Energy Efficient Area Monitoring Using Information 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.

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.

In large scale Wireless Sensor Networks (WSNs), the random deployment of the sensor nodes does not guarantee the desired level of the area coverage especially when the network is sparse. One of the solutions to overcome this problem is to use a mobile robot that is equipped with, in addition to the sensing unit, an actuator unit that controls its movement. In this case, the mobile robot is considered as a mobile sensor node that has the capability to monitor the uncovered regions in the sensor field. However, the main challenge in this case is how to control the movement of the mobile robot such that the area coverage is maximized and the time required to sample the uncovered regions is minimized. In this paper, we propose a fuzzy logic-based approach that controls the mobile robot movement in the sensor field in order to maximize the area coverage. The inputs of the fuzzy system determine the next point that the mobile node will select when it moves in the sensor field. Simulation results have shown that the proposed approach outperforms the random and standard moving patterns.

As the Internet of Things (IoT) evolves, more and more Wireless Sensor Networks (WSNs) are being deployed in the real world. Connected vehicles, smart grids, smart cities, smart healthcare, networks of robots, and disaster recovery networks are some examples. In WSNs, the area coverage is one of the most important quality of service metrics. A WSN without enough area coverage yields incorrect results. So calculating the covered area of a WSN is mandatory. Previous studies have used a simple approach: all nodes send their location to the sink, and it calculates the covered area centrally which makes huge unnecessary communication overhead. In our previous work titled Distributed Exact Coverage Rate Calculation, we calculated the covered area of a homogenous WSN in a distributed manner. In this paper, we provide a Heterogeneous Distributed Precise Coverage Rate (HDPCR) mechanism that calculates the covered area of a Heterogeneous Wireless Sensor Network by using a localized mechanism. With the use of boundary detection mechanisms, the HDPCR detects the boundary of the network and calculates its area. HDPCR also detects holes and calculates their area precisely. By subtracting these two calculated values, the covered area of the network can be computed. Many related studies have evaluated the coverage rate approximately with error and require more calculations to reduce the error rate. HDPCR calculates the coverage rate precisely without an error rate using simple arithmetic calculations. The exhaustive simulation also shows the superiority of HDPCR as compared to the previous approaches.

Exploring coverage and security challenges in wireless sensor networks: A survey

Efficient coverage and connectivity are two important factors that ensures better service quality especially during tracking targets or monitoring events in wireless sensor network. Although massive amount of studies has been carried out in the past to enhance coverage and connectivity issues, till date very few studies have witnessed a significant and standard outcomes that can opt further. Hence, this paper introduces a computationally efficient technique for jointly addressing both coverage and connectivity problems in large-scale wireless sensor network that ensures optimal network lifetime too. The proposed system has been empirically designed, and algorithms formulated to ensure energy efficient monitoring of event. The outcomes of the study are compared with standard energy efficient hierarchical protocol to benchmark the results.

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.

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.

Wireless sensor networks are a collection of specially designed nodes to provide support to sensor information propagation. Sensor nodes are basically designed to sense data as sensor and distribute to servers for further analysis. Based on the specific requirements, the numbers of sensor nodes can range from hundreds to thousands. All the communication will take place through predefined network characteristics. WSNs generally face energy issues. LEACH is designed for efficient cluster formation. It provides an efficient energy solution for data transmission from node to base station. Various enhanced version of LEACH has been proposed by Assuming LEACH as base protocol. The energy constraints are a critical aspect in developing the routing protocols for WSNs. Designers and researchers have contributed their effort through enhancement in energy saving procedure into WSNs. In this work, the LEACH based protocols and their performance in WSN has been analysed. This research paper evaluates recent advancement in LEACH based approaches for Wireless Wide Area Sensor Network with comparison table.

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.

Wireless sensor networks are widely used. In order to effectively deploy wireless sensor nodes, improve node utilization and network coverage, an improved sparrow search algorithm is proposed in this paper. Firstly, the optimal point set algorithm was introduced to initialize the sparrow population position, and the random generation was changed to optimal point set generation to make the initialization distribution more uniform. Then, the convergence factor μ was introduced to improve the search efficiency of the algorithm by changing the slow convergence due to the fixed producer ratio factor. Finally, double variation factors were introduced to track the target particles and reorder the individual fitness of sparrows to improve the search accuracy and keep the population fresh. The simulation results show that the proposed algorithm improves the coverage of wireless sensor networks. Compared with traditional methods, the proposed algorithm has stronger coverage ability, iteration ability and effectiveness.

To provide proper coverage of their random deployment regions, wireless sensor networks (WSN) should employ abundant nodes. In this paper we correct such situations by employing some mobile robots as mobile nodes in WSN which can actively move to desired locations for repairing the broken networks. According to the pre-research work we know that the number of nodes employed by WSN is closely relevant to quality of service (QoS) in sensing coverage when the nodes are randomly deployed in the target region. A modified particle swarm optimization (PSO) named particle swarm genetic optimization (PSGO), which imports selection and mutation operators in PSO to overcome the premature fault of classical PSO, is proposed to redeploy the mobile robots according to the node density for repairing the sensing coverage hole after their initial random deployment. It is suggested by the simulated experiment results that the WSN employing the mobile robots can improve the QoS in sensing coverage than the stationary WSN by redeploying mobile robots according to the node density.

Many sensor networks are deployed for the purpose of covering and monitoring a particular region, and detecting the object of interest in the region. In this paper, based on the probabilistic sensing model, we conduct comprehensive analytical and simulation studies on collaborative information coverage and object detection in wireless sensor networks. More specifically, we first define point information coverage and based on that we define p-coverage as a measure of the coverage performance for a randomly-deployed wireless sensor network. Then, we investigate the coverage and object detection performances of several simple decision fusion-based collaborative mechanisms and find that simple collaborations among active sensors indeed degrade the coverage performance due to the requirement of maintaining the target false detection probability. This motivates us to develop an on-demand collaborative framework for object detection, whose effectiveness is supported by detailed theoretical analysis and simulation-based validation. Finally, we investigate the energy efficiency performance of our proposed framework and identify the trade-offs among various system parameters in network power consumption.

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.

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.