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| Title | Oppositional Chaos Game Optimization Based Clustering With Trust Based Data Transmission Protocol For Intelligent Iot Edge Systems |
|---|---|
| ID_Doc | 40274 |
| Authors | Padmaa M.; Jayasankar T.; Venkatraman S.; Dutta A.K.; Gupta D.; Shamshirband S.; Rodrigues J.J.P.C. |
| Year | 2022 |
| Published | Journal of Parallel and Distributed Computing, 164 |
| DOI | http://dx.doi.org/10.1016/j.jpdc.2022.03.008 |
| Abstract | In the past decade, the Internet of Things (IoT) becomes essential in consumer and industrial applications. The accessibility of high bandwidth Internet connection particularly with the arrival of robust 5G networks rises to innovative IoT solutions such as smart city, automobiles, industry 4.0, etc. IoT analytics represents edge computing as a term commonly employed for defining intelligent computational resources placed closer to the source of data generation. Despite the benefits of the IoT edge systems, security and energy efficiency remains major challenging issues. With this motivation, this paper presents an energy-efficient clustering based secure data transmission protocol (EEC-SDTP) for Intelligent IoT Edge systems. The goal of the EEC-SDTP technique is to select an appropriate set of cluster heads (CHs) and optimally secure routes for data transmission in the network. The proposed model involves oppositional chaos game optimization-based clustering (OCGOC) technique for proper CH selection and cluster construction. Besides, a trust-based model is designed to determine the trustworthiness of the node in the IoT edge systems. The proposed OCGOC technique derives a fitness function utilizing 3 input parameters like trust level, distance to neighbors, and energy. Finally, a trust-based secure routing protocol using the quantum sand piper optimization (SRP-QSPO) technique is employed to derive routes for secure data transmission. For examining the better efficiency of the proposed EEC-SDTP algorithm, an extensive group of experimentations were performed and the outcomes are investigated under several performance measures. The experimental outcomes highlighted the improved efficiency of the proposed method over the other related techniques. © 2022 Elsevier Inc. |
| Author Keywords | Artificial intelligence; Edge computing; Internet of Things; Security; Trust sensing model |
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