TL;DR
Simulation results suggest that the proposed EGWO-CS-based approach offers significant improvements in packet delivery ratio, energy efficiency, trust precision, and network lifetime compared to conventional GWO, GWO-CS, and other standard schemes.
Wireless Sensor Networks (WSN) are significant for various applications, however ensuring data security and energy consumption remains a critical challenge. The conventional methods lacked sufficient security, exhibited communication overhead, and energy inefficiencies. Therefore, this research proposes the Distributed Fractional Hawk Optimization (DtFHO) algorithm to address the limitations in cluster head selection for secure WSN routing. The integration of fractional theory improves the convergence speed and exploitation balance in cluster head selection. To secure the data routing, a blockchain network is employed, which maintains a transparent record of routing paths while preventing malicious node entries. Furthermore, the modified End-to-End Homomorphic encryption enables secure data sharing without decrypting sensitive information at intermediate nodes. Through considering the multimetric factors, the DtFHO algorithm offers a secure routing path, making it highly effective for large-scale and sensitive network scenarios. The DtFHO showcases a robust performance by achieving a minimum transaction time of 2.013 seconds, memory usage of 347.95 Kilobytes, Gas usage of 345.84 Kilobytes, encryption time of 2.012 seconds, and a maximum throughput ratio of 0.748, normalized energy of 0.766 Joules, with 153 alive nodes compared to the conventional methods.
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.
Wireless Sensor Networks (WSNs) are increasingly deployed in resource-constrained and hostile environments for critical applications such as environmental monitoring, industrial automation, and structural auditing. However, their real-world efficacy is severely limited by battery energy constraints and high vulnerability to routing-layer security threats. Conventional cryptographic solutions incur excessive computational overhead, while standard energy-efficient routing protocols often neglect security. To resolve this trade-off, this paper proposes an integrated, multi-tier optimization framework combining three novel algorithms: Proficient Secure Cluster Head Selection (PSCH), Total Path Trust Assessment Routing (TPTAR), and Energy-Proficient, Secure, and Trust-Based Shortest Path Optimized Routing (EST-SPOR). The proposed architecture utilizes fuzzy logic for cluster-head selection, a dynamic trust evaluation model combining direct and reference trust metrics, and an optimized shortest-path routing mechanism based on Ant Colony Optimization (ACO) principles. Simulation analyses conducted in NS-3 demonstrate that the framework achieves superior performance in terms of Packet Delivery Ratio (PDR), energy conservation, end-to-end latency, and resilience against malicious routing attacks compared to existing baseline protocols.
Amid the explosive growth of latency-aware and computation-sensitive services, mobile edge computing (MEC) assisted by aerial networks, such as high-altitude platforms (HAPs) and low-altitude unmanned aerial vehicles (UAVs), has emerged as an effective solution for providing computational capabilities to regions with sparse terrestrial infrastructure. Nevertheless, aerial networks are highly sensitive to energy cost and inherently constrained in hosting dense computing resources, while the exposed wireless environment renders them particularly vulnerable to attacks from malicious nodes. Consequently, it is imperative to develop effective task scheduling and computing resources management mechanisms that satisfy users quality-of-service (QoS) requirements while minimizing system cost and ensuring network reliability. In this paper, we develop a multi-cell MEC network composed of multiple UAVs and multiple HAPs, and further propose a dual-layer blockchain-enabled, federated election (FE)-based (DBFE) group relative policy optimization (GRPO) algorithm to jointly reduce the task offloading latency and system energy expenditure. In particular, blockchain techniques enhance system resilience against non-Byzantine failures, whereas the FE mechanism suppresses the influence of Byzantine behaviors. Simulation results demonstrate that, compared with existing approaches, the proposed method reduces the overall system cost by 19% and 31% under scenarios without malicious nodes and with malicious nodes, respectively.
Secure routing is a critical requirement for Internet of Things (IoT) and wireless sensor networks (WSNs) that form the sensing layer of future internet and edge-assisted communication systems. Shortest-path routing alone cannot provide reliable delivery when intermediate nodes exhibit selective forwarding, malicious dropping, or inconsistent behavior. This paper presents HBTV-SR, a permissioned blockchain-assisted trust-evidence retrieval method for secure IoT/WSN routing. The proposed design keeps ordinary sensor nodes lightweight by allowing them to generate signed trust observations, while miner/gateway edge nodes verify signatures, freshness, the absence of duplicate hashes, evidence completeness, and trust-value ranges. Storage nodes maintain Merkle-rooted trust records for tamper-evident retrieval. A reproducible packet-level simulator evaluates HBTV-SR against AODV-like routing, TARF-style trust routing, a direct She et al. blockchain trust baseline, and other baselines surveyed in the literature for the same scenario. Across 30 fixed-seed Monte Carlo runs, HBTV-SR achieved 83.12% packet delivery ratio in the representative 100-node, 20% malicious-node scenario, compared with 78.21% for the She et al. baseline. The results indicate that edge-assisted ledger-backed trust retrieval improves secure routing reliability while avoiding excessive blockchain workload on constrained IoT/WSN nodes.
A new hybrid bioinspired optimization framework for efficient blockchain mining is presented, integrating Genetic Algorithm, Firefly optimization, and Particle Swarm Optimization into a unified architecture to take advantage of their complementary strengths.