In this paper, we propose a powerful method of estimating the model parameters for time synchronization in wireless sensor networks (WSNs). Joint estimation of clock offset and clock skew has been proposed in the literature using the standard regression framework. Here, we claim that simple regression poorly estimates the parameters because of the inherent correlation among successive time readings between two sensors. We propose an alternative autoregressive model and use generalized least squares for estimating the relative offset and skew parameters. A computationally efficient Bayesian approach is also proposed for the parameter estimation considering correlated readings between two sensors. The effectiveness of the proposed approach compared with the earlier approach has been investigated through extensive simulation studies.
An efficient deployment of a mobile ad-hoc network (MANET) requires a realistic approach towardsthe mobility of the hosts who want to communicate with each other over a wireless channel. Since ad-hoc networks are driven by human requirements, instead of considering the random movement of mobile nodes, we concentrate on the social desire of the nodes for getting connected with oneanother and provide here a framework for the mobility model of the nodes based on Social Network Theory. In this paper, we capture the preferences in choosing destinations of pedestrian mobility pattern on the basis of Social Factor (YF) and try to find out the essential impact of YF on the Pause Time of the nodes. Also, instead of considering an unobstructed terrain, we carry out our simulations in presence of obstacles which block the node movement. Thus, we present here a more realistic mobility distribution pattern. Further, a relative comparison of the proposed model with the popular Random Way-Point (RWP) Model is also done.
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