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.     

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.     

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.     

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.     

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.     

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.     

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.     

Hybrid Wireless Sensors Deployment Scheme with Connectivity and Coverage Maintaining in Wireless Sensor Networks

With the rapid growth of the internet of things (IoT), an impressive number of IoT’s application based on wireless sensor networks (WSNs) has been deployed in various domain. Due to its wide ranged applications, WSNs that have the capability to monitor a given sensing field, became the most used platform of IoT. Therefore, coverage becomes one of the most important challenge of WSNs. The search for better positions to assign to the sensors in order to control each point of an area of interest and the collection of data from sensors are major concerns in WSNs. This work addresses these problems by providing a hybrid approach that ensures sensors deployment on a grid for targets coverage while taking into account connectivity. The proposed sequential hybrid approach is based on three algorithms. The first places the sensors so as to all targets are covered. The second removes redundancies from the placement algorithm to reduce the number of sensors deployed. The third one, based on the genetic algorithm, aims to generate a connected graph which provide a minimal path that links deployed sensors and sink. Simulations and a comparative study were carried out to prove the relevance of the proposed method.

     

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.     

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.     

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.     

Effective sensor deployment based on field information coverage in precision agriculture

Coverage is an importance issue in wireless sensor networks. In this work, we first propose a novel notion of information coverage, which refers to the coverage efficiency of field information covered by deployed sensor nodes. On the basis of information coverage, we consider an optimization problem of how to partition the given field into multiple parcels and to deploy sensor nodes in some selected parcels such that the field information covered by the deployed sensor nodes meets the requirement. First, we develop two effective polynomial‐time algorithms to determine the deployed locations of source nodes for information 1‐coverage and q‐coverage of the field, respectively, without consideration of communication, where information q‐coverage implies that the field information in terms of information point is covered by at least q source nodes. Also, we prove the upper bound in the theoretical for the approximate solution derived by our proposed method. Second, another polynomial‐time algorithm is presented for deriving the deployed locations of relay nodes. In the theoretical, this proposed algorithm can achieve the minimized number of relay nodes. Further, the related information 1‐coverage algorithms are applied in our wireless sensor network‐based automatic irrigation project in precision agriculture. Experimental results show the major trade‐offs of impact factors in sensor deployment and significant performance improvements achieved by our proposed method. Copyright © 2013 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.     

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.     

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.     

Maximization of WSN Life Using Hybrid Evolutionary Programming

Of all the challenges faced by wireless sensor networks (WSN), extending the lifetime of the network has received the most attention from researchers. This issue is critically important, especially when sensors are deployed to areas where it is practically impossible to charge their batteries, which are their only sources of power. Besides the development and deployment of ultra low-power devices, one effective computational approach is to partition the collection of sensors into several disjoint covers, so that each cover includes all targets, and then, activate the sensors of each cover one at a time.. This maximizes the possible disjoint covers with an available number of sensors and can be treated as a set-K cover problem, which has been proven to be NP-complete. Evolutionary programming is a very powerful algorithm that uses mutation as the primary operator for evolution. Hence, mutation defines the quality and time consumed in the final solution computation. We have applied the self adaptive mutation strategy based on hybridization of Gaussian and Cauchy distributions to develop to develop a faster and better solution. One of the limitations associated with the evolutionary process is that it requires definition of the redundancy covers, and therefore, it is difficult to obtain the upper bound of a cover. To solve this problem, a redundancy removal operator that forces the evolution process to find a solution without redundancy is introduced. Through simulations, it is shown that the proposed method maximizes the lifespan of WSNs.     

一种无线传感器网络全连通群的休眠调度算法

该文针对无线传感器网络的覆盖性和连通性问题,在假设传感器节点地理位置信息已知的条件下,设计了一种包含全连通群的建立和维护以及群内节点休眠调度的全新算法。该算法采用保证群内节点彼此一跳可达的全连通群分群方法,以及分布式节能的休眠调度策略,最大程度上减少传感器网络的能量消耗,延长了网络寿命。仿真结果表明:该算法能较好地保证无线传感器网络的覆盖性和连通性,且能耗较低。     

WSN Deployment and Localization Using a Mobile Agent

To address the problem the sensors were typically deployed in fixed positions, but the robots can be used to calibrate, deploy and maintain the surrounding wireless sensor network (WSN) in disaster relief applications, a novel framework was proposed to obtain a wide coverage of the unknown environment by the sensors, which can help the robot during the disaster recovery activities, for the concurrent deployment and localization of a WSN by means of a mobile robot. During the mission, the robot explored an unknown environment, and was equipped with both proprioceptive sensors, range finders and wireless antennas. Moreover, the robot carried a set of nodes, and it can deploy them while exploring the unknown environment. Variou experimental results showd the proposed algorithm can outperform trilateration method in unknown environment exploration and network coverage problems.     

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.     

Zigbee-based intra-car wireless sensor networks: a case study

There is a growing interest in eliminating the wires connecting sensors to the microprocessors in cars due to an increasing number of sensors deployed in modern cars. One option for implementing an intra-car wireless sensor network is the use of ZigBee technology. In this article we report the results of a ZigBee-based case study conducted in a vehicle. Overall, the results of the experiments and measurements show that ZigBee is a viable and promising technology for implementing an intra-car wireless sensor network.