ENERGY EFFICIENCY AND ATTACK DETECTION USING DISTRIBUTION BASED CHICKEN SWARM OPTIMIZATION AND ENHANCED CRYPTOGRAPHY ALGORITHM ON HYBRID NETWORKS

Abstract

The Internet of Things (IoT) is suggested to sense the surroundings via Wireless Sensor Networks (WSNs). The data is collected by WSN and sent to the analysis and base station (BS) locations. However, there are still issues with different attack detection (AD) in the current system. Also, it has issues with the energy (E) consumption (EC) moreover, they cannot avoid the attacks from inside selfish or compromised nodes with legitimate identities. In order to address the aforementioned issue, this study presents a novel solution for optimal EC and efficient AD in heterogeneous networks using the Distribution based (CSO) Chicken Swarm Optimisation (DCSO) and Double Key based Advanced Encryption Standard (DKAES) algorithm. First, sensor nodes (SN), MANET nodes, IoT SN, and cloud servers (CS) are used to build the hybrid system model. Next, in an IoT-based WSN, the DCSO method is used for Cluster Head (CH) node selection (NS). By using the best fitness function (FF) values, it is utilised to boost energy efficiency (EE) and data transmission speed (DT). Fast DT over multipath (MP) routing (MPR) on MANETs is made more secure with DKAES. By successfully eliminating the attack nodes, the secured DT (SDT) is accomplished. The shortest path (SP) and trusted route between sensors in the Internet Cloud are being established via Mobile Ad Hoc Networks (MANET). Comprehensive simulations demonstrate that the integrated strategy outperforms conventional techniques in terms of end to end delay (E2ED), EC, network lifetime (NL), data throughput (T), and security. Its effectiveness in streamlining network operations in a range of deployment settings is confirmed by these results.