Fault-resilient localization for underwater sensor networks.

Localization is one of the critical issues in underwater sensor networks (UWSNs) on grounds of environmental characteristics, mobility of sensor nodes, and lack of global positioning system (GPS) services. Mobility modeling and fault tolerance with respect to underwater characteristics are extremely difficult. In this energy constraint network, an adaptive localization scheme with minimum communication and GPS support is required. We propose a fault-resilient localization scheme, which provides good localization accuracy with minimal communication overhead. To resile from an anchor node failure, a sensor node predicts its position by learning the mobility behavior of its neighbors using multiple linear regression. Therefore, the data dissemination process can continue even after an unexpected case of anchor node failure. We simulate the network and analyze the localization accuracy. Several experiments with different test cases are conducted to analyze the validity of the location prediction system. Experimental results show that the last 10 location information from three immediate neighbors are adequate for accurate prediction and the localization scheme is well suited for medium-range UWSNs.