Adaptive Non-myopic Quantizer Design for Target Tracking in Wireless Sensor Networks

In this paper, we investigate the problem of nonmyopic (multi-step ahead) quantizer design for target tracking using a wireless sensor network. Adopting the alternative conditional posterior Cramer-Rao lower bound (A-CPCRLB) as the optimization metric, we theoretically show that this problem can be temporally decomposed over a certain time window. Based on sequential Monte-Carlo methods for tracking, i.e., particle filters, we design the local quantizer adaptively by solving a particlebased non-linear optimization problem which is well suited for the use of interior-point algorithm and easily embedded in the filtering process. Simulation results are provided to illustrate the effectiveness of our proposed approach.Comment: Submitted to 2013 Asilomar Conference on Signals, Systems, and Computer

Resource aware distributed detection and estimation of random events in wireless sensor networks

In this dissertation, we develop several resource aware approaches for detection and estimation in wireless sensor networks (WSNs). Tolerating an acceptable degradation from the best achievable performance, we seek more resource efficient solutions than the state-of-the-art methods. We first define a multi-objective optimization problem and find the trade-off solutions between two conflicting objectives for the distributed detection problem in WSNs: minimizing the probability of error and minimizing the total energy consumption. Simulation results show that Pareto-optimal solutions can provide significant energy savings at the cost a slight increase in the probability of error from its minimum achievable value. Having detected the presence of the source, accurate source localization is another important task to be performed by a WSN. The state-of-the-art one-shot location estimation scheme requires simultaneous transmission of all sensor data to the fusion center. We propose an iterative source localization algorithm where a small set of anchor sensors first detect the presence of the source and arrive at a coarse location estimate. Then a number of non-anchor sensors are selected in an iterative manner to refine the location estimate. The iterative localization scheme reduces the communication requirements as compared to the one-shot location estimation while introducing some estimation latency. For sensor selection at each iteration, two metrics are proposed which are derived based on the mutual information (MI) and the posterior Cramer-Rao lower bound (PCRLB) of the location estimate. In terms of computational complexity, the PCRLB-based sensor selection metric is more efficient as compared to the MI-based sensor selection metric, and under the assumption of perfect communication channels between sensors and the fusion center, both sensor selection schemes achieve the similar estimation performance that is the mean squared error of the source location gets very close to the PCRLB of one-shot location estimator within a few iterations. The proposed iterative method is further extended to the case which considers fading on the channels between sensors and the fusion center. Simulation results are presented for the cases when partial or complete channel knowledge are available at the fusion center. We finally consider a heterogenous sensing field and define a distributed parameter estimation problem where the quantization data rate of a sensor is determined as a function of its observation SNR. The inverse of the average Fisher information is then defined as a lower bound on the average PCRLB which is hard to compute. The inverse of the average Fisher information is minimized subject to the total bandwidth and bandwidth utilization constraints and we find the optimal transmission probability of each possible quantization rate. Under stringent bandwidth availability, the proposed scheme outperforms the scheme where the total bandwidth is equally distributed among sensors

Permutation Trellis Coded Multi-level FSK Signaling to Mitigate Primary User Interference in Cognitive Radio Networks

We employ Permutation Trellis Code (PTC) based multi-level Frequency Shift Keying signaling to mitigate the impact of Primary Users (PUs) on the performance of Secondary Users (SUs) in Cognitive Radio Networks (CRNs). The PUs are assumed to be dynamic in that they appear intermittently and stay active for an unknown duration. Our approach is based on the use of PTC combined with multi-level FSK modulation so that an SU can improve its data rate by increasing its transmission bandwidth while operating at low power and not creating destructive interference for PUs. We evaluate system performance by obtaining an approximation for the actual Bit Error Rate (BER) using properties of the Viterbi decoder and carry out a thorough performance analysis in terms of BER and throughput. The results show that the proposed coded system achieves i) robustness by ensuring that SUs have stable throughput in the presence of heavy PU interference and ii) improved resiliency of SU links to interference in the presence of multiple dynamic PUs.Comment: 30 pages, 12 figure

Performance of Permutation Trellis Codes in Cognitive Radio Networks

In this paper, we investigate the error correction performance of Permutation Trellis Codes (PTC) combined with M -ary Frequency Shift Keying (M -FSK) modulation in Cognitive Radio Networks (CRNs). Using this modulation technique, a secondary user (SU) can improve its data rate by increasing its transmission bandwidth while operating at low power and without creating destructive interference to the primary users (PUs). Given an active PU, we first derive the bit error rate (BER) of the PTC based M-FSK system for a given SU link. For different PTCs, we compare the analytical BER with the corresponding simulation results. For the same transmitting power, bandwidth availability and transmission time, simulation results show that for a SU link, M-FSK scheme using PTC provides better protection against the interference caused by the PU than M-FSK schemes employing conventional error correction coding such as convolutional and low density parity check (LDPC) codes

A multiobjective optimization based sensor selection method for target tracking in wireless sensor networks

Abstract-In this paper, we propose a sensor selection strategy for target tracking in Wireless Sensor Networks by formulating it as a multiobjective optimization problem (MOP). At each time step of tracking, we obtain tradeoff solutions between two conflicting objectives: minimization of the number of selected sensors and minimization of the information gap between the Fisher Information when all the sensors transmit measurements and the Fisher Information when only the selected sensors transmit their measurements based on the sensor selection strategy. Simulation results show that the sensor selection strategy which is closest to the utopia point of the MOP adaptively decides on the required number of selected sensors at each time step of tracking and achieves estimation performance that is near the estimation performance when all the sensors transmit

Proportional time sharing with frame size adaptation for MB-OFDM based UWB WPANs

In this paper, we investigate Multi Band OFDM based UWB wireless personal area networks (WPANs) over time varying channels. We first improve the efficiency of the data link layer. In order to maximize the link efficiency, we consider an immediate acknowlegement policy with dynamically and optimally selected frame sizes based on channel conditions. We then propose an opportunistic multiple-link-time scheduling algorithm, where the network controller determines the time allocation of individual connections within the IEEE802.15.3 WPAN superframe structure. This algorithm determines time allocations proportional to the instantaneous efficiencies of operating links. Simulation results show that the proposed access method enhances the overall network performance compared to equal channel access schemes