Bang Wang

Coverage problems in sensor networks: A survey

Sensor networks, which consist of sensor nodes each capable of sensing environment and transmitting data, have lots of applications in battlefield surveillance, environmental monitoring, industrial diagnostics, etc. Coverage which is one of the most important performance metrics for sensor networks reflects how well a sensor field is monitored. Individual sensor coverage models are dependent on the sensing functions of different types of sensors, while network-wide sensing coverage is a collective performance measure for geographically distributed sensor nodes. This article surveys research progress made to address various coverage problems in sensor networks. We first provide discussions on sensor coverage models and design issues. The coverage problems in sensor networks can be classified into three categories according to the subject to be covered. We state the basic coverage problems in each category, and review representative solution approaches in the literature. We also provide comments and discussions on some extensions and variants of these basic coverage problems.

Information coverage for wireless sensor networks

Coverage is a very important issue in wireless sensor networks. Current literature defines a point to be covered if it is within the sensing radius of at least one sensor. In this paper we argue that this is a conservative definition of coverage. This definition implicitly assumes that each sensor makes a decision independent of other sensors in the field. However, sensors can cooperate to make an accurate estimation, even if any single sensor is unable to do so. We then propose a new notion of information coverage and investigate its implications for sensor deployment. Numerical and simulation results show that significant savings in terms of sensor density for complete coverage can be achieved by using our definition of information coverage compared to that by using the existing definition.

Performance of VoIP on HSDPA

This paper provides packet scheduler design and performance simulations for running VoIP services over high-speed downlink packet access (HSDPA) in WCDMA. The main challenge of supporting VoIP service on HSDPA is the tight delay requirement combined with the small VoIP packet size. A packet scheduler design incorporating VoIP packet aggregation and user multiplexing is proposed and the VoIP capacity is studied for a macro-cellular environment. Results are obtained for different delay budgets and packet scheduling settings, using either blind round robin or a slightly modified version of proportional fair scheduling. For proportional fair scheduling with code-multiplexing of 4-users, the downlink VoIP cell capacity on HSDPA is found to be in the range 72-104 users depending on whether the delay budget for the node-B scheduling and user reception equals 80 ms or 150 ms, respectively.

Coverage Control in Sensor Networks

This easy-to-read text focuses on challenges in coverage control in sensor networks, examines fundamental coverage problems, and presents the most recent advances and techniques in the field. Features: provides an introduction to sensors, sensor nodes, sensor networks, and sensor coverage models; supplies an informal definition and taxonomy for network-wide coverage control; explores the node placement optimization problem for coverage configuration before network deployment; investigates the coverage lifetime maximization problem by controlling coverage characteristics in a randomly deployed network; discusses the critical sensor-density problem for coverage configuration before network deployment; examines the sensor-activity scheduling problem of controlling network coverage characteristics in a randomly deployed network; introduces the node movement strategy problem for sensor networks containing mobile nodes; presents the challenges of building intrusion barriers and finding penetration paths.

Indoor Localization Based on Curve Fitting and Location Search Using Received Signal Strength

Indoor localization based on received signal strength (RSS) has attracted considerable attention in both academia and industry due to the wide deployment of wireless local area networks. In this paper, we propose a novel indoor localization scheme based on curve fitting (CF) and location search. In the offline phase, we divide the whole environment into some subareas and create a fingerprint for each subarea. We then apply the CF technique to construct a fitted RSS-distance function for each transmitter in each subarea. The online positioning phase consists of two steps. In the first step, we determine a subarea to which a mobile device belongs. In the second step, we propose two location search algorithms, namely exhaustive search and gradient descent search, to find a location within the selected subarea such that the sum of distance errors can be minimized. We conduct field experiments to examine the proposed algorithms. The results show that our algorithms can obtain approximately 20% improvement in localization accuracy compared with the classical fingerprinting-based and lateration-based localization algorithms.

Energy-efficient coverage for target detection in wireless sensor networks

In this paper we consider the coverage problem for target detection applications in wireless sensor networks. Unlike conventional coverage problems which assume sensing regions are disks around sensors, we define the sensing region according to detection constraints in terms of false alarm probability and missing probability. We show that exploiting cooperation between sensors can extend the overall sensing region while maintain the same constraints on false alarm probability and missing probability. We then propose an energy efficient cooperative detection scheme and study the trade-offs on energy consumption between cooperative and non-cooperative schemes. The cooperative scheme can use half the number of sensors to monitor the whole field compared to disk model in networks deployed on grids. We also study the communication overheads incurred by the cooperative scheme, and show that only cooperation between limited number of nearby sensors is profitable in terms of energy consumption. In our simulations on randomly deployed networks, cooperation reduces the number of sensors to cover the area by 30% and nearly doubles the number of disjoint sensor sets where each can fully cover the area. Appropriately trading off energy consumption with coverage extension, our cooperative detection scheme can increase the network lifetime by nearly 70%.

Information Coverage in Randomly Deployed Wireless Sensor Networks

Coverage is an important issue in wireless sensor networks. The most commonly used coverage model in the literature defines a point to be covered if its Euclidian distance to at least one sensor is less than a fixed threshold. This is a conservative definition of coverage which implicitly assumes that each sensor makes a decision independent of other sensors in the field. Sensors can cooperate to make an accurate estimation, even if any single sensor is unable to do so. We have previously proposed a new notion of information coverage and investigated its properties. In this paper, we study sensor density requirements for complete information coverage of a field with random sensor deployment. We provide an upper bound on the probability that an arbitrary point in a randomly deployed sensor field is not information covered and find the relationship between the sensor density and the average field vacancy. Simulation results validate our theoretical analysis and show that significant savings in terms of sensor density for complete coverage can be achieved with information coverage.

A novel range-free localization based on regulated neighborhood distance for wireless ad hoc and sensor networks

Range-free localization methods are suitable for large scale wireless ad hoc and sensor networks due to their less-demanding hardware requirements. Many existing connectivity- or hop-count-based range-free localization methods suffer from the hop-distance ambiguity problem where a node has a same distance estimation to all of its one-hop neighbors. In this paper, we define a new measure, called regulated neighborhood distance (RND), to address this problem by relating the proximity of two neighbors to their neighbor partitions. Furthermore, we propose a new RND-based range-free localization method, and compare our localization algorithm with peer classical algorithms in different network scenarios, which include grid deployment, random uniform deployment, non-uniform deployment and uniform deployment with a coverage hole. Simulation results show that ours can achieve better and reliable localization accuracy in these network scenarios.

A Coverage-Aware Clustering Protocol for Wireless Sensor Networks

In energy-limited wireless sensor networks, network clustering and sensor scheduling are two efficient techniques for minimizing node energy consumption and maximizing network coverage lifetime. When integrating the two techniques, the challenges are how to select cluster heads and active nodes. In this paper, we propose a coverage-aware clustering protocol. In the proposed protocol, we define a cost metric that favors those nodes being more energy-redundantly covered as better candidates for cluster heads and select active nodes in a way that tries to emulate the most efficient tessellation for area coverage. Our simulation results show that the network coverage lifetime can be significantly improved compared with an existing protocol.

Coverage for target localization in wireless sensor networks

Target tracking and localization are important applications in wireless sensor networks. Although the coverage problem for target detection has been intensively studied, few consider the coverage problem from the perspective of target localization. In this paper, we propose two methods to estimate the necessary sensor density which can guarantee a localization error bound over the sensing field. In the first method, we convert the coverage problem for localization to a conventional disk coverage problem, where the sensing area is a disk centered around the sensor. Our results show that the disk coverage model requires 4 times more sensors for tracking compared to detection applications. We then introduce the idea of sector coverage, which can satisfy the same coverage conditions with 2 times less sensors over the disk coverage approach. This shows that conventional disk coverage model is insufficient for tracking applications, since it overestimates the sensor density by two times. Simulation results show that the network density requirements derived through sector coverage are close to the actual need for target tracking applications