{"title":"Hybrid Optimization Enabled Routing Protocol for Enhancing Source Location Privacy in Wireless Sensor Networks","authors":"Chinnu Mary, Gayathri K M, Reeja S R","doi":"10.22247/ijcna/2023/218511","DOIUrl":null,"url":null,"abstract":"– Wireless sensor networks (WSN) are utilized in various application domains concerning monitoring and smart application, in which highly sensitive information in healthcare and military applications is also employed using the WSN. The openness and unattended nature of the WSN make security as a challenging task. The information eavesdropping is employed by the network intruder from the source node; hence the location of the source node needs to be protected for the acquisition of information security. Thus, this research introduces a privacy preservation of the source location method using the hybrid optimization based secure routing. For this, Shuffled Shepherd-Coot (SS-Coot) optimization is proposed by hybridizing the foraging behavior of the Coot, a water bird, with the shepherd's behavior in herding the animal community. The incorporation of the herding behavior of the shepherd with Coot's foraging behavior helps to enhance the diversification phase to obtain the best solution by avoiding premature convergence. In the proposed source location privacy preservation, the network boundary radiuses are obtained optimally using the SS-Coot algorithm during the network initialization. Then, the routing through the various boundaries of the network with multi-hop helps to protect the location of the source by confusing the intruder's backtrace process. The analysis is performed based on Packet Delivery Ratio (PDR), throughput, energy consumption, and delivery latency and obtained the values of 1.02867, 1.02909, 0.30171, and 0.00165, respectively.","PeriodicalId":36485,"journal":{"name":"International Journal of Computer Networks and Applications","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Networks and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22247/ijcna/2023/218511","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 1
Abstract
– Wireless sensor networks (WSN) are utilized in various application domains concerning monitoring and smart application, in which highly sensitive information in healthcare and military applications is also employed using the WSN. The openness and unattended nature of the WSN make security as a challenging task. The information eavesdropping is employed by the network intruder from the source node; hence the location of the source node needs to be protected for the acquisition of information security. Thus, this research introduces a privacy preservation of the source location method using the hybrid optimization based secure routing. For this, Shuffled Shepherd-Coot (SS-Coot) optimization is proposed by hybridizing the foraging behavior of the Coot, a water bird, with the shepherd's behavior in herding the animal community. The incorporation of the herding behavior of the shepherd with Coot's foraging behavior helps to enhance the diversification phase to obtain the best solution by avoiding premature convergence. In the proposed source location privacy preservation, the network boundary radiuses are obtained optimally using the SS-Coot algorithm during the network initialization. Then, the routing through the various boundaries of the network with multi-hop helps to protect the location of the source by confusing the intruder's backtrace process. The analysis is performed based on Packet Delivery Ratio (PDR), throughput, energy consumption, and delivery latency and obtained the values of 1.02867, 1.02909, 0.30171, and 0.00165, respectively.