Poster Abstract: Scalable Localization with Mobility Prediction for Underwater Sensor Networks

Localization of mobile sensor nodes is indispensable for underwater sensor networks. For example, in aquatic environment monitoring applications, localization is a must-do task in order to get useful location-aware data. Location information is also required for geo-routing, which is proved to be more scalable and efficient in mobile underwater sensor networks. So far, only a limited number of schemes have been proposed for the localization service in underwater acoustic networks [1], [3], [6]. Due to adverse aqueous environments, non-negligible node mobility and large network scale, localization for large-scale mobile underwater sensor networks is very challenging. Since radio does not work in water, acoustic communications have to be employed. The unique features of acoustic channels (largelatency, low-bandwidth, and long end-to-end delays) cause many constraints on the localization schemes for underwater sensor networks. Traditional multi-hop localization schemes for terrestrial sensor networks are inefficient because of their huge communication overhead. Meanwhile, underwater sensor networks are mobile networks and node locations change continuously. In such environments, most localization schemes designed for static sensor networks need to run periodically to update the location results, as will dramatically increase the communication overhead. Further, distributed localization schemes designed for small-scale underwater acoustic networks can not work well in large-scale underwater sensor networks due to their slow convergence speed and high communication overhead. In this work, we aim to design a scalable localization scheme with low communication overhead while good localization performance for large-scale underwater water sensor networks. Though the network conditions in underwater environments are extremely tough for localization (as we discussed above), some unique properties can be indeed effectively exploited. A very useful property we find is that objects under water move with predictable patterns, though these patterns are in a large part determined by environmental factors [2], [4]. This mobility property can actually provide us an alternative for high performance localization. In this work, we propose a scheme, called Scalable Localization scheme with Mobility Prediction (SLMP), for underwater sensor networks.. In SLMP, localization is performed in a hierarchical way, and the whole localization process is divided into two parts: anchor node localization and ordinary node localization. During the localization process, every node predicts its future mobility pattern according to its past known location information, and it can estimate its future location based on its predicted mobility pattern. Anchor nodes with known locations in the network will control the whole localization process in order to balance the tradeoff between localization accuracy, localization coverage and communication cost. Our simulation results show that SLMP can greatly reduce the communication cost while maintaining a relatively high localization coverage and localization accuracy.