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.

scholarly journals Maximizing network throughput by cooperative reinforcement learning in clustered solar-powered wireless sensor networks

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772110074
Author(s):  
Yujia Ge ◽  
Yurong Nan ◽  
Xianhai Guo
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Sensor Network ◽  
Cluster Head ◽  
Base Station ◽  
Network Throughput ◽  
Wireless Sensor ◽  
Learning Methods ◽  
Solar Powered

Power management in wireless sensor networks is very important due to the limited energy of batteries. Sensor nodes with harvesters can extract energy from environmental sources as supplemental energy to break this limitation. In a clustered solar-powered sensor network where nodes in the network are grouped into clusters, data collected by cluster members are sent to their cluster head and finally transmitted to the base station. The goal of the whole network is to maintain an energy neutrality state and to maximize the effective data throughput of the network. This article proposes an adaptive power manager based on cooperative reinforcement learning methods for the solar-powered wireless sensor networks to keep harvested energy more balanced among the whole clustered network. The cooperative strategy of Q-learning and SARSA( λ) is applied in this multi-agent environment based on the node residual energy, the predicted harvested energy for the next time slot, and cluster head energy information. The node takes action accordingly to adjust its operating duty cycle. Experiments show that cooperative reinforcement learning methods can achieve the overall goal of maximizing network throughput and cooperative approaches outperform tuned static and non-cooperative approaches in clustered wireless sensor network applications. Experiments also show that the approach is effective in response to changes in the environment, changes in its parameters, and application-level quality of service requirements.

scholarly journals An Energy Efficient Localization-Free Routing Protocol for Underwater Wireless Sensor Networks

2012 ◽  
Vol 8 (4) ◽  
pp. 307246 ◽  
Author(s):  
Abdul Wahid ◽  
Dongkyun Kim
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Research Attention ◽  
Underwater Environment

Recently, underwater wireless sensor networks (UWSNs) have attracted much research attention from both academia and industry, in order to explore the vast underwater environment. UWSNs have peculiar characteristics; that is, they have large propagation delay, high error rate, low bandwidth, and limited energy. Therefore, designing network/routing protocols for UWSNs is very challenging. Also, in UWSNs, improving the energy efficiency is one of the most important issues since the replacement of the batteries of underwater sensor nodes is very expensive due to the unpleasant underwater environment. In this paper, we therefore propose an energy efficient routing protocol, named (energy-efficient depth-based routing protocol) EEDBR for UWSNs. EEDBR utilizes the depth of sensor nodes for forwarding data packets. Furthermore, the residual energy of sensor nodes is also taken into account in order to improve the network lifetime. Based on the comprehensive simulation using NS2, we observe that EEDBR contributes to the performance improvements in terms of the network lifetime, energy consumption, and end-to-end delay. A previous version of this paper was accepted in AST-2011 conference.

Energy-Efficient Wireless Sensor Networks Using Learning Techniques

2014 ◽  
pp. 407-426 ◽  
Author(s):  
Sumit Tokle ◽  
Shamantha Bellipady ◽  
Rajeev Ranjan ◽  
Shirshu Varma
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Learning Techniques

scholarly journals Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 29355-29364 ◽  
Author(s):  
Claudio Savaglio ◽  
Pasquale Pace ◽  
Gianluca Aloi ◽  
Antonio Liotta ◽  
Giancarlo Fortino
Keyword(s):  
Wireless Sensor Networks ◽  
Reinforcement Learning ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Wireless Sensor

A New Energy-Efficient Hybrid and Clustering Routing for Wireless Sensor Networks

2017 ◽  
Vol 7 (3) ◽  
pp. 176
Author(s):  
Muneer Bani Yassein ◽  
Yaser Khamayseh ◽  
Ismail Hmeidi ◽  
Ahmed Al-Dubai ◽  
Mohammed Al-Maolegi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
New Energy
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