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Object Tracking in Random Access Networks: A Large-Scale Design | IEEE Journals & Magazine | IEEE Xplore

Object Tracking in Random Access Networks: A Large-Scale Design


Abstract:

We address a scenario in which networked sensor nodes measure the strength of the field generated by a number of moving objects and transmit their measurements to a fusio...Show More

Abstract:

We address a scenario in which networked sensor nodes measure the strength of the field generated by a number of moving objects and transmit their measurements to a fusion center (FC) in a random access manner for the final reconstruction of the objects' trajectories. To ensure scalability over an arbitrary coverage area, we divide the total observation area into design units (DUs), each consisting of several sensing cells. An extended Kalman filter is assigned to each cell, while neighboring cells communicate with each other to exchange current status, thus allowing state fusion whereby the Kalman filters adjust (overwrite) their local estimates at the end of each updating interval. In this manner, provisions are made for the objects leaving and entering a DU, yielding a system design that is scalable across a large coverage area without an increase in complexity (size) of the Kalman filters. We provide a step-by-step procedure for designing the system, taking into account the fact that sensors communicate to the FCs using random access over band-limited and imperfect channels where packet loss is inevitable due to collisions as well as communication noise. In addition, we study different rate control schemes in which the FC instructs the sensors to increase or decrease their sensing (transmission) rate in accordance with the currently estimated object locations. Performance is evaluated through simulation, showing the effectiveness of the approach proposed in terms of the mean squared localization error and data throughput, and quantifying the effect of limited bandwidth and lossy communication.
Published in: IEEE Internet of Things Journal ( Volume: 7, Issue: 10, October 2020)
Page(s): 9784 - 9792
Date of Publication: 17 April 2020

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