Tai-Lin Chin

Exposure for collaborative detection using mobile sensor networks

Sensor networks possess the inherent potential to detect the presence of a target in a monitored region. Although a stationary sensor network is often adequate to meet application requirements, it is not suited to many situations, for example, a huge number of nodes are required to monitor a large region. In such situations, mobile sensor networks can be used to resolve the communication and sensing coverage problems. This paper addresses the problem of detecting a target using mobile sensor networks. One of the fundamental issues in target detection problems is exposure, which measures how the region is covered by the sensor network. While traditional studies focus on stationary sensor networks, this paper formally defines and evaluates exposure in mobile sensor networks with the presence of obstacles and noise. To conform with practical situations, detection is conducted without presuming the targets activities and moving directions. As there is no fixed layout of node positions, a time expansion technique is developed to evaluate exposure. Since determining exposure can be computationally expensive, algorithms to calculate the upper and lower bounds on exposure are developed. Simulation results are also presented to illustrate the effectiveness of the algorithms

Analytic modeling of detection latency in mobile sensor networks

An envisioned usage of sensor networks is in surveillance systems for detecting a target or monitoring a physical phenomenon in a region. Traditionally, stationary sensor networks are deployed to carry out the sensing operations. In many applications, if the monitored region is relatively large compared to the sensing range of a node, a large number of nodes are required in the region to achieve high coverage. Using mobile nodes in such situations can be an attractive alternative. Mobility of sensor nodes has been studied in sensor networks for many purposes such as power saving, data collection, and packet delivery. However, nearly all research literature for the target detection problem has focused on stationary sensor networks. This paper investigates the problem of detecting the presence/absence of a target using mobile sensor networks. It presents an analytic method to evaluate the detection latency based on a collaborative sensing approach using nodes with uncoordinated mobility. We verify the analytic model through simulations. The analytic method provides a simple way of analyzing the tradeoff between number of nodes and detection latency in a mobile sensor network. The analysis is also used to compare the performance of mobile and stationary sensor networks with respect to these measures. Results show that if the target is present at the worst possible location in a given deployment, then detection latency of mobile sensor networks is considerably less as compared to that of stationary networks with the same number of nodes

Latency of Collaborative Target Detection for Surveillance Sensor Networks

Target detection is one of the most important topics in wireless sensor networks. Many studies in the literature have addressed the problem of evaluating the performance of a sensor network based on detection probability. However, it is difficult to guarantee detection probability in a sensor network since it depends on the topology of the sensor deployment and the location of the target. A sensor network without a careful sensor location arrangement may experience very low detection probability. This paper integrates collaborative fusion and sequential detection to guarantee the quality of the decisions made by a sensor network and analytically derives the average detection latency based on value fusion and decision fusion. Specifically, sensors periodically report their local measurements or decisions to a fusion center. The fusion center makes final decisions only when both the pre-defined false alarm probability and missing probability are satisfied. Otherwise, it will continue to collect data and repeat the decision making operations. Simple and elegant detection rules are provided for the collaborative sequential detection operations. Extensive simulations are conducted to show the performance of a sensor network in terms of detection latency. The correctness of the analytical results for detection latency is also verified by simulations.

Optimal Detector Based on Data Fusion for Wireless Sensor Networks

This paper investigates target detection problem in wireless sensor networks. Sensors carry out sensing operations and make consensus decisions about the presence or absence of a target or event. Most of previous studies for target detection either assume an unrealistic disk model for making detection decision or provide complicated numerical methods to evaluate detection performance. This paper develops the Uniformly Most Powerful(UMP) detector based on likelihood ratio test and derives simple and elegant test rules for target presence and absence. Moreover, detection performance measured by missing rate is also derived analytically. Simulations are conducted to show the performance of the UMP detector compared to a detector developed previously based on value fusion. The results show that the proposed detector dramatically outperforms the value fusion detector even in vulnerable locations.

WSN19-3: Optimal Sensor Distribution for Maximum Exposure in A Region with Obstacles

Sensor networks have been envisioned to enhance the ability of human beings in observing the environment and understanding the world. A potential application of a sensor network is to detect the presence or absence of a target in a region of interest. Many heuristics have been proposed in literature for placing sensors to achieve better coverage in the monitored region. However, none of them guarantee an optimal sensor deployment especially when there are obstacles in the region. Unlike the prior work, this paper focuses on the problem of determining the optimal sensor distribution in a region with or without obstacles. The detection performance is characterized using a metric called ldquoexposurerdquo, which is defined as the least probability of detecting a target over all possible target locations subject to a fixed false alarm probability. A linear programming based approach is proposed to find the optimal sensor distribution by maximizing the exposure in a given region with or without obstacles. The optimal sensor distribution can also be used as weights of sensor measurements taken at different locations for decision-making.

Modeling Detection Latency with Collaborative Mobile Sensing Architecture

Detection latency, which is defined as the time from the target arrival to the time of the first detection, is an important metric for the performance of sensor networks carrying out target detection, especially when the target is malicious or hostile. It characterizes the efficiency of detecting the presence of a target in a region of interest. Traditionally, stationary sensor networks are used to perform such sensing tasks. Consequently, nearly all research literature for the target detection problem has focused on stationary sensor networks. This paper addresses the problem of detecting the presence/absence of a target using a mobile sensor network. An analytic method is proposed to model the detection latency based on a collaborative sensing architecture. Detection latency for different node mobility models is presented. The accuracy of the analytic model is verified by simulations. This paper also compares the performance of mobile and stationary sensor networks. The comparison shows that if the target is present at the worst possible location in a given deployment, then detection latency of mobile sensor networks is considerably shorter as compared to that of stationary networks with the same number of nodes.

Optimal Target Detection with Localized Fusion in Wireless Sensor Networks

Detecting the presence/absence of an object in a region of interest is one of the important applications for sensor networks. A considerable amount of work has been seen in the literature for detecting events or objects using wireless sensor networks. Most of the prior work uses a simple binary detection model or an average signal strength model to make decisions of detection. Such methods are not optimal in terms of detection probability. This paper derives a detection approach which is optimal in the sense of Neyman-Pearson test and shows that the detection performance of the traditional average based method is much lower than the optimal. To reduce power consumption and communication cost, a localized fusion method is also developed by carefully selecting sensors in the vicinity of a target location. The paper shows that the localized fusion can dramatically reduce the number of sensors participating the fusion while maintain high detection performance.

Optimal Storage Placement for Tree-Structured Networks with Heterogeneous Channel Costs

This work considers data query applications in tree-structured networks, where a given set of source nodes generate (or collect) data and forward the data to some halfway storage nodes for satisfying queries that call for data generated by all source nodes. The goal is to determine an optimal set of storage nodes that minimizes overall communication cost. Prior work toward this problem assumed homogeneous channel cost, which may not be the case in many network environments. We generalize the optimal storage problem for a tree-structured network by considering heterogeneous channel costs. The necessary and sufficient conditions for the optimal solution are identified, and an algorithm that incurs a linear time cost is proposed. We have also conducted extensive simulations to validate the algorithm and to evaluate its performance.

Multicast scheduling for stereoscopic video in wireless networks

Stereoscopic video multicast over wireless network is a challenging issue due to large bandwidth requirement, limited resource, and heterogeneous user channel conditions. Recently, most existing methods for stereoscopic video multicast employ symmetric video coding that transmits the same video quality for stereo views. In this paper, we propose a novel rate scheduling method for stereoscopic video multicast in WiFi networks through asymmetric video coding to maximize users’ perceived video quality. We first formulated rate scheduling problem which has complexity in non-polynomial time subjected to playback time limit, block dependency, and the ratio of asymmetric video quality for stereo views. Then, a novel algorithm is proposed to assign a suitable rate for each frame per layer. Furthermore, we studied the impact of block dependency and asymmetric coding. Experimental results confirm that our approach resulted in promising perceived video quality while outperforming several existing video multicast techniques.

Patron Allocation for Group Services Under Lower Bound Constraints

Group services are highly important for a variety of computing application domains. In this paper, we study the fundamental problem of allocating a set of service patrons to a set of service groups in an attempt to maximize the total profit gained by the grouping platform. The problem under consideration is unique in that group service is not provided at all unless its lower bound requirement is satisfied. In addition, we allow each service patron to join multiple groups. In this paper, after proving the hardness property of the problem, we focus first on a special case of the problem. To this end, we propose two approaches. One aims at providing a suboptimal solution using a 1/2-approximation algorithm. The other approach turns to seeking an optimal solution using a branch and bound technique. For this purpose, we introduce a theorem that captures a useful property of an optimal allocation. Based on this theorem, we design an efficient branch and bound algorithm to find an optimal solution. We then extend these methods to solve the general problem. Extensive experiments show that our branch and bound algorithm is able to obtain an optimal solution with a small amount of computation time in many different settings.