This review article examines the state of blockchain-enabled identity management in Internet of Things (IoT) networks, focusing on decentralized and secure mechanisms for device identification, authentication, and access control. Traditional centralized identity systems face limitations such as single points of failure, scalability bottlenecks, and vulnerability to breaches. We systematically survey recent literature on blockchain-based frameworks applied to IoT, categorizing approaches by blockchain platform, identity credential models, consensus mechanisms, and smart contract implementations. The analysis highlights key performance metrics such as system latency, throughput, resource overhead, and energy consumption, and compares existing prototypes deployed across diverse IoT scenarios. We assess the security and privacy implications, including resistance to spoofing, Sybil attacks, unauthorized access, data tampering, and insider threats. Additionally, the review identifies open research challenges such as managing identity lifecycle in constrained devices, achieving interoperability across heterogeneous networks, balancing decentralization with scalability, and integrating with emerging technologies like edge computing and zero-knowledge proofs. Finally, we offer recommendations for future research directions and practical deployment strategies to advance blockchain-based identity solutions in IoT ecosystems. Our comprehensive synthesis aims to guide researchers and practitioners in developing robust, scalable, and trustworthy identity frameworks using blockchain for the evolving IoT landscape.
This paper introduces “SmartBLock”, a novel protocol that integrates smart lock management with the Bitcoin blockchain. By employing blockchain technology, the SmartBLock protocol eliminates the need for centralized databases (reducing the risk of data breaches) and ensures accountability for all access events. Authentication is accomplished through Bitcoin's cryptographic signature scheme. Additionally, SmartBLock is an open and manufacturer-agnostic protocol, relying on the open and permissionless Bitcoin blockchain rather than proprietary tools or protocols. Given the limited computational capabilities of Internet of Things (IoT) devices, achieving this integration presents a significant challenge. This paper provides a comprehensive review of the state of the art in smart lock protocols, details the design of the SmartBLock protocol, presents a proof-of-concept prototype to validate its feasibility, and offers a meticulous analysis of its security, privacy, and traceability features. • Literature review on blockchain-integrated smart locks. • Propose a smart lock protocol on Bitcoin for immutable and transparent management. • Provide a demonstration that the protocol is secure, private, and transparent. • Build a PoC for the proposal on an IoT device and gather evidence of its feasibility.
Ahmad Mutahhar, Tariq Jamil Saifullah Khanzada, Muhammad Farrukh Shahid
Large-scale events, such as festivals and public gatherings, pose serious problems in terms of traffic congestion, slow transaction processing, and security risks to transportation planning. This study proposes a blockchain-based solution for enhancing the efficiency and security of intelligent transport systems (ITS) by utilizing state channels and rollups. Throughput is optimized, enabling transaction speeds of 800 to 3500 transactions per second (TPS) and delays of 5 to 1.5 s. Prevent data tampering, strengthen security, and enhance data integrity from 89% to 99.999%, as well as encryption efficacy from 90% to 98%. Furthermore, our system reduces congestion, optimizes vehicle movement, and shares real-time, secure data with stakeholders. Practical applications include fast and safe road toll payments, faster public transit ticketing, improved emergency response coordination, and enhanced urban mobility. The decentralized blockchain helps maintain trust among users, transportation authorities, and event organizers. Our approach extends beyond large-scale events and proposes a path toward ubiquitous, Artificial Intelligence (AI)-driven decision-making in a broader urban transit network, informing future operations in dynamic traffic optimization. This study demonstrates the potential of blockchain to create more intelligent, more secure, and scalable transportation systems, which will help reduce urban mobility inefficiencies and contribute to the development of resilient smart cities.
Carlos Beis-Penedo, Francisco Troncoso‐Pastoriza, Rebeca P. Dı́az Redondo, Ana Fernández Vilas · 6 authors
The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the challenges associated with data privacy, security, and scalability. Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges by enabling decentralized, secure, and privacy-preserving model training on distributed data sources. In this paper, we present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with Ethereum blockchain technology to facilitate secure and efficient data sharing, model training, and prototype storage in a distributed environment. Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security. We assess the performance of our system through a series of experiments, showing its potential to enhance the accuracy and efficiency of machine learning tasks in IoT settings.
Blockchain technology has been widely explored for enhancing transparency, traceability, and security in food supply chains. However, existing blockchain implementations rely on single distributed ledgers, causing interoperability and privacy concerns. This paper introduces FoodFresh, a novel multi-chain blockchain approach that allows food supply chain stakeholders to maintain individual blockchains while ensuring interoperability via a decentralized relay hub. The system is evaluated using real-world supply chain datasets, analyzing efficiency, transaction latency, and security improvements. Results demonstrate enhanced traceability, improved data privacy, and increased scalability. Future work includes expanding cross-chain communication protocols and exploring AI integration for predictive analytics.
Bharat Bhasker, Patruni Muralidhara Rao, P. Vidhya Saraswathi, S. Gopal Krishna Patro · 8 authors
The Internet of Medical Things (IoMT) sector has advanced rapidly in recent years, and security and privacy are essential considerations in the IoMT due to the extensive scope and implementation of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have dramatically improved the functionalities and services of Healthcare 5.0, giving rise to a new domain termed Smart Healthcare. A proactive healthcare system may prevent long-term harm by recognizing issues early. This would improve patients' quality of life while alleviating their worry and healthcare expenses. The IoMT facilitates several capabilities in information technology, including intelligent and interactive healthcare. Consolidating medical information into a singular repository to train a robust ML model engenders apprehensions around privacy, ownership, and adherence to regulatory standards due to increased concentration. Federated learning (FL) addresses previous challenges using a centralized aggregate server to distribute global learning models. The local participant controls patient data, ensuring data confidentiality and security. Hence, this study proposes the Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS) for a secure health monitoring system. Additionally, this paper presents the Intrusion Detection System (IDS) as a tool for healthcare network intrusion detection, allowing doctors to track patients' vitals using medical sensors and anticipate when they could become sick so they can take preventative steps. The suggested system proves that the method is well-suited for medical monitoring. In contrast, the high prediction accuracy for intrusion detection and the high efficiency in disease detection achieved by the proposed FBI-SHS healthcare 5.0 system. The proposed method achieves data privacy and security by 98.73%, intrusion detection efficiency by 97.16%, disease detection accuracy by 96.425, proactive healthcare management by 98.37%, and interoperability by 96.74%.
The rapid evolution of telemedicine has enhanced healthcare accessibility, yet significant challenges persist, particularly in data security, patient engagement, latency, and scalability. Existing telemedicine solutions rely on centralized architectures, making Electronic Health Records (EHRs) susceptible to data breaches and unauthorized access. This research proposes a novel system which integrates the metaverse and blockchain into telemedicine which can be a transformative approach to solve problems in remote healthcare. By combining immersive virtual environments with decentralized data management, the proposed solution described in this paper aims to give users more ways to interact with each other, enhanced data security, increased efficiency, and higher scalability. The Metaverse serves as the foundation for the implementation of 3D consultation rooms, virtual training spaces, and individual care. Blockchain offers safe, transparent, and immutable data exchange that will create patient-empowered medical records for them. Real-time devices and analysis of real-time physiological data from wearables, sensors, Internet of Things (IoT) devices, and Artificial Intelligence (AI) analytics complete the system. The proposed solution extensively uses Virtual Reality (VR)/Augmented Reality (AR) devices, IoT sensors, Ethereum, and the Unity 3D platform, among others. Assessments indicate that system receives a significantly high level of satisfaction from its users, better secured data, increased automation of processes, and compliance with global standards such as General Data Protection Regulation (GDPR). Compliance with such global standards is achieved through smart contract-based access management, smart contract-based consent management, and immutable audit trails in the blockchain. Moreover, this research demonstrates that incorporating high-tech tools like AI and VR into telemedicine is currently feasible. This paves the way for the creation of even more secure and user-friendly telemedicine platforms that employ neural networks. This research sets a foundation for next-generation telemedicine ecosystems.
S M Mostaq Hossain, Amani Altarawneh, Maanak Gupta
As blockchain technologies are increasingly adopted in enterprise and research domains, the need for secure, scalable, and performance-transparent node infrastructure has become critical. While self-hosted Ethereum nodes offer operational control, they often lack elasticity and require complex maintenance. This paper presents a hybrid, service-oriented architecture for deploying and monitoring Ethereum full nodes using Amazon Managed Blockchain (AMB), integrated with EC2-based observability, IAM-enforced security policies, and reproducible automation via the AWS Cloud Development Kit. Our architecture supports end-to-end observability through custom EC2 scripts leveraging Web3.py and JSON-RPC, collecting over 1,000 real-time data points-including gas utilization, transaction inclusion latency, and mempool dynamics. These metrics are visualized and monitored through AWS CloudWatch, enabling service-level performance tracking and anomaly detection. This cloud-native framework restores low-level observability lost in managed environments while maintaining the operational simplicity of managed services. By bridging the simplicity of AMB with the transparency required for protocol research and enterprise monitoring, this work delivers one of the first reproducible, performance-instrumented Ethereum deployments on AMB. The proposed hybrid architecture enables secure, observable, and reproducible Ethereum node operations in cloud environments, suitable for both research and production use.
The Smart Mobility vision calls for dynamic resource and service discovery to cope with the intrinsic topology volatility of Internet of Things (IoT) platforms without sacrificing the required business continuity and service flexibility. For an extended automation of collaboration within and across enterprise boundaries, trust management is equally important, granting security, reliability and scalability at the same time. To tackle the above challenges, this paper proposes the integration of a semantic-based service management layer in an IoT infrastructure grounded on the Hyperledger Sawtooth blockchain. Every service in the outlined framework is annotated with reference to a domain ontology, so that smart contracts can exploit knowledge representation and non-standard reasoning for service registration, discovery, outcomes explanation and service selection. A case study on power management of Plug-in Electric Vehicles (PEVs) is proposed to clarify the benefits of the proposal. Early performance evaluation results support the feasibility and sustainability of the approach.
Carlos Beis-Penedo, Rebeca P. Díaz-Redondo, Ana Fernandez-Vilas, Manuel Fernández‐Veiga · 5 authors
Collaborative machine learning in sensitive domains demands scalable, privacy-aware and access-controlled solutions for enterprise-grade deployment. Conventional federated learning (FL) relies on a central server, introducing single points of failure and privacy risks, while split learning (SL) partitions models for privacy but scales poorly because of sequential training. We present HLF-FSL, a decentralized architecture that combines federated split learning (FSL) with the permissioned blockchain Hyperledger Fabric (HLF). Chaincode orchestrates split-model execution and peer-to-peer aggregation without a central coordinator, leveraging HLF’s transient fields and Private Data Collections (PDCs) to keep raw data and model activations off-chain and access-controlled. On CIFAR-10, MNIST and ImageNet-Mini, HLF-FSL matches the accuracy of a standard server-coordinated FSL baseline while reducing per-epoch training time versus Ethereum-based baselines. Performance and scalability tests quantify the Fabric coordination overhead via a component-level breakdown of SDK-facing latencies and communication volumes; empirically, this overhead increases wall-clock epoch time while preserving the same accuracy-vs-epoch behavior as a FedSplit Learning baseline.
The process of exchanging healthcare data introduces stringent requirements regarding users’ privacy. Federated learning (FL) is a novel model-sharing technique that aims to give additional privacy guarantees during machine learning process. Blockchain, as a form of distributed ledger technology, possesses the characteristic of trustworthiness; however, it is deficient in terms of computational capacity with a high-latency network due to its laborious consensus protocols. In this paper we present a distributed healthcare FL-based secure model sharing architecture to ensure healthcare data privacy and scalability. The solution relies on state channels technique to reduce on-chain transactions, contrast architecture latency, and reduce bandwidth consumption, alleviating the burden on the blockchain. State channels can be utilized to efficiently execute the tasks of federated learning models sharing and to solve the scalability problem.
The rapid integration of AI into IoT systems has outpaced the ability to explain and audit automated decisions, resulting in a serious transparency gap. We address this challenge by proposing a blockchain-based framework to create immutable audit trails of AI-driven IoT decisions. In our approach, each AI inference comprising key inputs, model ID, and output is logged to a permissioned blockchain ledger, ensuring that every decision is traceable and auditable. IoT devices and edge gateways submit cryptographically signed decision records via smart contracts, resulting in an immutable, timestamped log that is tamper-resistant. This decentralized approach guarantees non-repudiation and data integrity while balancing transparency with privacy (e.g., hashing personal data on-chain) to meet data protection norms. Our design aligns with emerging regulations, such as the EU AI Act’s logging mandate and GDPR’s transparency requirements. We demonstrate the framework’s applicability in two domains: healthcare IoT (logging diagnostic AI alerts for accountability) and industrial IoT (tracking autonomous control actions), showing its generalizability to high-stakes environments. Our contributions include the following: (1) a novel architecture for AI decision provenance in IoT, (2) a blockchain-based design to securely record AI decision-making processes, and (3) a simulation informed performance assessment based on projected metrics (throughput, latency, and storage) to assess the approach’s feasibility. By providing a reliable immutable audit trail for AI in IoT, our framework enhances transparency and trust in autonomous systems and offers a much-needed mechanism for auditable AI under increasing regulatory scrutiny.
Growing applications of Internet of Medical Things (IoMT) devices have revolutionized the healthcare sector because of remote patient tracking, diagnosis, and data-supported decision-making. The kind of medical data collected from these devices, however, is very sensitive, which makes it very vulnerable to issues of security, privacy, and integrity. This paper suggests a way to keep IoMT data safe using the Algorand blockchain, XChaCha20-Poly1305 encryption, and different types of decentralized storage. Using the platform's fast, highly scalable, and highly secure architecture, Algorand blockchain framework makes sure that encrypted patient medical records are stored permanently and cannot be changed. To properly encrypt sensitive IoMT data before storing the data in DSNs including IPFS, Storj, and Filecoin, a modern stream cipher called 'XChaCha20-Poly1305' is used. Decentralized storage ensures data accessibility and distribution simultaneously, minimizing reliance on associated server points that are susceptible to single points of failure. Besides data secrecy, accuracy, and anti-intrusion attack breakout measures, this work explores the security measures implied by this architecture. Additionally, it assesses the efficacy of various decentralized storage options and highlights their benefits and drawbacks when it comes to storing large amounts of medical data. It can be concluded that the proposed framework is cost-effective and capable of expansion and implementation in the modern healthcare environment of IoMT data protection.
Open access
Blockchain Technology Applications and Security
IoT and Edge/Fog Computing
Advanced Steganography and Watermarking Techniques
Evidence plays a crucial role in judicial systems, and managing it securely and efficiently ensures justice. This paper introduces Decentralized Trust, a framework that combines blockchain technology, Non-Fungible Tokens (NFTs), and fog computing to address common issues like tampering, delays, and reliance on centralized systems. Traditional methods that depend on cloud computing often face high latency and slow processing, especially in remote areas. This research also builds upon the challenges identified in previous studies, such as tampering vulnerabilities, inefficiencies in evidence processing, and accessibility issues in underserved regions, providing a novel and comprehensive solution through Decentralized Trust. Fog computing handles tasks closer to where data is created, reducing delays and improving response times. Blockchain ensures that evidence records cannot be altered, while NFTs make each piece of evidence unique and tamper-proof. The framework is organized into layers: edge nodes at police stations capture evidence, fog nodes process the data and create NFTs, and cloud storage, supported by the Interplanetary File System (IPFS), provides secure long-term storage. Results demonstrate that the framework achieves average transaction delays of 24.5 seconds on low-performance devices (Node A) and 168.9 seconds on high-performance devices (Node B), with margins of error showing efficient scalability even under significant processing loads. The observed transaction delays are due to differences in system architecture and processing priorities. High-performance devices (Node B) have more complex validation processes, increased security checks, or resource contention, contributing to longer transaction times. By combining these technologies, Decentralized Trust offers a reliable, fast, and secure way to manage judicial evidence, building trust in the framework while addressing the needs of remote and underserved areas.
The rapid evolution of smart cities has led to transformative advancements through the integration of IoT devices, sensors, and data-driven systems, yet has simultaneously exposed critical vulnerabilities in cybersecurity, data integrity, and trust management. This research proposes a Decentralized Trust Framework that leverages blockchain technology, AI-driven threat detection, and a Lightweight Adaptive Proof-of-Stake (LA-PoS) consensus mechanism to address these challenges. The framework integrates three key layers: a Blockchain Layer for decentralized trust and immutability, a Cybersecurity Layer employing cryptographic standards and AI-based anomaly detection, and a Data Integrity Protocol Layer for real-time synchronization and tamper-proof data validation. Performance evaluations indicate the framework achieves a threefold increase in transaction throughput, a 30% reduction in latency, and enhanced energy efficiency compared to traditional blockchain systems. Security metrics highlight a 98.2% threat detection rate and a substantial reduction in false positives, while resource optimization nearly doubles IoT device battery life. The framework demonstrates applicability in critical smart city use cases, including smart traffic management, energy systems, and public safety, providing secure, scalable, and efficient solutions for urban infrastructures. Despite these advancements, challenges such as interoperability among heterogeneous systems, computational overhead for IoT devices, and policy adoption persist. Future research will focus on optimizing interoperability protocols, incorporating quantum-resistant cryptographic techniques, and extending the framework to emerging domains such as autonomous systems and smart healthcare. The proposed framework provides a robust foundation for building sustainable, resilient, and trustworthy urban ecosystems, bridging gaps in current smart city technologies.
Internet of Things (IoT) has also brought about rapid adoption of technologies in agriculture that provides real-time monitoring and management of crop production, water usage, and soil health. Nevertheless, IoT devices combined with blockchain-based smart contracts create severe energy consumption issues, particularly when the resource is limited. This paper will present a framework that is energy-efficient to implement smart contracts in the network of IoT devices in the agriculture industry. “The proposed solution allows minimizing the computational overhead, preserving data integrity, transparency, and security by improving consensus mechanisms and scheduling transactions. According to the experimental simulations, the framework reduces the energy use by up to 35 percent of that of the existing blockchain execution models, without affecting the operational reliability. The results offer practical lessons towards sustainable precision farming whereby technological innovation is balanced with the environment.
Mohammad Nasrinasrabadi, Maryam A. Hejazi, Ehsan Chaharmahali, Mousa Hussein
The integration of blockchain and the Internet of Things (IoT) within smart grids offers transformative potential for enhancing energy management, security, and operational efficiency. Smart grids rely on advanced digital technologies to enable bidirectional communication between energy producers and consumers, optimizing the integration of renewable energy sources and promoting demand-side management. Blockchain technology, with its decentralized and immutable nature, ensures secure and transparent energy transactions while fostering trust without the need for centralized authorities. Meanwhile, IoT facilitates real-time data collection and monitoring, enabling dynamic energy management and transactive energy systems. This review explores the synergies between blockchain and IoT in addressing critical challenges such as cybersecurity, data security, and network security. By leveraging mechanisms like smart contracts and consensus algorithms, these technologies enhance grid resilience and privacy, providing robust solutions to manage distributed energy resources and decentralized energy markets. The integration also supports peer-to-peer energy trading, improves scalability, and reduces reliance on intermediaries, aligning with sustainability goals by promoting renewable energy adoption. Despite significant advancements, challenges such as regulatory barriers, high computational costs, and scalability limitations persist. This paper emphasizes the need for innovative approaches to overcome these issues and highlights emerging trends such as hybrid blockchain models and AI-enabled solutions. By addressing these gaps, blockchain and IoT can redefine smart grid infrastructures, ensuring a secure, efficient, and sustainable energy future.
Revolutionizing distributed agile software testing, we propose BCTestingPlus, a groundbreaking blockchain-based platform. In the traditional distributed agile software testing lifecycle, software testing has suffered from a lack of trust, traceability, and security in communication and collaboration. Furthermore, developers’ failure to complete unit testing has been a significant bottleneck, causing delays and contributing to project failures. Introducing BCTestingPlus, a transformative blockchain-based architecture engineered to overcome these challenges. This framework integrates blockchain technology to establish an inherently transparent and secure environment for software testing. BCTestingPlus operates on a private Ethereum blockchain network, offering superior control and privacy. By implementing smart contracts on this network, BCTestingPlus ensures secure payment verification and efficient acceptance testing. Crucially, it aligns development and testing teams toward shared objectives and guarantees equitable compensation for their efforts. The experimental results and findings conclusively show that this innovative approach demonstrates that BCTestingPlus significantly enhances transparency, bolsters trust, streamlines coordination, accelerates testing, and secures communication channels for all parties involved in the distributed agile software testing lifecycle. It delivers robust security for both development and testing teams, ultimately transforming the efficiency and reliability of distributed agile software testing.
Sunil P. Chinte, Prof. S. A. Thakare, Aarti R. Jaiswal, Nikunj Hasmukhrai Raja · 5 authors
The rapid expansion of Internet of Things (IoT) ecosystems has resulted in an unprecedented surge in data generation, necessitating reliable, scalable, and secure storage mechanisms. Traditional centralized storage systems suffer from inherent limitations such as single points of failure, limited scalability, and vulnerability to cyberattacks, which compromise the confidentiality and availability of critical IoT data. This study introduces a blockchain- based decentralized storage framework aimed at addressing these critical issues. By leveraging the distributed and immutable characteristics of blockchain technology, the proposed system enhances data integrity, ensures transparency, and facilitates trustless data exchange among heterogeneous IoT devices. The methodology includes mathematical modeling of key performance parameters such as latency, throughput, storage efficiency, and consensus delay. Smart contracts are integrated to automate validation and enforce rules among interconnected devices, while redundancy mechanisms like replication and erasure coding improve storage reliability and efficiency. The framework’s effectiveness is evaluated using simulation tools including Hyperledger Caliper and Ethereum Testnets for blockchain behavior, and NS-3 and OMNeT++ for modeling dynamic IoT network environments. Experimental results reveal a 30% improvement in data retrieval time, 25% gain in storage efficiency, 40% enhancement in system resilience, and a 50% increase in transaction throughput over conventional approaches. These metrics highlight the suitability of the proposed model for real-world applications requiring scalable and secure IoT data management, such as healthcare monitoring, smart cities, and industrial automation. The model’s reproducibility and modularity make it a robust solution for future research and deployment. Overall, this work demonstrates that blockchain-integrated decentralized storage frameworks present a transformative step toward resilient and scalable IoT infrastructures.
Juan de Anda-Suárez, José Luis López-Ramírez, Daniel Jiménez-Mendoza, José Manuel Benitez-Quintero · 7 authors
Autonomous Vehicles (AV) have been extensively studied in both scientific and social contexts. Over the past two decades, there has been a significant rise in their real-world applications, including neural networks, Blockchain, Internet of Things, autonomous navigation, computer vision, automation processes, and various other areas. Hence, it is imperative to investigate the interplay between software, hardware, and individuals. To guarantee secure and unaffected interactions within autonomous vehicle devices and networks, decentralized Blockchain technology is proposed. This study presents the introduction of a framework we named “DEMU-NAV” for an ecosystem that includes Artificial Intelligence (AI), humans, and robots. The framework makes use of a decentralized Blockchain, Smart-Contract (SC), and Internet of things (IoT) network. Our framework was implemented using Ethereum and Python, enabling us to oversee Blockchain, Smart-Contracts, and the IoT for the facilitation of autonomous vehicle navigation.
Blockchain or Distributed Ledger Technology’s (DLT) disruptive architecture will revolutionise both economic activity and social structure. Institutional crypto economics is a new analytic framework for studying that evolutionary process in general, and bitcoin in particular, it presents us with a new method of organising the world, just like the Internet did. Bitcoin will have a similar effect on economy, money and finance. Developing countries face multiple problems such as lack of financial services and infrastructure (road, railways, telecommunication, and others). The disruptive architecture of Blockchain or Distributed Ledger Technology is well suited to benefit developing countries. This will be clearly visible in the implementation and application of Internet of Things (IoT) in emerging services. The nature of innovation in service-sector-based technology in developing countries differs, and the nature of IoT as a potentially disruptive emergent service product technology enabler emphasises this difference. The conventional productprocess innovation divide may no longer be applicable: the true value in IoT rests in neither. It is present in the system as well as the data collected by all devices everywhere in the world. The services income, which is generated by a combination of intelligent apps, analytics, and system integration services, represents a considerably greater revenue possibility for both developers and consumers of IoT enabled use cases. This paper presents how a peer-to-peer network that provides coverage for low-power IoT devices bringing a new viewpoint to the cellular telecommunications market. The network is a decentralised IoT infrastructure that is built on a Blockchain or Directed Acyclic Graph (DAG) by the people, communities, and individuals to offer hotspots wireless to the communities that help creates opportunities in financial freedom, helps supply chains traceability, forestry control and others. The paper demonstrates that decentralised IoT networks based on Tangle DAG can reduce infrastructure costs by 35-40% while increasing wireless coverage by 60%, with 1.5 million devices per 100 hotspots. It makes a unique and significant contribution to the deployment of IoT on Blockchain or Distributed Ledger Technology, as well as its potential to reduce poverty by improving the effectiveness and efficacy of existing procedures in various sectors of developing countries.