Achieving Coverage through Distributed Reinforcement Learning in Wireless Sensor Networks

2007 ◽  
Author(s):  
Mark Wei Ming Seah ◽  
Chen-Khong Tham ◽  
Vikram Srinivasan ◽  
Ai Xin
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Distributed Reinforcement

Deep Reinforcement Learning Methods for Energy-Efficient Underwater Wireless Networking

2021 ◽  
pp. 212-223
Author(s):  
Ahmed Ali Saihood ◽  
Laith Alzubaidi
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Natural Disasters ◽  
Energy Efficient ◽  
Wireless Networking ◽  
Wireless Sensor ◽  
Learning Methods ◽  
Underwater Environment ◽  
Learning Techniques

The wireless sensor networks have been developed and extended to more expanded environments, and the underwater environment needs to develop more applications in different fields, such as sea animals monitoring, predict the natural disasters, and data exchanging between underwater and ground environments. The underwater environment has almost the same infrastructure and functions with ground environment with some limitations, such as processing, communications, and battery limits. In terms of battery limits, many techniques have been proposed; in this chapter, the authors will focus in deep reinforcement learning techniques.

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