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<p>Mobile ad hoc networks (MANETs) are wireless networks ideally designed for applications such as specific outdoor events, communication in areas without wireless infrastructure, crisis situations, natural disasters, and military operations, as they do not need preexisting network infrastructure and can be rapidly deployed. Security is a major problem in ad hoc networks, and extensive research has concentrated on lowering energy consumption in order to extend node a long lifespan. To overcome these challenges, this work proposes an energy-efficient and secure routing framework ASAO-DST for MANETs, addressing the dual challenge of prolonging network lifetime while ensuring trustworthy route selection. The proposed method uses Improved Fuzzy C-Means (IFCM) clustering, which efficiently organizes network nodes into clusters by enhancing membership functions for optimal formation, while Cluster Head (CH) selection is optimized through Electric Eel Foraging Optimization (EEFO), which emulates the foraging behavior of electric eels to determine the most energy-efficient CH. CH selection in EEFO utilizes CH selection because its multi-phase foraging model simultaneously satisfies the criteria of residual energy, mobility, and connectivity, which cannot be simultaneously optimized with a single-objective energy-aware heuristic, thus prolonging the network lifetime compared to conventional clustering techniques. Trust evaluation is performed using Dempster–Shafer Theory (DST) to integrate several evidence sources for evaluating node dependability. The routing path selection utilizes the Adaptive Snow Ablation Optimizer (ASAO), which replicates snow melting processes to determine optimum and secure paths while preserving Quality of Service (QoS) under fluctuating circumstances. In MANETs, Improved Fuzzy C-Means (FCM) clustering organizes network nodes into efficient clusters by refining membership functions for better formation, while CH selection is optimized using EEFO, which mimics the foraging behavior of electric eels to identify the most energy-efficient CH. The trust evaluation is conducted using DST to combine multiple evidence sources for assessing node reliability. For routing path selection, the ASAO is employed, which simulates snow melting patterns to identify optimal and secure routes, maintaining QoS under dynamic conditions. The suggested approach empirically compares the suggested approach to existing methods based on energy consumption 30&#xa0;mJ, throughput 0.96 Mbps, end-to-end delay 2.03063&#xa0;s, network lifetime 6100 rounds indicating LND criterion for the 100-nodes, packet delivery rate 99.8%, jitter 0.35ms, bit error rate 19%, efficiency rate 99.8% and attack detection rate 85%. The framework incurs a controlled computational overhead (2.9–4.6 ms vs. 1.3–2.5 ms for EACLRP) and memory overhead (560–930 KB vs. 310–640 KB for EACLRP), which constitute the computational cost of the additional security, trust evaluation, and adaptive optimization capabilities provided by the framework. The compared existing algorithms such as Energy Aware and Adaptive Cross Layer Routing Protocol (EACLRP), Optimal Fuzzy Clustering and Trust-Based Routing (OFC-TR), Ad hoc On-Demand Distance Vector with Rule-Based Optimization (AODV-RBO), Modified Mother Optimization with Online K-Means Clustering (MMO-OKMC), and Hybrid Sailfish-Whale Optimization Algorithm (HS-WOA) are less efficient than the proposed ASAO-DST.</p>

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An energy-efficient secure routing protocol using a hybrid Dempster–Shafer theory and adaptive snow ablation optimization routing framework for MANETs

  • 期刊论文

  • 发表日期：

    2026年5月8日

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  • S. D. Vijayakumar,
  • S. Anbu Karuppusamy

摘要

Mobile ad hoc networks (MANETs) are wireless networks ideally designed for applications such as specific outdoor events, communication in areas without wireless infrastructure, crisis situations, natural disasters, and military operations, as they do not need preexisting network infrastructure and can be rapidly deployed. Security is a major problem in ad hoc networks, and extensive research has concentrated on lowering energy consumption in order to extend node a long lifespan. To overcome these challenges, this work proposes an energy-efficient and secure routing framework ASAO-DST for MANETs, addressing the dual challenge of prolonging network lifetime while ensuring trustworthy route selection. The proposed method uses Improved Fuzzy C-Means (IFCM) clustering, which efficiently organizes network nodes into clusters by enhancing membership functions for optimal formation, while Cluster Head (CH) selection is optimized through Electric Eel Foraging Optimization (EEFO), which emulates the foraging behavior of electric eels to determine the most energy-efficient CH. CH selection in EEFO utilizes CH selection because its multi-phase foraging model simultaneously satisfies the criteria of residual energy, mobility, and connectivity, which cannot be simultaneously optimized with a single-objective energy-aware heuristic, thus prolonging the network lifetime compared to conventional clustering techniques. Trust evaluation is performed using Dempster–Shafer Theory (DST) to integrate several evidence sources for evaluating node dependability. The routing path selection utilizes the Adaptive Snow Ablation Optimizer (ASAO), which replicates snow melting processes to determine optimum and secure paths while preserving Quality of Service (QoS) under fluctuating circumstances. In MANETs, Improved Fuzzy C-Means (FCM) clustering organizes network nodes into efficient clusters by refining membership functions for better formation, while CH selection is optimized using EEFO, which mimics the foraging behavior of electric eels to identify the most energy-efficient CH. The trust evaluation is conducted using DST to combine multiple evidence sources for assessing node reliability. For routing path selection, the ASAO is employed, which simulates snow melting patterns to identify optimal and secure routes, maintaining QoS under dynamic conditions. The suggested approach empirically compares the suggested approach to existing methods based on energy consumption 30 mJ, throughput 0.96 Mbps, end-to-end delay 2.03063 s, network lifetime 6100 rounds indicating LND criterion for the 100-nodes, packet delivery rate 99.8%, jitter 0.35ms, bit error rate 19%, efficiency rate 99.8% and attack detection rate 85%. The framework incurs a controlled computational overhead (2.9–4.6 ms vs. 1.3–2.5 ms for EACLRP) and memory overhead (560–930 KB vs. 310–640 KB for EACLRP), which constitute the computational cost of the additional security, trust evaluation, and adaptive optimization capabilities provided by the framework. The compared existing algorithms such as Energy Aware and Adaptive Cross Layer Routing Protocol (EACLRP), Optimal Fuzzy Clustering and Trust-Based Routing (OFC-TR), Ad hoc On-Demand Distance Vector with Rule-Based Optimization (AODV-RBO), Modified Mother Optimization with Online K-Means Clustering (MMO-OKMC), and Hybrid Sailfish-Whale Optimization Algorithm (HS-WOA) are less efficient than the proposed ASAO-DST.