An Efficient Intra‐Cluster Data Aggregation and finding the Best Sink location in WSN using EEC‐MA‐PSOGA approach. (12th February 2022)

• Record Type: Journal Article
• Authors: Sreedevi, Pogula; Venkateswarlu, S.
• Is Part Of: International journal of communication systems. Volume 35:Number 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-12
• Subjects: ACO -- clone attack -- communication -- cyber security -- DMADA -- EEC‐MA‐PSOGA -- energy efficiency -- GA -- intra‐clustering -- LEACH -- LTAWSN -- MOPSO -- network lifetime -- optimization -- PSO -- varied communication range -- WSN; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.5110
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: Wireless sensor networks (WSNs) consist of nodes. Issues involved with the use of a sensor network are energy‐saving and the effective use of energy. Clustering in WSNs is a proven technique for energy optimization. The relationship between sensor nodes and cluster heads (CHs) has only been considered prior to cluster‐based routing protocols. However, most clustering algorithms have failed to address the routing overhead and the energy consumption rate between CH nodes and SINK. In this paper, a new framework named as EEC‐MA‐PSOGA ‐ an energy‐efficient (EE) intra‐cluster mobile agent (INC‐MA)‐based particle swarm optimization–genetic algorithm (PSO‐GA), has been presented to initiate the distance communication and place the SINK optimally in WSNs. The cluster members send the collected data towards their respective CHs for aggregation. To find the sink's best position, the PSO‐GA‐based location estimation algorithm is initiated based on the network structure. The limited capability of WSNs makes them more vulnerable to attackers. The prevention mechanism must be less complex to ensure the fair operations in the network. Here, the attack detection ability of the proposed solution has been tested against clone attack. With varied communication range, simulation time, and sensor nodes, the tests are conducted extensively on various scenarios of WSNs. When compared with the prior mechanisms, the framework provides better results and better prevention with less overhead by analyzing the experimental results such as energy consumption, network lifetime, and throughput. Abstract : Wireless sensor networks (WSNs) consist of nodes. Issues involved with the use of a sensor network are energy‐saving and the effective use of energy. Clustering in WSNs is a proven technique for energy optimization. The relationship between sensor nodes and CHs has only been considered prior to cluster‐based routing protocols. However, most clustering algorithms have failed to address the routing overhead and the energy consumption rate between CH nodes and SINK. In this paper, a new framework, namely, the energy‐efficient (EE) intra‐cluster mobile agent (INC‐MA)‐based particle swarm optimization–genetic algorithm (PSO‐GA), has been presented to initiate the distance communication and placing the SINK optimally in WSNs. The data are collected from cluster members by INC‐MA‐based PSO‐GA, and these have been transmitted towards the CHs. To find the sink's best position, the PSO‐GA‐based location estimation algorithm is initiated based on the network structure. The limited capability of WSNs makes them more vulnerable to attackers. The prevention mechanism must be less complex to ensure the fair operations in the network. Here, the attack detection ability of the proposed solution has been tested against clone attack. With varied communication range, simulation time, and sensor nodes, the tests are conducted extensively on various scenarios of WSNs. When compared with the prior mechanisms, the framework provides better results and better prevention with less overhead by analyzing the experimental results such as energy consumption, network lifetime, and throughput.