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.     

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.     

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.     

Maximal coverage hybrid search algorithm for deployment in wireless sensor networks

A vital design aspect in the setting up of a wireless sensor network is the deployment of sensors. One of the key metrics of the quality of deployment is the coverage as it reflects the monitoring capability of the network. Random deployment is a sub-optimal method as it causes unbalanced deployment and requires sensors in excess of the planned deployment to achieve the same level of coverage. To achieve maximum coverage with a limited number of sensors, planned deployment is a preferred choice. Maximizing the coverage of the region of interest with a given number and type of sensors is an optimization problem. A novel maximal coverage hybrid search algorithm (MCHSA) is proposed in this paper to solve this problem. The MCHSA is a hybrid search algorithm that achieves the balance between exploration and exploitation by applying the particle swarm optimization as a global search technique and using the Hooke–Jeeves pattern search method to improve the local search. The algorithm starts with a good initial population. The proposed MCHSA has low computational complexity and fast convergence. The performance of the MCHSA is analyzed by performing a comparison with the existing algorithms in the literature, in terms of coverage achieved and number of fitness function evaluations. The paper also discusses the tuning of parameters of the proposed algorithm.

     

Adaptive cost efficient deployment strategy for homogeneous wireless camera sensors

Availability of low cost low power camera sensors is likely to make possible applications that may otherwise have been infeasible. In this paper we investigate a cost efficient camera sensor deployment strategy based on random deployment of homogeneous sensors to monitor and/or surveillance a region of interest. We assume that there are costs associated with the sensors as well as with the deployments and our goal is to minimize the total cost while satisfying the desired coverage requirement. We consider two cases which assume the sensing field is obstacle free or with obstacles, and we develop analytical methods to derive the expected coverage of a single sensor as well as the joint coverage for a given number of homogenous camera sensors. Following this we propose an adaptive sensor deployment strategy, which deploys different number of sensors in each iteration, based on our analytical method. We then evaluate the expected cost of our deployment strategy by deriving expressions for the number of deployments and the number of sensors deployed during each deployment as a function of the probability distributions of joint coverage by sensors. We carry out simulation studies to validate the analytical results. Simulation studies are also used to demonstrate that our deployment strategy leads to near optimal values of sensors and deployments and hence achieves the overall low cost.     

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.     

Deterministic deployment based on information coverage in wireless sensor networks

In this study, a deterministic deployment problem in wireless sensor networks is examined. On the basis of information coverage, we study equilateral triangle and square deployment strategies, and we provide the maximum distance between sensors in order to reach the required detection probability for any point in the monitoring field. First, we provide a model of the signal attenuation. On the basis of the detected signal from the K sensors, the best linear and unbiased estimation is used to estimate the signal parameter with the corresponding error. For the equilateral triangle deployment, the maximum distance between sensors is computed and provided when the received signal data from two or three sensors is used. Similarly, we have computed and supplied the maximum distance between sensors in the square deployment. Simulations are performed to show the relationship between the number of sensors and the detection probability. The simulation results show that it is not a good choice to improve the detection probability with a larger number of sensors.Copyright © 2012 John Wiley & Sons, Ltd.     

Coverage and Connectivity-Based 3D Wireless Sensor Deployment Optimization

The wireless sensor network technology of Internet of Things (IoT) senses, collects and processes the data from its interconnected intelligent sensors to the base station. These sensors help the IoT to understand the environmental change and respond towards it. Thus sensor placement is a crucial device of IoT for efficient coverage and connectivity in the network. Many existing works focus on optimal sensor placement for two dimensional terrain but in various real-time applications sensors are often deployed over three-dimensional ambience. Therefore, this paper proposes a vertex coloring based sensor deployment algorithm for 3D terrain to determine the sensor requirement and its optimal spot and to obtain 100% target coverage. Further, the quality of the connectivity of sensors in the network is determined using Breadth first search algorithm. The results obtained from the proposed algorithm reveal that it provides efficient coverage and connectivity when compared with the existing methods.

     

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.     

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.     

A Pre-Determined Nodes Deployment Strategy of Two-Tiered Wireless Sensor Networks Based on Minimizing Cost

Nodes deployment is a fundamental factor in determining the connectivity, coverage, lifetime and cost of wireless sensor networks. In this paper, a two-tiered wireless sensor networks consisting of sensor clusters and a base station is considered. Within a sensor cluster, there are many sensor nodes and a relay node. We focus on the deployment strategy for sensor nodes and relay nodes to minimize cost under some constraints. Several means are used. The regular hexagonal cell architecture is employed to build networks. Based on the analysis of energy consumption of sensors and cost of network, an integer programming model is presented to minimize the cost. By the model, number of layers of sensor cluster is determined. In order to balance the energy consumption of sensors on the identical layer, a uniform load routing algorithm is used. The numerical analysis and simulation results show that the waste of energy and cost of wireless sensor networks can be effectively reduced by using the strategy.     

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.     

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.     

Distributed collaborative camera actuation for redundant data elimination in wireless multimedia sensor networks

Given the high cost of processing and communicating the multimedia data in wireless multimedia sensor networks (WMSNs), it is important to reduce possible data redundancy. Therefore, camera sensors should only be actuated when an event is detected within their vicinity. In the meantime, the coverage of the event should not be compromised. In this paper, we propose a low-cost distributed actuation scheme which strives to turn on the least number of cameras to avoid possible redundancy in the multimedia data while still providing the necessary event coverage. The basic idea of this scheme is the collaboration of camera sensors that have heard from scalar sensors about an occurring event to minimize the possible coverage overlaps. This is done by either counting the number of scalar sensors or determining the event boundaries with scalar sensors. Through simulation, we show how the distributed scheme performs in terms of coverage under several centralized and random deployment schemes. We also compare the performance with the case when all the cameras in the vicinity are actuated and when blockages in the region exist.     

Surveillance Wireless Sensor Networks: Deployment Quality Analysis

Surveillance wireless sensor networks are deployed at perimeter or border locations to detect unauthorized intrusions. For deterministic deployment of sensors, the quality of deployment can be determined sufficiently by analysis in advance of deployment. However, when random deployment is required, determining the deployment quality becomes challenging. To assess the quality of sensor deployment, appropriate measures can be employed that reveal the weaknesses in the coverage of SWSNs with respect to the success ratio and time for detecting intruders. In this article, probabilistic sensor models are adopted, and the quality of deployment issue is surveyed and analyzed in terms of novel measures. Furthermore, since the presence of obstacles in the surveillance terrain has a negative impact on previously proposed deployment strategies and analysis techniques, we argue in favor of utilizing image segmentation algorithms by imitating the sensing area as a grayscale image referred to as the iso-sensing graph. Finally, the effect of sensor count on detection ratio and time to detect the target is analyzed through OMNeT++ simulation of an SWSN in a border surveillance scenario.     

Connectivity-Preserved and Force-Based Deployment Scheme for Mobile Sensor Network

This paper considers the self-deployment of wireless sensor networks. Conventional deployment problem usually focuses on enhancing the coverage, while the conditions for network connectivity are largely simplified. We present a deployment scheme to enhance the coverage while keeping the network connected at each step of the deployment. Our scheme contains two parts. The coverage improvement part proposes an improved force-based mechanism. A limit is provided to determine the sensors which should attractive each other, so the wasted overlap and communication resource can be reduced. The connectivity preservation part provides constrains for the movement distance of each sensor, in order to take account of both connectivity and coverage enhancement. Some simulation results are presented to show the connectivity preservation and coverage maximization properties of our mechanism.     

Efficient Placement and Dispatch of Sensors in a Wireless Sensor Network

Sensor deployment is a critical issue because it affects the cost and detection capability of a wireless sensor network. In this work, we consider two related deployment problems: sensor placement and sensor dispatch. The former asks how to place the least number of sensors in a field to achieve sensing coverage and network connectivity, and the latter asks how to determine from a set of mobile sensors a subset of sensors to be moved to an area of interest with certain objective functions such that the coverage and connectivity properties are satisfied. This work is targeted toward planned deployment. Our solution to the placement problem allows an arbitrary-shaped sensing field possibly with arbitrary-shaped obstacles and an arbitrary relationship between the communication distance and sensing distance of sensors and, thus, significantly relaxes the limitations of existing results. Our solutions to the dispatch problem include a centralized one and a distributed one. The centralized one is based on adopting the former placement results and converting the problem to the maximum-weight maximum-matching problem with the objective of minimizing the total energy consumption to move sensors or maximizing the average remaining energy of sensors after movement. Designed in a similar way, the distributed one allows sensors to determine their moving directions in an autonomous manner.     

Chain-based barrier coverage in WSNs: toward identifying and repairing weak zones

Barrier coverage constructs a sensing barrier for detecting intruders crossing a belt region. Recent studies mostly focus on efficient algorithms to guarantee barrier coverage, with little consideration on the collaboration between individual nodes. Observing that in many situations, sensors naturally fall into several clusters (or components), for example when the sensors are deployed uniformly at random with a relatively low density, or when random sensors go down as a result of energy exhaustion, we propose to use chain as a basic scheduling unit for sensing and communication. A chain is a set of sensors whose sensing areas overlap with each other, and it can be extracted from a cluster. We present a distributed algorithm, named BARRIER, to provide barrier coverage with a low communication overhead for the wireless sensor networks (WSNs). The algorithm is able to detect weak zones that are often found in an initial deployment of a WSN, and repair them by adding an appropriate number of sensors. Theoretic analysis and simulations show that, compared with a representative previous algorithm, BARRIER significantly reduces the communication overhead and reparation cost in terms of number of sensors.     

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.

     

Guest Editorial: Geometry and Random Graphs for the Analysis and Design of Wireless Networks

Confronted with the difficulties of analyzing large wireless networks such as cellular, ad hoc, and sensor networks, researchers have realized that the mathematical techniques used need to incorporate and explicitly model the network geometry, which is crucial to their connectivity, capacity, and reliability. As a consequence, stochastic geometry and the theory of random geometric graphs have emerged as essential tools in the analysis and design of large wireless systems. In the last decade, these techniques have led to important results and insights into the fundamental limits of wireless networks and the coverage of sensor networks. Specifically, point process theory and percolation theory were instrumental in recent breakthroughs. The deployment of wireless networks will continually become more dense and ubiquitous as time progresses, and hence become increasingly interference-limited. Therefore, the importance of the geometry of the transmitters and receivers in the network will increase further and understanding its effects will be crucial to the design of future wireless communication systems.