Energy and throughput efficient mobile wireless sensor networks: A deep reinforcement learning approach

The efficient development of Mobile Wireless Sensor Networks (MWSNs) relies heavily on optimizing two key parameters: Throughput and Energy Consumption. The proposed work investigates network connectivity issues with MWSN and proposes two routing algorithms, namely Self-Organising Maps based-Optimised Link State Routing (SOM-OLSR) and Deep Reinforcement Learning based-Optimised Link State Routing (DRL-OLSR) for MWSNs. The primary objective of the proposed algorithms is to achieve energy-efficient routing while maximizing throughput. The proposed algorithms are evaluated through simulations by considering various performance metrics, including connection probability (CP), end-to-end delay, overhead, network throughput, and energy consumption. The simulation analysis is discussed under three scenarios. The first scenario undertakes 'no optimisation', the second considers SOM-OLSR, and the third undertakes DRL-OLSR. A comparison between DRL-OLSR and SOM-OLSR reveals that the former surpasses the latter in terms of low latency and prolonged network lifetime. Specifically, DRL-OLSR demonstrates a 47% increase in throughput, a 67% reduction in energy consumption, and a CP three times higher than SOM-OLSR. Furthermore, when contrasted with the 'no optimisation' scenario, DRL-OLSR achieves a remarkable 69.7% higher throughput and nearly 89% lower energy consumption. These findings highlight the effectiveness of the DRL-OLSR approach in wireless sensor networks. The proposed work investigates network connectivity issues with Mobile Wireless Sensor Network (MWSN) and proposes two routing algorithms, namely Self-Organising Maps based-Optimised Link State Routing and Deep Reinforcement Learning based-Optimised Link State Routing for MWSNs. image