An adaptive target tracking method for 3D underwater wireless sensor networks

Today, underwater target tracking using underwater wireless sensor networks (UWSNs) is an essential part in many military and non-military applications. Most of moving target tracking studies in UWSNs are considered in two-dimensional space. However, most practical applications require to be implemented in three-dimensional space. In this paper an adaptive method based on Kalman filter for moving target tracking in three dimensional space using UWSNs is proposed. Since, energy protection is a vital task in UWSNs; the proposed method reduces the energy consumption of the entire network by a sleep/wake plan. In this plan only 60% of the closer nodes along the path of the moving target will be waked up using a sink activation message and participate in the tracking, while the other nodes remain in sleep state. At each stage of tracking, the location of the target is estimated using a 3D underwater target tracking algorithm with the trilateration method. Subsequently, the estimations and target tracking results are inserted into the Kalman filter as measuring model to produce the final result. Performance evaluation and simulations results indicated that the proposed method improves the average location error by 45%, average estimated velocity by 86%, and average energy consumption by 33% in comparison to the trilateration method. However, computation time is increased as a result of improving tracking accuracy; and tracking accuracy is lost about 20% due to saving energy. It was shown that the proposed method has been able to adaptively achieve a trade-off between tracking accuracy and energy consumption based on real-time user requirements. Such adaption can be controlled trough the sink node based on real-time requirements.