Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing

Abstract

In wireless sensor networks, optimizing the network lifetime is an important issue. Most of the existing works define network lifetime as the time when the first sensor node exhausts all of its energy. However, such time is not necessarily important. This is because when a sensor node dies, the whole network is likely to work properly. In this article, we first make an overall consideration of the demand of applications and define the network lifetime in three aspects. Then, we construct a performance evaluation framework for routing protocols. To achieve the optimization of network lifetime in all defined aspects, we propose a reinforcement-learning-based routing protocol. Reinforcement-learning-based routing protocol takes advantage of the intelligent algorithm of reinforcement learning to search for the optimal routing path for data transmission. In the definition of reward function, factors such as link distance, residual energy, and hop count to the sink are taken into account to cut down the total energy consumption, balance the energy consumption, and improve the packet delivery. Simulation results demonstrate that compared with energy-aware routing, BEER, Q-Routing, and MRL-SCSO, reinforcement-learning-based routing protocol optimizes the network lifetime in three aspects and improves the energy efficiency.

Q-Learning-Based Data-Aggregation-Aware Energy-Efficient Routing Protocol for Wireless Sensor Networks

“…In neurofuzzy, they used a membership function comprising the communication distance and energy information of nodes to use the energy-efficient clusters to minimize packet loss. Guo et al [27] proposed an energy-efficient routing protocol based on a reinforcement learning algorithm. The nodes were reinforced to calculate the optimal routing path using a reward policy to maximize the energy efficiency and lifetime of the network.…”

The development of green wireless mesh network: A survey

“…The mode switch between sleep and active can help save energy [67] . To save energy, minimizing the energy consumption [68] or maximizing lifetime [69,70] can be the objective, and EH can be used to obtain energy. Energy will be consumed at different states: monitoring, processing, communication, and state transferring [71] .…”

“…Besides energy, other factors like delay [73] , throughput [72] and transmission rate [74] can also be considered together. The flooding control can be done to change the probability of forwarding control packets like RREQ to save energy [69,75] . When designing routing, few works consider EH.…”

Machine Learning-Based Optimized Hierarchical Routing Protocols for WSN Lifetime: A Review

“…The author by [34] has proposed, "an intelligent routing algorithm called RLLO". It uses the predominance of reinforcement learning (RL) and takes into account the residual power and hopping calculation to define the reward function".…”