Deep Reinforcement Learning Methods for Energy-Efficient Underwater Wireless Networking

View Sample PDF

Author(s): Ahmed Ali Saihood (University of Thi-Qar, Iraq)and Laith Alzubaidi (University of Information Technology and Communications, Iraq)
Copyright: 2021
Pages: 12
Source title: Energy-Efficient Underwater Wireless Communications and Networking
Source Author(s)/Editor(s): Nitin Goyal (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India), Luxmi Sapra (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India)and Jasminder Kaur Sandhu (Chitkara University Institute of Engineering and Technology, Chitkara University, India)
DOI: 10.4018/978-1-7998-3640-7.ch014

Keywords: Environmental Science and Technologies / Information Science Reference / Media & Communications / Wireless Systems

Purchase

View Deep Reinforcement Learning Methods for Energy-Efficient Underwater Wireless Networking on the publisher's website for pricing and purchasing information.

Abstract

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.

The IRMA Community

Research IRM

Deep Reinforcement Learning Methods for Energy-Efficient Underwater Wireless Networking

Purchase

Abstract

Related Content

IRMA Sponsors