But there's more at stake than just getting a piece of the blockchain pie. Artègo Pizza, 900 W. 39th St., Kansas City; photo by Tommy Felts, Startland News “I want people to understand how they can empower themselves and their communities through participating in these DAOs,” said Mary McCawley, a digital art curator at and founder of Digital Dreams KC and key organizer for Nouns KC DAO. “A lot of people are expressing that they are seeing and feeling a lack of funding that's been cut because of the recent (presidential) administration's policy decisions, and I feel that the answer is to take the power back on the grassroots level, to recognize that we don't have to wait for Big Brother to put his hand out and make certain things possible.” Nouns KC DAO's site is under construction but should go live at citynouns.wtf. It organizes party sites on May 22 each year to mark the day Bitcoin — or any digital asset — was first used to buy a tangible item. He bought two pizzas for 10,000 BTC (or roughly $782 million in today-dollars). Nouns DAO operates by creating a work of art — an NFT, or nonfungible token — each day that is then auctioned to raise funds for the DAO. Those funds are used for just about anything: film and TV projects, nationwide cleanup actions, extreme sport athlete sponsorships, web3 infrastructure, etc. Watch the video below for more on how Nouns DAO works. The term DAO is an acronym for “decentralized autonomous organization.” These organizations are cropping up across the globe and are championed by blockchain enthusiasts — blockchain being the nearly immutable ledgering tech behind cryptocurrency. Another group, KC Futures DAO, launched last year and is in a fundraising cycle. They're partnered with KC nonprofit KC Digital Drive, which has a mission of making Kansas City a digital leader and to improve the quality of life for all people in the region. Blockchain's primary strength is that it is a decentralized record of transactions or actions that each must be fully verified by multiple authorities — usually automated systems — before any subsequent transactions or actions can occur. And it's a way that people can empower themselves when they feel unempowered.” And blockchain's ledgers can be private, public or permissions-based, with a DAO's likely being set to the latter so that only members can view all DAO actions or transactions. This creates, at least upon inception and in effect, a bottom-up style of decision making. “It's a way that the funds can't be mismanaged,” she explained. And it's a way that people can empower themselves when they feel unempowered.” Haines Eason is the owner of startup content marketing agency Freelance Kansas. His work has appeared in publications like The Guardian, Eater and KANSAS!

Coinbase will suspend trading of Movement's MOVE token, citing "recent reviews," following a CoinDesk investigation into market-making deals that experts said incentivized price manipulation. Movement Labs is currently investigating how a market maker may have gained access to a significant number of its tokens, which were then dumped on retail investors, causing its price to tank. The market maker, Web3Port, appears in contracts previously reported by CoinDesk. According to the CoinDesk report, Movement Labs co-founder Cooper Scanlon told employees last month that the firm was investigating how Rentech, which Movement believed was a subsidiary of Web3Port, got a hold of over 5% of Web3Port's MOVE tokens. According to contracts obtained by CoinDesk, Rentech had the ability to liquidate all of its tokens under certain circumstances, which experts said could have created an incentive for the firm to increase the token's value. Crypto exchange Binance later banned Web3Port, the market-maker, after $38 million in MOVE tokens in wallets tied to Web3Port were liquidated following MOVE's exchange debut. Coinbase did not share many details about the trading suspension, just announcing that it would do so on May 15 by 2:00 p.m. Pacific Time (21:00 UTC). Coinbase said it has already switched its order books to "limit-only mode" for MOVE tokens, meaning trades will only be executed at certain prices, rather than a token's spot price. Read more: Inside Movement's Token-Dump Scandal: Secret Contracts, Shadow Advisers and Hidden Middlemen He won a Gerald Loeb award in the beat reporting category as part of CoinDesk's blockbuster FTX coverage in 2023, and was named the Association of Cryptocurrency Journalists and Researchers' Journalist of the Year in 2020.

“The world looks to New York, and the world looks to [its] Department of Financial Services,” veteran crypto regulator Ken Coghill told a Cornell audience. Ten years ago, the state created the United States' first comprehensive regulatory framework for firms dealing in cryptocurrencies, including key consumer protection, anti-money laundering compliance and cybersecurity guidelines. It's against that background that Ken Coghill, NYDFS's deputy superintendent for virtual currencies, appeared at Cornell Tech's blockchain conference on April 25 to discuss “A New Era of U.S. Most of the firms that have come to the NYDFS for a BitLicense are crypto-native firms, and often, they are new to the financial world and not used to dealing with regulators. Many times they don't fully understand that they are in control of someone else's asset, noted Coghill at the New York City conference, adding: “We set the guardrails,” Coghill said, and it's the industry's job to figure out how to stay within those guardrails. The NYDFS can't possibly contemplate every element that's going to go wrong in a business. These days, more conventional financial institutions are becoming interested in crypto as well, added Coghill. Related: Trump's first 100 days ‘worst in history' despite crypto promises “On a per capita basis, we have more supervisory resources focused on crypto businesses than we do for all of those other [non-crypto] businesses,” said Coghill. This includes 3,000 banks, insurance companies and other financial institutions. It wasn't a direct route that brought Coghill to the NYDFS in July 2024. “I went for three years and stayed for 12 years,” spending that time primarily as an official regulating global systemically important banks, or G-SIBs. There, he was called upon to develop a cryptocurrency supervision model, and so he “spent the last six years regulating cryptocurrency in the Middle East.” Panel moderator Neil DeSilva asked Coghill what good regulation looks like. One can't eliminate risk entirely; to do so would quash all business activity. Related: Institutions break up with Ethereum but keep ETH on the hook There seem to be some “positive tailwinds” behind cryptocurrencies and stablecoins, noted DeSilva, himself a former chief financial officer for PayPal's Digital Currencies and Remittances business. “For DFS, it's largely business as usual,” Coghill commented. That's because New York State has long had crypto rules in place. “We have a team that practically sits in Washington and has discussions with Congressional members, talking about what we think will work and what won't work.” The NYDFS' crypto initiatives have influenced other US states. California's crypto reform legislation (AB 1934), signed into law in late September 2024, for instance, builds on New York State's BitLicense and its limited-purpose trust charter regulations for digital currency businesses — even though BitLicense's licensing requirements are relatively strict. Its application fee is $5,000 — too strict with its detailed anti-money laundering protocols and required audits and generally too much of an obstacle for innovative crypto-native firms. Crypto exchange Kraken exited the state when New York implemented its BitLicense requirement, for instance. Coghill was asked by DeSilva how the NYDFS actually looks at decentralized protocols compared with how it views the centralized financial institutions that it has historically regulated. “And so it's incumbent on us to filter those out.” It's not our job to look at and say, ‘Yes, this is fantastic. '” Rather, they examine a potential product and ask, “How is this bad for efficiency?” or “How is this bad for inclusion?” How does he think things will play out at the federal level this year regarding crypto and stablecoin legislation? Magazine: Crypto wanted to overthrow banks, now it's becoming them in stablecoin fight

Connecting decision makers to a dynamic network of information, people and ideas, Bloomberg quickly and accurately delivers business and financial information, news and insight around the world Americas+1 212 318 2000 EMEA+44 20 7330 7500 Asia Pacific+65 6212 1000 Connecting decision makers to a dynamic network of information, people and ideas, Bloomberg quickly and accurately delivers business and financial information, news and insight around the world Americas+1 212 318 2000 EMEA+44 20 7330 7500 Asia Pacific+65 6212 1000 Last year on the campaign trail, Donald Trump promised to be a champion of the Bitcoin mining industry in the US. Yet as American crypto miners begin to release their first quarterly earnings reports since Trump returned to the White House, it's clear the group is struggling. Seven of the eight biggest publicly traded miners that are based in the US are expected to post a loss when they report first-quarter results, according to analysts' estimates compiled by Bloomberg. The financial struggles come even after Bitcoin reached a record above $109,000 in January, and its price in the quarter averaged about 75% higher than it was in the first quarter of 2024.

Strict editorial policy that focuses on accuracy, relevance, and impartiality Quisque arcu lorem, ultricies quis pellentesque nec, ullamcorper eu odio. This is particularly noteworthy because the company's odds for a $SOL ETF were just 70% back in February, i.e., only a couple of months ago. Keep reading to find out all the reasons for being bullish on Solana right now. We'll also point you towards the best altcoins you can buy today to make the most of $SOL's bright future. While six asset managers await the SEC's permission to list $SOL ETFs, the crypto's future open interest reached $5.75B (or $40.5M $SOL). Moreover, this data is hot off the oven (recorded just a few days ago), so it's dead accurate and proof of strong institutional interest in the blockchain. With rising DEX volumes and a staggering $9.5B in total value locked (TVL), $SOL can easily surge past $200 well before a potential spot ETF approval on October 10. Technical analysis on $SOL's charts paints a pretty picture, too. The aforementioned bounce is also at a major support/resistance zone, further proving that the current bullish rally could hold itself nicely. However, as per the 4-hour chart, it should easily be able to do that, seeing as it's above all the major EMAs on that time frame. If you want to ride its growth, here are three Solana-based coins that could give you the best results in an already-pumping market. It's still in presale, which has turned out to be a massive success thanks to over $30M in early investor funding. Unlike other coins on Solana, Solaxy ($SOLX) won't just use the blockchain's low-cost, high-speed architecture to further its interests. Solana has become too popular for its own good, ironically. A recent massive influx of new investors to the network has overloaded it. As a result, it has been struggling with congestion and scalability issues. It will process some of Solana's transactions on a sidechain, reducing the burden on its mainnet and increasing transaction speed while lowering traffic congestion. In one word, Solaxy will optimize Solana's key areas. The utility-based nature of $SOLX significantly contributes to its uniqueness and makes it one of the best cryptos to buy today. Remember, prices are at their lowest during the presale stage, offering insane risk-to-reward possibilities (like a 29x increase by the end of 2026). With over $32.6M in presale funding so far, one $SOLX is currently available for just $0.00171. ai16z ($AI16Z) really stands out because it's the first-ever venture capital firm run solely by AI agents. A decentralized trading fund, ai16z uses AI agents to collect and analyze investment-related data, including current community sentiment. This speeds up the entire process of deciding whether a certain company is worth investing in. Aside from extra speed, AI will also reduce human error. What's more, these AI agents are also equipped to execute transactions both on-chain and off-chain, further improving flexibility and transaction speed. As a leader in the AI-powered decentralized trading segment, $AI16Z has unsurprisingly been one of the most successful cryptos of late. It reached a record market cap of around $2.6B in January 2025. Only $TRUMP, $PEPE, $SHIB, and $DOGE are above it. Launched in December 2022, $BONK currently boasts over 17,000% in lifetime returns, which more than justifies its massive popularity among crypto degens. After a nearly two-month-long steady decline, the token is climbing once again. Its actual launch, therefore, could be a canon event that sets forth a new future for $SOL and Solana-based cryptos. None of the above is financial advice, after all. Disclaimer: The information found on NewsBTC is for educational purposes only. It does not represent the opinions of NewsBTC on whether to buy, sell or hold any investments and naturally investing carries risks. You are advised to conduct your own research before making any investment decisions. Use information provided on this website entirely at your own risk. Le monde des mystery boxes crypto vient de passer à la vitesse supérieure. In april hebben Cardano whales voor meer dan $289 miljoen aan ADA gekocht. Strict editorial policy that focuses on accuracy, relevance, and impartiality Quisque arcu lorem, ultricies quis pellentesque nec, ullamcorper eu odio. NewsBTC is a cryptocurrency news service that covers bitcoin news today, technical analysis & forecasts for bitcoin price and other altcoins. Here at NewsBTC, we are dedicated to enlightening everyone about bitcoin and other cryptocurrencies.

In 2025, altcoins are where the action is, offering fresh utility, bold innovation, and serious upside potential. At TOKEN2049 Dubai, BlackRock's Robert Mitchnick touched on the ETF space, noting lukewarm appetite for altcoin ETFs. Projects like Dawgz AI ($DAGZ) are stepping up with real tech, staking rewards, and meme-powered momentum—mixing fun with function in a way that fits today's market. In this guide, we'll break down the best altcoins to buy now—tokens that combine narrative strength, advanced features, and high-growth potential. Bitcoin and Ethereum may be the pillars of crypto investing, but with their massive market caps, their ability to deliver exponential gains during bull runs is limited. Altcoins, on the other hand, are smaller and often more volatile, offering opportunities for investors willing to take calculated risks. Altcoins span multiple blockchain use cases, from AI-powered platforms to gaming ecosystems. By diversifying into altcoins, investors can explore emerging trends like DeFi, tokenization, and interoperability. This not only reduces portfolio risk but also captures value from groundbreaking innovations. For those looking to get started with a smaller budget, altcoins provide an attractive alternative. Many have lower price points, allowing investors to own entire tokens rather than fractional shares of Bitcoin or Ethereum. BlackRock's IBIT ETF alone consumed a staggering $643 million in one day. Yet, institutional players like BlackRock have indicated minimal interest in altcoin ETFs for now. This dynamic creates an interesting marketplace where retail and institutional investors may have differing priorities, opening opportunities for savvy altcoin investments. Here are the most promising ones to keep on your radar. Unlike typical meme coins, $DAGZ offers real utility through AI-powered tools like advanced trading bots and market analysis features. Early adopters gain exclusive staking rewards, while a comprehensive audit by SolidProof ensures strong security. Pro Tip: Grab $DAGZ during the presale phase to maximize long-term gains. TokenFi, developed by the Floki team, focuses on making tokenization accessible. The platform enables users to create tokens and tokenize real-world assets (RWAs) like real estate or artwork without technical knowledge. Its governance token, SAFE, plays a pivotal role in decision-making and platform improvements. This earned $GHX can be spent in a gamer-focused marketplace, blending gaming activity with blockchain utility. Investing in the right altcoins requires a balance of due diligence and timing. Start by understanding the fundamentals of each project, including its tokenomics, utility, and market appeal. Align your investments with emerging trends like AI, gaming, and tokenization for the best chances of long-term growth. Among the options, Dawgz AI ($DAGZ) not only encapsulates 2025's strongest crypto narratives but also provides genuine utility, making it a top altcoin to consider. Early movers always see the most significant gains, so don't wait too long. Coins with low market caps and innovative technology usually have the potential for exponential growth. If you're looking for both utility and growth, Dawgz AI is a strong contender. Its blend of AI-powered features and low-entry cost positions it as a standout investment.

HOUSTON, May 01, 2025 (GLOBE NEWSWIRE) -- KULR Technology Group, Inc. (NYSE American: KULR) (the "Company" or "KULR"), a global leader in advanced energy management solutions, today announced the launch of a blockchain-secured supply chain initiative to safeguard product-related data being offered to their customers. By recording each inventory item as a tamper-proof, timestamped entry on a distributed ledger, KULR eliminates the risk of data manipulation and ensures that all stakeholders have access to a single source of truth. This reduces reliance on centralized systems that are vulnerable to outages or unauthorized access. Additionally, blockchain's traceability features enable KULR to verify product authenticity, monitor asset movement, and streamline audits. One of the initial products that will be recorded on the blockchain will include lithium-ion batteries screened to meet NASA's WI 37A rigorous methodology. Utilizing the blockchain to create a secure and immutable record of the testing data will benefit all future users of these batteries, who will have access to proven testing results. "After launching our bitcoin treasury strategy last December, we became curious about what other aspects of the crypto ecosystem could benefit our business operations. Moving our supply chain tracking onto the blockchain seemed like a natural move for us,” KULR CEO Michael Mo commented on today's news. “We are leveraging proven blockchain technologies to deliver commercial applications that provide our customers with a trustworthy data source and improved operational efficiency. Each battery that KULR manufactures will have its metadata minted as a non-fungible token (NFT) on the blockchain. For large quantity customers, KULR will establish wallets to easily transfer NFTs associated with their orders. KULR designed an internal UI tool that synchronizes with encrypted KULR-owned wallets to view the current inventory. As a result of KULR's Bitcoin Treasury, CEO Michael Mo will be speaking at Strategy World next week in Orlando, Florida. For more information about KULR Technology Group and its bitcoin strategy, please visit www.kulr.ai. strategic collaboration with AstroForge, an asteroid resource extraction pioneer, to develop a custom 500 watt-hour (Wh) KULR ONE Space (K1S) battery pack NYSE American: KULR announced today that it has been awarded $6,703,500 by the Texas Space Commission as part of a $26 million grant

Unlock stock picks and a broker-level newsfeed that powers Wall Street. - 47 3D Egg Experiences Enabled by Data Vault Teach about U.S. National Parks, Protected Endangered Species, Geography and American History - BEAVERTON, Ore., May 01, 2025--(BUSINESS WIRE)--Datavault AI Inc. (Nasdaq: DVLT), a trailblazer in data sciences and Web 3.0 asset monetization, proudly announces its innovative VerifyU™ team has minted three complete sets of 47 3D NFT eggs, which are displayed at www.databunny.us, immutably honoring the shared educational mission of both Datavault AI and the White House Historical Association to teach American history while supporting the association's mission of using technology to tell its stories through The People's House. The experiential and educational NFT Eggs leverage DVHolo™ holographic display and ADIO® cryptoanchor technologies used to authenticate the "2025 Natural Heritage Preservation NFT Easter Egg Basket" and engage Americans in learning about the nation's National Natural Heritage. This initiative, showcased at the official White House Historical Association's Easter brunch on April 21, 2025, highlights the bipartisan legacy of U.S. Presidents in preserving national parks and endangered species through an interactive, blockchain-secured digital collectible. The Educational NFT Egg Strategy introduces a collection of digital Easter eggs, each a unique non-fungible token (NFT) accessible via Datavault AI's DVHolo holographic displays and delivered securely through ADIO's cryptoanchor technology. Web 3.0 Security: Powered by Datavault AI's Information Data Exchange® (IDE) and ADIO technologies, each NFT is a crypto-anchored, tamper-proof digital asset, ensuring authenticity and secure ownership for young collectors. The Chia blockchain was selected for this project for its energy effective management of a large and reliable blockchain that matched perfectly with the project's requirements. The unique qualities of our NFTs reflect Datavault AI's blockchain agnostic approach of a need, budget and commitment to delivering customer focused Web 3.0 solutions. 3D Experiential Educational NFT Eggs were accessible through DVHolo stations at the event, where participants' mobile phones could bring to life Yellowstone (est. This secure connection remains tied to the NFT—represented as an egg—for future activations and engagement. The initiative is designed to support multiple generations by serving as a U.S.-based time capsule, educating users on contemporary conservation efforts and the historical importance of iconic landmarks in a gamified and interactive format. The platform now supports educational technology initiatives at institutions such as Arizona State University and Lane College, utilizing Web 3.0 tools—like our Easter Eggs—to enhance learning experiences. "We're redefining how young people connect with America's natural heritage," said Nathaniel Bradley, CEO of Datavault AI. "By combining DVHolo's immersive visuals with ADIO's Web 3.0 data indexing and cybersecurity, along with the immersive WiSA HD audio environment, interactive soundscapes transported users through 47 National Parks in about 8 minutes. I'm most certainly grateful to our marketing and creative teams who worked over this holiday to make this exhibit a success and make an immutable mark in our history. Datavault AI participated at the White House Historical Association's Easter Egg Roll brunch, where it further demonstrated its technologies to interested attendees. This collaboration underscores the company's commitment to blending innovation with cultural and educational initiatives. Through innovative exhibits, objects, and interactive media, visitors can gain a deeper understanding of the White House's role in American history. The Data Science Division leverages the power of Web 3.0 and high-performance computing to provide solutions for experiential data perception, valuation and secure monetization. Datavault AI's cloud-based platform provides comprehensive solutions serving multiple industries, including HPC software licensing for sports & entertainment, events & venues, biotech, education, fintech, real estate, healthcare, energy and more. The Information Data Exchange® (IDE) enables Digital Twins, licensing of name, image and likeness (NIL) by securely attaching physical real-world objects to immutable metadata objects, fostering responsible AI with integrity. This press release contains "forward-looking statements" within the meaning of the Private Securities Litigation Reform Act of 1995, as amended, and other securities laws. Words such as "expect," "will," "anticipates," "estimates" and variations of such words and similar future or conditional expressions are intended to identify forward-looking statements. Readers are cautioned not to place undue reliance on these forward-looking statements. Actual results may differ materially from those indicated by these forward-looking statements as a result of various risks and uncertainties including, but not limited to, the following: the risk that we are unable to satisfy all closing conditions in connection with the senior notes issuance described above, and the acquisition of certain assets from CSI; our ability to successfully integrate all IP that we have acquired; risks regarding our ability to utilize the assets we acquire to successfully grow our market share; risks regarding our ability to open up new revenue streams as a result of the various agreements we have entered into and assets we have acquired; our current liquidity position and the need to obtain additional financing to support ongoing operations; general market, economic and other conditions; our ability to continue as a going concern; our ability to maintain the listing of our common stock on Nasdaq; our ability to manage costs and execute on our operational and budget plans; our ability to achieve our financial goals; the degree to which our licensees implement the licensed technology into their products, if at all; the timeline to any such implementation; risks related to technology innovation and intellectual property, and other risks as more fully described in our filings with the U.S. Securities and Exchange Commission. The information in this press release is provided only as of the date of this press release, and we undertake no obligation to update any forward-looking statements contained in this communication based on new information, future events, or otherwise, except as required by law. Investors: David Barnard, Alliance Advisors Investor Relations(415) 433-3777datavaultinvestors@allianceadvisors.com Media Inquiries: Sonia Choi(844) DATA-400sonia@vault.email

According to local news outlet Malay Mail, the raids on two separate premises led to the seizure of 45 Bitcoin mining machines worth roughly $52,145 (RM225,000), alongside other equipment. The operation was carried out in collaboration with Tenaga Nasional Berhad's (TNB) Special Engagement Against Losses (SEAL) unit. However, tampering with the grid's electricity connectivity is punishable by up to five years' imprisonment and/or a fine of up to $21,500 (RM100,000.) Illegal Bitcoin mining operations, which siphon energy from the national grids, are a growing problem in East and Southeast Asia. A 2025 report from the United Nations Office on Drugs and Crime (UNODC) highlighted that international criminal groups operating in the region are attracted to Bitcoin mining as it allows them to circumvent anti-money laundering laws compared to more traditional forms of crime. A Bloomberg report from last year indicated that China's decision to ban Bitcoin mining in 2021 may have helped to push this type of illegal activity into Southeast Asia. This trend has had real-world consequences for Malaysia before. In February of this year, an explosion in the Bandar Puncak Alam city, Malaysia, revealed a nine-rig illegal Bitcoin mining operation. Akmal Nasrullah Mohd Nasir, the country's deputy energy transition and water transformation minister, told Malay Mail in July 2024 that illegal crypto mining has cost the country at least $722 million (RM3.4 billion) in electricity costs between 2018 and 2023. Neighboring Thailand has also had its fair share of high-profile crypto mining crackdowns, including one involving 1,000 machines earlier this year thought to have stolen $3 million from the nation's grid. The latest news, articles, and resources, sent to your inbox weekly.

Bitcoin has surged toward $100,000 per bitcoin, soaring this week to levels not seen since before the markets' tariff tantrum (and helped by a predicted $10 trillion Wall Street surprise). Front-run Donald Trump, the White House and Wall Street by subscribing now to Forbes' CryptoAsset & Blockchain Advisor where you can "uncover blockchain blockbusters poised for 1,000% plus gains!" Now, after a leak revealed growing establishment “panic” over U.S. president Donald Trump's plans for bitcoin and crypto, analysts are warning a Federal Reserve “nightmare” is coming true as data reveals the worst U.S. quarterly economic performance in three years. Commerce department data showed U.S. gross domestic product (GDP) for the first quarter contracted at a 0.3% annualized rate, weighed down by a record surge in imports. In September, Fed chair Jerome Powell surprised markets with an interest rate cut, kicking off a monetary policy loosening cycle that's been on pause for months. “We have rising inflation with a weakening economy,” they wrote following this week's data drop. The Fed will meet next week to decide whether to change interest rates, with the market currently predicting it will leave rates on hold. "For bitcoin, such a scenario is a positive factor, since the easing of monetary policy traditionally leads to an influx of liquidity into risky assets," Tracy Jin, chief operating officer of bitcoin and crypto exchange MEXC, said in emailed comments. Bitcoin's performance in recent months at first disappointed traders as the bitcoin price fell along with stocks in the face of Trump's escalating trade war. “Since president Trump's Liberation Day announcement, bitcoin has charted its own course, surging past $90,000 and demonstrating remarkable resilience against the headwinds affecting traditional markets," David Hernandez, crypto investment specialist at 21Shares, said via email. “This outperformance relative to the Nasdaq represents a significant departure from historical patterns. As the impacts of president Trump's tariff policies begin to materialize more fully across the economy, we anticipate bitcoin could further disassociate from equities. The asset shows strong potential to outperform other risk assets as investors seek hedges against policy-driven market volatility.”

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. Smart waste management is vital for reducing environmental impact and improving quality of life in smart cities. This study presents an AI-driven waste classification model that integrates IoT and Blockchain technologies. IoT-connected bins transmit data to a central server, which uses blockchain to ensure secure, transparent data storage. AI algorithms, including machine learning (ML) and deep learning (DL), classify waste in real-time, optimizing waste collection and recycling. Blockchain ensures data integrity, while ML and DL models enhance sorting efficiency. The system aims to improve waste management and sustainability through intelligent decision-making and secure data handling. Performance will be assessed using retrieval metrics and visualization tools to evaluate the impact of hybrid ML and DL models on waste detection and classification. As urban populations continue to grow, the need for smarter and more efficient waste management systems has become increasingly critical1. The integration of IoT and AI offers a transformative solution for creating greener, safer, and more efficient cities2,3,4. This research proposes an innovative, IoT-based smart container system, designed to optimize waste collection processes and reduce environmental and operational inefficiencies. The smart container is equipped with an ultrasonic sensor that automatically and periodically scans the fill level inside the waste container, providing real-time updates on waste accumulation5. Once the sensor detects that the container has reached a certain fill threshold, it sends an immediate notification to the waste collector, allowing for more responsive and timely waste collection. This real-time data-driven approach minimizes unnecessary collection trips, ensuring that waste collection vehicles only operate when needed, thereby saving fuel, labor, and time. Furthermore, AI enhances the system by optimizing collection routes in real-time, helping waste collection vehicles to follow the most efficient paths6. This improvement not only speeds up the collection process but also contributes to reducing carbon emissions, preserving the city's landscape, and mitigating health and environmental risks associated with waste accumulation. CNNs are a powerful deep learning model specifically designed for image recognition tasks. They are chosen for their ability to efficiently learn patterns from visual data, making them particularly effective for classifying waste materials into categories such as recyclable and non-recyclable. CNNs have been widely used in similar applications due to their high accuracy and ability to generalize from labeled datasets. The choice of CNNs over other AI models, such as recurrent neural networks (RNNs) or transformers, is based on their superior performance in processing and classifying visual data, which is critical for this waste management system where image-based waste identification is essential. The CNN model also benefits from transfer learning, enabling it to be fine-tuned using pre-trained models, thus reducing the computational load while maintaining high classification accuracy. CNNs not only improve waste classification accuracy but also contribute significantly to energy optimization in waste management systems. By predicting waste accumulation patterns, optimizing collection routes, and reducing unnecessary vehicle trips, CNN-based models help lower fuel consumption and CO2 emissions. Additionally, lightweight deep learning architectures minimize computational overhead, making real-time waste classification more energy-efficient7,8,9. The integration of AI with IoT-enabled smart bins further enhances sustainability by optimizing sensor operations and reducing energy wastage in data transmission. These improvements align with smart city initiatives, promoting eco-friendly and efficient waste management solutions. The novelty of this system lies not only in the use of AI for waste classification but also in its integration with IoT and Blockchain technologies. The system incorporates Blockchain to organize and securely store waste-related data into distinct blocks, ensuring data integrity and security. By analyzing collected data and applying sophisticated algorithms, the system can predict optimal collection schedules and routes, adapt to changes, and continuously improve its functionality, resulting in a more efficient and user-friendly waste management solution. In summary, this study introduces a novel waste management system that combines real-time monitoring, smart data processing, secure blockchain technology, and AI-based waste classification, offering significant improvements over existing systems in terms of efficiency, scalability, and security. The key contributions of this study are as follows: Real-time monitoring of waste container fill levels through IoT-enabled ultrasonic sensors. Optimization of waste collection routes using AI algorithms, reducing fuel consumption, labor, and environmental impact. Development of a hybrid AI system that continuously learns and adapts to improve waste management functionality over time. Reduction of operational costs by minimizing unnecessary waste collection trips and optimizing vehicle routes. Simulation results, discussion, conclusion and future work are given in “Simulation results”, “Discussion”, “Conclusion” and “Future work” sections respectively. Artificial Intelligence (AI) technology has seen a significant rise in its application across various sectors, one of which is solid waste management10. This integration of AI into the waste management process has been instrumental in enhancing the efficiency of the systems from the initial stages of waste collection to its final disposal, as discussed in research by Kolekar et al.11 and Vitorino et al.12. In an effort to enforce waste segregation policies more effectively, researchers have been exploring the use of AI for the classification and recycling of waste. This shift towards AI-driven methodologies is seen as an essential response to counter the mounting issues of waste accumulation and the limitations of manual classification systems. A detailed literature review by Abdallah et al.13 identifies several AI models that are commonly used for waste classification, including Artificial Neural Networks, Support Vector Machines, Linear Regression, Decision Trees, and Genetic Algorithms. These models significantly improve the accuracy and efficiency of waste sorting, which is a crucial step in the recycling process. From a commercial perspective, there are primarily three types of AI solutions in the market that cater to waste classification and recycling needs. Each product serves a vital role in optimizing the waste management process: AI-based waste classification software enhances the precision in identifying and categorizing different types of waste materials. AI-powered waste sorting machinery is utilized in advanced sorting facilities, where it further segregates waste into recyclable and non-recyclable materials, thereby fine-tuning the recycling operations. These AI advancements not only bolster the effectiveness of waste management practices but also promote environmental sustainability. They contribute to higher recycling rates and reduce dependency on landfills. The ongoing development and integration of AI in waste management are anticipated to bring forth substantial improvements in the sector, enhancing waste handling and resource conservation on a global scale. Moreover, recent advancements in the field of machine learning, especially through the application of supervised learning techniques and deep convolutional neural networks (CNNs), have shown promising results. Further comparisons within the study assessed the performance of various machine learning models, including Decision Trees, Random Forests, Support Vector Machines (SVM), and Deep Neural Networks. Among these, the CNNs displayed superior accuracy, achieving a remarkable 90% accuracy rate, thus underscoring their potent capability in managing complex data sets more effectively than other popular algorithms. Building on the effectiveness of CNN, a study by Sandler et al. In15 highlighted the application of a specific CNN architecture known as Xception Net. This model was tested on a Synthetic Aperture Radar Target Recognition Dataset, presenting a multi-class classification challenge. Xception Net was evaluated alongside prominent transfer learning models including VGG16, Resnet152, and Inception V3. The analysis demonstrated that Xception Net surpassed these models in critical performance metrics like Top-1 Accuracy and Top-5 Accuracy, showcasing its superior classification capabilities at various thresholds. The absence of fully connected layers in Xception Net's architecture might contribute to its effectiveness, indicating a potential advantage in complex image recognition tasks. These results emphasize the progressive capabilities of CNNs and their evolving designs in addressing complex machine learning and image recognition problems, setting new standards for future research and applications. In a later study proposed in16, an advanced system for classifying waste using image processing and CNNs was developed, focusing particularly on identifying different types of plastics, primarily polyethylene. Similarly, in17, Sreelakshmi and her team introduced an approach using Capsule Neural Networks (Capsule-Net) for solid waste management, effectively distinguishing between plastic and non-plastic materials. This innovation marks a significant advancement in waste management technology. The study achieved high accuracy rates on two publicly available datasets and tested the integration across various hardware platforms. Additional research in18 by Huiyu, O. G., and Kim S. H. introduced a novel waste classification model using deep learning techniques aimed at recycling applications. In the same vein, Adedeji and Wang19 proposed a deep learning framework that autonomously recognized and classified waste materials, proving effective in identifying recyclables. Furthermore, Nowakowski and Pamuła20 presented a waste classification method using a pre-trained CNN model, ResNet-50, combined with Support Vector Machines (SVM), achieving 87% accuracy on a public dataset. These studies predominantly focus on the architectural design of waste classification systems using deep learning, without integrating IoT for waste management. Conversely, Samann22 described a significant advancement in automated waste management processes with a smart trash bin equipped with sensors and a real-time monitoring system, though this did not incorporate machine learning. Similarly, Malapur and Pattanshetti23 introduced a cost-effective smart trash bin enhanced with IoT technology, capable of notifying users via SMS when waste levels exceeded set thresholds, incorporating additional features like a PIR motion sensor and audio messages for user interaction. In their research24 introduced an economical and efficient waste management approach for smart cities. Similarly, ALFoudery et al.25 developed a Raspberry Pi and infrared sensor-based IoT model to enhance waste collection, with the system manager overseeing the scheduling and routing to maximize efficiency. In another study, Balaji et al.26 created a smart trash bin that could detect fill levels using an infrared sensor, with data sent to an Android app via a Wi-Fi and web server setup. Hong et al.27 also presented a smart trash can utilizing IoT technology and a Raspberry Pi. Additionally, Bai et al.28 implemented an IoT-based smart garbage system to minimize food waste, using mesh technology for effective component management and integrating a router and server to gather and analyze data related to food poisoning, resulting in a 33% reduction in food waste. Several studies have advocated for IoT-based waste management systems, though none have explored structural designs using deep learning. Muthugala et al.29 introduced a waste collection robot that navigated autonomously and used deep learning to detect waste with 95% accuracy. Spanhol et al.30 proposed a floor cleaning robot that used a fuzzy inference system to optimize area coverage and energy consumption, employing the Weighted Sum Model (WSM) for decision-making based on user-defined preferences. While the works of29 and30 presented innovative robotic solutions, they did not focus on IoT contributions. Zhu et al.31 discussed the fundamental aspects of blockchain and IoT, reviewing interconnection, interoperability, reliability, and security in daily operations. Reyna et al.32 highlighted the challenges, future prospects, and benefits of integrating blockchain with IoT, proposing a lightweight blockchain framework for IoT devices that significantly reduces overhead and processing time while enhancing security, as shown in research by33. Samaniego et al.34 focused on blockchain as a service within IoT, exploring various case studies and simulations with reported accuracies. Novo35 detailed an architecture for managing roles and permissions in realistic IoT scenarios, proposing a scalable architecture with clear advantages. A decentralized solution has been presented in36 for solid waste management by integrating blockchain technology with Vehicular Ad Hoc Networks (VANETs). It utilizes advanced ultra-high frequency (UHF) technology and Internet of Things (IoT) devices to enable real-time tracking of waste vehicles and detection of waste bins. Geo-fencing techniques are employed to monitor and ensure timely waste collection from designated spots. The application of blockchain enhances the security, reliability, and trustworthiness of machine-to-machine (M2M) communications across IoT devices. Experimental results from a pilot project in Karachi, Pakistan, demonstrate the system's effectiveness in real-time tracking, intelligent identification of waste bins, trash weighing, and monitoring waste collection using geo-fencing. The study suggests that blockchain-enabled VANETs could be applied to route management, intelligent transportation, and fleet management systems in the future. Heidari et al.37 addresses the challenges of rapid urbanization and inadequate solid waste management by proposing a smart waste management system that leverages blockchain technology. The system aims to mitigate the adverse environmental impacts associated with traditional waste management services. By utilizing blockchain and smart contracts, the proposed system enhances transparency, accountability, and efficiency in waste management processes. The study emphasizes the potential of blockchain to revolutionize waste management by providing a secure and transparent framework for waste tracking and disposal. The IoT architecture of the proposed waste management system is designed to support efficient data collection, processing, and transmission from sensor nodes installed in waste containers. The system integrates various sensor types, each serving a specific function to monitor and optimize waste management operations. These images are sent to the system for analysis, where deep learning models, specifically CNNs, classify the waste into recyclable and non-recyclable categories. The proposed system incorporates a blockchain-based architecture to ensure data integrity, security, and transparency in waste classification and management. This architecture consists of multiple layers, each serving a distinct function. The Application Layer hosts decentralized applications (DApps) and smart contracts, which automate data logging and waste classification verification. The Consensus Layer ensures secure validation of transactions using a consensus mechanism, preventing unauthorized modifications to recorded data. The Network Layer facilitates peer-to-peer communication between IoT-enabled waste bins, cloud servers, and blockchain nodes, enabling real-time data sharing. Finally, the Data Layer is responsible for securely storing waste classification records in an immutable ledger, ensuring traceability and accountability. By leveraging this layered architecture, the system enhances security and operational efficiency while supporting automated, data-driven decision-making in smart waste management (see Fig. Blockchain technology ensures secure, transparent, and tamper-proof waste management. However, like any distributed system, blockchain is vulnerable to various security threats at different layers. To facilitate efficient data transmission, the system uses two primary communication protocols: MQTT and CoAP. It ensures low-latency data transmission, allowing for timely decision-making. CoAP, on the other hand, is designed for resource-constrained devices and supports simple request/response communication, making it a suitable choice for transmitting data from the various sensor nodes. Once the data is collected by the sensors, it is processed by a microcontroller and transmitted to a cloud-based platform for further analysis and storage. Both MQTT and CoAP are utilized depending on the sensor's capabilities and the data's specific requirements. The data is then analyzed using AI algorithms to generate optimized waste collection schedules, identify inefficiencies, and trigger automated actions, such as notifying waste collection personnel or adjusting collection routes. Blockchain technology is integrated into the system to ensure the integrity and security of the transmitted data, storing it in a tamper-proof ledger for transparency and accountability. In response to the growing challenges of waste classification in smart cities, this study proposes an AI-driven waste management framework. The methodology is structured around two primary components: waste classification using a convolutional neural network (CNN) and the architectural design of smart trash bins equipped with real-time data monitoring via the Internet of Things (IoT). This dual approach enhances efficiency in waste management systems. CNNs are particularly suitable for this task as they can learn and generalize patterns associated with various waste types from labeled image datasets. Since large-scale waste classification datasets are limited, transfer learning techniques were employed, utilizing pre-trained CNN models that are fine-tuned for this specific application. This approach not only enhances classification accuracy but also reduces computational overhead. The loss function for classification is cross-entropy: where yi is the true label (one-hot encoded), \(\hat{y}_{i}\) is the predicted probability for class iii. These bins are equipped with IoT-enabled sensors that facilitate real-time waste monitoring and data transmission. Camera Module: Captures images of waste items and transmits them to the microcontroller for processing. Load Sensor: Determines the total weight of waste accumulated over time. Microcontroller & Servo Motor: Processes CNN classification results and controls the bin's sorting mechanism. Based on classification outputs, the servo motor directs waste to the appropriate bin (digestible or indigestible). A block diagram of the system architecture illustrates the interaction between these components, ensuring seamless integration of AI and IoT functionalities (see Fig. The collected data is transmitted to a cloud-based platform and accessed via the Blynk application, enabling remote waste monitoring and management. The total accumulated weight of waste is measured using: This study introduces an AI-enabled waste classification management framework encompassing waste collection, sorting, and disposal. AI algorithms continuously analyze waste data, improving classification accuracy and optimizing resource allocation. Integration with Smart City Infrastructure: The system enables real-time tracking of waste levels and disposal patterns. Sustainable Waste Processing: AI enhances recycling strategies by improving material recovery rates and minimizing landfill contributions. Sorting accuracy improvement using AI can be represented as: AI optimizes waste collection using predictive analytics. where di is the distance to waste bin i, Wi is the weight of waste at iii. The AI-driven system classifies waste into four categories: Food waste: Requires specialized processing due to decomposition properties. Hazardous waste: Demands careful handling to prevent contamination. Residual waste: Often directed to incineration or landfills, but AI-based reclassification reduces waste disposal inefficiencies. Recyclable waste: Advanced sorting technologies facilitate material recovery, supporting circular economy initiatives. The efficiency of AI-enhanced recycling is: where Mr is the mass of successfully recycled materials, Mt is the total recyclable waste input. Once classified, waste is directed to appropriate treatment facilities, including recycling plants, hazardous waste centers, and municipal sanitation systems. The AI-driven system replaces traditional manual sorting, reducing human error and improving waste management efficiency. By leveraging AI and IoT, this methodology paves the way for a more sustainable and cost-effective waste management system, aligning with smart city initiatives and environmental sustainability goals. The system used for simulation was designed to evaluate various aspects of the model, including classification accuracy, processing time, data integrity, waste collection efficiency, and environmental impact. The simulation environment included high-performance computing hardware to run deep learning models, enabling the efficient processing of images for waste classification through Convolutional Neural Networks (CNN) with transfer learning. This setup was key to achieving a low latency of 1.2 s per image, ensuring that the waste sorting process was efficient and real-time. In addition to the waste classification component, the system incorporated Blockchain technology to ensure secure and tamper-proof data management. The Blockchain framework was employed to store and track waste management data on a decentralized ledger, providing data integrity and real-time traceability. The system also included optimized waste collection mechanisms, such as route planning and resource allocation algorithms, aimed at improving collection efficiency and minimizing environmental impact. 3, the proposed AI-driven waste classification model, utilizing a CNN with transfer learning, significantly improves accuracy compared to previous methods. The 202136 and 202237 models relied on traditional machine learning approaches, such as SVM and decision trees, which lacked deep feature extraction capabilities. By leveraging pre-trained deep learning models and fine-tuning them for waste classification, our approach achieves an accuracy of 95%, outperforming the 88% (2021) and 90% (2022) methods. The higher accuracy ensures that recyclable materials are correctly classified, leading to improved waste sorting efficiency and reduced contamination in recycling streams. Figure 4 depicts the processing time over three different mechanisms. The 2021 model used traditional feature extraction techniques, which required additional processing time. The 2022 method incorporated deep learning but lacked optimization for real-time execution. Our methodology optimizes CNN inference using lightweight architectures and edge computing, reducing computational overhead and making real-time classification feasible. This low latency is essential for practical deployment in smart bins, allowing waste to be sorted instantaneously without significant delays. 5, the integration of Blockchain technology in our system ensures secure and tamper-proof data management. The proposed framework achieves a data integrity score of 98%, surpassing 85% (2021) and 90% (2022). Previous methods stored data on centralized cloud servers, making them vulnerable to data breaches and manipulation. Our decentralized ledger system provides real-time traceability, ensuring that waste collection and classification data remain authentic and immutable. This feature is particularly beneficial in waste management contracts and audits. The improved efficiency of the proposed approach suggests enhanced optimization in waste collection strategies, potentially due to better route planning, resource allocation, or technological advancements. The superior performance of the proposed approach highlights its effectiveness in minimizing environmental impact, likely due to improved operational efficiency, optimized routing, and reduced fuel consumption. Precision, which measures the proportion of correctly identified recyclable items among all predicted recyclables, is crucial for minimizing contamination in recycling streams. The improvement in precision can be attributed to the use of Convolutional Neural Networks (CNNs) with transfer learning, allowing the system to capture complex waste patterns and reduce false positives. This enhancement ensures that only actual recyclable materials are classified, improving the efficiency and quality of waste sorting and contributing to more sustainable waste management. Recall is a crucial performance metric that measures the ability of the model to correctly identify all relevant instances, specifically the proportion of actual positive instances (True Positives) that are correctly detected by the model (True Positives + False Negatives). In the context of waste management, a higher recall ensures that most recyclable materials are accurately identified and categorized, minimizing the risk of recyclable items being discarded as waste. This improvement is primarily due to the fine-tuning of pre-trained deep learning models through transfer learning, which allows the system to better recognize and classify a wider range of waste materials, reducing the number of false negatives. This enhancement in recall plays a vital role in ensuring that recycling systems are more efficient and reliable, ultimately contributing to the reduction of contamination in recycling streams and supporting the goals of sustainable waste management in smart cities. The F1-score is the harmonic mean of precision and recall, providing a balanced measure of a model's ability to correctly classify recyclable waste while minimizing both false positives and false negatives. A higher F1-score indicates that the proposed system not only accurately classifies recyclable materials but also reduces misclassification, leading to more reliable waste sorting and better overall waste management efficiency. Latency, defined as the time delay between data input and system response, is a critical factor in real-time waste management. This improvement is primarily due to the use of lightweight deep learning architectures and the integration of edge computing, which allows for faster data processing closer to the data source. Despite the high classification accuracy of 95%, the proposed method successfully balances accuracy and speed, maintaining real-time processing capabilities essential for smart city waste management operations. However, it is important to note that as model complexity increases (e.g., deeper CNNs or hybrid DL models), processing speed may be impacted if not optimized, highlighting the need for efficient model design and hardware acceleration. The proposed system demonstrates higher computational complexity, primarily due to the integration of deep learning models, real-time processing requirements, and the addition of blockchain technology. The use of advanced convolutional neural networks (CNNs) with transfer learning increases the depth and number of parameters in the model, enhancing classification accuracy to 95% but also requiring greater computational resources. Additionally, the system performs real-time waste classification and monitoring, which demands high-performance processing to maintain a low latency of 1.2 s per image. The incorporation of blockchain, utilizing a Proof of Stake (PoS) consensus mechanism, introduces further processing overhead to ensure secure, decentralized data management. Despite these complexities, the system balances trade-offs between accuracy, processing speed, and efficiency through optimized CNN architectures, edge computing, and lightweight blockchain protocols. As a result, while the computational complexity is higher in the proposed framework, it is justified by significant improvements in accuracy, data integrity, and operational efficiency, making the system well-suited for real-time smart waste management applications. Table 4 presents a detailed comparison of classification accuracy, processing time, blockchain security, waste collection efficiency, CO2 reduction, and other critical performance metrics. IoT technologies in smart cities is revolutionizing urban management, with waste management being one of the key areas benefiting from this transformation. IoT-based waste management systems typically follow a layered architecture to ensure seamless operation and efficiency2. The perception layer involves sensors and smart trash bins that collect real-time data on waste levels, types, and fill status. The network layer is responsible for transmitting this data through various communication protocols such as Wi-Fi, ZigBee, or LoRaWAN, ensuring efficient and reliable data transfer to cloud platforms or local processing units. The edge computing layer processes this data closer to the source, reducing latency and optimizing decision-making for real-time waste sorting. Finally, the application layer analyzes thedata to provide actionable insights, including waste classification and optimization of collection schedules. The proposed AI-driven waste management framework enhances this IoT infrastructure by incorporating CNN for waste classification and IoT-enabled smart bins for seamless integration of real-time monitoring and sorting, addressing key challenges in waste management and contributing to the overall sustainability of smart cities. MNASNet is another effecient CNN model optimized for mobile and embedded devices, leveraging neural architecture search (NAS) to balance accuracy and computational efficiency. Unlike traditional CNNs, which may require significant computational resources, MNASNet reduces power consumption and latency, making it ideal for real-time mobile applications. However, for this study, we selected a traditional CNN because of its proven robustness, versatility, and ability to handle large datasets with high accuracy. CNNs have a long track record in various domains, providing reliable and consistent results, which is crucial for achieving optimal performance in our specific application. The use of CNNs for waste classification shows a substantial leap in accuracy over traditional machine learning methods. This improvement can be attributed to the deep feature extraction capabilities of CNNs, which allow for more nuanced and accurate categorization of waste types, especially when dealing with complex materials. This level of precision ensures that recyclable materials are properly identified, reducing contamination and enhancing the overall recycling process. As a result, the AI-driven waste classification not only increases the effectiveness of waste sorting but also supports sustainable recycling efforts by diverting more materials from landfills. This reduction is essential for practical implementation in real-time waste sorting, where delays in waste categorization could hinder the effectiveness of smart bins. The optimization of CNN inference through lightweight architectures and edge computing makes real-time processing feasible, ensuring that waste sorting can occur without significant delays and enhancing the system's responsiveness in dynamic environments. The integration of blockchain technology further strengthens the proposed system by ensuring secure, tamper-proof data management. enhances the security and authenticity of waste management data, offering traceability and reducing the risk of data manipulation or breaches. This is particularly important in waste management contracts and audits, where accurate and reliable data is crucial for monitoring compliance and optimizing operational efficiency. The improvements in cryptographic mechanisms and consensus algorithms in our framework contribute to the higher data integrity score, ensuring more robust and trustworthy waste management operations. This improvement can be attributed to better route optimization, smarter resource allocation, and more accurate waste level monitoring, facilitated by the integration of IoT sensors. The system's ability to predict optimal collection schedules based on real-time data and AI algorithms allows for more efficient waste collection, reducing unnecessary trips and optimizing fleet management. The higher efficiency also suggests that the AI system is able to better prioritize waste collection in areas where bins are nearing full capacity, minimizing the risk of overflows and improving overall service quality. Finally, the proposed methodology demonstrates a significant reduction in CO2 emissions, with a 30% reduction compared to 15% and 20% reductions in the 2021 and 2022 methods, respectively. This is a direct result of improved operational efficiency, including optimized routing and reduced fuel consumption. Overall, the proposed AI-driven waste management framework represents a significant advancement in waste management systems, offering improvements in classification accuracy, sorting efficiency, data integrity, and environmental sustainability. By integrating cutting-edge AI and IoT technologies, this framework addresses the growing challenges of waste management in smart cities, paving the way for more sustainable, efficient, and cost-effective waste management solutions. Future work could explore further optimizations, such as incorporating additional machine learning models for waste prediction or expanding the use of renewable energy sources to power the smart bins, further aligning with sustainability goals. This study introduces an innovative AI-driven waste management framework that integrates CNNs for waste classification with IoT-enabled smart trash bins for real-time monitoring. The incorporation of IoT sensors, such as ultrasonic and load sensors, ensures effective monitoring of bin fill levels and waste weight, further optimizing waste collection and sorting efficiency. The proposed system demonstrates significant advantages over previous methods, including improved accuracy, lower latency, and enhanced data security through the integration of blockchain technology. Additionally, the AI-based framework enhances resource allocation, supports sustainable waste processing, and contributes to the reduction of CO2 emissions, with a notable reduction of 30% in emissions compared to previous approaches. Furthermore, the AI-driven waste management system aligns with the principles of smart cities by facilitating real-time waste tracking, automated sorting, and efficient recycling, all while reducing human error. The overall performance improvements, including the increase in waste collection efficiency and data integrity, highlight the potential for widespread deployment of this system in urban environments, promoting sustainability and contributing to environmental goals. In conclusion, the proposed methodology sets a new standard for smart waste management systems, combining AI, IoT, and blockchain to optimize waste classification, collection, and recycling processes. This approach not only enhances operational efficiency but also contributes to building smarter, more sustainable cities. Future work can explore further integration with city-wide infrastructure and the use of additional AI techniques to refine waste sorting and improve the scalability of the system. Future work will focus on expanding the system's capabilities by integrating additional AI techniques for further optimization of waste sorting and improving the scalability of the framework. Future research can explore the potential of deploying the system on a larger scale across various urban settings and incorporating additional sensors for more comprehensive waste data collection. Additionally, further advancements in blockchain technology may enhance the system's resilience and enable better integration with smart city infrastructure. The use of edge computing for more efficient data processing and the development of predictive analytics models for waste generation and collection scheduling could also be explored to further optimize system performance. Moreover, it is important to note that the integration of IoT and blockchain in the waste classification system inherently enhances data integrity and security through blockchain's decentralized and tamper-resistant architecture. However, large-scale IoT deployments can still be susceptible to potential risks such as data breaches and cyber-attacks, which should be considered in future enhancements of the system. Ethical concerns, such as AI bias in waste classification, are addressed by using diverse and representative training datasets to ensure fairness and accuracy in classification decisions. 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Dankrad Feist, a researcher at the Ethereum Foundation (EF), believes that Ethereum will become irrelevant in five or ten years from now if its current trajectory does not change. Earlier this month, Feist introduced Ethereum Improvement Proposal 7938, which aims to increase the gas limit by 100x over the course of four years. This would make it possible to conduct a much bigger number of transactions per block. Feist has admitted that the proposal is "unconvetional," but he argues that such unorthodox decisions are needed since the layer-1 chain is at risk of fading into irrelevance. If liquidity gets fragmented across layer-2s, Ethereum will likely end up losing to other competing ecosystems, the researcher warns. Feist is convinced that Ethereum will be able to scale without compromising its crucial properties (censorship resistance and verifiability). As reported by U.Today, Cardano Founder Charles Hoskinson recently predicted that Ethereum would not be able to survive 10 years from now, with "parasitic" layer-2 solutions being cited as one of the key reasons behind its possible demise. Matt Hougan, chief investment officer at Bitwise, says that Ethereum has "realized that it is in a hole and has stopped digging" in response to Feist's proposal. Every investment and all trading involves risk, so you should always perform your own research prior to making decisions. U.Today is not liable for any financial losses incurred while trading cryptocurrencies.

The wider cryptocurrency market may have slowed the relief rally that began after United States (US) President Donald Trump paused reciprocal tariffs for 90 days on April 9, but select altcoins such as Fartcoin, Virtuals Protocol (VIRTUAL), Curve DAO (CRV) do not show any signs of a waning bullish momentum. Fartcoin's price hovers at $1.22 at the time of writing on Thursday, amid an extended rally from its April low of $0.35. Fartcoin's price remains above the key moving averages, indicating a potential long-term bullish trend, which has been recently reinforced by breaking through the critical $1.00 and $1.20 levels. The SuperTrend indicator presented a buy signal on April 10, which continues to influence Fartcoin's bullish structure. On the upside, supply zones at $1.50 and $1.80 are key to monitor as Fartcoin aims for its $2.00 target. However, profit-taking at these levels might hinder the uptrend or trigger a reversal. Virtuals Protocol's price is up a staggering 292% from its April low of $1.00, currently exchanging hands at $1.63 at the time of writing. Traders would anticipate higher support at $1.50 to hold in the event of a reversal. At the same time, VIRTUAL's bullish momentum could accelerate towards the $2.00 level, which was tested in December and January as both support and resistance. Curve DAO's uptrend steadies at $0.72, supported by a golden cross pattern in the daily time frame. CRV cemented the bullish outlook in April after breaking out of a cup and handle pattern, as shown in the chart below. Curve DAO may encounter resistance at $0.75, where traders could decide to take profit, thus weakening the uptrend. Beyond this level, investors would monitor CRV's reaction to the supply zones at $0.80 and $1.00, which have previously been tested as support and resistance levels. Token launches influence demand and adoption among market participants. A hack is an event in which an attacker captures a large volume of the asset from a DeFi bridge or hot wallet of an exchange or any other crypto platform via exploits, bugs or other methods. Such events often involve an en masse panic triggering a sell-off in the affected assets. If the US Dollar index declines, risk assets and associated leverage for trading gets cheaper, in turn driving crypto prices higher. Information on these pages contains forward-looking statements that involve risks and uncertainties. Markets and instruments profiled on this page are for informational purposes only and should not in any way come across as a recommendation to buy or sell in these assets. You should do your own thorough research before making any investment decisions. FXStreet does not in any way guarantee that this information is free from mistakes, errors, or material misstatements. It also does not guarantee that this information is of a timely nature. 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It also does not guarantee that this information is of a timely nature. All risks, losses and costs associated with investing, including total loss of principal, are your responsibility. The author will not be held responsible for information that is found at the end of links posted on this page. The author has not received compensation for writing this article, other than from FXStreet. FXStreet and the author do not provide personalized recommendations. The author and FXStreet are not registered investment advisors and nothing in this article is intended to be investment advice. Solana (SOL) price is stabilizing at around $149 at the time of writing on Thursday, after finding support around its 50-day Exponential Moving Average (EMA) the previous day. On-chain data support a bullish thesis as SOL's stablecoin market capitalization has surged to $13 billion. The wider cryptocurrency market may have slowed the relief rally that began after United States President Donald Trump paused reciprocal tariffs for 90 days on April 9, but select altcoins such as Fartcoin, Virtuals Protocol (VIRTUAL), Curve DAO (CRV) do not show any signs of a waning bullish momentum. Crypto exchange Coinbase filed an amicus brief on Wednesday urging the Supreme Court to cut back on the third-party doctrine, a rule often used by the Internal Revenue Service (IRS) to demand customer information from exchanges. Bitcoin price is consolidating around $94,000 at the time of writing on Friday, holding onto the recent 10% increase seen earlier this week. This week's rally was supported by strong institutional demand, as US spot ETFs recorded a total inflow of $2.68 billion until Thursday. Our list features brokers with competitive spreads, fast execution, and powerful platforms. Note: All information on this page is subject to change. 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At first glance, this appears to mark a decline from the previous year's $46.1 billion. However, our 2024 figures reflect-lower bound estimates based on identified illicit addresses — when accounting for likely undiscovered activity, the true number is closer to $51.3 billion. As blockchain adoption accelerates and decentralized infrastructure expands, so does the attack surface. With every new protocol, smart contract, and wallet user added to the ecosystem, the need for robust blockchain security grows more urgent. From smart contract exploits and cross-chain bridge hacks, to phishing campaigns that drain users wallets, the risks facing blockchain technology are evolving in real time. However, with the right tools, many attacks can be stopped before they strike. In this blog, we'll explore the most pressing blockchain security challenges and the steps participants can take now to build more resilient infrastructure. Keep reading to learn more about the following topics: Blockchain security refers to the combination of cybersecurity principles, tools, and best practices aimed at mitigating risk, avoiding malicious attacks, and preventing unauthorized access while operating on blockchain networks. The codebase of public blockchains is open source — publicly available and continually vetted by developers who review the code for bugs, vulnerabilities, and other issues. In contrast, private blockchains are exclusive, permissioned networks with limited access, making them more centralized. The centralized nature of these blockchains means that there is a single point of failure, making it crucial for the institution to implement strong security measures. Although private blockchains may not benefit as much from the decentralized and security-by-numbers approach of public blockchains, they are generally faster and more efficient due to less computational work required for consensus algorithms. Blockchain technology runs on a distributed digital ledger system. That's where consensus mechanisms, such as Proof-of-Work and Proof-of-Stake, come into play. Miners and stakers are incentivized with rewards to secure the network. This process ensures everyone agrees (or reaches consensus) on the validity of each transaction. By linking each block using cryptography and distributing the ledger across numerous computers, any attempt to tamper with a block would disrupt the entire chain. Unlike traditional finance — which operates on permissions to pull funds — a crypto transaction is a push transaction, initiated peer-to-peer without the need for an intermediary. Participants in blockchain networks control their digital assets on the blockchain with a private key — a cryptographically secured method of authentication and access. Because no intermediary is required, personal responsibility becomes much more important when transferring value on-chain. This makes it notoriously difficult to recover funds that are lost or stolen. The notion that blockchain technology is inherently immune to security threats is somewhat misleading, but several of its unique structural features bolster its intrinsic security: New protocols, features, and use cases emerge faster than they can be thoroughly vetted, leaving gaps that attackers are quick to exploit. Anyone can deploy a contract, launch a token, or interact with protocols, creating low barriers for malicious actors to operate at scale. As users move assets across multiple blockchains, visibility into transactions can become fragmented. Monitoring and securing these flows requires coordination across protocols — something the current infrastructure isn't fully equipped to handle. Finally, one of the most persistent challenges is real-time detection. This ambiguity makes it increasingly difficult to distinguish between malicious and benign actions before damage is done. The following threats target applications operating on-chain — such as DeFi protocols, bridges, and phishing tokens — rather than the underlying blockchain infrastructure itself. Smart contracts are self-executing code that power everything from decentralized finance (DeFi) protocols to non-fungible tokens (NFTs). But once deployed, a single bug can expose millions in locked assets. Without code audits, even well-intentioned projects can become exposed. If attackers influence or corrupt these inputs, they can trigger faulty contract logic. For instance, manipulating the reported price of an asset could allow an attacker to buy it at a discount or trigger a liquidation cascade. In DeFi, where value is algorithmically tied to oracle data, the consequences can be immediate and catastrophic. Cross-chain bridges allow assets to move between blockchains, but they've also become prime targets for attackers. Many bridge architectures rely on complex smart contracts and custodial mechanisms, creating large honeypots of funds. Some of the most damaging attacks are social in nature. Rug pulls occur when developers abruptly withdraw liquidity or abandon a project after attracting user investment, leaving holders with worthless tokens. These schemes often masquerade as legitimate startups, leveraging hype, influencer marketing, and anonymous teams to build trust before vanishing with user funds. Moreover, approximately 94% of DEX pools involved in suspected pump-and-dump schemes appear to be rugged by the address that created the DEX pool. Phishing remains one of the most effective attack vectors in crypto. As we recently revealed in our 2025 Crypto Crime Report, nearly $10 billion worth of crypto was lost in 2024 due to fraud and scams, although we estimate this number is likely closer to $12.4 billion, which would be a slight increase from amounts stolen in 2023. Whether through fake wallet apps, malicious airdrops, or impersonated support accounts, attackers trick users into revealing private keys or signing malicious transactions. On-chain data provides an invaluable window into blockchain activity, helping security teams identify red flags early — whether that's unusual transaction patterns, interactions with known malicious contracts, or sudden liquidity movements. Real-time monitoring and transaction analysis are essential for catching threats in motion. Chainalysis plays a critical role in blockchain analytics by connecting activity across chains and platforms, enabling faster detection, deeper investigations, and more coordinated responses. As threats grow more complex, this kind of integrated, data-driven approach is key to building safer blockchain infrastructure. Chainalysis Hexagate brings proactive defense to the forefront of blockchain security. Chainalysis Hexagate is built for a range of security-focused teams operating across the blockchain ecosystem: The cost of a major blockchain attack goes far beyond lost funds — it can shatter user trust, damage reputations, and set back entire ecosystems. The industry is shifting from reactive cleanup to proactive prevention, recognizing that real-time intelligence and early detection are essential to staying ahead of threats. But this requires continuous investment in data security infrastructure, tooling, and collaboration across the ecosystem. At Chainalysis, we're committed to enabling safe, scalable blockchain adoption. By equipping teams with the data and tools they need to prevent attacks before they happen, we're helping build a future where innovation and security grow together. Book a demo of Chainalysis Hexagate's fraud prevention and security solution here. 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