Energy-aware task scheduling by a true online reinforcement learning in wireless sensor networks

2017 ◽  
Vol 25 (4) ◽  
pp. 244 ◽  
Author(s):  
Muhidul Islam Khan ◽  
Kewen Xia ◽  
Ahmad Ali ◽  
Nelofar Aslam
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Task Scheduling ◽  
Wireless Sensor ◽  
Energy Aware

An Intelligent Routing Algorithm in Wireless Sensor Networks Based on Reinforcement Learning

2014 ◽  
Vol 678 ◽  
pp. 487-493 ◽  
Author(s):  
Wen Jing Guo ◽  
Cai Rong Yan ◽  
Yang Lan Gan ◽  
Ting Lu
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Algorithm ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Hop Count ◽  
Packet Delivery ◽  
Intelligent Routing

Lifetime enhancement has been a hot issue in Wireless Sensor Networks (WSNs). To prolong the network lifetime of WSNs, this paper proposes an intelligent routing algorithm named RLLO. RLLO makes uses of the superiority of reinforcement learning (RL) and considers residual energy and hop count to define the reward function. It is to uniformly distribute the energy consumption and improve the packet delivery without additional cost. This proposed algorithm has been compared with Energy Aware Routing (EAR) and improved EAR (I-EAR). Simulation results show that RLLO gains a significant improvement in terms of network lifetime and packet delivery over these two algorithms.

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