# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
###### Abstract  
In modern trends, wireless sensor networks (WSNs) are interesting, and distributed in the environment to evaluate received data. The sensor nodes have a higher capacity to sense and transmit the information. A WSN contains low-cost, low-power, multi-function sensor nodes, with limited computational capabilities, used for observing environmental constraints. In previous research, many energy-efficient routing methods were suggested to improve the time of the network by minimizing energy consumption; sometimes, the sensor nodes run out of power quickly. The majority of recent articles present various methods aimed at reducing energy usage in sensor networks. In this paper, an energy-efficient clustering/routing technique, called the energy and distance based multi-objective red fox optimization algorithm (ED-MORFO), was proposed to reduce energy consumption. In each communication round of transmission, this technique selects the cluster head (CH) with the most residual energy, and finds the optimal routing to the base station. The simulation clearly shows that the proposed ED-MORFO achieves better performance in terms of energy consumption (0.46 J), packet delivery ratio (99.4%), packet loss rate (0.6%), end-to-end delay (11 s), routing overhead (0.11), throughput (0.99 Mbps), and network lifetime (3719 s), when compared with existing MCH-EOR and RDSAOA-EECP methods. c 2022 2022 2022 2022 2022 20222 2022 20222 2022 2022 2022 2022 202  
more favorable for future applications in effectively detecting environmental fluctuation [7]. WSNs used for environmental monitoring are made up of excess battery-operated nodes that are closely distributed in inaccessible or remote locations [8]. The fundamental problem in WSNs, however, is the sensor nodes' limited power resources. As, in many cases, the nodes are deployed in hostile environments, it is not possible to recharge or replace their batteries after they have completely depleted their energy [9; 10]. As a result, while traditional networks strive for excellent quality of service (QoS), sensor network protocols must prioritize energy conservation, in order to extend the network's lifetime [11; 12]. This topic developed a number of research concerns. The most promising challenge among them is the development of energy-efficient clustering and routing algorithms [13]. Clustering in WSNs entails efficiently grouping sensor nodes into different clusters, with each cluster commanded by a cluster head (CH) [14]. The CHs gather data from all of their corresponding members, combine them, and send them to the BS. Each sensor node belongs to a single cluster and only connects with that cluster's CH [15]. As a result, effective CH selection is required to balance the CHs' energy consumption; otherwise, they expire quickly, because of supplementary data aggregation and transmission [16]. Most cluster-based routing algorithms choose CHs at random, or based on prospects, before forming clusters [17]. However, in such instances, all CHs may be concentrated in a small area, a fact that leads to the isolation of certain conventional nodes, potentially causing network failure [18; 19]. Moreover, the energy efficiency and quality of service are deliberated as a multi-objective optimization issue, in order to maintain the tradeoffs between network coverage, energy efficiency, and network lifetime; these are analyzed in previous research works. This research work proposes an energy and distance based multi-objective red fox optimization algorithm (ED-MORFO) for overcoming the problems caused by network failure. The research modeled a global search by simulating a red fox searching for prey over land. The local search simulates disguising the prey when hunting for the maxima or minima test functions, which is efficient for overcoming the optimization problems. The algorithm locates the individuals in the direct surroundings of the optimum after a short interval of time, and provides a good advantage for improving the final precision results.