Energy efficient scheme to Jointly Optimize Coverage and Connectivity in Large Scale Wireless Sensor Network

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.

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.

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.

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.

Coverage quality is one critical metric to evaluate the Quality of Service (QoS) provided by wireless sensor networks. In this paper, we address maximum support coverage problem (a.k.a. best case coverage) in wireless sensor networks. Most of the existing work assume that the coverage degree is 1, i. e. every point on the resultant path should fall within the sensing range of at least one sensor node. Here we study the k-coverage problem, in which every point on the resultant path is covered by at least k sensors while optimizing certain objectives. We present tackle this problem under both centralized and distributed setting. The time complexity is bounded by O(k2n log n) where n is the number of deployed sensor nodes. To the best of our knowledge, this is the first work that presents polynomial time algorithms that find optimal k-support paths for a general k.

Parallel and distributed computing has been the basis to many emerging areas, such as smart networks, cloud computing, big data analysis, and blockchain technology. Without the development of parallel and distributed computing technologies and various types of systems, it is not possible to meet the requirement on efficiency, accuracy, scalability, and reliability for various critical applications that support our modern economy and society. While parallel and distributed computing played a vital role in modern science, engineering, biology, medicine, pharmacy, astronomy, geology, and archaeology, its application has also been extended to business, finance, economics, management, government, and defense, covering all aspects of our modern society and life. Furthermore, parallel and distributed computing has emerged in recent advances of many hotspot research directions including artificial intelligence, machine learning, Internet of Things, bioinformatics, digital medicine, cybersecurity, and social computing, resulting in numerous ground-breading discoveries that are changing our society and life.

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.

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.

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#.

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

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.

Solving energy issues for sweep coverage in wireless sensor networks

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.

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.