Enhanced algorithms for deploying the minimum sensors to construct a wireless sensor network having full coverage of critical square grids

With the rapid technological development of sensors, many applications have been designed to use wireless sensor networks to monitor a certain area and provide quality-of-service guarantees. Therefore, the coverage problem had an important issue for constructing wireless sensor networks. Recently, a coverage problem of constructing a minimum size wireless sensor network to fully cover critical squares in a sensor field, termed CRITICAL-SQUARE-GRID COVERAGE, has received much attention. CRITICAL-SQUARE-GRID COVERAGE is shown to be NP-Complete, and an approximation algorithm, termed Steiner-tree-based critical grid covering algorithm (STBCGCA), is proposed accordingly. In STBCGCA, a sensor is selected to cover critical squares only if at least one of the critical squares is fully covered by the sensor. However, a critical square grid can be cooperatively covered by two or more sensors; that is, one sensor covers one part of the critical square, and the other sensors cover the other part of the critical square. This motivates us to propose two efficient algorithms based on STBCGCA, termed critical-grid-partitioned (CGP-STBCGCA) and reference-point-covered (RPC-STBCGCA), that select sensors that can cooperatively cover critical squares in an attempt to minimize the size of the wireless sensor network. The theoretical analysis shows that sensors deployed by CGP-STBCGCA and RPC-STBCGCA can form a connected wireless sensor network that fully covers all critical grids. In addition, a performance guarantee for CGP-STBCGCA is provided. Simulation results show that the ratio of the average number of deployed sensors in STBCGCA to that in CGP-STBCGCA and RPC-STBCGCA in about 90 % of the cases was between 1.08 and 2.52 for CRITICAL-SQUARE-GRID COVERAGE.     

An incremental deployment algorithm for wireless sensor networks using one or multiple autonomous agents

The paper studies the deployment problem of wireless sensor networks using one or multiple autonomous agents. An online incremental algorithm based on Voronoi partition is proposed to solve the problem, for which each agent deploys sensors one-at-a-time with the objective of using less number of sensors to cover an area and maintain communication connectivity. A probabilistic sensor sensing model is applied for area coverage evaluation. The shape of target area is assumed to be known by the agents, but how the environment affects the communication is unknown a priori. Therefore, the agents are desired to autonomously place every new sensor at an appropriate location based on deployed sensors to ensure connectivity and coverage specifications. Both simulations and experiments using our self-made wireless sensors are conducted to validate the algorithm.     

Barrier coverage with line-based deployed mobile sensors

Barrier coverage of a wireless sensor network is a critical issue in military and homeland security applications, aiming to detect intruders that attempt to cross the deployed region. While a range of problems related to barrier coverage have been investigated, little effort has been made to explore the effects of different sensor deployment strategies and mechanisms to improve barrier coverage of a wireless sensor network after it is deployed. In this paper we study the barrier coverage of a line-based sensor deployment strategy and explore how to exploit sensor mobility to improve barrier coverage. We first establish a tight lower bound for the existence of barrier coverage under the line-based deployment. Our results show that the barrier coverage of the line-based deployment significantly outperforms that of the Poisson model when the random offsets are relatively small compared to the sensor’s sensing range. To take advantage of the performance of line-based deployment, we further devise an efficient algorithm to relocate mobile sensors based on the deployed line so as to improve barrier coverage. The algorithm finds barrier gaps and then relocates mobile sensors to fill the gaps while at the same time balancing the energy consumption among mobile sensors. Simulation results show that the algorithms can effectively improve the barrier coverage of a wireless sensor network for a wide range of deployment parameters. Therefore, in wireless sensor network applications, the coverage goal, possible sensor deployment strategies, and sensor mobility must be carefully and jointly considered. The results obtained in this paper will provide important guidelines and insights into the deployment and performance of wireless sensor networks for barrier coverage.     

Leveraging data fusion to improve barrier coverage in wireless sensor networks

Intruder detection and border surveillance are amongst the most promising applications of wireless sensor networks. Barrier coverage formulates these problems as constructing barriers in a long-thin region to detect intruders that cross the region. Existing studies on this topic are not only based on simplistic binary sensing model but also neglect the collaboration employed in many systems. In this paper, we propose a solution which exploits the collaboration of sensors to improve the performance of barrier coverage under probabilistic sensing model. First, the network width requirement, the sensor density and the number of barriers are derived under data fusion model when sensors are randomly distributed. Then, we present an efficient algorithm to construct barriers with a small number of sensors. The theoretical comparison shows that our solution can greatly improve barrier coverage via collaboration of sensors. We also conduct extensive simulations to demonstrate the effectiveness of our solution.     

Connected sensor cover for area information coverage in wireless sensor networks

Coverage is an important issue in wireless sensor networks (WSNs) and is often used to measure how well a sensor field is monitored by the deployed sensors. If the area covered by a sensor can also be covered by some other sensors, this sensor can go into an energy‐saving sleep state without sacrificing the coverage requirement. In this paper, we study the problem of how to select active sensors with the constraints that the selected active sensors can provide complete field coverage and are completely connected. We propose to use the notion of information coverage, which is based on estimation theory to exploit the collaborative nature of WSNs, instead of using the conventional definition of coverage. Owing to the use of information coverage, a point that is not within the sensing disk of any sensor can still be considered to be covered without loss of estimation reliability. We propose a heuristic to approximately solve our problem. The basic idea is to grow a connected sensor tree to maximize the profit from the covered points of the selected sensors in each step. Simulations are used to validate the effectiveness of the proposed algorithm and the results illustrate that the number of active sensors to provide area coverage can be greatly reduced by using the notion of information coverage compared with that by using the conventional definition of coverage. Copyright © 2008 John Wiley & Sons, Ltd.     

An Energy Efficient Barrier Coverage Algorithm for Wireless Sensor Networks

Intrusion detection is one of the most important applications of wireless sensor networks. When mobile objects are entering into the boundary of a sensor field or are moving cross the sensor field, they should be detected by the scattered sensor nodes before they pierce through the field of sensor (barrier coverage). In this paper, we propose an energy efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select best node to guarantee barrier coverage, at any given time. To apply our method, we used coverage graph of deployed networks and learning automata of each node operates based on nodes that located in adjacency of current node. Our algorithm tries to select minimum number of required nodes to monitor barriers in deployed network. To investigate the efficiency of the proposed barrier coverage algorithm several computer simulation experiments are conducted. Numerical results show the superiority of the proposed method over the existing methods in term of the network lifetime and our proposed algorithm can operate very close to optimal method.     

Connectivity maintenance and coverage preservation in wireless sensor networks

In wireless sensor networks, one of the main design challenges is to save severely constrained energy resources and obtain long system lifetime. Low cost of sensors enables us to randomly deploy a large number of sensor nodes. Thus, a potential approach to solve lifetime problem arises. That is to let sensors work alternatively by identifying redundant nodes in high-density networks and assigning them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In a single wireless sensor network, sensors are performing two operations: sensing and communication. Therefore, there might exist two kinds of redundancy in the network. Most of the previous work addressed only one kind of redundancy: sensing or communication alone. Wang et al. [Intergrated Coverage and Connectivity Configuration in Wireless Sensor Networks, in: Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys 2003), Los Angeles, November 2003] and Zhang and Hou [Maintaining Sensing Coverage and Connectivity in Large Sensor Networks. Technical report UIUCDCS-R-2003-2351, June 2003] first discussed how to combine consideration of coverage and connectivity maintenance in a single activity scheduling. They provided a sufficient condition for safe scheduling integration in those fully covered networks. However, random node deployment often makes initial sensing holes inside the deployed area inevitable even in an extremely high-density network. Therefore, in this paper, we enhance their work to support general wireless sensor networks by proving another conclusion: “the communication range is twice of the sensing range” is the sufficient condition and the tight lower bound to ensure that complete coverage preservation implies connectivity among active nodes if the original network topology (consisting of all the deployed nodes) is connected. Also, we extend the result to k-degree network connectivity and k-degree coverage preservation.     

Compound event barrier coverage in wireless sensor network

In wireless sensor networks (WSN),more and more people utilize barrier coverage to monitor compound events.The data of compound event barrier coverage (CEBC) comes from different types of sensors.It will be subject to multi-constraints under complex conditions in real-world application.Aiming at the merging problem of compound event confidence,a computational model based on joint probability density was proposed.In order to solve the optimization problem of compound event barrier coverage under multiple complex constraints,an active set multiplier policy (ASMP) was proposed.The algorithm can calculate the coverage ratio efficiently and allocate the sensor resources reasonably in compound event barrier coverage.The algorithm can simplify complex problems to reduce the computational load of the network and improve the efficiency of the network.The simulation results demonstrate that the ASMP algorithm is more efficient in the allocation of sensor resources and network optimization.     

一种三角形网格空洞修复算法

无线传感器网络由大量传感器节点组成,在网络初始化时节点随机部署在目标区域中,导致某一区域未被覆盖而形成覆盖空洞.针对目标区域中存在覆盖空洞问题,设计了一种基于三角形网格的无需地理信息的空洞探测算法ATN和空洞修复算法TNR.利用ATN算法检测节点与其邻居形成的三角形网格是否被完全覆盖,TNR算法以ATN算法理论为基础,向三角形网格中添加节点使目标区域完全覆盖.理论与仿真实验分析表明,ANR算法能够探测出目标区域中所有空洞,TNR算法在部署密集的传感网络中能够快速完成空洞修复.     

Barrier coverage with wireless sensors

When a sensor network is deployed to detect objects penetrating a protected region, it is not necessary to have every point in the deployment region covered by a sensor. It is enough if the penetrating objects are detected at some point in their trajectory. If a sensor network guarantees that every penetrating object will be detected by at least k distinct sensors before it crosses the barrier of wireless sensors, we say the network provides k-barrier coverage. In this paper, we develop theoretical foundations for k-barrier coverage. We propose efficient algorithms using which one can quickly determine, after deploying the sensors, whether the deployment region is k-barrier covered. Next, we establish the optimal deployment pattern to achieve k-barrier coverage when deploying sensors deterministically. Finally, we consider barrier coverage with high probability when sensors are deployed randomly. The major challenge, when dealing with probabilistic barrier coverage, is to derive critical conditions using which one can compute the minimum number of sensors needed to ensure barrier coverage with high probability. Deriving critical conditions for k-barrier coverage is, however, still an open problem. We derive critical conditions for a weaker notion of barrier coverage, called weak k-barrier coverage.     

Impact of Interference on Coverage in Wireless Sensor Networks

The quality of surveillance is dependent on the sensing coverage of a wireless sensor network. In the present paper, we examine how interference affects the coverage of a wireless sensor network. The coverage fraction and required number of sensors for randomly deployed and well-planned deployed wireless sensor networks in the presence of interferers are computed. The required number of sensors to achieve higher level of coverage increases drastically for randomly distributed sensor nodes where the interference effect is high. In the case of well-planned distributed sensor network, required sensors increases linearly as interference effects become more pronounced. Algorithms for computing the required number of sensors to obtain the desired level of coverage in the presence of non-uniform interference is presented. The simulation results suggest that the coverage per subregion and coverage per sensor approaches towards, the improvement achieved is constant. The sensor saving ratio is independent of the level of the desired coverage provided the coverage per subregion is larger than or equal to the coverage per sensor.     

Static wireless sensor networks deployment using an improved binary PSO

A major issue in designing wireless sensor networks is the deployment problem. Indeed, many performances of the sensor network, such as coverage, are determined by the number and locations of deployed sensors. This paper reviews existing deterministic deployment strategies and devises a modified binary particle swarm optimization, which adopts a new position updating procedure for a faster convergence and exploits the abandonment concept to avoid some drawbacks such as premature convergence. The devised approach combines, in a meaningful way, the characteristics of the binary particle swarm optimization with the wireless sensor networks deployment requirements in order to devise a lightweight and efficient sensor placement algorithm. The effectiveness and efficiency of the proposed approach are evaluated through extensive simulations. The obtained results show that the proposed algorithm outperforms the state‐of‐the‐art approaches, especially in the case of preferential coverage. Copyright © 2015 John Wiley & Sons, Ltd.     

Polytype target coverage scheme for heterogeneous wireless sensor networks using linear programming

Sensing coverage is one of fundamental problems in wireless sensor networks. In this paper, we investigate the polytype target coverage problem in heterogeneous wireless sensor networks where each sensor is equipped with multiple sensing units and each type of sensing unit can sense an attribute of multiple targets. How to schedule multiple sensing units of a sensor to cover multiple targets becomes a new challenging problem. This problem is formulated as an integer linear programming problem for maximizing the network lifetime. We propose a novel energy‐efficient target coverage algorithm to solve this problem based on clustering architecture. Being aware of the coverage capability and residual energy of sensor nodes, the clusterhead node in each cluster schedules the appropriate sensing units of sensor nodes that are in the active status to cover multiple targets in an optimal way. Extensive simulations have been carried out to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

A Learning Automata Based Area Coverage Algorithm for Wireless Sensor Networks

One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.     

Enhancing Quality of Coverage for Target Coverage Problem Using Discrete Haar Wavelet

Quality of coverage is one of the fundamental issues in wireless sensor networks, particularly for the deterministic placement of sensors. One of the methods to improve the quality of coverage is to place the minimum number of sensors in the optimal position to cover the entire target. This paper proposes a discrete Haar wavelet transform for deterministic sensor placement in the target coverage problem. Dilation and translation of Haar wavelet transform are used for identifying the optimal position of sensors. Simulation results validate the performance of discrete Haar wavelet transform better than random placement in terms of optimal placement, quality of coverage and network traffic reduction.     

Coverage and connectivity aware critical target monitoring for wireless sensor networks: Novel NSGA‐II–based approach

As sensor nodes have limited sensing and transmission capability, their efficient deployment takes an important role in proper monitoring of the critical targets in various applications of wireless sensor networks (WSNs). The key issues that need to be taken care during deployment are the lesser number of deployed sensors, coverage of the targets, and connectivity between the sensor nodes. In this paper, we have proposed NSGA‐II with modified dominance to solve the node deployment problem with the aforementioned three conflicting objectives. The conventional domination method is modified for better performance of the NSGA‐II. An intelligent representation of chromosome is provided. Three conflicting objectives are derived to evaluate the chromosomes. Extensive simulation on the proposed algorithm and the statistical test, and analysis of variance (ANOVA) followed by post hoc analysis are performed.     

Scheduling sensor activity for information coverage of discrete targets in sensor networks

In this paper, we study the problem of scheduling sensor activity to cover a set of targets with known locations such that all targets can be monitored all the time and the network can operate as long as possible. A solution to this scheduling problem is to partition all sensors into some sensor covers such that each cover can monitor all targets and the covers are activated sequentially. In this paper, we propose to provide information coverage instead of the conventional sensing disk coverage for target. The notion of information coverage is based on estimation theory to exploit the collaborative nature of geographically distributed sensors. Due to the use of information coverage, a target that is not within the sensing disk of any single sensor can still be considered to be monitored (information covered) by the cooperation of more than one sensor. This change of the problem settings complicates the solutions compared to that by using a disk coverage model. We first define the target information coverage (TIC) problem and prove its NP‐completeness. We then propose a heuristic to approximately solve our problem. Simulation results show that our heuristic is better than an existing algorithm and is close to the upper bound when only the sensing disk coverage model is used. Furthermore, simulation results also show that the network lifetime can be significantly improved by using the notion of information coverage compared with that by using the conventional definition of sensing disk coverage. Copyright © 2008 John Wiley & Sons, Ltd.     

CWSC: Connected k-coverage working sets construction algorithm in wireless sensor networks

One of the most important issues for wireless sensor networks is to get a long network lifetime without affecting either communication connectivity or sensing coverage. Many sensors that are deployed randomly in a dense sensor network in a redundant way waste a lot of energy. One effective way to save energy is to let only a subset of sensors work at any given time. In this paper, we mainly consider such a problem. Selecting the minimum number of connected sensor nodes that can provide k-coverage (k ≥ 1), i.e., selecting a subset S of working sensors, such that almost every point in the sensing region can be covered by at least k sensors and the sensors in S can form a connected communication subgraph. We propose a connected k-coverage working sets construction algorithm (CWSC) based on Euclidean distance to k-cover the sensing region while minimizing the number of working sensors. CWSC can produce different coverage degrees according to different applications, which can enhance the flexibility of the sensor network. Simulation results show that the proposed algorithm, which can conserve energy and prolong the lifetime of the sensor network, is better than the previous algorithms.     

On Connected Multiple Point Coverage in Wireless Sensor Networks

We consider a wireless sensor network consisting of a set of sensors deployed randomly. A point in the monitored area is covered if it is within the sensing range of a sensor. In some applications, when the network is sufficiently dense, area coverage can be approximated by guaranteeing point coverage. In this case, all the points of wireless devices could be used to represent the whole area, and the working sensors are supposed to cover all the sensors. Many applications related to security and reliability require guaranteed k-coverage of the area at all times. In this paper, we formalize the k-(Connected) Coverage Set (k-CCS/k-CS) problems, develop a linear programming algorithm, and design two non-global solutions for them. Some theoretical analysis is also provided followed by simulation results.     

A Learning Automata-Based Solution to the Priority-Based Target Coverage Problem in Directional Sensor Networks

In recent years, directional sensor networks composed of directional sensors have attracted a great deal of attention due to their extensive applications. The main difficulties associated with directional sensors are their limited battery power and restricted sensing angle. Moreover, each target may have a different coverage quality requirement that can make the problem even more complicated. Therefore, satisfying the coverage quality requirement of all the targets in a specific area and maximizing the network lifetime, known as priority-based target coverage problem, has remained a challenge. As sensors are often densely deployed, organizing the sensor directions into several cover sets and then activating these cover sets successively is a promising solution to this problem. In this paper, we propose a learning automata-based algorithm to organize the directional sensors into several cover sets in such a way that each cover set can satisfy coverage quality requirement of all the targets. In order to verify the performance of the proposed algorithm, several simulations were conducted. The obtained results showed that the proposed algorithm was successful in extending the network lifetime.