Leveraging data fusion to improve barrier coverage in wireless sensor networks

Solving energy issues for sweep coverage in wireless sensor networks

Many wireless sensor network applications must resolve the inherent conflict between energy efficient and nodes of coverage in wireless sensor networks to achieve desired quality of service (QoS). The wireless sensor networks (WSNs) have been the main focus in the past few years, and it is becoming a special issue. We study the problem of controlling the node locations and energy within ad-hoc sensor networks. Network survivability is a very important measure for deciding the efficiency of network protocols. It includes a measure of the network lifetime as well as the quality of service. In fact, these dynamics are very important in evaluating networks. The wireless network applications can be found in battlefield communication, homeland security, pollution sensing and traffic monitoring. As such, there has been an increasing need for defining and developing simulation frameworks for carrying out high-fidelity WSN simulation. In this cause, we present a simulation by including factors such as energy-consumption, Bandwidth, nodes, and coverage and provides an object-oriented definition of target, sensor and sink nodes, sensor and wireless communication nodes, and physical radio such as relating of nodes, mobility model and power model both energy-producing and energy-consuming components, the simulation was developed using C#.

SummaryAs 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.

Forest fire detection system using barrier coverage in wireless sensor networks

Although most of the studies on coverage and connectivity in wireless sensor networks (WSNs) considered two-dimensional (2D) settings, such networks can in reality be accurately modeled in a three-dimensional (3D) space. The concepts of continuum percolation theory best fit the problem of connectivity in WSNs to find out whether the network provides long-distance multihop communication. In this paper, we focus on percolation in coverage and connectivity in 3D WSNs. We say that the network exhibits a coverage percolation (respectively, connectivity percolation) when a giant covered region (respectively, giant connected component) almost surely spans the entire network for the first time. Because of the dependency between coverage and connectivity, the problem is not only a continuum percolation problem but also an integrated continuum percolation problem. Thus, we propose an integrated-concentric-sphere model to address coverage and connectivity in 3D WSNs in an integrated way. First, we compute the critical density lambdaC con above which coverage percolation in 3D WSNs will almost surely occur. Second, we compute the critical density lambdac con above which connectivity percolation in 3D WSNs will almost surely occur. Third, we compute the critical density lambdac cov-con above which both coverage and connectivity percolation in 3D WSNs will almost surely occur. For each of these three problems, we also compute their corresponding critical network degree. Our results can be helpful in the design of energy-efficient topology control protocols for 3D WSNs in terms of coverage and connectivity.

In this paper, we study the barrier lifetime maximisation (BLM) and barrier gap mending (BGM) problem in a sensor network, where sensors can choose to use different sensing ranges when constructing a sensor barrier. In the BLM problem, for small–scale sensor networks, we propose a linear programming optimisation method. For large–scale sensor networks, we propose two distributed heuristics to select a next node to construct barriers. Simulation results show that compared with the randomised one, the greedy scheme can achieve longer lifetime. In the BGM problem, we study how to efficiently mend barrier gaps in a hybrid sensor network consisting of both stationary and mobile sensors. We propose two gap mending schemes: the min–max scheme to minimise the maximal energy consumption to move sensors, and the max–lifetime scheme to maximise the lifetime of barrier coverage after mending all gaps. Simulation results show that the min–max scheme can achieve a lower maximal moving distance and the max–lifetime scheme can efficiently extend the barrier lifetime.

Minimizing maximum movement of sensors for line barrier coverage in the plane

Barrier coverage, as one of the most important applications of wireless sensor network (WSNs), is to provide coverage for the boundary of a target region. We study the barrier coverage problem by using a set of n sensors with adjustable coverage radii deployed along a line interval or circle. Our goal is to determine a range assignment $$\mathbf {R}=({r_{1}},{r_{2}}, \ldots , {r_{n}})$$ of sensors such that the line interval or circle is fully covered and its total cost $$C(\mathbf {R})=\sum _{i=1}^n {r_{i}}^\alpha $$ is minimized. For the line interval case, we formulate the barrier coverage problem of line-based offsets deployment, and present two approximation algorithms to solve it. One is an approximation algorithm of ratio 4 / 3 runs in $$O(n^{2})$$ time, while the other is a fully polynomial time approximation scheme (FPTAS) of computational complexity $$O(\frac{n^{2}}{\epsilon })$$ . For the circle case, we optimally solve it when $$\alpha = 1$$ and present a $$2(\frac{\pi }{2})^\alpha $$ -approximation algorithm when $$\alpha > 1$$ . Besides, we propose an integer linear programming (ILP) to minimize the total cost of the barrier coverage problem such that each point of the line interval is covered by at least k sensors.

Aiming at the uneven random coverage distribution of wireless sensor networks sensor nodes, the sparrow search algorithm (SSA) is used to optimize the node coverage of wireless sensor networks. To improve the global search capability of SSA, the algorithm is improved and applied to the wireless sensor networks of bridge monitoring. For enhancing the coverage of the wireless sensor networks, this article uses two improved methods. One is to use the good point set theory to make the initial population evenly distributed; another is to introduce a weight factor to speed up its convergence. The experiments have proved the reliability and rationality of the algorithm. The improved algorithm is superior to other meta-heuristic algorithms and provides a new idea for optimizing bridge monitoring wireless sensor networks coverage.

Achieving optimal field coverage is a significant challenge in various sensor network applications. In some specific situations, the sensor field (target) may have coverage gaps due to the random deployment of sensors; hence, the optimized level of target coverage cannot be obtained. Given a set of sensors in the plane, the target coverage problem is to separate the sensor into different groups and provide them specific time intervals, so that the coverage lifetime can be maximized. Here, the constraint is that the network should be connected. Presently, target coverage problem is widely studied due to its lot of practical application in Wireless Sensor Network (WSN). This paper focuses on target coverage problem along with the minimum energy usage of the network so that the lifetime of the whole network can be increased. Since constructing a minimum connected target coverage problem is known to be NP-Complete, so several heuristics, as well as approximation algorithms, have been proposed. Here, we propose a heuristic for connected target coverage problem in WSN. We compare the performance of our heuristic with the existing heuristic, which states that our algorithm performs better than the existing algorithm for connected target coverage problem. Again, we have implemented the 2-connected target coverage properties for the network which provide fault tolerance as well as robustness to the network. So, we propose one algorithm which gives the target coverage along with 2-connectivity.

Intruders detection is one of the very important applications in Wireless Sensor Networks (WSNs). Sometimes detecting intruders is not sufficient; distinguishing whether an intruder is legal or illegal is necessary. Since k-barrier coverage is widely used in detecting intruders, a barrier construction algorithm is needed, which can not only detect an intruder but also judge an illegal intruder. An intruder is defined as illegal if and only if it crosses straightly through the monitored region from the special side to another side. On the contrary, it is a legal intruder. To detect an intruder and distinguish whether the intruder is legal or illegal, a strong k-barrier coverage algorithm is proposed. The strong k-barrier coverage is a local barrier constructing algorithm and can detect any intruder crossing the k-barrier with a full probability. The strong k-barrier coverage detects all intruders penetrating the annular region for k times. What is more, the proposed strong k-barrier algorithm can provide a reliable judgement on whether an intruder is legal or illegal, and the constructed k-barrier coverage is different from the traditional one-way barrier coverage using binary barriers that are intersected but not overlapped. Some simulation tests show that the proposed algorithm can construct strong k-barrier coverage very well.

Barrier coverage, an important research area with respect to camera sensor networks, consists of a number of camera sensors to detect intruders that pass through the barrier area. Existing works on barrier coverage such as local face-view barrier coverage and full-view barrier coverage typically assume that each intruder is considered as a point. However, the crucial feature (e.g., size) of the intruder should be taken into account in the real-world applications. In this paper, we propose a realistic resolution criterion based on a three-dimensional (3D) sensing model of a camera sensor for capturing the intruder’s face. Based on the new resolution criterion, we study the barrier coverage of a feasible deployment strategy in camera sensor networks. Performance results demonstrate that our barrier coverage with more practical considerations is capable of providing a desirable surveillance level. Moreover, compared with local face-view barrier coverage and full-view barrier coverage, our barrier coverage is more reasonable and closer to reality. To the best of our knowledge, our work is the first to propose barrier coverage for 3D camera sensor networks.

Energy-efficiency, Coverage-quality, and Faulttolerance Issues (ECFI) are the three primary and crucial quality assurance standards in a Wireless Sensor Network (WSN). These three standards ensure the network design of a self-configurable and sustainable WSN. First, energy-efficiency mechanisms ensure the prolonging of the overall network lifetime. Second, the coverage quality means how well a region of interest (RoI) is covered. Third, fault-tolerance refers to the ability of a network to continue operating without interruption when one or more components fail. With rapid innovations and developments in Wireless Sensor Network (WSN) applications, many researchers' most sustainable objective is to provide an adeptly operational sensor network. A broad range of applications of WSNs can be seen in the fields of academic, military, industry, medical, and daily needs worldwide. This analysis and survey article's main objective is to highlight different promising challenges on various fundamental characteristics, designing limitations, and their impacts on applications of WSNs. The authors also highlight the energy depletion and energy-provision issues with a concise survey of coverage and fault-related issues in WSNs. The authors finally summarized with discussion and analysis of the overall impacts of ECFI and other parameters on WSNs with simulator availability and future research direction.

Wireless Sensor Networks (WSNs) can be used effectively to form barriers that monitor strips of land for applicaions such as homeland security and critical infrastructure protection. Sensors are highly constrained in their energy usage, so when they harvest energy, it is neccessary that this energy be used effectively. When WSNs are used as barriers, they are typically deployed for extended period of time, so it is of great importance that the network has a long lifetime. The lifetime problem for WSNs has been studied extensively. In this paper we give an exact solution to the problem of achieving maximum lifetime for the application of k-barrier coverage in energy harvesting wireless sensor networks.

Minimizing the energy consumption of battery-powered sensors is an essential consideration in sensor network applications like coverage, and sleep/wake scheduling mechanism has been proved to an efficient approach to handling this issue. Nevertheless, the frequent switching between states, during scheduling, leads also to significant energy consumption. In this article, a coverage-guaranteed distributed sleep/wake scheduling scheme is presented with the purpose of prolonging network lifetime while guaranteeing network coverage. Our scheme mitigates scheduling process to be more stable by avoiding useless transitions between states without affecting the coverage level required by the application. The simulation results illustrate that out scheme outperforms some other existed algorithms in terms of coverage guarantee, energy conservation and stability.