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 — 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.
W. C. Yang, J. F. Qiao, J. F. Hu, Jie Wang · 5 authors
This study presents a multi-level verification system for secure communication protocols in energy billing infrastructures. The proposed framework integrates device attestation, network integrity verification, privacy-preserving aggregation, billing validation, and immutable auditing to address security vulnerabilities across Advanced Metering Infrastructure (AMI) communication chains. A Hybrid Secure-Efficient Protocol (HSEP) combining elliptic curve cryptography, homomorphic encryption, and zero-knowledge proofs is developed to provide secure authentication, privacy protection, and verifiable data integrity while maintaining low computational overhead. Experimental evaluation using a large-scale AMI testbed demonstrates that the proposed system significantly improves tampering detection capability, achieving an intrusion detection AUC of 0.94 while maintaining an average energy consumption of 1.55 J per transaction and acceptable communication latency for large-scale deployment. The architecture exhibits strong scalability, robustness, and rapid dispute-resolution performance under multiple attack scenarios. The proposed framework is particularly applicable to wireless smart metering networks and antenna-enabled AMI communication infrastructures, where reliable data transmission, secure protocol verification, and resilience against communication-layer attacks are essential for trustworthy energy billing and grid operation. This work provides an effective engineering solution for secure, privacy-preserving, and verifiable communication in modern intelligent energy systems.
The use of a wireless sensor network is increasingly supporting e-governance functions such as municipal utility monitoring, environmental monitoring, grievance-based field reporting, and smart public service delivery. Most wireless sensor network architectures rely on a gateway or database. However, this introduces vulnerabilities to data integrity, node accountability, and auditability. This study examines transparency through a blockchain-enabled WSN architecture for e-governance. The study applies a reproducible Python-based Monte Carlo simulation with a fixed random seed, five node densities, three architectural scenarios, and 450 observations. The scenarios that are compared in this work are a normal WSN, a centralized secure WSN, and a permissioned blockchain-enabled WSN with smart-contract-based identity registration, hash-linked data records, trust scoring, and tamper verification. Descriptive statistics, one-way ANOVA, Welch t-tests, Pearson correlation, and multiple linear regression analysis. The blockchain-assisted WSN, as evidenced by the simulation findings of our project, produced the highest mean data integrity score, tampering detection rate, trust score, malicious node detection rate, and packet delivery ratio. The architecture also improved the composite service efficiency index relative to the conventional baseline, even though it introduced higher latency, transaction confirmation time, and energy consumption. The research indicates that the permissioned blockchain can enhance public-sector WSN transparency with edge aggregation and lightweight cryptographic operations along with carefully tuned endorsement rules. The methods presented in this study allow for scrutiny of secure WSN designs tailored for e-governance.
Over the past few years, Wireless Sensor Networks (WSNs) have been increasingly deployed for numerous sensing and monitoring purposes in environmental monitoring, industrial automation, health monitoring, military surveillance, smart agriculture and disaster management among others. The inherent limitations in terms of processing power, memory, communication bandwidth and energy of sensor nodes make WSNs highly susceptible to malware attacks. A wide variety of malware such as sensor network worms, Trojans, viruses, botnets and ransomware can easily propagate in a network through inter node communication. Such malware can cause serious damage to communication, compromise sensitive data, consume energy of the infected nodes thereby reducing the lifetime of network among others. In the last decade, numerous approaches have been proposed for the detection of malware infecting sensor nodes. These approaches range from traditional signature-based detection and behavior-based detection to more advanced approaches such as machine learning (ML)-based, deep learning (DL) -based, blockchain-based, trust management-based and federated learning-based detection. Most of the existing approaches for malware detection in WSNs have been designed to work on WSNs and have not been tested on real scenarios. Most of the approaches have their own strengths and weaknesses and the most suitable approach for a given application depends on various factors. In this paper, we present a comprehensive review of approaches for the detection of malware infecting sensor nodes in WSNs. We present a taxonomy of reviewed approaches for detection of malware. We also present a discussion on approaches for modeling malware propagation in a WSN as well as review on various categories of malware that have been designed to attack sensor nodes in WSNs along with detection frameworks for different categories of malware. We also present a comparative study of approaches used for the detection of malware in WSNs on the basis of various parameters such as detection accuracy, computational complexity, energy efficiency, scalability, detection latency and deployability. The review and taxonomy presented in this paper will be highly beneficial for researchers and practitioners designing approaches and systems for the detection of malware in WSNs. Various open research challenges in this area have also been discussed in this paper including detection of zero-day malware, designing of intelligent models to be light enough to be deployed on sensor nodes, use of explainable artificial intelligence for detection of malware in WSNs, designing approaches for privacy-preserving collaborative learning in WSNs and designing adaptive security approaches for WSNs.
Ahmed A. Jasim, Noor Riyadh Issa, Hisham A. Shehadeh, Fadhil Mukhlif · 6 authors
Abstract The Wireless Sensor Networks (WSNs), which are deployed in harsh environments, are extremely susceptible to localized battery exhaustion as well as intelligent inside routing threats, especially sinkhole and data falsification attacks. In this paper, we present ECHVM (Enhanced Cluster Head selection by Voting and an ECC-based Blockchain Mechanism), a novel, secure, and energy-efficient routing framework to achieve an optimal trade-off between network security and hardware resource efficiency. We present the ECHVM protocol that combines Elliptic Curve Cryptography (ECC) with a lightweight, local distributed ledger to mitigate risks of centralized authority by transferring the verification of crucial network events from a single, highly vulnerable centralized alert sink node to a decentralized, voting-based consensus among neighboring nodes. In this study, a weighted voting algorithm based on node and distance proximity metrics is applied to cluster head selection, where a 51% neighbor consensus rule is leveraged to validate local ledger transactions against malicious acts. Moreover, a local energy density and topological communication geometry-oriented energy-efficient cluster head (CH) selection algorithm is crafted to ensure balanced CH distribution in the high-density areas of the network structure so as to avoid the premature energy hole problem. Using the standard first-order radio energy model, quantitative simulations performed in MATLAB show that ECHVM achieves a malicious node detection rate of 98.2% and extends the network lifetime by 30% compared to state-of-the-art protocols (e.g., ELSO and SEC-HDT) because of proactive topology defense and rapid node sleeping. The results of statistical validation using the Wilcoxon Signed-Rank test confirm that both the reduction in energy consumption and network longevity offered by ECHVM are highly significant ( p = 0.019), justifying ECHVM as a mathematically sound, permanent, scalable security framework suitable for resource-constrained, dense Internet of Things (IoT) and smart sensing applications for next-generation communication systems.
P. Thanalakshmi, V. G. Kiruthika, J. C. Gokul Abinash, P. Saravanan · 6 authors
Wireless Sensor Networks (WSNs) are vulnerable to malicious nodes and sensor node failures, which compromise data integrity and network reliability. These threats result in incorrect decisions and reduce system trust. To address this, machine learning algorithms enable anomaly detection by identifying abnormal sensor nodes, while blockchain ensures secure and tamper-proof data storage. However, reliable consensus is essential before data validation in the blockchain. A hybrid framework combining ML, blockchain, and a modified HotStuff consensus algorithm with post-quantum cryptographic systems provides secure, fault-tolerant, and quantum-resistant consensus, ensuring trustworthy and resilient WSN operations.
Blockchain interoperability remains a major challenge because heterogeneous blockchain networks cannot securely and efficiently exchange cross-chain data and transactions. Existing interoperability solutions often rely on central relays or trusted intermediaries, creating security vulnerabilities, limited fault tolerance, and a single point of failure. To address these limitations, this paper proposes VeriMesh, a decentralised mesh-based interoperability framework that combines trust-adaptive routing, multi-path relay verification, and Zero-Knowledge Proof (ZKP)-based validation for secure cross-chain communication. VeriMesh models relay nodes as a trust-weighted graph in which routing decisions dynamically adapt based on node behaviour and delivery reliability. Multi-path routing improves resilience against adversarial relay nodes, while transport-layer ZKP verification enables privacy-preserving validation without exposing sensitive information. The framework was implemented using Python relay nodes, Solidity smart contracts, and an Ethereum (Ganache) environment. Experimental evaluation using structured event-driven workloads demonstrated stable latency below 34 ms and delivery success rates above 85% up to 40% malicious node presence. Comparative evaluation against single-path and random multi-path relay baselines showed improved fault tolerance and routing reliability. The results demonstrate favourable scalability and robustness within the evaluated network range ( N = 10–30), while larger-scale evaluation remains future work. All experiments were conducted in a controlled local Ganache blockchain environment rather than on a public Ethereum testnet or mainnet, so the reported latency, gas, and delivery figures characterise protocol-layer behaviour under controlled conditions and should not yet be interpreted as representative of performance under public-network conditions such as real gas markets, block propagation delays, or network congestion.