Dynamic Bit Allocation for Object Tracking in Wireless Sensor Networks

Abstract

In this paper, we study the target tracking problem in wireless sensor networks (WSNs) using quantized sensor measurements under limited bandwidth availability. At each time step of tracking, the available bandwidth R needs to be distributed among the N sensors in the WSN for the next time step.The optimal solution for the bandwidth allocation problem can be obtained by using a combinatorial search which may become computationally prohibitive for large N and R. Therefore, we develop two new computationally efficient suboptimal bandwidth distribution algorithms which are based on convex relaxation and approximate dynamic programming (A-DP). We compare the mean squared error (MSE) and computational complexity performances of convex relaxation and A-DP with other existing suboptimal bandwidth distribution schemes based on generalized Breiman, Friedman, Olshen, and Stone (GBFOS) algorithm and greedy search. Simulation results show that, A-DP, convex optimization and GBFOS yield similar MSE performance, which is very close to that based on the optimal exhaustive search approach and they outperform greedy search and nearest neighbor based bandwidth allocation approaches significantly. Computationally, A-DP is more efficient than the bandwidth allocation schemes based on convex relaxation and GBFOS, especially for a large sensor network.

Sensor Selection for Estimation with Correlated Measurement Noise

“…Moreover, if we drop the source statistics Σ from the MSE matrix (5) and impose the assumption s ≥ n, the proposed formulation (P0) is then applicable for sensor selection in a non-Bayesian framework, where the unknown parameter is estimated through the best linear unbiased estimator [24].…”

“…We also remark that although the dynamical system (36)-(37) is assumed to be linear, it will be evident later that the proposed sensor scheduling framework is also applicable to non-linear dynamical systems. The PDF of the initial state x 0 at time step t 0 is assumed to be Gaussian with meanx 0 and covariance matrixP 0 , wherex 0 andP 0 are estimates of the initial state and error covariance from the previous measurements obtained using filtering algorithms, such as a particle filter or a Kalman filter [37], [38]. At time step t 0 , we aim to find the optimal sensor schedule over the next τ time steps t 0 + 1, t 0 + 2, .…”

“…Therefore, the problem of sensor selection/scheduling arises, which aims to strike a balance between estimation accuracy and sensor activations over space and/or time. The importance of sensor selection has been discussed extensively in the context of various applications, such as target tracking [4], bit allocation [5], field monitoring [6], [7], optimal control [8], power allocation [9], [10], optimal experiment design [11], and leader selection in consensus networks [12].…”

Particle filtering with applications in networked systems: a survey

“…When there is a constraint on the total number of bits that can be transmitted, bit allocation becomes necessary. In [142], a dynamic bit allocation algorithm based on approximate dynamic programming has been presented. It is shown that the proposed algorithm can save much of the computational cost while achieving comparable accuracy with other existing methods.…”

Target Tracking via Crowdsourcing: A Mechanism Design Approach

“…In (10), the FIM is decomposed into two parts, where, J D t represents the FIM corresponding to the sensor measurements, and J P t represents the FIM corresponding to the a priori information. The FIM corresponding to the sensor measurements, J D t , can be further written as the summation of each sensor's individual FIM [18], [21] as,…”