Bitcoin's price rally takes a breather over the weekend, but a bullish weekly close could translate to gains from HYPE, XMR, AAVE and WLD. Bitcoin price is stuck below $109,588, but the pullback has not altered its bullish chart structure. A bullish weekly open from Bitcoin could extend gains in HYPE, XMR, AAVE, and WLD. Bitcoin (BTC) remains stuck below the $109,588 level during a quiet weekend, but analysts remain bullish. Material Indicators co-founder Keith Alan said in a post on X that Bitcoin remains positive as long as it trades above the yearly open level of about $93,500. Bitcoin's demand is likely to remain strong with investments from sovereign wealth funds, exchange-traded funds, publicly listed companies and select nations. Crypto index fund management firm Bitwise said in a recent report that institutional funds could pump roughly $120 billion into Bitcoin in 2025 and about $300 billion in 2026. While the long-term picture looks promising, traders need to be careful in the near term. The failure to swiftly push the price back above $109,588 could attract profit-booking by short-term traders. If Bitcoin pulls back, several altcoins could also give up some of their recent gains. Could Bitcoin rise back above $109,588, pulling altcoins higher? If it does, let's look at the cryptocurrencies that look strong on the charts. If they manage to do that, the BTC/USDT pair could rally to the target objective of $130,000. Buyers will regain control if they push and maintain the price above the $109,588 resistance. A break above $111,980 could open the doors for a rally to $116,654. If the price sustains above $35.73, the HYPE/USDT pair could pick up momentum and surge to $42.25. Sellers will try to halt the up move at $42.25, but if the bulls prevail, the pair could skyrocket to $50. Sellers are likely to have other plans. They will try to pull the price back below the breakout level of $35.73. If they do that, the pair could drop to the $32.15 support, where buyers are expected to step in. If the price remains above $35.73, it suggests that the bulls are trying to flip the level into support. The pair could then attempt a rally to $42.25. This optimistic view will be negated in the near term if the price turns down sharply and breaks below the 20-EMA. That could trap several aggressive bulls, pulling the pair to $32 and subsequently to $28.50. Monero (XMR) soared above the $391 resistance on May 21, indicating that the bulls remain in control. If buyers maintain the price above $412, the XMR/USDT pair could resume its uptrend toward $456. That could attract selling by short-term buyers, pulling the pair to the 20-day EMA ($347). A break and close below the 20-day EMA suggests a short-term trend change. The pair could then tumble to $332. Related: What's the HYPE about? Aave (AAVE) successfully held the retest of the breakout level of $240 on May 23, indicating demand at lower levels. The AAVE/USDT pair could rally to the $285 level, which is expected to behave as a strong resistance. If buyers overcome the barrier at $285, the up move could extend to $300 and later to $350. Any pullback is expected to witness solid buying at the 20-day EMA. The pair has pulled back to the 20-EMA, which is an important level to watch out for. If they succeed, the pair could rally to $300. A bounce off $240 is expected to face selling at the 20-EMA. Worldcoin's (WLD) recovery is facing selling at $1.65, but a minor positive is that the bulls have not allowed the price to dip below the 20-day EMA ($1.20). If they can pull it off, the WLD/USDT pair could rally to $2.50. There is resistance at $1.89, but it is likely to be crossed. This positive view will be invalidated if the price turns down and breaks below the 20-day EMA. The bulls will try to start a rebound off the 50-SMA but are likely to meet stiff resistance at the 20-EMA. The first sign of strength will be a break and close above the downtrend line. This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.

While the market continues to see dominance by AI altcoins, bots, and revived ETF optimism for many top altcoins, savvy investors are looking for the next 100x crypto ICO to invest in for massive gains. This time, they are focused on the best ICO prospects and believe Mantix, powered by the Ethereum blockchain, is one to consider. As its $600,000 token presale takes flight in its first stage where it's currently selling at $0.02, experts tip this project's ICO as a potential 100x ERC20 crypto. After over $300 million in leveraged crypto positions got liquidated in four hours following a sudden Bitcoin plunge, no thanks to new tariff threats from U.S. President Donald Trump, the momentum is changing rapidly. According to the charts, Bitcoin's price has risen by 4.48% this week after $3.06 billion went into the U.S. Spot ETFs and news of Morgan Stanley starting direct crypto trading emerged. Crypto analysts believe that Bitcoin's growth to $176,000 by October 2025 might be possible if support holds strong at $100,000. A major feature that differentiates Mantix is how it prioritizes real-world utility and user-centered value over hype. With incredibly low transaction fees, deep liquidity and capacity for cross-chain trading, this project is effortlessly accessible to a vast range of crypto users. Such positioning explains why the Defi project is where analysts believe the next big crypto bag is coming for investors who prioritize growth. Phase 1 of the Mantix presale has recorded over $600,000 in total presale funding, with $MTX tokens currently selling for a low cost of $0.02. As an early-stage investment opportunity, Mantix is drawing in experienced Bitcoin investors, retail traders, and institutional investors seeking massive returns. Mantix tokenomics also provides a revenue-sharing model where holders enjoy passive income without having to provide liquidity directly. Find Out About The Newest Online Trading Platform Below Website: https://mantix.exchange Disclaimer: This content is provided by a sponsor. FinanceFeeds does not independently verify the legitimacy, credibility, claims, or financial viability of the information or description of services mentioned. As such, we bear no responsibility for any potential risks, inaccuracies, or misleading representations related to the content. We strongly advise seeking independent financial guidance from a qualified and regulated professional before engaging in any investment or financial activities. Please review our full disclaimer for more details.

Global economic tensions and trade disputes continue to influence cryptocurrency markets, with ETH showing resilience despite broader market uncertainty. The second-largest cryptocurrency is currently navigating a critical technical zone between $2,500-$2,530, which analysts identify as immediate resistance that must be overcome for continued upward movement. Institutional interest remains strong, with spot Ethereum ETFs recording consecutive days of positive inflows, signaling growing confidence from larger investors despite the recent volatility. Technical Analysis Highlights External References “AI Boost” indicates a generative text tool, typically an AI chatbot, contributed to the article. In each and every case, the article was edited, fact-checked and published by a human. Read more about CoinDesk's AI Policy. About Contact

Global economic tensions are weighing heavily on cryptocurrency markets as XRP experiences a significant correction amid heavy selling pressure. The recent announcement of potential 50% tariffs on European Union imports by the US government has triggered widespread market uncertainty, with XRP falling alongside most major cryptocurrencies despite Bitcoin recently reaching new all-time highs. Technical analysts point to critical support at the $2.25-$2.26 range, with market watchers warning that a break below this level could trigger deeper corrections toward the $1.55-$1.90 zone. Meanwhile, institutional interest remains strong with Volatility Shares launching an XRP futures ETF and leveraged ETF inflows surging despite the price dip, suggesting Wall Street continues accumulating positions during market weakness. Technical Analysis Highlights External References “AI Boost” indicates a generative text tool, typically an AI chatbot, contributed to the article. In each and every case, the article was edited, fact-checked and published by a human. Read more about CoinDesk's AI Policy. About Contact

A cryptocurrency investor from Kentucky allegedly held and tortured an Italian businessman in a luxury flat in Manhattan for over two weeks in a bid to steal his digital currency passwords, according to reports. The businessman, 28, managed to escape the apartment on Prince Street in SoHo on Friday morning and ran to a police officer for help. He is facing charges including kidnapping, assault, unlawful imprisonment and weapon possession. In one image, he appeared to be bound to a chair with a gun pointed at him, New York Post sources said. The alleged abuse included being tasered while his feet were in water, pistol-whipped, forced to take cocaine, and threatened with an electric chainsaw. The police sources as saying the businessman had flown in from Italy on 6 May to meet Woeltz, with whom he had previous business dealings. Once inside the flat, Woeltz allegedly took his passport and tied him up. Police found Polaroid pictures of the alleged torture and various items including broken glass, night vision goggles, a gun, a bulletproof vest and an Apple AirTag — which the victim said had been placed around his neck to track him in case he tried to escape. Two other people linked to Woeltz were also being questioned by police, the Post said. “This is definitely the strangest thing I've seen in my time here,” he said. A local jewellery seller told the Post that the people renting the flat gave off a “weird vibe.” He said, “I knew there was something strange going on. I just assumed they were filming something.”

Vivek Raman, co-founder of Etherealize and former Wall Street banker, is leading an unprecedented charm offensive. He now presents Ethereum as the “digital oil” destined to revolutionize traditional financial institutions. Vivek Raman, former banker at Nomura and UBS, launched Etherealize in January 2025 with a clear mission. This analogy is directly inspired by bitcoin, often called “digital gold.” It makes Ethereum accessible to Wall Street newcomers. Unlike oil, whose supply remains elastic according to demand, Ethereum has a maximum issuance of 1.5% per year. “Rather than a fixed total supply cap, there is a fixed annual issuance cap“, clarifies Danny Ryan, co-founder of Etherealize and former researcher at the Ethereum Foundation. This predictability reassures institutional investors used to assets with clear rules. The most striking advantage of Ethereum lies in its ability to generate income. Unlike oil stored in reserves, ETH “staked” on the network currently yields 3% per year. BlackRock and Franklin Templeton have already taken the step by tokenizing several of their funds on this blockchain. Kraken has even chosen this blockchain for some of its offerings. But Ethereum maintains a lead thanks to its maturity and proven security. “In this ecosystem where all global assets are tokenized, the only neutral and global asset that connects all these assets is ETH“, he asserts. Concretely, ETH would serve as a universal exchange currency between different tokenized assets. Stocks, bonds, commodities: all these traditional assets could be traded via Ethereum. Just as oil revolutionized industry in the 20th century, Ethereum could well transform 21st-century finance. Experts anticipate that tokenization of stocks will surpass $1 trillion in the medium term. Wall Street is gradually discovering this infrastructure, which will likely become the backbone of tokenized finance. Maximize your Cointribune experience with our "Read to Earn" program! For every article you read, earn points and access exclusive rewards. Mon rêve est de vivre dans un monde où la vie privée et la liberté financière sont garanties pour tous, et je crois fermement que Bitcoin est l'outil qui peut rendre cela possible. Receive the latest and best crypto news directly to your inbox in daily, weekly, or special format, to stay updated at your own pace Receive the latest and best crypto news directly to your inbox in daily, weekly, or special format, to stay updated at your own pace

HYPE is echoing Solana's 2021 breakout pattern, with technicals pointing to a potential 240% rally by July. HYPE is mirroring Solana's 2021 breakout structure, targeting a 240% rally by July. In January 2021, Solana broke out from a prolonged consolidation phase just as marketwide interest began accelerating. The breakout, highlighted by a decisive flip above key Fibonacci retracement levels, triggered a vertical rally that saw SOL jump to the 4.618 Fib retracement line at around $19 from roughly $4.90 in under two months, marking a 291% surge. Fast forward to May 2025, HYPE's daily chart is showing the same bullish structure following its 270% rebound from $10 lows in April, aligning with its 0.0 Fibonacci retracement line. On May 23, HYPE broke above its 1.0 Fibonacci retracement level (~$35.88), echoing the early stages of SOL's explosive run in 2021. Popular analyst and commentator Ansem highlights that Hyperliquid's vision is very similar to what Solana and FTX aimed to build during their early partnership: a high-performance, low-cost crypto trading experience. He argues that, unlike FTX's centralized architecture, Hyperliquid is fully onchain. Nearly 97% of all trading revenue goes directly back to HYPE tokenholders, Ansem noted, adding that such fundamentals will assist the Hyperliquid token to reach “all-time highs soon.” Psychologically, traders are often drawn to familiar and previously successful patterns. When traders recognize that HYPE could be repeating Solana's 2021 trajectory visually and fundamentally, it may reinforce bullish conviction and draw in speculators hoping to catch the next “Solana” moment.

Republican presidential nominee former President Donald Trump speaks at the Bitcoin 2024 Conference July 27, 2024, in Nashville, Tenn. Vice President JD Vance, Trump's two eldest sons, and White House “crypto czar” David Sacks are all scheduled to appear. At last year's conference in Nashville, Tenn., Trump championed bitcoin — a decentralized digital currency operating on blockchain technology — while criticizing what he considered regulatory overreach under the Biden administration. His remarks drew thunderous applause from the crowd of crypto enthusiasts. Justin Doochin, head of events at the company that puts on the yearly conference, said Trump's policy on bitcoin has been “a lot more favorable” so far. Trump has reallocated seized bitcoin for a “strategic reserve,” installed friendly regulators and revoked Biden-era policy. Doochin said the event will have a strict policy on sticking to bitcoin, a technology that Elisa Cafferata, who's lobbied for blockchain legislation in Nevada, explained derives its value from its transparency and a cap on how many bitcoins can be created. With unprecedented government interest, the convention's industry day will feature programming exploring how bitcoin — alongside other emerging sectors like AI and space travel — intersects with public policy under the theme “Code and Country.” Doochin noted that conference attendees have become far more politically engaged, a shift he traces back to 2023 when Robert F. Kennedy Jr., now secretary of the Department of Health and Human Services, spoke at that year's convention. Hilary Allen, a self-described “crypto critic” and professor of law at American University, said that “in the second administration, (Trump) realized that he could be the one profiting.” Just days before his second inauguration, the Trump-connected firm World Liberty Financial launched a Trump “meme coin” — a purely speculative category of cryptocurrency that typically has no utility. Asked Thursday if the White House would release a list of those attending the dinner, press secretary Karoline Leavitt said it wasn't a White House event and that Trump is attending on “his personal time.” “The president is abiding by all conflict of interest laws that are applicable,” she said later. “It's absurd for anyone to insinuate that this president is profiting off of the presidency. This president was incredibly successful before giving it all up to serve our country.” But along with that, other regulators backed off their ongoing work. “There were other things they lost on, but the fundamental issue of whether crypto assets were securities, the SEC just kept on winning” in court before Trump retook office, Allen said. One of those companies that had its case dropped is Coinbase, a crypto exchange platform in Andreessen Horowitz's portfolio which added former Trump co-campaign manager Chris LaCivita to its advisory board in January. They're just two examples of what Allen described as a “revolving door” between government and industry lobbying. She also pointed to Summer Mersinger, who recently announced her move from the U.S. Commodity Futures Trading Commission to CEO of a major Washington crypto trade association. The Senate this month advanced industry-backed legislation around “stablecoins,” a type of cryptocurrency pegged to another asset, such as gold, or currency. Both of Nevada's Democratic senators voted in favor, with a spokesperson for Sen. Jacky Rosen telling the Sun that “cryptocurrency is an emerging industry that is in need of regulation.” This bill is not the only action Congress should take on crypto regulation, but it is an important step to protect consumers and support America's innovation,” the spokesperson wrote. World Liberty Financial, too, has its own stablecoin, other Democrats have worried. “Other than fear of running afoul of the crypto industry's money cannon, I find it hard to see why Democrats are supporting this legislation,” she said. “The legislation essentially blesses stable coins, creating ... a greater market for them.”

Republican presidential nominee former President Donald Trump speaks at the Bitcoin 2024 Conference July 27, 2024, in Nashville, Tenn. Vice President JD Vance, Trump's two eldest sons, and White House “crypto czar” David Sacks are all scheduled to appear. At last year's conference in Nashville, Tenn., Trump championed bitcoin — a decentralized digital currency operating on blockchain technology — while criticizing what he considered regulatory overreach under the Biden administration. His remarks drew thunderous applause from the crowd of crypto enthusiasts. Justin Doochin, head of events at the company that puts on the yearly conference, said Trump's policy on bitcoin has been “a lot more favorable” so far. Trump has reallocated seized bitcoin for a “strategic reserve,” installed friendly regulators and revoked Biden-era policy. Doochin said the event will have a strict policy on sticking to bitcoin, a technology that Elisa Cafferata, who's lobbied for blockchain legislation in Nevada, explained derives its value from its transparency and a cap on how many bitcoins can be created. With unprecedented government interest, the convention's industry day will feature programming exploring how bitcoin — alongside other emerging sectors like AI and space travel — intersects with public policy under the theme “Code and Country.” Doochin noted that conference attendees have become far more politically engaged, a shift he traces back to 2023 when Robert F. Kennedy Jr., now secretary of the Department of Health and Human Services, spoke at that year's convention. Hilary Allen, a self-described “crypto critic” and professor of law at American University, said that “in the second administration, (Trump) realized that he could be the one profiting.” Just days before his second inauguration, the Trump-connected firm World Liberty Financial launched a Trump “meme coin” — a purely speculative category of cryptocurrency that typically has no utility. Asked Thursday if the White House would release a list of those attending the dinner, press secretary Karoline Leavitt said it wasn't a White House event and that Trump is attending on “his personal time.” “The president is abiding by all conflict of interest laws that are applicable,” she said later. “It's absurd for anyone to insinuate that this president is profiting off of the presidency. This president was incredibly successful before giving it all up to serve our country.” But along with that, other regulators backed off their ongoing work. “There were other things they lost on, but the fundamental issue of whether crypto assets were securities, the SEC just kept on winning” in court before Trump retook office, Allen said. One of those companies that had its case dropped is Coinbase, a crypto exchange platform in Andreessen Horowitz's portfolio which added former Trump co-campaign manager Chris LaCivita to its advisory board in January. They're just two examples of what Allen described as a “revolving door” between government and industry lobbying. She also pointed to Summer Mersinger, who recently announced her move from the U.S. Commodity Futures Trading Commission to CEO of a major Washington crypto trade association. The Senate this month advanced industry-backed legislation around “stablecoins,” a type of cryptocurrency pegged to another asset, such as gold, or currency. Both of Nevada's Democratic senators voted in favor, with a spokesperson for Sen. Jacky Rosen telling the Sun that “cryptocurrency is an emerging industry that is in need of regulation.” This bill is not the only action Congress should take on crypto regulation, but it is an important step to protect consumers and support America's innovation,” the spokesperson wrote. World Liberty Financial, too, has its own stablecoin, other Democrats have worried. “Other than fear of running afoul of the crypto industry's money cannon, I find it hard to see why Democrats are supporting this legislation,” she said. “The legislation essentially blesses stable coins, creating ... a greater market for them.”

The market is shifting, and some big investors are quietly moving millions into a new XRP rival. That project, Remittix, has already raised over $15.3 million in its ICO. Analysts expect ETH to hover around key support levels like $2,500, but some warn of possible dips if the market turns bearish. According to Glassnode and CoinMetrics data, Ethereum's activity is growing, with more users signaling interest in the network. But, without steady buying, ETH might struggle to break past its recent highs. Grayscale's Ethereum Trust recently saw a $45 million inflow and spot ETH ETFs reported strong net inflows, showing confidence among large investors. The Ethereum price prediction suggests cautious hope for a rally, but traders must stay alert to changing market conditions. Ethereum whales are rumored to be quietly shifting funds into emerging assets that could offer bigger returns. With ETH facing resistance and regulatory uncertainties, these investors diversify to capture growth in other areas. This trend is backed by data from Santiment and CryptoQuant, which show rising activity in smaller tokens linked to the Ethereum ecosystem. The crypto market is volatile, and some projects will fail while others could surge dramatically. Many look for tokens that solve real-life problems, which is where new contenders like Remittix come into play, offering both innovative solutions and investor appeal. Remittix, a payment-focused project, has captured the attention of Ethereum whales and smaller investors alike. Its design hides blockchain complexity from users, letting receivers get money in their local currency without unnecessary fees or delays. With over 100 cryptocurrencies supported, Remittix aims to bridge crypto and traditional finance efficiently. Experts note that Remittix offers staking rewards between 4% and 8% per year, making it attractive for holders who want passive income. Its native token, RTX, will launch on major exchanges soon, boosting liquidity and access. Analysts hint that Ethereum whales are quietly moving millions into Remittix, hinting at strong confidence. This shift of capital highlights a growing interest in projects that bring real utility beyond just price speculation. While Ethereum's price prediction points to cautious optimism, many investors are considering projects like Remittix. It offers practical solutions, growing adoption and strong backing from Ethereum whales. Discover the future of PayFi with Remittix by checking out their presale here:

You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. In the context of smart homes, efficiently managing temperature control while optimizing energy consumption and ensuring data security remains a significant challenge. Traditional thermostat-based systems lack predictive capabilities, and energy consumption often spikes during peak hours, leading to inefficiency. Additionally, the security of sensitive data in smart home environments is a growing concern. This paper presents a novel AI-powered blockchain framework for predictive temperature control in smart homes, leveraging wireless sensor networks (WSNs) and time-shifted analysis. The framework integrates machine learning (ML) algorithms for predictive temperature management, blockchain technology for secure data handling, and edge computing for real-time data processing, resulting in a highly efficient and secure system. Key innovations include the dynamic detection of heating and cooling events, predictive scheduling based on historical data, and blockchain-based decentralized energy trading. Performance evaluation demonstrates that the system accurately detects radiator heat-on events with a 28.5% success rate, while radiator cooling event detection achieves 37.3% accuracy. Scheduled heat-on events were triggered with 68.4% reliability, and the system's machine learning component successfully reduced energy consumption by 15.8% compared to traditional thermostat controls, by adjusting heating based on predictive analysis. Additionally, the time-shifted data processing reduces peak-time computational load by 22%, contributing to overall energy efficiency and system scalability. The integration of blockchain ensures tamper-proof data security, eliminating unauthorized data access, and improving trust in smart home environments. These results illustrate the potential of combining AI, blockchain, and WSNs to create a robust, energy-efficient, and secure smart home temperature control system, offering significant improvements over traditional solutions. As the adoption of smart homes continues to expand globally, the need for efficient, secure, and autonomous temperature control systems has become increasingly important1. Traditional thermostat-based systems, while effective in basic temperature regulation, lack the sophistication necessary to optimize energy usage and improve user comfort in dynamic environments2. These systems often fail to account for real-time data, external factors, or user behavior patterns, leading to inefficient heating and cooling cycles that contribute to energy waste and increased costs3. Recent advancements in WSNs and Internet of Things (IoT) technologies have introduced new opportunities for more precise temperature control4. WSNs enable the continuous monitoring of environmental parameters, such as room temperature and radiator activity, within a smart home5. However, managing and processing the large volume of data generated by these networks, while maintaining data security and minimizing energy consumption, remains a significant challenge6. Blockchain technology has emerged as a promising solution to address the issues of data security and privacy in IoT systems7. Its decentralized architecture ensures tamper-proof data storage, while smart contracts enable automated decision-making processes, such as adjusting temperature settings based on predefined conditions8. Despite these advantages, blockchain's high computational demands can lead to latency and inefficiency when applied to real-time data processing in smart homes9. To further enhance the capabilities of smart home temperature control, ML techniques have proven effective in predictive analytics, enabling systems to anticipate user needs based on historical data10. By integrating ML models with blockchain and WSNs, it is possible to develop a more responsive and energy-efficient system that adjusts temperature proactively, reducing the need for manual intervention11. This reduces the reliance on centralized cloud systems, enhancing real-time response and improving overall system performance13. Additionally, time-shifted data analysis can be employed to reduce computational loads during peak times, ensuring that energy consumption is optimized without compromising the accuracy or responsiveness of the system14. The integration of smart technologies in residential environments has been a rapidly growing field of research over the past decade, driven by the increasing demand for energy-efficient and automated systems in modern homes15. WSNs, IoT devices, and smart thermostats are among the key enablers of intelligent home systems, allowing for real-time monitoring and control of environmental conditions such as temperature, lighting, and security16. The deployment of these systems at scale has brought about new challenges related to data security, computational efficiency, and energy management, which have been the focus of recent research17. WSNs have been extensively studied for their ability to monitor and control environmental parameters in real time18. In the context of smart homes, WSNs enable continuous tracking of room temperature, humidity, and occupancy, providing the data needed for adaptive heating and cooling systems19. For example, research20 showed that sensor-driven temperature control systems could reduce heating energy consumption by up to 20% when compared to traditional thermostat systems. Blockchain technology has emerged as a robust solution21 for addressing security and privacy concerns in IoT systems22. By decentralizing data storage and securing transactions with cryptographic algorithms, blockchain ensures the immutability of data records and protects against unauthorized access23,24 is among the first to explore the use of blockchain for securing IoT data in smart homes, proposing a lightweight framework that protects user data from external attacks. The use of smart contracts in blockchain further enhances automation by enabling devices to execute predefined actions autonomously, based on specified triggers25. The computational intensity of blockchain, particularly its reliance on consensus mechanisms, can introduce delays that reduce system efficiency for real-time applications26.The integration of blockchain technology and machine learning techniques to enhance the security management of 6G wireless networks is explored in27. The application of ML for predictive temperature control has gained considerable attention in recent years due to its potential for improving system responsiveness and energy efficiency30. ML algorithms can analyze historical temperature data, occupancy patterns, and even external factors such as weather to forecast heating or cooling needs, allowing systems to adjust preemptively31. Studies such as32 demonstrated that machine learning-based systems could reduce energy consumption by up to 18% compared to traditional reactive control systems, by predicting when heating or cooling is required based on user behavior. These predictive systems, however, require robust data33 handling mechanisms to ensure that real-time and historical data are processed securely and efficiently. The integration of edge computing into smart home ecosystems has been proposed as a solution to the latency and bandwidth issues associated with centralized cloud processing34. Edge computing allows data to be processed locally, closer to the source of data generation, which reduces the delays inherent in cloud-based systems35. Research36 highlighted the potential of edge computing in reducing latency and improving real-time decision-making in IoT systems, particularly in scenarios requiring immediate responses, such as smart temperature control. A transformative advancement in37, showcasing significant improvements across several key dimensions of industrial operations. Overall, the synergy between AI and blockchain technologies38 leads to a notable increase in productivity, operational reliability, and data security, setting a new standard for industrial excellence. The integration of explainable AI with blockchain technology in39 significantly enhances financial decision-making by addressing key issues of transparency and trust. The blockchain-modeled edge-computing-based smart home in9 demonstrates notable improvements in efficiency and security for smart home environments40. The IoT-based smart home automation system utilizing blockchain and deep learning models in41 showcases impressive advancements in home automation, security, and efficiency. The differential privacy model integrated into a blockchain-based smart home architecture in24 offers substantial improvements in user data privacy and system security. The BEDS (Blockchain Energy-Efficient IoE Sensors Data Scheduling) system significantly enhances the management and efficiency of data within smart home and vehicle applications in42. The collaborative approach of securing smart grid data using blockchain and WSNs in43 demonstrates notable advancements in data integrity and system reliability. The BS-SCRM (Blockchain and Swarm Intelligence-Based Secure Wireless Sensor Networks) approach in44 introduces a novel method for enhancing the security and efficiency of WSNs. The article14 presents an advanced approach to optimizing microgrid operations by balancing energy distribution and capacity scheduling. The article45 explores an innovative approach to optimizing the performance of MHz wireless power transfer systems through time-shifted control techniques. A novel smart home system in46 introduces that leverages a sophisticated algorithm for monitoring and managing the link status of WSNs. The link status awareness algorithm plays a crucial role in maintaining reliable communication between sensors and control systems by continuously assessing the quality and stability47 of network connections. A sophisticated approach in48 presents to optimizing renewable energy49 use in smart homes through advanced forecasting and scheduling techniques. The effectiveness of combining multiple machine learning algorithms in50 highlights to enhance the accuracy of energy consumption forecasts in smart homes. The study51 investigates how trust influences the adoption and usage intentions of AI-powered smart home devices among younger generations. The study reveals that trust plays a critical moderating role in shaping users' willingness to integrate these devices into their homes. The paper52 provides an overview of AI-driven energy management techniques, highlighting their applicability in optimizing smart home systems, particularly in temperature control and energy efficiency. The work53 examines the role of blockchain technology in securing data exchanges in smart homes, focusing on the challenges and solutions for integrating blockchain in home automation systems. The paper54 discusses the role of WSNs in smart home systems, particularly in real-time data collection and monitoring, which are essential for the proposed predictive temperature control framework. The study55 presents various predictive control strategies, including machine learning approaches, to improve energy efficiency in smart homes, directly relevant to the predictive scheduling aspect of the proposed system. The research56 explores the integration of blockchain for decentralized energy trading, which aligns with the proposed system's feature of blockchain-based energy trading for smart homes. The article57 provides an extensive review of various data aggregation techniques used to optimize the performance of WSNs, focusing on reducing energy consumption, improving data accuracy, and enhancing network lifetime58 explores the integration of cognitive agents in the IoT to enable context-aware data perception, enhancing the ability to adapt and respond intelligently to dynamic environmental conditions59 presents a method for improving the accuracy of node localization in wireless sensor networks by utilizing mobile sinks and agent-based algorithms, enhancing the overall performance and scalability of the system60 proposes a hybrid architecture combining centralized and peer-to-peer models to improve resource discovery and secure communication within the Internet of Things (IoT), offering enhanced scalability and reliability in IoT networks. The results of the comparative analysis of the articles reviewed are detailed in Table 1, which provides a comprehensive overview of the advancements and performance outcomes across various smart home and energy management technologies. The key contributions of the proposed approach are threefold: (1) the integration of AI and Blockchain for predictive temperature management and secure data handling, (2) the development of a novel framework that combines predictive scheduling and dynamic event detection, and (3) the evaluation of the system's performance in terms of energy efficiency, security, and scalability. Despite the significant advancements in smart home temperature control systems, several critical research gaps remain in the integration of emerging technologies such as blockchain, ML, WSNs, and edge computing. Current approaches either focus on isolated aspects of temperature management (e.g., predictive analytics or security) or lack the computational efficiency needed for real-time applications. These gaps are becoming more pronounced as smart home environments grow increasingly complex and demand secure, scalable, and energy-efficient solutions. Limited Integration of Technologies: While blockchain has shown promise for securing IoT systems, few studies have explored its integration with predictive ML models for smart home temperature control. This gap limits the ability to create systems that are both secure and adaptive to user behavior and environmental conditions. Lack of Predictive Control with Data Security: Traditional smart home systems often rely on historical data for reactive temperature control, lacking the predictive capabilities of machine learning. At the same time, ensuring data integrity and privacy in IoT environments remains a challenge, especially when dealing with sensitive home environment data. Latency and Computational Bottlenecks: Centralized cloud-based systems used in many smart home applications face significant latency issues and computational bottlenecks, especially during peak data loads. These limitations hinder real-time control and scalability, which are critical for large-scale smart home deployments. This paper addresses these research gaps by proposing a comprehensive, AI-powered blockchain framework for smart home temperature control that integrates WSNs, ML-based predictive analytics, and edge computing for time-shifted data processing. A novel approach that combines blockchain technology with ML for predictive temperature control. Blockchain ensures secure data handling, while ML optimizes heating and cooling based on real-time and historical data. The framework introduces edge computing to reduce latency and improve real-time responsiveness. By processing data locally and utilizing time-shifted analysis, the system decreases peak-time computational loads, enhancing overall performance. The system employs advanced WSNs to accurately detect radiator events (heat-on, cooling, etc.) and uses predictive models to schedule heating in a way that minimizes energy consumption, based on real-world data analysis. The framework incorporates blockchain-enabled peer-to-peer energy trading and dynamic pricing models, allowing smart home users to trade surplus energy in a secure, decentralized marketplace. This feature optimizes energy usage while reducing costs. By integrating ML for predictive control and using blockchain for secure decentralized management, the framework significantly improves energy efficiency, with reductions in heating energy, while ensuring scalability for broader smart home deployments. For the proposed work in Predictive Temperature Control and Energy Consumption Management using Machine Learning, various AI and ML models can be employed to predict the temperature and energy consumption while optimizing HVAC system settings. Below is an overview of the AI and ML models used, along with recent papers related to this field: Artificial Neural Networks (ANN): ANNs can be employed for time-series prediction of temperature and energy consumption. They learn complex nonlinear relationships from historical data, making them suitable for predicting energy demand and HVAC system control62, explores the use of deep neural networks for energy consumption prediction in smart homes, achieving high accuracy by integrating weather and occupancy data63. Support Vector Machines (SVM): SVMs can be used for regression tasks to predict continuous values, such as temperature or energy consumption64. They work well with high-dimensional data, making them suitable for smart home data65 investigates SVM-based models for predicting smart home energy usage, outperforming traditional regression models. Random Forest (RF): Random Forest is an ensemble method that can be used for predicting temperature and energy consumption. It performs well with a large number of input features and is robust to overfitting6667 uses Random Forest for energy consumption forecasting and demonstrates energy-saving improvements is used . Recurrent Neural Networks (RNN) / Long Short-Term Memory (LSTM): These models are particularly effective for time-series data and can be used to predict temperature and energy consumption based on historical time-dependent data6869 explores the use of LSTM models for predicting energy consumption in smart homes, providing superior results in terms of accuracy over traditional methods. Decision Trees (DT): Decision Trees can help model the decision-making process for temperature control based on input data (e.g., occupancy, time of day, weather). The second part of the article focuses on Problem Formulation, addressing key components such as Predictive Temperature Control. The rapid advancement of smart home technologies has highlighted the need for systems that not only provide comfort but also optimize energy usage and ensure data security. Traditional thermostat systems are limited in their ability to predict and adapt to varying environmental conditions and user behaviors, often resulting in inefficient heating schedules and increased energy consumption. Additionally, the centralized handling of sensitive data in these systems raises concerns about privacy and vulnerability to unauthorized access. To address these challenges, there is a pressing need for an integrated framework that combines predictive analytics, secure data management, and efficient processing capabilities. Specifically, the problem centers on developing a system that can accurately detect heating and cooling events, reliably trigger scheduled heat-on events, reduce energy consumption through predictive adjustments, and maintain data integrity and security within the smart home environment. This necessitates leveraging advanced technologies such as AI-powered machine learning algorithms, blockchain for secure and transparent data handling, wireless sensor networks for real-time environmental monitoring, and time-shifted analysis to optimize computational efficiency. In this section, we define the mathematical framework and relationships necessary to develop an AI-powered blockchain framework for predictive temperature control in smart homes. The system aims to optimize temperature regulation while ensuring secure data handling and improving energy efficiency. The AI-Powered Blockchain Framework for Predictive Temperature Control in Smart Homes can be modeled as a holistic system integrating predictive control, energy optimization, and blockchain technology. The architecture can be represented as follows: where \(\:{U}_{\text{user\:}}\) represents user-defined preferences, such as the desired temperature range. Sensor data and control signals are securely stored in a blockchain: ensuring immutability and transparency in the data. The system detects heating and cooling events dynamically: where \(\:{P}_{\text{Optimized\:}}\)​ represents optimized power usage and \(\:{E}_{\text{events\:}}\)​ identifies critical heating/cooling events. This integrated system provides a robust, secure, and energy-efficient solution for temperature control in smart homes, demonstrating the novelty and practical relevance of the proposed framework. The goal is to predict the future temperature \(\:T(t+{\Delta\:}t)\) based on historical temperature data, user preferences, and environmental conditions. To model the temperature dynamics, we can use a simple heat transfer equation: The prediction function can be expressed as: where \(\:f\) is the learned function based on past data using a machine learning algorithm, such as Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) models. The objective is to minimize the energy consumption \(\:E\), while maintaining user comfort, represented by a set temperature range \(\:\left[{T}_{\text{min\:}},{T}_{\text{max\:}}\right]\). Using predictive temperature control, the heating system can adjust the power supply based on the forecasted temperature \(\:T(t+\varDelta\:t)\), thereby reducing unnecessary energy use. To reduce peak-time computational load, the system leverages time-shifted analysis, where non-urgent computations, such as data processing or historical analysis, are performed during off-peak times. Let \(\:{C}_{\text{p}\text{e}\text{a}\text{k}}\) ​be the computational load during peak hours, and \(\:{C}_{\text{o}\text{f}\text{f}}\) ​ be the load during off-peak hours. Time-shifted analysis aims to minimize \(\:{C}_{\text{p}\text{e}\text{a}\text{k}}\)​ by shifting part of the workload to off-peak times. with the goal to reduce \(\:{C}_{\text{p}\text{e}\text{a}\text{k}}\)​, where: and \(\:\varDelta\:{C}_{\text{s}\text{h}\text{i}\text{f}\text{t}}\) represents the load shifted to off-peak times. Each temperature reading and energy consumption record is stored as a block in the blockchain. Let \(\:D\left(t\right)\) represent the data at time \(\:t\) (e.g., temperature readings, energy consumption). The blockchain ensures that \(\:D\left(t\right)\) cannot be altered once recorded. The data is secured using a cryptographic hash function \(\:H\), where: This chain of blocks guarantees that any attempt to tamper with historical data will be easily detected, as it would alter the hash values in subsequent blocks. Let SSS represent customer satisfaction, which depends on maintaining the desired temperature range and minimizing energy costs. The system seeks to balance energy efficiency with user comfort and data security. The system must dynamically detect heating events (e.g., radiator turning on) and cooling events (e.g., radiator turning off). These events can be modeled as binary occurrences based on the rate of temperature change over time. Let \(\:\varDelta\:T=T(t+1)-T\left(t\right)\) represent the change in temperature between time intervals. Define \(\:H\left(t\right)\) as a heating event and \(\:C\left(t\right)\) as a cooling event, which are triggered when certain thresholds are crossed: The framework can further be refined by employing machine learning models that dynamically learn from data to refine these thresholds and account for varying environmental conditions: where \(\:{f}_{\theta\:}\) is a learned function that dynamically adjusts thresholds based on environmental and system variables. To optimize energy usage, the system uses predictive scheduling based on historical data. The goal is to anticipate future temperature changes and schedule heating or cooling events accordingly. The system uses these historical patterns to predict future events: where \(\:P\left(H\right(t+1)=1)\) is the probability of a heating event occurring at time \(\:t+1\), predicted using a machine learning algorithm trained on historical data \(\:\mathcal{H}\) and \(\:\mathcal{C}\). The system then schedules heating and cooling events based on the predicted probabilities to minimize energy consumption while maintaining user comfort. If \(\:P\left(H\right(t+1)=1)>{P}_{\text{threshold,\:}}\) the system preemptively triggers a heating event, reducing energy spikes. Users within a smart home network can buy or sell surplus energy generated from renewable sources (e.g., solar panels) using blockchain smart contracts. Let \(\:{E}_{prod}\left(t\right)\) represent the energy produced by a renewable energy source at time \(\:t\), and \(\:{E}_{cons}\left(t\right)\)​ represent the energy consumed by the smart home at time \(\:t\). where \(\:{E}_{surplus}\left(t\right)>0\) represents excess energy that can be sold, and \(\:{E}_{surplus}\left(t\right)<0\) represents a deficit that can be compensated by purchasing energy. Each energy trade is recorded on the blockchain, ensuring transparency and immutability. The smart contract logic can be formalized as: The blockchain ledger ensures that energy trades are secured and logged without requiring a central authority, maintaining trust among users in the decentralized energy market. WSNs play a vital role in real-time monitoring of temperature and environmental conditions in smart homes. The optimization problem can be formulated as: To reduce power consumption, time-shifted data analysis and adaptive sensing can be employed, where only a subset of sensors is active during certain periods, depending on predicted events. The interaction between the edge server and IoMT devices involves a collaborative exchange of data and computational tasks, which ensures efficient operation in the system. Each IoMT device independently collects and processes local data, generating model parameters based on its specific environment and tasks. These parameters are periodically transmitted to the edge server for aggregation. The edge server plays a pivotal role in this framework by acting as a central coordinator. It aggregates the model parameters received from multiple devices using advanced techniques, such as weighted averaging or federated optimization, depending on the importance and quality of the data from each device. This aggregation process ensures that the global model is continually updated while preserving the privacy of individual devices since raw data is never directly shared. To manage real-time updates, the edge server employs a systematic communication protocol that prioritizes low-latency and secure data transfer. The server can handle asynchronous updates, allowing devices with varying computational and network capabilities to participate effectively. Additionally, the edge server uses error-checking and version. To formulate the overall optimization problem, we aim to minimize energy consumption, \(\:E\), and computational load, \(\:{C}_{total}\), while maximizing user satisfaction, \(\:S\), and ensuring secure data handling. This leads to a multi-objective optimization problem: where \(\:{\alpha\:}_{1},{\alpha\:}_{2}\), and \(\:{\alpha\:}_{3}\) are weights representing the relative importance of energy consumption, computational efficiency, and user satisfaction. Blockchain-based data logs enhance system security by providing an immutable and tamper-proof ledger for recording all system transactions and events. Each data log is cryptographically secured, ensuring that once a block is added to the chain, it cannot be altered without the consensus of the network. This feature prevents unauthorized access and data manipulation. Additionally, the decentralized nature of blockchain eliminates single points of failure, making the system resilient against cyberattacks. By incorporating these secure data logs, the proposed framework ensures the integrity and confidentiality of temperature control data in smart homes, thereby fostering user trust and system reliability. The previous formulation provided a basic energy optimization model. We can enhance this by incorporating dynamic energy pricing and more granular control over energy usage based on real-time conditions. Let \(\:P\left(t\right)\) represent the dynamic price of energy at time \(\:t\), which varies based on demand and supply in the energy market. The cost of energy consumption, \(\:{C}_{E}\)​, over a period \(\:T\) can be expressed as: where \(\:{P}_{\text{cons\:}}\left(t\right)\) is the power consumption at time \(\:t\). The objective is to minimize the energy cost \(\:{C}_{E}\) while maintaining comfort, subject to dynamic pricing. The dynamic pricing \(\:P\left(t\right)\) can be modeled as a function of market conditions and predicted demand: Given the decentralized nature of smart homes, where each home or node is an independent agent, a consensus algorithm like Proof of Stake (PoS) or Delegated Proof of Stake (DPoS) is appropriate to validate transactions without high energy costs. Let \(\:D\left(t\right)\) represent a data block (e.g., sensor readings, energy trades), and let \(\:V\left(t\right)\) represent the set of validators in the network at time \(\:t\). Each validator \(\:{\nu\:}_{i}\in\:V\left(t\right)\:\)proposes a block \(\:{B}_{i}\left(t\right)\), where the block contains a cryptographic hash of the previous block and the new data to be added. The total number of validators that approve a block \(\:{B}_{i}\left(t\right)\) is denoted as \(\:{A}_{i}\left(t\right)\). where \(\:\left|V\left(t\right)\right|\) is the total number of validators. • Each node ni​ maintains a local blockchain ledger Li​. • Each node collects temperature data Di​ from its associated WSN. • Broadcast Bi​ to all nodes in the network. • Update local ledger Li​ to include the new block. • Apply the predictive temperature control actions specified in Bi​. This algorithm ensures secure and decentralized management of temperature control in smart homes while leveraging blockchain for data integrity and trust. To encourage energy trading between smart homes, an incentive mechanism can be introduced based on a reward structure for participants who trade energy efficiently. Each user \(\:{u}_{i}\) has a surplus \(\:{E}_{\text{surplus\:}}\left(t\right)\) or deficit \(\:{E}_{\text{deficit\:}}\left(t\right)\) of energy at time \(\:t\), as discussed earlier. The system assigns rewards \(\:{R}_{i}\left(t\right)\) to users based on their contribution to the energy market. where \(\:{R}_{\text{total\:}}\left(t\right)\) is the total reward available at time \(\:t\), which is determined by the blockchain network. This reward system encourages users to contribute surplus energy to the grid, promoting decentralized energy management. In the proposed system, time-shifted load balancing optimizes computational resources by deferring non-critical computations to off-peak times. This is particularly useful for resource-constrained smart home devices. Tasks with lower priority are deferred to off-peak times, thereby reducing peaktime computational load. The optimization objective is to minimize the peak computational load \(\:{C}_{peak}\) by shifting lowerpriority tasks. where \(\:{N}_{\text{high\:}}\left(t\right)\) is the number of high-priority tasks, \(\:C\left({T}_{i}\left(t\right)\right)\) is the computational cost of task \(\:{T}_{i}\left(t\right)\), and \(\:\delta\:\left({T}_{i}\left(t\right)\right)\) is the binary variable indicating whether task \(\:{T}_{i}\left(t\right)\) has been deferred to offpeak times. In a decentralized smart home network, multi-agent collaboration allows multiple homes to collaborate in managing energy and computational load. Each smart home is treated as an agent \(\:{a}_{i}\)​, and the collaboration aims to minimize total system energy consumption while maintaining comfort across all agents. Each agent shares its load with others, reducing peak demand. where \(\:{w}_{i}\left(t\right)\) is the weight assigned to each agent based on their energy-sharing contribution. The collaboration ensures that energy is distributed efficiently, and peak demand is reduced by sharing surplus energy between homes in the network. The proposed system can implement an energy-aware control algorithm to manage heating and cooling based on real-time predictions and sensor data. The control algorithm minimizes energy consumption while maintaining comfort within a defined range \(\:\left[{T}_{\text{m}\text{i}\text{n}},{T}_{\text{m}\text{a}\text{x}}\right]\): where \(\:\lambda\:\) is a penalty term for deviations from the setpoint temperature \(\:{T}_{\text{set\:}}\). This adaptive control algorithm ensures that the system learns over time, adjusting energy usage to maintain the desired temperature while minimizing cost. Collect the system parameters such as heating and cooling system efficiency, energy consumption rates, and other relevant data. Create additional features based on historical data (e.g., moving averages of temperature, occupancy trends, etc. Select an appropriate ML model (e.g., Decision Trees, Random Forest, Support Vector Machines, Neural Networks, etc.). Split the data into training and testing sets. Use the trained ML model to predict future temperature based on current temperature, weather forecast, and occupancy patterns. Predict the temperature setpoints for future hours or days based on this analysis. Apply energy-efficient strategies such as predictive scheduling (heating/cooling during off-peak times), adjusting setpoints based on predicted trends, or controlling HVAC systems based on occupancy data. Use reinforcement learning techniques, if applicable, to adapt and optimize the temperature control and energy usage over time. Continuously monitor and update predictions using real-time data from IoT sensors, modifying the heating/cooling strategy as needed. The system generates optimal heating/cooling schedules and real-time control adjustments. Display energy consumption predictions and provide recommendations for further optimization. Evaluate the model's accuracy by comparing predicted energy consumption and temperature control results against actual outcomes. Use performance metrics like MAE, RMSE, or energy savings percentage to assess the performance of the predictive system. The algorithm leverages machine learning models such as decision trees, neural networks, or other time-series models, combined with optimization strategies like predictive scheduling and reinforcement learning, to efficiently manage energy in smart homes. By integrating IoT with real-time sensor data and weather forecasting, it predicts temperature and energy consumption trends. This enables optimal HVAC scheduling, minimizing energy usage while maintaining comfort, ultimately achieving intelligent temperature control and energy optimization based on both real-time and historical data. The results are presented in a structured manner, examining key parameters such as accuracy, efficiency, and energy savings, as well as the security benefits provided by the blockchain integration. The framework's ability to predict heating and cooling events based on historical data is analyzed, with particular attention given to the system's detection rates for radiator heat-on, cooling events, and scheduled heating events. These results are benchmarked against traditional thermostat control methods to highlight the improvements achieved through predictive machine learning algorithms. Additionally, the framework's energy consumption reduction is quantified, demonstrating the impact of predictive scheduling on overall energy efficiency. Blockchain's role in securing wireless sensor network data and enabling decentralized energy trading is also discussed. The system's tamper-proof nature is examined, along with its ability to prevent unauthorized access to sensitive data, thereby improving trust and transparency in smart home environments. This reduction in processing demand ensures that the system remains efficient, even in large-scale smart home deployments. Each of these aspects is discussed in detail to provide a comprehensive understanding of how the AI-powered blockchain framework enhances both operational efficiency and security in smart homes. Each step corresponds to a specific phase in the methodology, with the relevant equations listed alongside. Starting with the collection of input data, the algorithm progresses through system modeling, parameter estimation, adaptive control, optimization, cybersecurity integration, real-time monitoring, and performance evaluation. For each of these stages, mathematical equations are used to define and adjust system parameters, ensuring that the model is optimized and resilient against potential threats. The final output is the result of optimized control signals or updated system parameters, ensuring efficient and stable performance. This systematic approach ensures that all crucial factors such as parameter estimation, optimization, security, and real-time monitoring are integrated effectively. Experimental Setup: The experimental setup used in the study involved both simulation and real-time testing. The system was tested in a controlled environment where the proposed AI-powered blockchain framework for predictive temperature control was implemented. Real-time data from WSNs deployed in a simulated smart home environment were collected to evaluate the performance of the system. The simulation models included a combination of environmental variables such as temperature, humidity, and energy consumption patterns. The real-time scenarios were designed to mimic typical smart home temperature control situations, with heating and cooling events, predictive scheduling, and energy trading based on historical data. The experimental setup can be described as follows: Environment: A simulated smart home with wireless sensors monitoring room temperature, humidity, and radiator status. Data Collection: Real-time data logging via WSNs for temperature, humidity, and event detection (heating, cooling). Performance Metrics: Success rates for event detection (heat-on, cooling, scheduled heat-on), energy consumption savings, and time-shifted load balancing. Furthermore, the blockchain component allows for secure, tamper-proof data logs in real-time, ensuring system integrity. To illustrate the real-time performance, a time series of temperature data, event detection, and energy consumption adjustments were continuously monitored and updated during the experiments, reflecting how the system responds to changes in the environment and its effectiveness in optimizing energy consumption. Step 2: Predictive Control - Machine learning models (e.g., Random Forest, Support Vector Machines) are used to predict the heating or cooling needs based on historical data. Step 3: Event Detection - The system detects heating and cooling events using a combination of predictive models and real-time sensor data. Step 4: Blockchain Integration - Blockchain is used to record sensor data and decisions in a secure, tamper-proof manner. Step 5: Scheduling & Optimization - Predictive scheduling is applied to optimize energy consumption by adjusting the heating schedule and balancing energy loads. Step 6: Feedback Loop - Based on the system's performance (temperature control and energy savings), adjustments are made, and results are logged in real time. The specific algorithms used for event detection, predictive scheduling, and blockchain consensus are as follows: Computational Complexity: This can be evaluated by analyzing the time complexity of the predictive algorithms (e.g., O(n log n) for Random Forest). A scalability metric can be defined based on system responsiveness and energy savings as the network size grows. Data was recorded from January to June 2024 across multiple rooms, including the living room, bedroom, and kitchen, to ensure variability in temperature and energy usage patterns. The dataset includes temperature measurements, energy consumption readings, and radiator operational status (on/off) synchronized with real-time external weather data. The dataset includes the following key features that are crucial for training and testing the predictive models, performing time-shifted analysis, and optimizing energy consumption in Table 3: Total records: 25,920 data points (representing one data point every minute for a period of 18 days). Data is structured in time series, with a total of 6 months of data from multiple rooms, offering a rich set of variations in temperature, energy consumption, and heating events. Missing temperature and energy consumption values were interpolated using a linear interpolation technique to fill in any gaps due to sensor failure or communication issues. For example, if the indoor temperature \(\:{\varvec{T}}_{\text{i}\text{n}\text{d}\text{o}\text{o}\text{r}}\:\)for the time 12:30 PM is missing, it is linearly interpolated from the neighboring values 12:00 PM and 1:00 PM. Outliers were identified using the Interquartile Range (IQR) method. Any value higher than 3 times the IQR from the upper quartile (e.g., values > 10 kWh in a 2-hour period) was removed to prevent distortion of the analysis and prediction accuracy. All temperature values were normalized using Min-Max scaling. Temporal features such as time of day (morning, afternoon, evening), weekday/weekend status, and holidays were extracted to enhance the predictive models. These features were used to adjust heating predictions during high-demand periods like weekends or holidays. Historical data was employed to train machine learning models (such as LSTM or Random Forest) to predict the indoor temperature and heating durations, providing predictive scheduling for energy-efficient heating control. Blockchain technology was used to securely log and share data related to temperature and energy usage across decentralized nodes within the smart home network, ensuring tamper-proof and traceable data handling. The radiator status and event detection were analyzed using the dataset to identify the exact times heating events occur (heat-on, cooling, and heat-off events), which are integral to time-shifted load balancing. The structure of the network is designed to process input data efficiently and optimize system performance through adaptive learning. All essential details of the model, including activation functions, weight adjustments, and training iterations, are clearly depicted, ensuring a thorough understanding of the network's functionality and optimization process. The AI-powered framework uses historical temperature data, real-time sensor readings, and predictive machine learning algorithms to control the heating system more efficiently. First, the room temperature dynamics need to be modeled based on heat transfer principles. This differential equation governs how the room temperature changes over time based on heating power and heat loss to the environment. To optimize energy use, a machine learning model (e.g., a recurrent neural network, RNN) is trained using historical temperature data and heating system responses. The goal is to predict the future temperature \(\:T(t+1)\) based on current and past data, allowing for pre-emptive heating adjustments. The predictive control algorithm then adjusts the heating power \(\:P\left(t\right)\) to maintain the desired temperature \(\:{T}_{\text{set\:}}\) within a predefined comfort range \(\:\left[{T}_{\text{m}\text{i}\text{n}},{T}_{\text{m}\text{a}\text{x}}\right]\). This feedback control loop ensures that the heating system responds dynamically to temperature predictions, optimizing energy use. To ensure that all data exchanged between smart home devices (e.g., sensors, heating systems) is secure, the system integrates blockchain. Each temperature reading and heating event is recorded as a transaction in the blockchain. Let \(\:{D}_{i}\)​ represent the data block for the iii-th transaction, containing temperature data and heating control decisions. A blockchain consensus algorithm (such as Proof of Stake) is used to validate each transaction. The final step is to optimize energy consumption while maintaining room comfort. The cost function for energy consumption over a period \(\:T\) is: where \(\:{P}_{\text{price\:}}\left(t\right)\) is the dynamic energy price at time \(\:t\). where \(\:{P}_{\text{m}\text{a}\text{x}}\) is the maximum power that the heating system can provide. The AI-powered system adjusts the heating power \(\:P\left(t\right)\) to minimize energy costs by scheduling heating during periods of lower energy prices and leveraging the predictive model to avoid unnecessary heating during peak times. To evaluate the performance of the system, key metrics such as energy savings, temperature control accuracy, and system scalability are computed. For example, energy consumption can be reduced by 15.8%, as mentioned in the abstract, by optimizing heating schedules and predictive controls. Additionally, the system can reduce computational load by 22% through time-shifted data processing. The simulations were conducted using MATLAB for predictive temperature control and blockchain implementation. A smart home model was developed, incorporating realistic thermal dynamics, energy consumption profiles, and user preferences. These simulation settings highlight the robustness and practical feasibility of the proposed framework, providing a clear basis for evaluating its performance. Figure 3 illustrates the system's ability to maintain room temperature under varying external conditions, demonstrating the novelty of our predictive AI-driven approach. The machine learning component anticipates temperature fluctuations and adjusts heating in real-time, enabling precise temperature control even as outside temperatures drop from 10 °C to 0 °C. The smooth curves indicate the system's rapid response to temperature deviations, showcasing the model's capacity for fine-grained temperature regulation, a significant improvement over traditional thermostat controls that react only after deviations occur. Room Temperature vs. Time for Different Outside Temperatures. The novelty lies in the dynamic power management based on predictive models and time-shifted analysis, which reduces unnecessary energy spikes. Unlike conventional systems that apply heating continuously to counter temperature drops, our system optimally adjusts heating power, minimizing energy consumption while maintaining comfort. The gradual power adjustments across scenarios reveal the system's ability to adapt its energy output intelligently based on anticipated needs. Heating Power vs. Time for Different Outside Temperatures. The comparison of room and outside temperatures highlights the system's capability to maintain indoor comfort despite significant external fluctuations in Fig. The novelty of this framework is in the integration of WSNs with AI and blockchain, which enables real-time environmental monitoring and secure data handling. The system responds predictively to outside temperature changes, reducing heating power when external temperatures are higher, and increasing it when external temperatures drop, thereby optimizing energy efficiency. This dynamic response mechanism showcases how the system outperforms traditional static thermostat controls. 6 showcases the overall energy efficiency of the system, with a clear trend showing increased consumption as outside temperatures decrease. The novelty of the approach is evident in how it minimizes energy consumption through predictive analysis and intelligent scheduling. Even though more heating power is required in colder conditions, the energy usage is optimized due to the system's ability to foresee heating demands and avoid overcompensation. The blockchain component ensures secure and transparent monitoring of energy usage, further enhancing the system's efficiency by allowing decentralized energy management in a smart home environment. Figure 7 depicts the living room radiator temperature over time, showcasing how the temperature dynamically adjusts as external conditions change. This figure highlights the impact of predictive temperature control, comparing power consumption with and without time-shifted analysis at varying outside temperatures. The proposed AI-powered system effectively manages the radiator's heat output based on predictive algorithms, ensuring optimal comfort while minimizing energy consumption. By anticipating temperature variations and adjusting the heating schedule accordingly, the system reduces energy use during periods of low demand. This is especially evident in the temperature curves, where the power consumption with time-shifted analysis demonstrates smoother transitions and fewer peaks. The method's superiority stems from its ability to balance energy efficiency and comfort. Traditional non-predictive systems react only after significant temperature changes occur, leading to more energy being consumed to restore the desired room temperature. In contrast, the predictive method preemptively adjusts the heating output, resulting in a more stable and efficient control. Additionally, the integration of blockchain technology enhances system security without compromising performance. By securing sensor data in a decentralized manner, the system eliminates vulnerabilities that may exist in conventional smart home setups, ensuring trust and transparency in data handling. Ultimately, this combined approach of predictive AI, time-shifted analysis, and blockchain demonstrates significant improvements over traditional methods, with lower energy consumption, higher system security, and a smoother user experience. This event detection is key to optimizing energy use, as it allows the system to adjust heating schedules based on real-time and predicted temperature needs. The proposed method's superiority lies in its combination of machine learning and time-shifted analysis. By leveraging AI algorithms, the system not only detects heat-on events but also predicts future heating needs, minimizing unnecessary energy use. Unlike traditional systems that activate heating based purely on current temperature, this approach proactively schedules heat-on events in advance. Additionally, the integration of blockchain ensures that the heat-on event data is securely logged, preventing unauthorized tampering and adding an extra layer of trust to the system. This hybrid approach, combining event detection, predictive scheduling, and secure data management, results in a more efficient and reliable temperature control system that outperforms conventional smart home solutions. Figure 9 displays the detection of scheduled heat-on events, where the system identifies and activates heating based on predefined schedules. This is achieved through predictive algorithms that optimize heating times according to historical data and anticipated temperature changes. The superiority of the proposed method lies in its ability to precisely control heating schedules while adapting to real-time conditions. Unlike conventional systems that rely on fixed schedules, the AI-powered framework dynamically adjusts heat-on timings to better match energy demand, reducing wastage. The system also leverages time-shifted analysis to further improve efficiency by minimizing peak energy loads. Moreover, blockchain integration ensures that all scheduled events are securely recorded, preventing unauthorized alterations and enhancing transparency. This combination of predictive control, time-shifted analysis, and secure scheduling significantly outperforms traditional thermostat systems, offering enhanced energy efficiency, reliability, and trust. This figure highlights the system's predictive capability to preemptively activate heating based on forecasted temperature needs. By forecasting temperature changes and adjusting the radiator's operation in advance, the system ensures a more consistent and efficient indoor climate. This proactive approach contrasts with traditional systems that often react too late, leading to less optimal energy use. The combination of advanced predictive algorithms and secure, real-time adjustments leads to faster responses and reduced energy consumption, showcasing the system's superior performance and efficiency. Time delay before radiator activation, showing efficient preemptive heating. This feature is crucial for maintaining optimal heating performance and preventing overheating. The proposed method stands out by providing accurate and timely detection of hot events, ensuring that heating levels are adjusted promptly to avoid excessive temperatures. This proactive management prevents energy wastage and enhances safety. By integrating predictive algorithms and real-time monitoring, the system not only optimizes energy efficiency but also improves overall reliability compared to traditional heating controls that may lack such precision. Radiator hot event detection, ensuring optimal heating and preventing overheating. Figure 12 depicts radiator cooling event detection, illustrating how the system identifies when the radiator's temperature decreases. The method's advantage lies in its precise detection and timely response to cooling events, allowing for effective management of temperature transitions. By accurately monitoring and adjusting the radiator's cooling process, the system enhances energy efficiency and maintains a stable indoor climate. This proactive capability surpasses traditional systems, which may react sluggishly to cooling needs, ensuring better comfort and reduced energy consumption. This figure demonstrates the system's ability to synchronize radiator output with living room temperature, ensuring consistent and efficient heating. The advanced predictive control not only keeps the room temperature stable but also reduces energy usage by adjusting radiator settings proactively. This approach provides superior performance compared to traditional systems, which may result in fluctuating temperatures and higher energy consumption. Figure 14 shows room temperatures during a single heating event, illustrating the system's precise control.This figure highlights how the proposed method maintains stable room temperatures throughout the heating process. The system's predictive algorithms ensure that temperature changes are smooth and well-managed, avoiding abrupt fluctuations. This capability improves comfort and energy efficiency compared to traditional systems, which may struggle to regulate temperature consistently during heating events. Table 5 is a table comparing the proposed methods in the article against traditional temperature control methods. This table highlights key features and performance metrics to illustrate the advantages of the proposed approach. The proposed method, leveraging predictive AI, time-shifted analysis, and blockchain, demonstrates significant improvements over traditional temperature control systems. It offers enhanced energy efficiency, better temperature stability, faster response times, and improved data security. These features collectively contribute to a more effective and reliable smart home temperature management solution. Table 6 is a numerical table comparing the performance of the proposed method with traditional temperature control methods. This numerical comparison illustrates the proposed method's superior performance across several key metrics. It also provides better temperature stability and faster response times compared to traditional methods. 15 clearly demonstrate the superior performance of the proposed AI-Powered Blockchain Framework for Predictive Temperature Control compared to traditional thermostat and PID control methods. In contrast, the thermostat and PID methods exhibit higher deviations. This reduction in energy consumption, combined with improved temperature control accuracy, underscores the effectiveness of the proposed system in optimizing energy usage while maintaining comfort, making it an ideal solution for modern smart homes. Comparison of temperature regulation and energy efficiency among control methods. To analyze the algorithm complexity for the proposed AI-powered blockchain framework for predictive temperature control in smart homes, we need to evaluate both the time complexity and space complexity of the key components involved. Here is a general approach to analyzing algorithm complexity for the proposed framework: Time Complexity: Collecting data from WSNs involves reading sensor data periodically, which is typically O(n) for n sensors. Space Complexity: Storing sensor data requires O(n) space, as data from each sensor must be stored temporarily for processing. Machine Learning Model for Predictive Temperature Control. Training the Model: The time complexity for training a machine learning model (e.g., decision trees, SVMs, or neural networks) is usually dependent on the number of data points (m) and features (d). For a model like neural networks, the complexity can be O(m.d.k), where k is the number of epochs. For tree-based models, the complexity is generally O(m.log(m)). However, as the blockchain grows in size, block verification and consensus can introduce additional computational complexity. Space Complexity: The space required for blockchain storage is proportional to the number of blocks and transactions. Event Detection: Detecting heat-on or cooling events involves analyzing the sensor data in real-time. The complexity of event detection is typically O(n), where n is the number of sensors. If using a more sophisticated model for anomaly detection, the complexity could increase to O(n.d). Adjustment Calculation: The time required to adjust temperature settings or initiate control actions is generally O(1) for each event. This is typically O(1) for each prediction, as it requires only the current state of the system. Network Lifetime Analysis: The complexity of analyzing network lifetime depends on the number of edge devices (n) and their energy consumption rates. For systems with multiple devices, the complexity could be O(n) or higher, depending on the communication and processing requirements. Space Complexity: The space complexity primarily depends on the data storage for sensor readings (O(n)) and the size of the blockchain (O(b)), resulting in an overall space complexity of O(n + b). The framework is designed to be efficient, with optimizations for both real-time control and secure data logging. This paper presents a novel AI-powered blockchain framework for predictive temperature control in smart homes, integrating wireless sensor networks and time-shifted analysis. The proposed system demonstrates significant advancements in energy efficiency, temperature stability, and data security compared to traditional methods. The key innovations of the framework include dynamic detection of heating and cooling events, predictive scheduling, and secure data handling through blockchain technology. Performance evaluations reveal that the system reduces energy consumption, achieves higher accuracy in event detection, and provides reliable temperature control with minimal fluctuations. The time-shifted analysis further enhances efficiency by reducing peak-time computational loads. Overall, the proposed method offers a robust and efficient solution for smart home temperature management, addressing the limitations of conventional systems. By combining advanced AI algorithms, secure blockchain integration, and real-time data processing, this approach sets a new standard in optimizing energy use and improving user comfort in smart homes. While the proposed AI-powered blockchain framework demonstrates significant potential for predictive temperature control in smart homes, there are several avenues for future research and improvements. One important direction is the enhancement of the framework's scalability, particularly when dealing with larger networks of IoT devices and edge nodes. The system's ability to efficiently handle a growing number of sensors and devices, while maintaining real-time performance and data integrity, will be crucial for large-scale deployments. Additionally, future work could explore the integration of more advanced machine learning models, such as deep reinforcement learning, to further optimize predictive control and improve energy efficiency. This would allow the system to continuously learn from real-time data and adapt to environmental changes more effectively. Another area for further investigation is the incorporation of more robust cybersecurity measures. Although blockchain technology provides a secure data logging mechanism, potential vulnerabilities may still exist in other components of the system. Future research could focus on enhancing the security protocols, such as incorporating secure multi-party computation or homomorphic encryption to protect sensitive data throughout the network. Lastly, real-world testing and validation of the framework in diverse smart home environments are essential. While the current results are promising, future work should include experimental validation in various settings to ensure the practical applicability and robustness of the system. This would also involve assessing the system's performance under different energy consumption scenarios, network conditions, and user behaviors. 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Al-Manara College for Medical Sciences, Amarah, Maysan, Iraq Faculty of Data Science and Information Technology, INTI International University, Persiaran Perdana BBN, Putra Nilai, Nilai, 71800, Malaysia Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar You can also search for this author inPubMed Google Scholar Cong Feng: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft.Ahmed Kateb Jumaah Al-Nussairi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft.Mustafa Habeeb Chyad: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft.Narinderjit Singh Sawaran Singh: Software, Validation, Visualization, Supervision, Writing- Reviewing and Editing.Jianyong Yu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft.Amirfarhad farhadi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft. 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There are no new alerts at this time A man was arrested Friday after being accused of kidnapping an Italian tourist and torturing him for weeks inside a Manhattan home in a bid to steal the alleged victim's bitcoin, according to officials, law enforcement sources with direct knowledge of the investigation and a criminal complaint. John Woeltz, 37, was taken into custody that same day and was arraigned Saturday on one count each of second-degree assault, kidnapping, unlawful imprisonment and criminal possession of a firearm, court records show. Law enforcement sources close to the investigation said Woeltz and the tourist, along with a third person, had been in business together for years. Police are still searching for the third business partner, who has not been publicly identified. An attorney for Woeltz declined to comment Saturday. The tourist, a 28-year-old man visiting from Italy, met his alleged kidnapper on May 6, law enforcement sources said. That's when, they said, Woeltz took the younger man to a home he was renting in Nolita, a neighborhood in Manhattan. According to a criminal complaint, Woeltz and an unidentified, “unapprehended male” kidnapped the victim, took his electronics and passport, and tortured him until he managed to escape weeks later. Law enforcement sources added that the men urinated on the victim and put an Apple AirTag around his neck to ensure he did not escape. Upon his escape, the 28-year-old ran into a traffic agent who called police to the home on Prince Street. The alleged victim was taken to a hospital in stable condition, police said. Law enforcement sources said Woeltz is a prominent crypto currency trader from Kentucky, estimated to be worth around $100 million. The victim has an estimated worth of $30 million, they said. Woeltz had reportedly been renting out the Prince Street residence at a monthly rate of between $30,000 and $40,000, the sources said. Law enforcement sources said there were cases of alcohol throughout the six-story residence, as well as stripper poles in the basement. Those sources said officers turned up multiple Polaroid pictures of the victim being tied up and tortured at the home. “A lot does go on here in New York. You can imagine, I've seen a lot, but this is the most unusual thing I've seen in 30 years in this neighborhood. I mean, that's just beyond what you can even describe or imagine.” Crawford said she couldn't imagine the motive of someone with so much wealth committing this type of crime. “All that money in the world didn't do anything. It's not going to help him,” she said. A few even took selfies in front of the home by the iron fence wrapped in police tape. Police officials and investigators were in and out of the house Saturday, several standing guard in the front and recommending to those passing by to Google the home address to learn more. Jonathan Dienst is chief justice contributor for NBC News and chief investigative reporter for WNBC-TV in New York. Marc Santia is a reporter for NBC New York. Maya Eaglin is a New York correspondent for NBC News and “Stay Tuned” on Snapchat. Samantha Cookinham is an NBC News assignment editor.

There are no new alerts at this time A man was arrested Friday after being accused of kidnapping an Italian tourist and torturing him for weeks inside a Manhattan home in a bid to steal the alleged victim's bitcoin, according to officials, law enforcement sources with direct knowledge of the investigation and a criminal complaint. John Woeltz, 37, was taken into custody that same day and was arraigned Saturday on one count each of second-degree assault, kidnapping, unlawful imprisonment and criminal possession of a firearm, court records show. Law enforcement sources close to the investigation said Woeltz and the tourist, along with a third person, had been in business together for years. Police are still searching for the third business partner, who has not been publicly identified. An attorney for Woeltz declined to comment Saturday. The tourist, a 28-year-old man visiting from Italy, met his alleged kidnapper on May 6, law enforcement sources said. That's when, they said, Woeltz took the younger man to a home he was renting in Nolita, a neighborhood in Manhattan. According to a criminal complaint, Woeltz and an unidentified, “unapprehended male” kidnapped the victim, took his electronics and passport, and tortured him until he managed to escape weeks later. Law enforcement sources added that the men urinated on the victim and put an Apple AirTag around his neck to ensure he did not escape. Upon his escape, the 28-year-old ran into a traffic agent who called police to the home on Prince Street. The alleged victim was taken to a hospital in stable condition, police said. Law enforcement sources said Woeltz is a prominent crypto currency trader from Kentucky, estimated to be worth around $100 million. The victim has an estimated worth of $30 million, they said. Woeltz had reportedly been renting out the Prince Street residence at a monthly rate of between $30,000 and $40,000, the sources said. Law enforcement sources said there were cases of alcohol throughout the six-story residence, as well as stripper poles in the basement. Those sources said officers turned up multiple Polaroid pictures of the victim being tied up and tortured at the home. “A lot does go on here in New York. You can imagine, I've seen a lot, but this is the most unusual thing I've seen in 30 years in this neighborhood. I mean, that's just beyond what you can even describe or imagine.” Crawford said she couldn't imagine the motive of someone with so much wealth committing this type of crime. “All that money in the world didn't do anything. It's not going to help him,” she said. A few even took selfies in front of the home by the iron fence wrapped in police tape. Police officials and investigators were in and out of the house Saturday, several standing guard in the front and recommending to those passing by to Google the home address to learn more. Jonathan Dienst is chief justice contributor for NBC News and chief investigative reporter for WNBC-TV in New York. Marc Santia is a reporter for NBC New York. Maya Eaglin is a New York correspondent for NBC News and “Stay Tuned” on Snapchat. Samantha Cookinham is an NBC News assignment editor.

As the cryptocurrency world continues to mature, Ripple's XRP token is once again a strong contender for mainstream adoption. Once overshadowed by regulatory uncertainty, XRP is now experiencing a remarkable resurgence driven by global partnerships, growing utility, and growing investor interest. Meanwhile, cloud-based mining platform BlockchainCloudMining is offering crypto enthusiasts, especially XRP holders, a whole new way to benefit from the digital asset ecosystem without actively trading or managing mining hardware. This article explores the current state of XRP, the importance of its growth to the broader crypto economy, and how BlockchainCloudMining enables passive income generation that fits perfectly with XRP's expanding footprint. The past few years have been nothing short of an up-and-down journey for XRP. The U.S. Securities and Exchange Commission (SEC) lawsuit that began in 2020 cast a shadow over Ripple's development. XRP is being relisted on mainstream exchanges such as Coinbase, and global financial institutions are returning to RippleNet and its On-Demand Liquidity (ODL) solution. By reducing transaction fees and enabling instant settlement, XRP continues to position itself as a transformative tool for cross-border finance. While XRP itself is not a mineable coin (unlike Bitcoin or Litecoin), investors looking to benefit from the overall growth of the cryptocurrency market often seek a diversification strategy. BlockchainCloudMining is a UK-registered platform that offers cloud-based cryptocurrency mining contracts. Instead of buying expensive mining machines or managing electricity, software, and cooling systems, users simply rent a portion of a global mining operation. These operations mine popular proof-of-work cryptocurrencies such as Bitcoin, Dogecoin, and Litecoin. This structure allows XRP enthusiasts to participate in the wider crypto mining economy and benefit from daily returns – while keeping an eye on XRP's long-term potential. For many members of the XRP community, passive income is a core strategy. Compared to day trading or constantly monitoring price fluctuations, BlockchainCloudMining offers a hands-free solution with tangible advantages: Registered users: Sign up and get an instant $12 bonus, which can be used for free mining, earning $0.6 per day. No technical skills required: Users do not need to understand hash rate, mining difficulty, or cooling infrastructure – all of which is managed by the platform. Environmental focus: BlockchainCloudMining utilizes green energy whenever possible, in line with the crypto industry's increasingly important sustainability goals. This makes the platform an attractive option for both cryptocurrency newcomers and experienced investors looking to optimize portfolio performance. The platform allows users to choose their preferred withdrawal currency, providing flexibility that matches personal investment strategies. There's a perfect storm brewing for XRP and platforms like BlockchainCloudMining. On one hand, XRP is seeing a return of institutional investors and renewed public interest. On the other hand, cloud mining is gaining traction as a convenient and reliable way to earn crypto without the need for large upfront capital or maintenance headaches. BlockchainCloudMining bridges the gap between passive income and active asset allocation. Investors no longer have to choose between mining, trading, or holding — they can combine all three strategies on one platform. BlockchainCloudMining stands out not only for its ease of use, but also for its adaptability — allowing users to easily earn across major assets and convert profits into XRP. As XRP recovers in utility, price, and ecosystem strength, combining it with a reliable income stream like BlockchainCloudMining is not only strategic, but also smart portfolio design. Visit blockchaincloudmining.com for more details and start earning daily income while connecting with the future of global finance. The statements, views and opinions expressed in this column are solely those of the content provider and do not necessarily represent those of Bitcoinist. Bitcoinist does not guarantee the accuracy or timeliness of information available in such content.