Nianxia Cao

Target Tracking via Crowdsourcing: A Mechanism Design Approach

In this paper, we propose a crowdsourcing-based framework for myopic target tracking by designing an optimal incentive-compatible mechanism in a wireless sensor network (WSN) containing sensors that are selfish and profit-motivated. In typical WSNs which have limited bandwidth, the fusion center (FC) has to distribute the total number of bits that can be transmitted from the sensors to the FC among the sensors. In the formulation considered here, the FC conducts an auction by soliciting bids from the selfish sensors, which reflect how much they value their energy cost. Furthermore, the rationality and truthfulness of the sensors are guaranteed in our model. The final problem is formulated as a multiple-choice knapsack problem (MCKP), which is solved by the dynamic programming method in pseudo-polynomial time. Simulation results show the effectiveness of our proposed approach in terms of both the tracking performance and lifetime of the sensor network.

Compressive Sensing Based Probabilistic Sensor Management for Target Tracking in Wireless Sensor Networks

In this paper, we consider the problem of sensor management for target tracking in a wireless sensor network (WSN). To determine the set of sensors with the most informative data, we develop a probabilistic sensor management scheme based on the concepts developed in compressive sensing. In the proposed scheme where each sensor transmits its observation with a certain probability via a coherent multiple access channel (MAC), the observation vector received at the fusion center becomes a compressed version of the original observations. In this framework, the sensor management problem can be cast as the problem of finding the probability of transmission at each node so that a given performance metric is optimized. Our goal is to determine the optimal values of the probabilities of transmission so that the trace of the Fisher information matrix (FIM) is maximized at any given time instant with a constraint on the available energy. We consider two cases, where the fusion center has i) complete information and ii) only partial information, regarding the sensor transmissions. The expression for FIM is derived for both cases and the optimal values of the probabilities of transmission are found accordingly. With nonidentical probabilities, we obtain the results numerically while under the assumption that each sensor transmits with equal probability, we obtain the optimal values analytically. We provide numerical results to illustrate the performance of the proposed probabilistic sensor management scheme.

Sensor Selection for Target Tracking in Wireless Sensor Networks With Uncertainty

In this paper, we propose a multiobjective optimization framework for the sensor selection problem in uncertain Wireless Sensor Networks (WSNs). The uncertainties of the WSNs result in a set of sensor observations with insufficient information about the target. We propose a novel mutual information upper bound (MIUB)-based sensor selection scheme, which has a low computational complexity, same as the Fisher information (FI)-based sensor selection scheme, and gives an estimation performance similar to the mutual information-based sensor selection scheme. Without knowing the number of sensors to be selected a priori, the multiobjective optimization problem (MOP) gives a set of sensor selection strategies that reveal different tradeoffs between two conflicting objectives: minimization of the number of selected sensors and minimization of the gap between the performance metric (MIUB and FI) when all the sensors transmit measurements and when only the selected sensors transmit their measurements based on the sensor selection strategy. Illustrative numerical results that provide valuable insights are presented.

An incentive-based mechanism for location estimation in wireless sensor networks

In this paper, we propose a framework for target location estimation by designing an incentive-compatible mechanism in a wireless sensor network containing sensors that are selfish and profit-motivated. To accomplish the task, the fusion center (FC) conducts an auction by soliciting bids from the selfish sensors, where the bids reflect the information available at the sensor and the remaining energy of the sensors. Furthermore, the truthfulness of the sensors is guaranteed in our model. Computationally efficient algorithms to implement our mechanism are provided. Simulation results show the effectiveness of our proposed approach.

Sensor placement for field estimation via Poisson disk sampling

In this paper, we study the problem of sensor placement for field estimation, where the best subset of potential sensor locations is chosen to strike a balance between the number of deployed sensors and estimation accuracy. Potential sensor locations are generated by sampling a continuous field of interest. We investigate the impact of sampling strategies on sensor placement, and show that compared to other commonly-used sampling strategies, the Poisson disk sampling method can provide a more accurate (discretized) representation of the random field. Based on the sampled locations, we propose an efficient placement algorithm that scales gracefully with problem size using the alternating direction method of multipliers and the accelerated gradient descent method. Numerical results are provided to demonstrate the effectiveness of our approach for sensor placement.

Portfolio theory based sensor selection in Wireless Sensor Networks with unreliable observations

In this paper, we propose a portfolio theory based sensor selection framework in Wireless Sensor Networks (WSNs) with unreliable sensor observations for target localization. Fisher information (FI) is used as the sensor selection metric in our work. Our objective is to find a sensor selection scheme that considers both the expected FI gain and the reliability of the sensors, where we observe that the FI variability captures the reliability of the sensors. Based on portfolio theory, we formulate our sensor selection problem as a multiobjective optimization problem (MOP), which is solved by the normal boundary intersection (NBI) method. Simulation results show the advantages of performing portfolio theory based sensor selection.

Optimal Auction Design With Quantized Bids

This letter considers the design of an auction mechanism to sell the object of a seller when the buyers quantize their private value estimates regarding the object into binary values prior to communicating them to the seller. The designed auction mechanism maximizes the utility of the seller (i.e., the auction is optimal), prevents buyers from communicating falsified quantized bids (i.e., the auction is incentive compatible), and ensures that buyers will participate in the auction (i.e., the auction is individually rational). The letter also investigates the design of the optimal quantization thresholds using which buyers quantize their private value estimates. Numerical results provide insights regarding the influence of the quantization thresholds on the auction mechanism.

Market based sensor mobility management for target localization

In this paper, we propose a framework for the mobile sensor scheduling problem in target location estimation by designing an equilibrium based two-sided market model where the fusion center (FC) is modeled as the consumer and the mobile sensors are modeled as the producers. To accomplish the task, the FC provides incentives to the sensors to motivate them to optimally relocate themselves in a manner that maximizes the information gain for estimating the location of the target. On the other hand, the sensors calculate their own best moving distances that maximize their profits. Price adjustment rules are designed to compute the equilibrium prices and moving distances, so that a stable solution is reached. Simulation experiments show the effectiveness of our model.

Towards cloud sensing enabled target localization

In this paper, we introduce “cloud sensing” as a paradigm for enabling sensing-as-a-service in the context of target localization in wireless sensor networks (WSNs). We present a bilateral trading mechanism consisting of a sensing service provider (fusion center) that “sells” information regarding the target through sensor management, and a user who seeks to “buy” information regarding the target. Our mechanism, aware of resource costs involved in service provisioning, maximizes the expected total gain from the trade while assuring individual rationality and incentive compatibility. The impossibility of achieving ex post efficiency is also shown in the paper. Design of the mechanism enables the study of the tradeoff between information gain and the costs of the WSN for sensor management. Simulation results provide insights into the dynamics of the proposed model.