The growing interests in underwater wireless sensor networks (UWSNs) for a wide variety of purposes from Tsunami monitoring to commercial oilfield exploration projects have been encouraging. This paper deals with the problem of accurately tracking a single target moving through UWSNs employing acoustic sensors. This paper addresses the issues of estimating the target position, improving energy efficiency by applying a Kalman filter in a distributed architecture. Each underwater wireless sensor node composing the UWSNs is battery-powered, so the energy conservation problem is a critical issue. This paper provides an algorithm which increases energy efficiency of each sensor through a wake-up/sleep (WuS) and a valid measurement selecting (VMS) scheme. Simulation results illustrate the performance of the tracking filter according to the sensor node displacement and sensor detecting area.
Advances in micro electromechanical systems (MEMS) and wireless technologies have allowed for the emergence of inexpensive micro-sensors with embedded processing and communication capabilities (Chong, Zhao, Mori, and Kumar, 2003). Sensor networks consist of many of these micro-sensors communicating with one another over wireless links. Wireless sensor networks (WSNs) are emerging technology for monitoring physical world with a densely deployed network of sensor nodes. The main advantages of WSNs include its low cost, rapid deployment, self-organization, and fault tolerance. It can be used in a variety of applications, such as environment monitoring, traffic monitoring in intelligent transportation systems, industrial sensing and diagnostics, healthcare, navigation and control of mobile robots, and military surveillance. One of the main constraints for WSNs is energy. It typically is impractical to replace the battery on each sensor node, so the lifetime of the network is tied to the battery life. The authors in (Brooks, Ramanathan, and Sayeed, 2003) argue for the need to study the problem of energy-aware target tracking for wireless sensor networks.
