Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Abstract

Real-time applications based on Wireless Sensor Network (WSN) technologies quickly lead to the growth of an intelligent environment. Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks. The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things (IoT) network is widely recognized. Sensor nodes are low-power devices with low power devices, storage, and quantitative processing capabilities. The existing system uses the Artificial Immune System-Particle Swarm Optimization method to minimize the energy and improve the network's lifespan. In the proposed system, a hybrid Energy Efficient and Reliable Ant Colony Optimization (ACO) based on the Routing protocol (E-RARP) and game theory-based energy-efficient clustering algorithm (GEC) were used. E-RARP is a new Energy Efficient, and Reliable ACO-based Routing Protocol for Wireless Sensor Networks. The suggested protocol provides communications dependability and high-quality channels of communication to improve energy. For wireless sensor networks, a game theory-based energy-efficient clustering technique (GEC) is used, in which each sensor node is treated as a player on the team. The sensor node can choose beneficial methods for itself, determined by the length of idle playback time in the active phase, and then decide whether or not to rest. The proposed E-RARP-GEC improves the network's lifetime and data transmission; it also takes a minimum amount of energy compared with the existing algorithms.

Game Theory-Based Multi-Hop Routing Protocol with Metaheuristic Optimization-Based Clustering Process in WSN for Precision Agriculture

“…In the study [25], each SN is regarded as an individual participant in the game. The SN can use advantageous strategies based on the duration of idle listening time during the active phase, and then decide on whether or not to enter a resting state.…”

“…In Table 1, the simulation factors employed for the accomplishment of the recommended GO_CGT and the benchmarked MSGE-LS [25], DivNCGL [26], and GTRP [23] approaches are analyzed.…”

A Novel Particle Swarm Optimization-Based Clustering and Routing Protocol for Wireless Sensor Networks

“…Clustering and routing processes are considered as an NP-hard problem [5,6], and the two critical issues faced by clustering and routing protocols are CHs selection and routing paths finding, which are exactly what the protocols have been trying to solve during the last decades [3,7]. Nowadays, the swarm intelligence optimization based approaches such as ant colony optimization [8], grey wolf optimization [9] and whale optimization [10] are increasingly used in clustering and routing protocols to find the optimal solutions for these problems [11]. Especially, due to its simplicity, fast convergence and easy implementation, the particle swarm optimization (PSO) can provide a more optimized solution for WSNs [6,7,12].…”