Abstract
Underwater acoustic sensor networks (UWASNs) can revolutionize the subsea domain by enabling low-cost monitoring of subsea assets and the marine environment. Accurate localization of the UWASNs is essential for these applications. In general, range-based localization techniques are preferred for their high accuracy in estimated locations. However, they can be severely affected by variable sound speed, multipath spreading, and other effects of the acoustic channel. In addition, an inefficient localization scheme can consume a significant amount of energy, reducing the effective life of the battery-powered sensor nodes. In this paper, we propose robust, efficient, and practically implementable localization schemes for static UWASNs. The proposed schemes are based on the Time-Difference-of-Arrival (TDoA) measurements and the nodes are localized passively, i.e., by just listening to beacon signals from multiple anchors, thus saving both the channel bandwidth and energy. The robustness in location estimates is achieved by considering an appropriate statistical noise model based on a plausible acoustic channel model and certain practical assumptions. To overcome the practical challenges of deploying and maintaining multiple permanent anchors for TDoA measurements, we propose practical schemes of using a single or multiple surface vehicles as virtual anchors. The robustness of localization is evaluated by simulations under realistic settings. By combining a mobile anchor(s) scheme with a robust estimator, this paper presents a complete package of efficient, robust, and practically usable localization schemes for low-cost UWASNs.
Highlights
IntroductionWith advances in wireless communication and networking technologies, powerefficient edge-computing devices, miniaturization of sensor payloads, and implementations of advanced signal processing algorithms and machine learning inferences on edge-computing devices, the Internet of Things (IoT) has taken the world by storm: from home automation to industrial automation to environmental monitoring
In the case of shallow sea environment, the receivers are at depth of 40 m, and the seabed is at depth of 75 m, while in the case of the moderately deep sea environment, the receivers are at the depth of 200 m, and the seabed was at the depth of 230 m
We proposed robust silent localization schemes for large-scale Underwater acoustic sensor networks (UWASNs), which is based on a fairly suitable statistical model of the possible errors in the rangedifference measurements due to varying sound speed and multipath propagation
Summary
With advances in wireless communication and networking technologies, powerefficient edge-computing devices, miniaturization of sensor payloads, and implementations of advanced signal processing algorithms and machine learning inferences on edge-computing devices, the Internet of Things (IoT) has taken the world by storm: from home automation to industrial automation to environmental monitoring. One of the significant achievements of such sensor networks is remote monitoring and controlling of processes in hazardous environments without human interventions for long-term. Underwater sensor networks have the potential to revolutionize the monitoring of subsea assets and marine environments, and enhance the prevention of disasters, security, and navigation
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