Target Coverage in Wireless Sensor Networks

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.

Node placement for connected target coverage in wireless sensor networks with dynamic sinks

One of the major issues in a wireless sensor network (WSN) is how to cover an interested area. In this paper, we consider the area coverage problem for variable sensing radii WSN. With variable sensing range, the difficulties to cover a continuous space in the area coverage problem becomes exceptionally harder than covering discrete points in the target (or point) coverage problem. Very few papers have paid effort for the former problem. Wang and Medidi have solved the area coverage problem based on Delaunay triangulation structure [2]. However, due to the boundary effect their proposed algorithms cannot always provide complete surveillance for the whole network. In this work, we improve the work in [2] so that the monitored area can be completely covered. A theorem confirms that our improved algorithm provides complete coverage for all the cases. In addition, the simulation further shows that our energy-efficient algorithm has an obvious improvement on coverage status with very small compensation of network lifetime.

Target Coverage and Network Connectivity in Wireless Sensor Networks (WSNs) plays a momentous role in the field of monitoring environment, habitant observing, disaster recovery, surveillance, etc. Coverage and Network Connectivity collectively can be considered as a proportion of Quality of Service (QoS) in a sensor network. It implies the coverage of each point in the detecting field while in the meantime fulfilling the measure that each node is surrounded by the equivalent neighbourhood of at least one other node. Many primary issues influence the outline and performance of WSN. A Comprehensive survey is made based on the scheduling algorithm and set cover approach, in which we analyze various approaches and their algorithm complexity. Firstly, the scheduling algorithm intermittently provides the required data service with guaranteed efficiency. This methodology can efficiently reduce the entire number of active nodes in the network, resulting in maximizing the network lifetime. Secondly, the set cover approach plays a major role in solving target coverage problem which group sensors into each cover set thereby, target coverage was achieved. Typical issues and open research challenges are addressed by classifying the approaches available in literature into three broad categories namely, adaptable coverage radius, coverage deployment strategy, and sleep scheduling. This survey aims to provide a better perceptive of the existing open challenges and possible futuristic approaches for target coverage and network connectivity in wireless sensor networks.

In this paper, we consider the connected target coverage (CTC) problem with the objective of maximizing the network lifetime by scheduling sensors into multiple sets, each of which can maintain both target coverage and connectivity among all the active sensors and the sink. We model the CTC problem as a maximum cover tree (MCT) problem and prove that the MCT problem is NP-Complete. We determine an upper bound on the network lifetime for the MCT problem and then develop a (1+ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</i> ) <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> (M circ) approximation algorithm to solve it, where <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</i> is an arbitrarily small number, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> (M circ)= <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lesilesM</sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">circ</sub> (1/i) and M circ is the maximum number of targets in the sensing area of any sensor. As the protocol cost of the approximation algorithm may be high in practice, we develop a faster heuristic algorithm based on the approximation algorithm called Communication Weighted Greedy Cover (CWGC) algorithm and present a distributed implementation of the heuristic algorithm. We study the performance of the approximation algorithm and CWGC algorithm by comparing them with the lifetime upper bound and other basic algorithms that consider the coverage and connectivity problems independently. Simulation results show that the approximation algorithm and CWGC algorithm perform much better than others in terms of the network lifetime and the performance improvement can be up to 45% than the best-known basic algorithm. The lifetime obtained by our algorithms is close to the upper bound. Compared with the approximation algorithm, the CWGC algorithm can achieve a similar performance in terms of the network lifetime with a lower protocol cost.

Finding an optimal node deployment strategy in wireless sensor networks (WSNs) that would reduce cost, be robust to node failures, reduce computation, and communication overhead, and guarantee a high level of coverage along with network connectivity is a difficult problem. In fact, sensing coverage and network connectivity are two of the most fundamental problems in WSNs as they can directly impact the network lifetime and operation. In this paper, we consider deriving optimal conditions for connectivity with coverage in WSNs. Most versions of this problem are (NP-complete), while approximation algorithms cannot be developed for some versions of polynomial time, unless P = NP. Hence, we also develop a heuristic for some versions of the problem and the efficacy of the heuristic will be evaluated through extensive simulations. We are also interested in determining the probability of finding a path between a given pair of nodes over a given topology of WSNs. This will serve as a measure of connectivity with coverage of the network. Hence, we derive necessary and sufficient conditions for connectivity with coverage over a clustered structure in WSNs. Then, employing queuing networks modeling techniques, we present a dynamic programming study of the connectivity with coverage of clustered structure and its effect on routing in generalized WSNs. The performance evaluation of the proposed schemes shows that availability of nodes, sensor node coverage, and the connectivity were sufficiently enhanced to maximize network lifetime.

Data aggregation is an important process for wireless sensor networks (WSNs). The important aspects with WSNs are to increase the lifetime and to reserve the battery power of sensor nodes. The energy of nodes is mainly utilized in the process of sensing and transmitting the data. Clustering is one of the processes in which the total WSNs are divided into the form of groups and each group has a cluster head (CH) and advantage of doing this is decrease of energy consumption and reduce the data collision. The blockchain technology is utilized in WSN by so many ways for example security, data storing, node recovery etc. because the role of blocks in blockchain is same as the role of node in WSN. In this paper, we propose a cluster-based data aggregation framework for wireless sensor networks based on the blockchain technique. The proposed scheme is based on cluster head (CH) selection based on energy, and nodes communicate with each other via the shortest path. Further, the proposed scheme is also using the concept of blockchain technology for the purpose of maximum data storage. KeywordsWireless sensor networksLow energy adaptive clustering hierarchy (LEACH)Data aggregationCluster headBlockchainAnd Energy consumption

The coverage problems have been studied in the recent years, most of them with limited detection capability therefore some pivotal sensors consume energy rapidly and there exist a large amount of partially unused sensors when the network lifetime terminated. We introduce an M-M (multiple sensors to multiple targets) probabilistic target coverage problem and its service solution for the wireless sensor networks (WSNs) in this paper to improve the utilization of randomly deployed sensors and to maximize the network lifetime as well. The intention of the M-M probabilistic target coverage problem is to detect/cover multiple targets by multiple sensors cooperatively and simultaneously, where each target is given a realistic detection probability threshold. We designed a heuristic algorithm to solve this problem. Simulation results show that the resulting network lifetime of our algorithm are longer than the target coverage approach, and our algorithm is more appropriate for the demand of the different observed target importance.

Target coverage is an important issue in wireless sensor networks (WSNs). This work copes with the target coverage problem which detaches a set of mobile sensors to targets efficiently. Given a set of disconnected targets with different quality of monitoring (QoM) and a set of mobile sensors which are distributed randomly in the networks, this paper develops two efficient algorithms for detaching mobile sensors to the targets such that the QoM of each target is satisfied. Performance study reveals that the proposed mechanisms outperform known mechanisms in terms of the satisfied QoMs of all targets and the movement cost of all mobile sensors as well as the life time of the wireless mobile sensor networks (WMSNs).

Wireless Sensor Network is useful in broad range of applications such as natural disaster relief, military, environmental and health monitoring. Coverage is one of the fundamental problem and an active research area in wireless sensor network. WSN is an emerging field due to its large contribution in dealing with coverage. It consist of low cost, low power, small size and multifunction sensor nodes. The critical aspect with wireless sensor network is energy conservation. In power constrained WSN, scheduling of sensors to be done effectively and efficiently so as to maximize network lifetime. In this paper we give an introduction to WSN and its fundamental problems that is target coverage problem together with energy constraint. The target coverage problem is proven to be NP-Complete problem by many researchers. We propose a new energy-efficient heuristic for target coverage problem in wireless sensor network to maximize total network lifetime.

In this paper, we are concerned with energy efficient area monitoring using information coverage in wireless sensor networks, where collaboration among multiple sensors can enable accurate sensing of a point in a given area-to-monitor even if that point falls outside the physical coverage of all the sensors. We refer to any set of sensors that can collectively sense all points in the entire area-to-monitor as a full area information cover. We first propose a low-complexity heuristic algorithm to obtain full area information covers. Using these covers, we then obtain the optimum schedule for activating the sensing activity of various sensors that maximizes the sensing lifetime. The scheduling of sensor activity using the optimum schedules obtained using the proposed algorithm is shown to achieve significantly longer sensing lifetimes compared to those achieved using physical coverage. Relaxing the full area coverage requirement to a partial area coverage (e.g., 95% of area coverage as adequate instead of 100% area coverage) further enhances the lifetime.

The target coverage problem in wireless sensor networks is concerned with maximizing the lifetime of the network while continuously monitoring a set of targets. We add a QoS requirement to the target coverage such that the targets are covered by more than one sensor at any time. Given an integer vector Q, where q i is the minimum number of sensors to simultaneously cover targets i, the problem becomes Q-coverage problem. In this paper, we study the target Q-coverage problem. The objective is to maximize sensor network lifetime satisfying Q-coverage requirement. The problem is shown to be NP-complete and there is no known practical algorithm. We propose a heuristic and show that the proposed algorithm yields solution very near to the optimal solution. Our algorithm has two important features. It uses a greedy heuristic to generate Q-covers by prioritizing sensors in terms of the residual battery life and the algorithm assigns a small constant of lifetime to Q-covers so generated. In this process, it allows the sensors to participate in many Q-covers. We observe that the smaller the constant, the closer is the solution to optimal solution. Through experiments on randomly generated problem instances, we show that the proposed algorithm yields near-optimal solution.KeywordsTarget Q-coverage problemWireless sensor networksenergy efficiencynetwork lifetimeQoS requirement

Sensor deployment is a fundamental issue in Wireless Sensor Networks (WSNs). Studies on the coverage problem are concentrated on deploying sensors to cover determined areas or a set of determined points (targets). On the other hand, the sensor network should have a lifetime long enough to fulfill the application requirements. Therefore, prolonging the network lifetime must be taken into account in designing a solution for a WSN that covers determined points. The problem of determining the location of sensor nodes such that the targets are covered and the network lifetime is maximized is called the sensor deployment problem. So far, various approaches, such as heuristics and approximation algorithms, have been used to solve the sensor deployment problem in WSNs. In this paper, we use two improved versions of the Particle Swarm Optimization (PSO) algorithm to solve the sensor deployment problem. The first one is cooperative PSO and the second one is improved cooperative PSO using fuzzy logic. The simulation results show that the algorithms proposed in this paper solve the target coverage problem with a longer network lifetime compared to the other similar algorithms.

Coverage in wireless sensor networks is a critical issue since it reflects the cost and detection capability of the network. Sensor coverage model is closely to the sensing functions of different types of sensors while network coverage is a collaborative measure from geographically distributed sensors. The coverage problems in wireless sensor networks have been formulated in various ways with different assumptions, constraints and objectives. In this work, a collaborative coverage model is proposed in the target surveillance application, while sensor sensing probabilistic model is introduced. Furthermore, a collaborative target coverage optimization algorithm (CTCOMSN) is presented to prolong the network lifetime in energy constraint mobile sensor networks. Simulation results show that CTCOMSN can prolong the lifetime greatly while improving the sensing quality.

The activity scheduling of sensors to alternately wake up for sensing obligation such that the network lifetime can be efficiently prolonged is a very important issue in wireless sensor networks (WSNs). Target area coverage is a new coverage problem in wireless sensor networks. The paper addresses the target area coverage problem and schedules sensors to alternatively wake up to collaboratively cover and sense the target area. A geometric-based activity scheduling scheme, named GAS scheme, for WSNs to fully cover a target area is proposed. By means of computational geometry, the sensors can self-determine when to sleep or wake up while preserving the sensing coverage. GAS is able to find as few sensors as possible to cover the target area, which is termed a cover set. In addition, GAS can find as many number of cover sets as possible to be alternately in charge of the sensing task. Simulation results show that GAS can efficiently schedule the sensor when to switch between active and sleep modes. Furthermore, the network lifetime can be prolonged significantly in comparison with the state-of-the-art schemes.