EATMR: an energy-aware trust algorithm based the AODV protocol and multi-path routing approach in wireless sensor networks

Yin, Huaying; Yang, Hongmei; Shahmorad, Saeid

doi:10.1007/s11235-022-00915-0

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…The Taylor C-SSA [19] had incompetent to optimize efficient path selection. EATMAR [20] failed to examine the more objective function for achieving the efficient routing. Taylor-SHO [21] failed to determine an analysis of computation overhead.…”

Section: Discussionmentioning

confidence: 99%

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Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Srinivasan R.

2024

jes

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Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

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Section: Discussionmentioning

confidence: 99%

“…Huaying Yinet al [20] developed an AODV protocol and Multipath Routing approach-based Energy-Aware Trust algorithm (EATMR) for an enhancement of WSN security. Initially, the nodes were estimated according to Open-Source Development Model Algorithm (ODMA) and then, the clustering-based routing was substituted.…”

Section: Iiliterature Surveymentioning

confidence: 99%

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Srinivasan R.

2024

jes

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“…The outcomes proved that the model performed better than the previous related schemes. Huaying Yin et al [18] suggested an ad-hoc on-demand distance vector protocol and multipath routing technique to provide energy-efficient and trust-based routing in WSN. The framework selected optimal and secure routes for DA by considering trust, energy, count of hops, and distance.…”

Section: Related Workmentioning

confidence: 99%

A trust and optimal energy efficient data aggregation scheme for wireless sensor networks using QGAOA

Kumar,

Srimanchari

2023

Int J Syst Assur Eng Manag

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In recent years, Wireless Sensor Networks (WSNs) have become a key technology for monitoring and tracking applications in a wide application range. A wireless sensor network (WSN) senses the environment, collects data, and sends it to a base station (BS) for analysis. A fundamental challenge in designing WSNs is maximizing their lifetimes, especially when energy is limited. Managing trust in the WSN is also a difficult task because trust is used when collaboration is critical to achieving reliable communication. As a result, this paper proposed a secure and energy-aware data aggregation and optimal routing scheme for WSNs based on the Quantum behaviour and Gaussian Mutation based Archimedes Optimization Algorithm (QGAOA). The proposed system comprises three parts: cluster formation, cluster heads (CHs) selection, and optimal routing. Initially, clusters are formed using the Voronoi-included K-means clustering algorithm (VKMCA). Then CHs are selected, and optimal routing is selected using QGAOA. Simulations are carried out to analyze the performance effectiveness of the proposed system with existing related techniques. The results revealed that the proposed system outperformed existing techniques regarding network lifetime (NLT), energy consumption (ECP), throughput, delay, and packet delivery rate (PDR) factors.

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“…Yin [14] developed an energy-aware trust technique that depends on 𝐴𝑂𝐷𝑉 protocol and multipath routing method (EATMR) for improving the security in wireless sensor networks. The developed model includes two major steps, first open-source development model algorithm (ODMA) was utilized for nodes clustering and second clustering-based routing was utilized.…”

Section: Literature Surveymentioning

confidence: 99%

Multi-objective Sand Cat Swarm Optimization Algorithm for Cluster Head and Routing Path Selection in WSN

2023

IJIES

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A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements that are utilized for sensing, tracking, and data collection in a variety of situations. Sensor energy is considered a significant resource due to the difficulty in providing battery a backup to sensors when they are deployed in an uncontrolled or wild area. Integrating clustering & routing strategies are the optimal techniques for saving sensor node energy. Cluster heads (CHs) are selected carefully for load balancing throughout the clustering process. Therefore, multi-objective sand cat swarm optimization (MSCSO) algorithm is proposed to maximize the efficiency of energy in WSN. The optimum cluster heads from the network are selected and the path through the cluster head is identified by utilizing MSCSO. The proposed MSCSO decreases the usage of energy of nodes when maximizing the transmission of data in the WSN. The performances of MSCSO are evaluated based on alive nodes, energy consumption, throughput, and life expectancy for 100 nodes and attained better performance than other methods like low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC) and threshold-distributed energy efficient clustering (T-DEEC). The MSCSO is compared with existing methods such as fractional artificial lion (FAL) algorithm, taylor-spotted hyena optimization (Taylor-SHO) algorithm, energy centric multi objective salp swarm algorithm (ECMOSSA) and whale based tunicate swarm algorithmaquila optimizer (WTSA-AO) and shows that the MSCSO algorithm improves the performance by reducing energy consumption of 0.0208J, 0.0350J and 0.04045J for 200, 400 and 600 rounds respectively.

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EATMR: an energy-aware trust algorithm based the AODV protocol and multi-path routing approach in wireless sensor networks

Cited by 11 publications

References 34 publications

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

A trust and optimal energy efficient data aggregation scheme for wireless sensor networks using QGAOA

Multi-objective Sand Cat Swarm Optimization Algorithm for Cluster Head and Routing Path Selection in WSN

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