An intelligent trust sensing scheme with metaheuristic based secure routing protocol for Internet of Things

被引:0
作者
Sowmya Gali
Venkatram Nidumolu
机构
[1] Koneru Lakshmaiah Education Foundation,Department of ECE
[2] Koneru Lakshmaiah Education Foundation,undefined
来源
Cluster Computing | 2022年 / 25卷
关键词
IoT; Routing protocol; Metaheuristics; Trust sensing scheme; Chaotic optimization; Malicious node;
D O I
暂无
中图分类号
学科分类号
摘要
The Internet of Things (IoT) defines the network of physical objects, commonly used to interconnect and communicate with other devices through the internet. Security is highly essential in IoT based communication owing to the massive heterogeneity of devices involved in the network. The interlinked systems in IoT, requires the trusted model for assuring security, authenticity, authorization and confidentiality of interconnected things, irrespective of the functionalities. Considering the challenges in the provision of security in the IoT network, this paper proposes a new chaotic bumble bees mating optimization (CBBMO) algorithm for secure data transmission with trust sensing model, called CBBMOR-TSM model. The BBMO is stimulated by the mating nature of a swarm of bumble bees. To improve the convergence rate of the BBMO technique, the CBBMO model is defined by the integration of chaotic concept into the classical BBMO technique. The aim of the proposed model is to design a trust sensing model and perform secure routing using the CBMO algorithm. The proposed model initially designs a trust sensing model by incorporating indirect and direct trusts that are utilized to determine the trust values of the IoT nodes and thereby the malicious node can be identified. In addition, the secure routing process is invoked using the CMBO algorithm by using the trust sensing model to determine an optimal and secure path for data transmission. To examine the superior performance of the presented method, an extensive set of experiments are performed and the results are investigated in terms of different measures. The CBBMOR-TSM model has attained a higher average PDR of 0.931 and lower average PLR of 0.069 whereas the TRM_IOT, OSEAP_IOT and MCTAR-IOT methods have achieved a maximum PLR of 0.219, 0.161 and 0.110 respectively.
引用
收藏
页码:1779 / 1789
页数:10
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