Reinforcement Learning-Based Data Forwarding in Underwater Wireless Sensor Networks with Passive Mobility.

Haotian Chang,Chaofan Duan,Jing Feng

doi:10.3390/s19020256

Haotian Chang, Chaofan Duan + Show 1 more

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https://doi.org/10.3390/s19020256

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Abstract

Data forwarding for underwater wireless sensor networks has drawn large attention in the past decade. Due to the harsh underwater environments for communication, a major challenge of Underwater Wireless Sensor Networks (UWSNs) is the timeliness. Furthermore, underwater sensor nodes are energy constrained, so network lifetime is another obstruction. Additionally, the passive mobility of underwater sensors causes dynamical topology change of underwater networks. It is significant to consider the timeliness and energy consumption of data forwarding in UWSNs, along with the passive mobility of sensor nodes. In this paper, we first formulate the problem of data forwarding, by jointly considering timeliness and energy consumption under a passive mobility model for underwater wireless sensor networks. We then propose a reinforcement learning-based method for the problem. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate the validity of the proposed method. Our method outperforms the benchmark protocols in both timeliness and energy efficiency. More specifically, our method gains 83.35% more value of information and saves up to 75.21% energy compared with a classic lifetime-extended routing protocol (QELAR).

Highlights

Nowadays, marine surveillance, water contamination detection and monitoring, and oceanographic data collection are indispensable to the exploration, protection and exploitation of aquatic environment [1].Because of the huge amount of unexploited resources in the ocean, there is an urgent need for research in the field of sensors and sensor networks [2]
We evaluate the performance of our proposed method compared with two well-known routing protocols: (i) Q-Learning-based Energy-Efficient and Lifetime-Aware Routing (QELAR), a machine learning-based protocol designed for minimizing and balancing node energy consumption [7]; (ii) DBR, a data forwarding method for Underwater Wireless Sensor Networks (UWSNs) based on the depth of the sender [15]
We proposed the data forwarding method in joint consideration of value of information (VoI) of packets and energy consumption, with passive mobility of sensors in UWSNs

Summary

Introduction

Because of the huge amount of unexploited resources in the ocean, there is an urgent need for research in the field of sensors and sensor networks [2]. Underwater Wireless Sensor Networks (UWSNs) has become a main approach to gain information from previously inaccessible waters. Traditional wireless sensor networks (WSNs) consist of a large number of sensor nodes randomly distributed in a detection field, and these nodes are usually either stationary or moving in limited ranges. Stationary nodes are anchored to the water bottom while moving nodes can move in a preset velocity, such as Autonomous Underwater Vehicles (AUVs). Only a few researchers take passive mobility of nodes into account. Underwater nodes have no access to GPS signals, and the network topology is completely time varying due to irregular mobilities of water currents, which is essentially different from terrestrial

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