SUMMARYTarget tracking in wireless sensor networks is a well-known application. In real life scenario, target mobility can be predicted using well-known filters. In this paper, we explain an approach to model the pattern of movement of a target on the basis of target data available. This method utilizes filter techniques to predict the target and a curve-fitting algorithm to model the mobility of a target in both linear and non-linear motion patterns. Two alternate strategies to achieve mobility approximation have been proposed and compared. The efficacy of the algorithm is, further, adjudged by comparing its mobility prediction vis-a-vis the Kalman filter. Simulation results show that with sufficient data, the mobility pattern of the target can be fairly calculated even if the target moves unpredictably.

In target tracking applications of wireless sensor networks (WSNs), one of the important but overlooked issues is the estimation of mobility behavior of a target inside a coverage hole. The existing approaches are restricted to networks with effective coverage by wireless sensors. Additionally, those works implicitly considered that a target does not change its mobility pattern inside the entire tracking region. In this paper, we address the above lacunae by designing a stochastic learning weak estimation-based scheme, namely mobility prediction inside a coverage hole (MIRACLE). The objectives of MIRACLE are two fold. First, one should be able to correctly predict the mobility pattern of a target inside a coverage hole with low computational overhead. Second, if a target changes its mobility pattern inside the coverage hole, the proposed estimator should give some estimation about all possible transitions among the mobility models. We use the trajectory extrapolation and fusion techniques for exploring all possible transitions among the mobility models. We validate the results with simulated traces of mobile targets generated using network simulator NS-2. Simulation results show that MIRACLE estimates the mobility patterns inside coverage hole with an accuracy of more than 60% in WSNs.

Wirelesses Local Area Networks (WLANs) have become more prevalent and are widely deployed and used in many popular places like university campuses, airports, residences, cafes etc. With this growing popularity, the security of wireless network is also very important. In this work, the evaluation of CRC-32 and SHA-1 is performed by calculating the throughput and by running dictionary attack on both the algorithms. The analysis shows that SHA-1 is more secure that CRC-32.