Dynamic Bit Allocation for Object Tracking in Bandwidth Limited Sensor Networks

TL;DR

This paper introduces efficient algorithms for dynamic bandwidth allocation in sensor networks to improve object tracking accuracy under limited bandwidth constraints.

Contribution

Develops two novel computationally efficient suboptimal algorithms for bandwidth distribution in sensor networks, based on convex relaxation and approximate dynamic programming.

Findings

A-DP, convex optimization, and GBFOS achieve near-optimal MSE performance.

A-DP is more computationally efficient for large sensor networks.

Proposed methods outperform greedy and nearest neighbor schemes significantly.

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.