Q-Coverage Problem 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.

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.

Target coverage problem in wireless sensor networks remains a challenge. Due to nonlinear nature, previous work has mainly focused on heuristic algorithms, which remain difficult to characterize and have no performance guarantee. To solve the problem, this paper offers two important contributions. The first contribution is to have two lifetime upper bounds, which could be used to justify performance of previously proposed heuristic algorithms. One upper bound is based on the relaxation and reformulation technique while the other is derived by relaxing coverage constraints. We study the interesting connection between those two bounds and thus endow them with physical meanings. The second contribution is proposing a column generation based (CG) approach. The objective is to find an optimal schedule, defined as a time table specifying from what time up to what time which sensor watches which targets while the maximum lifetime has been obtained. We also offer an in-depth theoretic analysis as well as several novel techniques to further optimize the approach. Numerical results not only demonstrate that the lifetime upper bounds are very tight, but also verify that the proposed CG based approach constantly yields the optimal or near optimal solution.

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.

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).

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

Energy consumption is a fundamental and critical issue in wireless sensor networks. Mobile sensors consume much more energy during the movement than that during the communication or sensing process. Thus how to schedule mobile sensors and minimize their moving distance has great significance to researchers. In this paper, we study the target coverage problem in mobile sensor networks. Our goal is to minimize the moving distance of sensors to cover all targets in the surveillance region. Here initially all the sensors are located at k base stations. Thus we define this problem as k-Sink Minimum Movement Target Coverage. To solve this problem, we propose a PTAS, named Energy Effective Movement Algorithm (EEMA). We can divide EEMA into two phases. In the first phase, we partition the surveillance region into some subareas. In the second phase, we select subareas and schedule sensors to the selected subareas. We also prove that the approximation ratio of EEMA is 1 + e and the time complexity is ηO(1/e2 Finally, we conduct experiments to validate the efficiency and effectiveness of EEMA.

Energy consumption is a fundamental and critical issue in wireless sensor networks. Mobile sensors consume much more energy during the movement than that during the communication or sensing process. Thus how to schedule mobile sensors and minimize their moving distance, while keeping the coverage requirement has great significance to researchers. In this paper, we study the target coverage problem in mobile sensor networks where all the targets need to be covered by sensors continuously. Our goal is to minimize the moving distance of sensors to cover all targets in the surveillance region, which is in Euclidean space. Here initially all the sensors are located at $k$ base stations. Thus, we define this problem as $k$ -Sink Minimum Movement Target Coverage . To solve this problem, we propose a polynomial-time approximation scheme, named Energy Effective Movement Algorithm (EEMA). We prove that the approximation ratio of EEMA is $1+\varepsilon $ and the time complexity is $n^{O(1/\varepsilon ^{2})}$ . We also propose a distributed version of EEMA (D-EEMA) for large-scale networks where EEMA is not efficient enough in practice. Finally, we conduct experiments to validate the efficiency and effectiveness of EEMA and D-EEMA.

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.

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.

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.

Personal health status is becoming a matter of concern while pressure of work growing gradually in everyday life, in the meantime, the ageing population needs more attention. Thanks to many challenging problems of wireless sensor networks (WSNs) solved, exploring work of human body monitoring based on WSNs achieved great progress, we called it wireless body sensor networks(BSNs). In this paper, we propose a novel health-monitoring system based on BSNs; we named it SENS-U. It consists of wireless multi-parameters monitoring terminals and two medical care centers; with human body, they form a remote human in loop 4 vital signs and ECG monitoring system.

Wireless sensor networks consist of a large number of low-cost, low-power, and multifunctional sensor nodes that communicate over short distances through wireless links. Such sensor networks are ideal candidates for a wide range of applications such as monitoring of critical infrastructures, data acquisition in hazardous environments, industry control systems, vehicular networks, and military operations. Wireless sensor networks introduce new security challenges due to their dynamic topology, severe resource constraints, and absence of a trusted infrastructure. The purpose of this special issue is to publish high-quality research papers as well as review articles addressing recent advances of wireless sensor network security. In this special issue, we will explore the topic of security challenges and solutions for the wide application of wireless sensor networks in different areas, such as RFID and smart grid. RFID has been widely used in logistics and internet of things. Smart grid leverages sensor networks to better balance the load of the power grid. Both of them have great impact on the daily life and areweak points of thewhole economy,which attract the attention of terrorists. The feasible attacks and defense solutions are still not clear.The papers in this volume also cover several important foundational security services for wireless sensor networks, such as key management, key distribution, secure cluster formation, secure architecture for multihop communication, intrusion prediction, and secure localization. All these foundational security services are critical bases for application of wireless sensor networks. We hope that the papers in this volume engender further thinking about new security challenges for wireless sensor networks and even further “out-of-the-box” ideas about how to solve new security problems for wireless sensor networks.

Sensing coverage is one of fundamental problems in wireless sensor networks. In this paper, we investigate the polytype target coverage problem in heterogeneous wireless sensor networks where each sensor is equipped with multiple sensing units and each type of sensing unit can sense an attribute of multiple targets. How to schedule multiple sensing units of a sensor to cover multiple targets becomes a new challenging problem. This problem is formulated as an integer linear programming problem for maximizing the network lifetime. We propose a novel energy-efficient target coverage algorithm to solve this problem based on clustering architecture. Being aware of the coverage capability and residual energy of sensor nodes, the clusterhead node in each cluster schedules the appropriate sensing units of sensor nodes that are in the active status to cover multiple targets in an optimal way. Extensive simulations have been carried out to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.

Wireless sensor network constitute a class of real time embedded systems having limited resources. Target coverage problem is concerned with the continuous monitoring of a set of targets such that the network lifetime is maximized with the consideration of resource constraints. In this paper we propose a node scheduling protocol for target coverage problem on the basis of node contribution, coverage probability and trust values, where the set covers are computed dynamically using time stamping. The time stamping is a factor of threshold of the coverage level. We have evaluated the performance of the proposed protocol by varying the number of nodes and targets. The results show that the proposed scheme improves the network lifetime in terms of energy consumption and the reliability of the data communicated in comparison to the naive approach in which all the nodes are activated at once. The results show that the network lifetime is proportional to the energy savings under a constant environment.