Research On Network Design For Energy-constrained Underwater Wireless Sensor Networks

Underwater wireless sensor networks(UWSNs)are self-organized networks which consist of several underwater sensors.UWSNs are used in collecting underwater information,and play an important role in many applications in recent years,such as ocean environment monitoring and underwater target tracking.UWSNs are usually deployed in remote environments,and the batteries of sensor nodes cannot be maintained or replaced after the deployment.As a result,the available energy in underwater sensor nodes is limited.Prolonging the network lifetime under this energy constraint becomes the main design concern in UWSNs field.On the other hand,due to the high attenuation of radio waves and optical signals in underwater environments,UWSNs usually use acoustic waves for communications.Acoustic waves propagate slowly in underwater environments,leading to a very large delay for data transmission.Focusing on the above challenges on network design for UWSNs,this paper proposes two algorithms:Firstly,this paper proposes a cross-layer design for network lifetime maximization in UWSNs.The proposed method jointly considers the routing layer,link layer and physical layer in network protocol stack.With the consideration of constraints in UWSNs,a network lifetime optimization problem is formulated,and then a network lifetime maximization algorithm is proposed.The proposed algorithm iteratively optimizes the transmission rate,transmission power of sensor nodes and the link schedules,and finally obtains the optimal transmission scheme for network lifetime maximization.The simulation results show that the proposed algorithm outperforms state-of-art algorithms in term of prolonging network lifetime.Secondly,the paper investigates that how to raise the network performance in terms of some other performance indexes,such as transmission delay in networks,with keeping a long network lifetime.This paper proposes a reinforcement learning based routing algorithm for UWSNs,which is named as qDBR(Q-learning and Depth based routing).qDBR jointly considers the residual energy and depth information of sensor nodes,and avoids the multipath transmissions in network via setting the priority levels of nodes in data transmissions.The simulation results show that qDBR can efficiently prolong the network lifetime and reduce the transmission delay.