Sharif Digital Repository

We study the problem of power efficient tracking interval management for distributed target tracking wireless sensor networks (WSNs). We first analyze the performance of a distributed target tracking network with one moving object, using a quantitative mathematical analysis. We show that previously proposed algorithms are efficient only for constant average velocity objects; however, they do not ensure an optimal performance for moving objects with acceleration. Towards an optimal performance, first, we derive a closed-form mathematical expression for the estimation of the minimal achievable power consumption by an optimal adaptive tracking interval management algorithm. This can be used as... 

In the literature of model selection, Vuong’s test and Akaike information criterion aim to find the best statistical model. Both of them are related to the expectation of the log-likelihood function of the rival models, but they are not sensitive to the small difference between rival models. The goal of this study is to develop a simple model selection approach which does not assume a true distribution for data. We have introduced a nonparametric tracking interval in the context of model selection. We have shown that this interval is compatibale with the known Vuong’s test results, but here we let the magnitude of the data enhance the performance of statistical inference. Simulation study... 

In the literature of model selection, Vuong’s test and Akaike information criterion aim to find the best statistical model. Both of them are related to the expectation of the log-likelihood function of the rival models, but they are not sensitive to the small difference between rival models. The goal of this study is to develop a simple model selection approach which does not assume a true distribution for data. We have introduced a nonparametric tracking interval in the context of model selection. We have shown that this interval is compatibale with the known Vuong’s test results, but here we let the magnitude of the data enhance the performance of statistical inference. Simulation study... 

We consider the minimization of power consumption in target tracking wireless sensor networks (WSNs) using dynamic modification of tracking interval. In this context, we first analyze the performance of such networks, using a quantitative mathematical analysis. Then we calculate an upper bound for the achievable improvement in total power consumption, when using an adaptive time interval modification algorithm for tracking moving objects with acceleration. Towards this optimum functionality, we propose a novel adaptive algorithm (AHC) to adapt the tracking interval such that it minimizes power consumption while keeping an acceptable accuracy. Simulation results show that using the proposed...