ABSTRACT Smart cities are digitally advanced urban environments that are equipped with sensor networks to gather, share, and analyze extensive data across interconnected systems. Among various smart city applications, the intelligent transportation system represents one of the most critical and securityâsensitive domains. An intelligent transportation system relies heavily on continuous vehicular communication, a lowâlatency decisionâmaking process, as well as realâtime traffic monitoring. Existing Internet of Things security methods encounter significant computational overhead and limited scalability, making them unfit for realâtime applications. To address these issues, this paper proposes a novel security model, named Deep Residual Stacked Bidirectional Network. The proposed system is integrated into a blockchainâsupported hybrid system to ensure security and privacy for users and systems in smart cities. This enhanced DeepâLearning model combines the residual learning power with bidirectional long shortâterm memory layers. To effectively manage deeper networks, residual connections help mitigate the vanishing gradient problem, while bidirectional long shortâterm memory provides sequential dependencies in backward and forward directions. This allows the model to detect patterns in data, especially in security environments where data is highly dynamic and timeâsensitive. Four Internet of Thingsârelated datasets are used to evaluate the efficiency of the developed algorithm. These datasets offer various realâworld network traffic and attack scenarios that allow comprehensive performance evaluation of the proposed approach in comparison with existing methods. The test outcomes revealed that the blockchainâsupported proposed method outperforms traditional methods with an accuracy of 98.21%, specificity of 97.39%, and F1âscore of 97.46%.
With the explosive growth of online education resources, traditional education platforms that rely on centralized servers for resource distribution and storage gradually expose issues such as inefficient resource management, lack of trust in sharing, high storage costs, and a high risk of single-point failure. In response, this study designs a decentralized education resource sharing platform by integrating blockchain technology and distributed file systems. It leverages the distributed ledger, immutability, and traceability features of blockchain, along with the high data availability and low storage cost advantages of distributed file systems. Results show that the proposed education platform reaches 1,388 transactions per second when the number of nodes is 200, with a latency response time of 9.8 seconds and an average memory overhead of 268 MB. In practical performance evaluation, the platform achieves a top-10 hit rate of 97.2%, a business interruption probability as low as 4.8% under unexpected conditions, and an average resource throughput efficiency of 176 Mbps. Overall, the platform performs well in education resource sharing and demonstrates strong algorithm fault tolerance, practicality, robustness, and service stability, providing reliable technical support for global education resource sharing.
This paper explores the application of blockchain technology to manage distributed edge computing resources. The core claim is that blockchain can facilitate dynamic resource allocation and efficient utilization within edge computing environments. The proposed mechanism involves constructing a blockchain-based resource management system leveraging smart contracts to automate and optimize resource distribution. This approach addresses the challenges of centralized control, inefficient resource utilization, and security vulnerabilities commonly found in traditional edge computing models. The research investigates the potential benefits of blockchain's decentralized, transparent, and immutable ledger for enhancing edge computing resource management, ultimately leading to improved performance, scalability, and trust within distributed edge systems. The key focus is on establishing a secure and automated framework for resource sharing and access control, significantly improving the overall efficiency and reliability of edge computing deployments. ---
Nelli Yaswanth Kumar, Dr. Singothu Jhansi Rani, Setti Sarika
The rapid proliferation of Internet of Things (IoT) devices under sixth-generation (6G) networks introduces a highly dynamic, decentralized environment in which static, perimeter-based security models are no longer adequate. This paper proposes AZTM-v3 an adaptive Zero Trust framework that couples behavior-driven trust management with a Random Forest classifier to identify and isolate malicious nodes in real time. The framework is evaluated on an NS-3 simulation of a 150-node 6G IoT network subjected to Sybil, Denial-of-Service (DoS), spoofing, replay and ON-OFF attacks. Unlike prior trust-management proposals that report only qualitative or partial outcomes this work quantifies performance across five dimensions i.e detection accuracy, F1-score, false-positive rate, end-to-end latency and consensus-convergence time and benchmarks AZTM-v3 against PKI-based, centralized-trust and static-blockchain baselines. AZTM-v3 attains a 98.1% overall detection accuracy with a 1.6% false-positive rate at 150 nodes and sustains 95.4% accuracy at 200 nodes outperforming the PKI baseline by 12â18 percentage points across all tested loads. These results indicate that combining tiered trust evaluation with machine learning based classification yields a measurably more scalable and resilient security layer for 6G-enabled IoT deployments than existing static or purely cryptographic approaches.
Smart Cities (SCs) leverage advanced technologies and data analytics to optimize infrastructure and services for economic and quality of life benefits. However, realizing the potential of SCs requires interoperability between different systems, which remains challenging due to fragmentation. Thus, unified architectures are needed to enable effective coordination through common languages and protocols. Digital Twin (DT) models, bidirectional virtual representations of physical assets, show immense promise for unifying SCs by integrating massive heterogeneous data streams. Although there have been numerous studies investigating unified models for SCs, in the context of DT, most studies narrowly focus on using DT for different systems within cities rather than citywide implementation. As a response, this study identified 34 recent papers investigating interoperability and unified models in SCs, out of which 19 papers were focused on developing unified models for SCs and 15 papers were focused on unified DT models in SCs. These 15 papers were systematically reviewed, identifying the key factors, benefits, and challenges of such models. To help city leaders and to make focused, context-aware decisions aligned to their objectives, whole factors were categorized into four groups, including relevance-based, influence-based, complexity-based, and risk-based. To guide future research, the study highlights edge computing and implementing blockchains as underrepresented areas within the realm of SCs.
Marco Scarpa, Mohammad Sadeghzadeh, Saeed Javanmardi, Bahareh Pahlevanzadeh ¡ 5 authors
Blockchain provides secure and decentralized data storage. Normal blockchains permanently store data. Mutable blockchains allow users to change data, but this reduces tamper resistance. This paper tests both methods in PaB-PIF, a hybrid architecture for IoT-Fog networks. Our design uses an immutable mainchain in the cloud layer and mutable sidechains in the fog layer. We analyze throughput, latency, and tamper resistance using math models and simulations. Results show that blockchain greatly improves network security. Without blockchain, the network has zero tamper resistance. The mutable blockchain in the fog layer has a tamper resistance of 0.58. The immutable blockchain in the cloud layer reaches 0.99. However, this extra security increases latency and reduces throughput. The mutable blockchain has lower latency, so it is a good fit for the fog layer. The immutable blockchain provides maximum security, which is best for the cloud layer. This trade-off works well for IoT systems like the Internet of Vehicles, where data integrity and legal rules are essential. We also compare PaB-PIF with an IoT-Fog network that has no blockchain.
The rapid expansion of fourth industrial revolution (4IR) technologies has intensified the expectation that artificial intelligence (AI), blockchain, the Internet of Things (IoT), big data analytics, and automation can accelerate the process of achieving the United Naitons Sustainable Development Goals (SDGs), particularly in developing nations. Whether these technologies live up to their expectation, however, depends not only on technological capability but also on the legal, regulatory, and institutional environment in which they operate. However, the governance of 4IR technologies has gained far less scholarly attention than their technological potential. The present study examines how legal frameworks, policy instruments, and governance arrangements influence the contribution of 4IR technologies to sustainable development in developing countries. Following the PRISMA 2020 guidelines, literature published between 2015 and 2025 was identified through searches of Web of Science, Scopus, Google Scholar, Pub Med, and arXiv. From 721 retrieved records, 50 peer reviewed studies met the eligibility criteria and were synthesised using a narrative approach. The analysis reveals three consistent patterns. First, legal authority is fragmented within and across jurisdictions. Second, policy commitments frequently outstrip implementation capacity, a performativity in which governments announce SDG ambitions without building the institutional means to deliver them. Third, governance is constrained by limited expertise, weak enforcement, and poor coordination between agencies. The review also identifies three important gaps in the literature: a predominant focus on artificial intelligence at the expense of other technologies, limited empirical testing of the links between fourth industrial revolution technologies and SDG outcomes, and minimal attention to how rules are enforced in practice. These finding suggest that the prime difficulty of harnessing 4IR technologies for sustainable development in developing nations are institutional rather than technological. Therefore, it is not only about advancing technological innovation but also strengthening regulatory coherence, governance capacity, and the effective implementation of legal frameworks to achieve SDGs in developing countries.
There are several types of multimodal data such as text, image, audio, video, and sensor streams. It is evolving at a very high rate, which has resulted in not only complicated security issues but also enormous potential opportunities in advanced analytics and intelligent applications. The significant security issues in multimodal data management are analyzed in this chapter, such as data integrity, data confidentiality, authentication, access control, and privacy protection. Multimodal data is vulnerable to attacks such as adversarial attacks, data breaches, unauthorized access, and cross-modal inference, and due to its dispersed and heterogeneous nature, such attacks can compromise sensitive information. The chapter examines diverse security controls to address these challenges, such as safe multimodal fusion, blockchain architecture, cryptography, federated learning, differential privacy, and robust authentication processes. It is concentrated on scalable and real-time protection methods that are effective in edge-cloud designs and big data environments. This combination of approaches will enable businesses to ensure dependable multimodal data analytics that will safeguard user privacy and system reliability and allow making safe and efficient decisions in any field of application.
In the age of fast industrial digitalization, securing the heterogeneous and high-volume data produced by the Industrial IoT systems is a basic need. The chapter is dedicated to the application of machine learning and deep learning methods in the process of securing multimodal data within the context of Industrial Internet of Things (IIoT). It includes a detailed discussion of multimodal sources of data and the corresponding cyber threat environment, and then it introduces the machine learning (ML)-based and deep learning (DL)-based anomaly detection and intrusion prevention techniques. The chapter reviews the secure architectural designs, which combine edge, fog, and cloud intelligence and privacy-sensitive and trust management schemes like federated learning and blockchain. The practical applicability of such approaches is pointed out by the real-life industrial applications and case studies. The main implementation issues and the performance evaluation metrics are examined to ensure a successful implementation. The chapter ends by highlighting the future directions and new trends, focusing on adaptive, explainable, and resilient intelligent security solutions in next-generation IoT systems of the industrial world.
Aasem N. Alyahya, Muidh Awadh Algahtani, Amani Ibraheem, Naglaa F. Soliman ¡ 8 authors
Industry 4.0 is evolving rapidly, 6G networks are emerging, and this has led to a dramatic increase in ultra-latency-critical, computationally demanding jobs in Industrial Internet of Things (IIoT) environments such as real-time digital twins, collaborative robots, and augmented reality-guided assembly. However, the conventional D2D-assisted mobile edge computing (MEC) systems suffer from the severe performance degradation due to the harsh factory propagation environment, severe security threats on the open D2D links, strict industrial data privacy requirements, selfish resource sharing behaviour, and frequent service migration due to device mobility. In this research, we propose a holistic secure task co-offloading system that integrates Reconfigurable Intelligent Surfaces (RIS), permissioned blockchain with smart contracts, and federated learning, into an integrated D2D-MEC architecture for the IIoT. A federated secure multi-armed bandit algorithm enables privacy-preserving decentralized decision-making without revealing sensitive industrial data. Blockchain records off-load transactions through immutable ledgers and enables smart-contract-based incentive enforcement. RIS renders unreliable wireless channels dynamic with minimum energy overhead. In scenarios with hostile and imprecise information, the collaborative design can lower the long-term cost of the system, including task latency, energy consumption, migration overhead, and blockchain transaction fees. The proposed framework, compared to state-of-the-art baselines, reduces the average job completion latency by 29.6%, energy consumption by 36.8%, and migration cost by 41.2%, as demonstrated by extensive trace-driven simulations in actual 6G-IIoT manufacturing scenarios. The experimental results also show the robust performance under the simulated willingness manipulation and poisoned federated updates. The permissioned blockchain architecture provides the architectural security against the Sybil attack and other trust-related threats.
Security in resource-constrained IoT deployments remains a persistent challenge: devices used in industrial control, smart healthcare, and transportation must authenticate quickly, consume minimal energy, and resist physical attacks â yet existing protocols rarely address all three requirements at once. To the best of current knowledge, no prior protocol jointly optimises security, energy, and latency within a single formally verified framework. This paper presents OPAQUE-IoT, an Optimization-driven PUF-Blockchain AKA Protocol for constrained IoT networks. The framework integrates PUF-based hardware identity verification, a permissioned blockchain for decentralized trust management, and the Adaptive Security-Energy Trade-off Optimizer (ASETO), which jointly minimizes authentication latency and energy consumption under formal security constraints. Convergence of ASETO is proven under Lipschitz-continuous objective functions. Formal security analysis under the Real-or-Random (RoR) model with explicit Random Oracle and ECDH hardness assumptions demonstrates resistance to replay, impersonation, man-in-the-middle, PUF modeling, insider, and side-channel attacks, with a security advantage bound of approximately 2^(-68). Simulation results across heterogeneous IoT topologies ( N = 50 to 5000 devices) show 31.8% lower energy consumption, 30.2% reduced authentication latency, and 41.1% higher throughput compared to the best-performing blockchain-capable baseline, with O(log N) Merkle-indexed blockchain query complexity and O(T_max¡N¡P) per-epoch optimiser complexity.
Abstract The integration of Internet of Things (IoT) and blockchain technologies enables secure, decentralized data management for real-time applications. However, limitations in processor, memory, and energy resources restrict the direct processing of large datasets. Notably, Garbage Collector (GC) mechanisms in high-level languages increase variance in P99 queue latencies, while expanding data volumes can result in system crashes due to Out-of-Memory (OOM) errors. This study introduces a hybrid Rust-Python Simplified Payment Verification (SPV) native hashing engine deployed on resource-constrained edge devices (Raspberry Pi Zero W) and high-capacity gateways (Raspberry Pi 5). Laboratory evaluations demonstrate that the hybrid system achieves a verification capacity of 34,000 Merkle nodes per second, representing a 4.35-fold speed improvement over pure Python on the Pi Zero W. Additionally, the system reduces GC-induced latency fluctuations and offers up to 75% potential energy savings, as indicated by theoretical modeling based on active processor cycle analyses. Bottleneck analyses on Raspberry Pi 5 indicate that Foreign Function Interface (FFI)-related data transfer costs limit parallel processing benefits for low-volume datasets. In contrast, the scalability of the hybrid architecture is evident with datasets containing 1.5 million records. The memory-mapped streaming architecture minimizes OOM risks and achieves a cache miss rate of 0.34%. Memory safety was assessed using the MIRI tool.
Haitham A. Mahmoud, Ahmed Soliman, Mohammed El-Meligy, Azhar Imran ¡ 5 authors
Abstract Modern digital ecosystems rely mostly on blockchain technology, such as decentralized and immutable ledger systems. This technology avails guarantees of secure transaction and data administration in keeping with the privacy of consumers. Thus, the blockchain systems often suffer in resource-constrained environments to experience considerable computational overhead along with low scalability and issues in handling real-time data. To overcome these restrictions, this research incorporates federated learning, decentralized storage using IPFS, and lightweight cryptographic methods to deliver secure, scalable, and real-time analytics in the IoT system. This research has proposed a novel framework based on blockchain, privacy-preserving techniques, and predictive maintenance models to address some of the security, scalability, and reliability challenges observed in IoT ecosystems. The framework guarantees secure data management, efficient real-time analytics, and robust anomaly detection by using the most advanced technologies such as federated learning, decentralized storage, and lightweight cryptographic methods. The suggested technique exceeds traditional methods by means of accuracy and error reduction with the astonishingly low FPV value of 0.005954% and FNR value of 0.000274% while giving extraordinary performance metrics that reach 99.88% accuracy, 99.89% precision, 99.97% recall, and 99.93% F1-score. This solution establishes secure, scalable, and tamper-proof infrastructure for all the applications from industrial automation, healthcare to vehicular networks, hence enabling smart and sustainable IoT governance for these applications.
Smart cities are quickly becoming data-driven environments that are dependent on intelligent technologies to make cities efficient and their citizens happy. In this chapter, the author introduces a comprehensive concept of deep learning and blockchain that will be used to secure and improve multimodal smart city applications. It explores the heterogeneity issues of Internet of Multimedia Things (IoMT) data, such as security, privacy, and trust, and shows how deep learning can facilitate intelligent analysis by means of feature extraction, multimodal fusion, and real-time decision-making. Data integrity and transparency, as well as decentralized governance, are guaranteed by blockchain and secure access control and policy automation through smart contracts. It is also in this chapter that mechanisms of identity and trust management, secure data and model management, and privacy are discussed. Applied benefits are demonstrated by use cases in surveillance, transportation, and energy management, whereas challenges and future research discussions provide a basis for secure, resilient, and intelligent urban ecosystems.
Blockchain is one important building blocks of the Internet of the future, called Web3. The Blockchain technology supports a wide range of applications, spanning from Smart Cities and automotive industries, from agriculture to energy. The healthcare sector, in particular, has experienced a profound impact from blockchain-based technologies, paving the way for the development of true digital healthcare systems. By enabling secure and immutable data storage, and facilitating the sharing of this information among all nodes possessing a local copy of the distributed ledger, blockchain plays a vital role in the analysis of healthcare data. This paper provides a comprehensive survey of the main blockchain platforms utilized in the digital healthcare, integrated with a comparative analysis. In addition, the implementation of Innovative permissioned Blockchain for eHealth (IBEH) is presented and discussed in detail. IBEH addresses key challenges in digital health data management, including secure and controlled access to sensitive health information, ensuring data integrity and traceability, and secure sharing between different healthcare institutions and organizations. This is made possible by decoupling the application and blockchain layers and by a flexible, customizable, and easily deployable infrastructure. IBEH integrates the application-oriented and embedded layer with that of a blockchain network built with the MultiChain platform, which uses smart contracts with permissions, REST APIs, and RPC calls. The main features and its associated smart contracts within the healthcare domain are discussed. Finally, the analysis of performance is provided.