ReLeC: A Reinforcement Learning-Based Clustering-Enhanced Protocol for Efficient Energy Optimization in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) are a widely studied area in the field of networked embedded computing. They are made up of several sensor nodes, which keep track of a variety of physical and environmental parameters, like temperature and humidity. The nodes are autonomous, self-configuring, and wireless. A significant problem in WSNs is that sensors in these networks consume a lot of energy. Energy consumption is a big issue when it comes to the deployment of sensor networks. The reason for this is the cost of operating a sensor node and the cost incurred due to energy consumption. Energy optimization is based on intelligent energy management. This paper presents a reinforcement learning-based and clustering-enhanced method. Reinforcement learning is a set of algorithms inspired by operant conditioning in animal behavior, and clustering-based methods have been extensively used for devising energy-efficient protocols. The proposed method is able to plan and schedule the nodes to ensure an extended network lifetime. In this work, we aim to assess and increase the efficiency of power consumption and reduce sensor node energy loss. The simulation results prove that the presented protocol effectively reduces the energy consumption of sensor nodes and ensures a prolonged lifetime of the sensor network.

Interrogation of Dynamic Data Loss in Long Range Wireless Sensor Networks by Utilizing CatBoost-MLGBA to Detect Anomalies and Unusual Patterns

“…This chapter deals with the various parameter evaluation results of the novel QKD optimization CatBoost-MLGBA and how it overcomes and addresses the drawbacks of the prevailing models such as DL-IDS-WSN (14) , RM-ANN (15) and ReLeC-WSN (16) in terms of detecting anomalies and unusual patterns in LoRa-WSNs. The data loss and error rate are drastically minimized, and the robustness level to withstand attacks is improved.…”

“…The sensor node will be idle and save energy until the next data arrives. (16) 44 46 48 50 52 RM-ANN (15) 40 42 44 46 49 DL-IDS-WSN (14) 35…”

“…The comparative analysis is done for the proposed QKD optimization model CatBoost-MLGBA against the prevailing models such as DL-IDS-WSN (14) , RM-ANN (15) and ReLeC-WSN (16) using the OMNET++ simulator tool, which helps to create realistic simulation scenarios with real-time and historical network data. The tool is integrated with ML and DL algorithms for optimization, anomaly detection, real-time simulations, etc.…”

“…The performance and comparative analysis of the proposed CatBoost-MLGBA model is done against the baseline models such as DL-IDS-WSN (14) , RM-ANN (15) and ReLeC-WSN (16) which was chosen in the preceding section. The following are PEM and equation to measure the protocol.…”

Swarm energy efficient power efficient gathering in sensor information systems protocol in wireless sensor networks

“…When compared to alternative chain building strategies, such as PEGASIS, which builds chains using a greedy algorithm, the performance of the suggested method is shown to be superior. ReLeC protocol, a reinforcement learningbased, clustering-enhanced method for energy-efficient routing in WSNs and maybe other geographically dispersed IoT networks, is proposed by the author in [27]. The protocol uses clustering and RL to try to find a reliable data transmission path.…”