Improved Energy-Efficient Target Coverage in Wireless Sensor Networks

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

Coverage problems are fundamental and crucial in designing a wireless sensor networks. The target coverage problem is finding an optimal scheduling for sensors such that the time (called lifetime) to monitor every target can be as long as possible. Unfortunately, the target coverage problem has been proved to be NP-complete. Most of previous work only considers one or two factors exclusively and thus fails to prolong the lifetime to near the optimum. The main objective of this work is to design efficient scheduling algorithms to maximize the lifetime of a given whole wireless sensor network by considering adjusting sensing range, locations of target and sensors, residue battery power of sensor nodes, and assignment between sensors and targets simultaneously. A maximum weighted matching algorithm is devised by considering full coverage and the maximum total monitored duration for each target-sensor assignments. We also conduct simulations to demonstrate that the proposed algorithms can achieve very high network lifetime closed to the optimum.

Solving energy issues for sweep coverage 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.

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.

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).

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.

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.

Deployment is an important issue in wireless sensor networks (WSNs). Most recent researches focus on the optimal deployment strategy to achieve full coverage in WSNs. However, the sensing devices in practical applications are finite, and cannot achieve full coverage in the whole monitored region. Moreover, the monitored objects distribute unevenly, and thus the monitored significance differs from region to region. How to deploy limited number of sensors efficiently has been to an important issue. In this study, we propose a new coverage problem in WSNs, named N-node coverage, to acquire the maximum information about monitored objects with N sensors. In this problem, the captured information is used as the new evaluation metric for network coverage. On the basis of the metric, we establish the N-node network coverage model, and prove that the N-node coverage problem is NP-hard. The authors propose a heuristic algorithm for deployment, named optimisation schEme of N-node (OPEN), to obtain the near-optimal solution of the problem. Experimental results show that the OPEN can effectively improve the coverage performance of the WSNs, and is able to provide practical guideline for deployment of WSNs with limited number of nodes.

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.

Network life time is a crucial perspective in target specified applications of wireless sensor network. Expedient sensor scheduling and power management can efficaciously prolong network lifetime. Sensors are divided into cover sets in which all targets are covered by a set of sensors and targets are monitored by each cover set for a period of time. The coverage problem proved to be NP-Complete. In present study, a unified elucidation of prior algorithms is given an efficient and new heuristic is proposed. The proposed heuristic prioritizes sensors based on the value of heuristic function. Its shown analytically that proposed heuristic outperforms other heuristic in terms of solutions quality. Our experimental analysis over a randomly generated large set of problem instances demonstrate that proposed heuristic solution is close to the definite optimal solution.

Leveraging data fusion to improve barrier 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#.

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

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.