Abstract
Underwater sensor networks (UWSNs) have become a hot research topic because of their various aquatic applications. As the underwater sensor nodes are powered by built-in batteries which are difficult to replace, extending the network lifetime is a most urgent need. Due to the low and variable transmission speed of sound, the design of reliable routing algorithms for UWSNs is challenging. In this paper, we propose a Q-learning based delay-aware routing (QDAR) algorithm to extend the lifetime of underwater sensor networks. In QDAR, a data collection phase is designed to adapt to the dynamic environment. With the application of the Q-learning technique, QDAR can determine a global optimal next hop rather than a greedy one. We define an action-utility function in which residual energy and propagation delay are both considered for adequate routing decisions. Thus, the QDAR algorithm can extend the network lifetime by uniformly distributing the residual energy and provide lower end-to-end delay. The simulation results show that our protocol can yield nearly the same network lifetime, and can reduce the end-to-end delay by 20–25% compared with a classic lifetime-extended routing protocol (QELAR).
Highlights
Underwater wireless sensor networks (UWSNs) have attracted significant interest.Many applications of underwater wireless sensor networks (UWSNs), including commercial exploitation, marine mammal studies and oceanography data collection [1,2] allow humans to sense the vast underwater domain and motivate research on UWSN design.because of the harsh environment and limited spectrum source, communications in UWSNs are much more difficult than those in terrestrial sensor networks
Since Modified energy weight routing (MEWR) does not take the residual energy of sensor nodes into account, it cannot optimize the energy distribution, which is crucial for network lifetime extension
We examine how the underlying MAC protocol layer affects the performance of Q-learning based delay-aware routing (QDAR) and Q-learning-based lifetime-aware (QELAR) in terms of total energy consumption
Summary
Underwater wireless sensor networks (UWSNs) have attracted significant interest. The main contributions of QDAR can be summarized as follows: (1) it defines a data collection phase and designs the packet structure before routing decisions to quickly adapt to the dynamic underwater environment; (2) it takes both delay and residual energy into consideration by defining two kinds of cost functions: delay-related cost and energy-related cost; (3) it uses an adaptive mechanism to ensure a longer network lifetime and a relatively shorter delays: when the residual energy is enough, the end-to-end delay is restricted, while the residual energy of some nodes is lower than the threshold, an adequate path consisting of nodes with longer delays but more remaining energy is determined; (4) QDAR is extendible: energy consumption, channel capacity, communication reliability and many other metrics can be integrated into the action-utility functions in future research for different targets.
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