Bayesian network based energy efficient ship motion monitoring

It is extremely important to safeguard our harbours from intruders and smugglers who aim to benefit from unlawful activities and cause harm. Theft, terror attacks in commercial boats and cargo ships docked on harbours needs to be prevented. Camera surveillance, radar, satellites images have not been very reliable so far as they fail to work in drastic weather conditions and can be manipulated as well. Underwater Wireless Sensor Network (UWSN) could be installed in both shallow and deep water of the harbour for detecting various types of harbour activities. Ship movements namely heave, sway, surge, yaw, pitch and roll, could be detected and classified using pressure, position and underwater sensors. Such information can help in tracking ship motions, movements, loading and unloading activities. Any unplanned unloading activity can thus be detected and necessary alarms can be raised for ship owners and harbour official's attention. However designing such a network needs one to ensure that the severe energy constraints of the UWSN are well addressed. Bayesian Network based approach is explored in this paper for scheduling the sleep and active cycle of network nodes. Our proposed technique of ship motion monitoring system reduces energy consumption of the network nodes and enhances the network lifetime by balancing network load intelligently. The tracking mechanism proposed here explores the fundamental behaviour of ships motions in waves with reference to translating coordinate system.