Expected k-coverage in wireless sensor networks

We are concerned with wireless sensor networks where n sensors are independently and uniformly distributed at random in a finite plane. Events that are within a fixed distance from some sensor are assumed to be detectable and the sensor is said to cover that point. In this paper, we have formulated an exact mathematical expression for the expected area that can be covered by at least k out of n sensors. Our results are important in predicting the degree of coverage a sensor network may provide and in determining related parameters (sensory range, number of sensors, etc.) for a desired level of coverage. We demonstrate the utility of our results by presenting a node scheduling scheme that conserves energy while retaining network coverage. Additional simulation results have confirmed the accuracy of our analysis.     

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.     

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 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.     

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.     

Optimal barrier coverage for critical area surveillance using wireless sensor networks

Intrusion detection using barrier coverage is one of many applications existed in wireless sensor networks. The main purpose of using barrier coverage is to monitor the borders of a specific area against the intruders that are trying to penetrate this critical area by ensuring the total coverage with a low cost and extending the lifetime of the network, many solutions have been proposed in the literature in order to solve the coverage problem in wireless sensor networks, which became a vital field of research. In this paper, we present a new technique based on geometric mathematical models, in order to guarantee the total coverage of our deployed barriers with a minimum possible number of sensors. The idea is to calculate the number of sensors adequate to cover a barrier before deployment. We then formulate the problem to minimize the number of sensors to be deployed to properly cover a barrier; the calculation makes it possible to solve this problem in polynomial using our own heuristic. Additionally, we propose a new mechanism for ensuring a fault‐tolerant network by detecting the faulty sensors and select other suited sensors to close the existing gaps inside the barriers and detecting the sensors whose energy is low before the failure. The obtained simulation results prove the effectiveness of the proposed algorithms.     

On determining cluster size of randomly deployed heterogeneous WSNs

Clustering is an efficient method to solve scalability problems and energy consumption challenges. For this reason it is widely exploited in Wireless Sensor Network (WSN) applications. It is very critical to determine the number of required clusterheads and thus the overall cost of WSNs while satisfying the desired level of coverage. Our objective is to study cluster size, i.e., how much a clusterhead together with sensors can cover a region when all the devices in a WSN are deployed randomly. Therefore, it is possible to compute the required number of nodes of each type for given network parameters.     

Prediction-Based Object Tracking in Visual Sensor Networks

This paper is to investigate the mobile object tracking in visual sensor networks. When visual sensors equipped with cameras are randomly deployed in a monitoring environment, many sensors are involved in covering the same mobile object. In a visual sensor network, images of the object may be captured by different sensors in different orientations simultaneously, and the captured images are then sent back to a base station or server. However, achieving full coverage for a set of selected characteristic points of an object invariably involves a great deal of redundant image data consuming the transmission energy for a visual sensor network. A novel approach is proposed to overcome this problem. The minimal number of sensors required for set coverage can be determined by predicting the direction and speed of the mobile object. Such sets are capable of covering the maximal number of characteristic points of view related to the mobile object at one time. The simulation results show that this approach reduces transmission cost while preserving the maximal coverage range of mobile objects.     

Distributed Energy Efficient Tracking in Hybrid wireless sensor network (DEETH)

In wireless sensor network (WSN), it is a complex task to track the target when it is moving randomly in an unknown environment. It also becomes difficult to cover a complete searching area because of the limited searching range and energy of sensor nodes as they are few in number. The author proposes a distributed energy efficient tracking in a hybrid WSN (DEETH) to track a randomly moving target in an unknown searching. Hybrid WSN that is proposed has both static sensor nodes (SSNs) and mobile sensor nodes (MSNs), which are deployed in the searching area. The MSNs move collectively using particle swarm techniques to search a target. The SSNs are deployed for tracking the presence of a target and giving this information to the base station. As per the information given by SSN, MSNs travel to the target and track it. Simulation results prove that proposed technique successfully tracks the target using less number of nodes and also less amount of energy.     

Distributed Optimal Self-Organization in Ad Hoc Wireless Sensor Networks

This work is motivated by the idea of using randomly deployed wireless networks of miniature smart sensors to serve as distributed instrumentation. In such applications, often the objective of the sensor network is to repeatedly compute and, if required, deliver to an observer some result based on the values measured at the sensors. We argue that in such applications it is important for the sensors to self-organize in a way that optimizes network throughput. We identify and discuss two main problems of optimal self-organization: (1) building an optimal topology, and (2) tuning network access parameters, such as the transmission attempt rate. We consider a simple random access model for sensor networks and formulate these problems as optimization problems. We then present centralized as well as distributed algorithms for solving them. Results show that the performance improvement is substantial and implementation of such optimal self-organization techniques may be worth the additional complexity.     

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.     

Coverage by randomly deployed wireless sensor networks

One of the main applications of wireless sensor networks is to provide proper coverage of their deployment regions. A wireless sensor network k-covers its deployment region if every point in its deployment region is within the coverage ranges of at least k sensors. In this paper, we assume that the sensors are deployed as either a Poisson point process or a uniform point process in a square or disk region, and study how the probability of the k-coverage changes with the sensing radius or the number of sensors. Our results take the complicated boundary effect into account, rather than avoiding it by assuming the toroidal metric as done in the literature.     

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.     

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.     

基于冗余节点休眠和分阶段唤醒策略的传感器网络三维覆盖控制方法

该文针对无线传感器网络中节点能量有限且密集布点时存在大量冗余节点的情况,提出了基于冗余节点休眠和分阶段唤醒策略的无线传感器网络3维覆盖控制方法。在3维待监测区域中随机配置大量传感器节点,达到高密度分布,使冗余节点处于休眠状态,等待活跃节点能量耗尽之后,分阶段唤醒休眠节点,直至整个传感器网络中所有节点的能量都耗尽为止。仿真结果表明,该方法提高了传感器网络的网络性能,且对相同的传感器节点数,分阶段唤醒策略优于不分阶段的唤醒策略,先使冗余节点休眠之后再唤醒方法的网络性能高于直接唤醒方法的网络性能。     

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.     

A distributed lightweight Redundancy aware Topology Control Protocol for wireless sensor networks

WSN consists of a large number of sensor nodes randomly deployed, and, in many cases, it is impossible to replace sensors when a node failure occurs. Thus, applications tend to deploy more nodes than necessary to cope with possible node failures and to increase the network lifetime, which leads to create some sensing and communication redundancy. However, sensors in the same region, may collect and forward the same information, which will waste more energy. In this paper, we propose a distributed Lightweight Redundancy aware Topology Control Protocol (LRTCP) for wireless sensor networks. It exploits the sensor redundancy in the same region by dividing the network into groups so that a connected backbone can be maintained by keeping a minimum of working nodes and turning off the redundant ones. LRTCP identifies equivalent nodes in terms of communication based on their redundancy degrees with respect of some eligibility rules. Simulation results indicate that, compared with existing distributed topology control algorithms, LRTCP improves network capacity and energy efficiency.     

An Integrated Approach to Sensor Role Selection

Many sensor network applications require consistent coverage of the region in which they are deployed over the course of the network lifetime. However, because sensor networks may be deployed randomly, node distribution and data redundancy in some regions of the network may be lower than in others. The sensors in the sparsest regions should be considered more critical to the sensor network application since their removal would likely result in unmonitored regions in the environment. For this reason, sensors in the more densely deployed regions should be considered more favorable as candidates to route the traffic of other nodes in the network. In this work, we propose several coverage-aware routing costs that allow traffic to be routed around the sparsely deployed regions so that the coverage of the environment can remain high for a long lifetime. We also propose an integrated route discovery and sensor selection protocol called DAPR that further lengthens network lifetime by jointly selecting routers and active sensors, again with the goal of minimizing the use of sensors in sparsely covered areas. Simulation results show the effectiveness of our approach in extending network lifetime nearly to the extent that can be reached using a centralized approach based on global network knowledge.     

Design of Probability Density Function Targeting Energy Efficient Network for Coalition Based WSNs

Energy consumption is one of the important issues in wireless sensor network that rely on non chargeable batteries for power. Also, the sensor network has to maintain a desired sensing coverage area along with periodically sending of the sensed data to the base station. Therefore, coverage and the lifetime are the two important issues that need to be addressed. Effective deployment of wireless sensors is a major concern as the coverage and lifetime of any wireless sensor network depends on it. In this paper, we propose the design of a Probability Density Function (PDF) targeting the desired coverage, and energy efficient node deployment scheme. The suitability of the proposed PDF based node distribution to model the network architecture considered in this work has been analyzed. The PDF divides the deployment area into concentric coronas and provides a probability of occurrence of a node within any corona. Further, the performance of the proposed PDF is evaluated in terms of the coverage, the number of transmissions of packets and the lifetime of the network. The scheme is compared with the existing node deployment schemes based on various distributions. The percentage gain of the proposed PDF based node deployment is 32\(\%\) more than that when compared with the existing schemes. Thus, the simulation results obtained confirm the schemes superiority over the other existing schemes.     

Distributed Sensor Networks Deployment Using Fuzzy Logic Systems

The effectiveness of distributed wireless sensor networks highly depends on the sensor deployment scheme. Given a finite number of sensors, optimizing the sensor deployment will provide sufficient sensor coverage and ameliorate the quality of communications. In this paper, we apply fuzzy logic systems to optimize the sensor placement after an initial random deployment. We use the outage probability due to co-channel interference to evaluate the communication quality. Fenton–Wilkinson method is applied to approximate the sum of log-normal random variables. Our algorithm is compared against the existing distributed self-spreading algorithm. Simulation results show that our approach achieves faster and stabler deployment and maximizes the sensor coverage with less energy consumption. Outage probability, as a measure of communication quality gets effectively decreased in our algorithm but it was not taken into consideration in the distributed self-spreading algorithm.