Sixsoid: A new paradigm for kk-coverage in 3D Wireless Sensor Networks

Coverage in 3D wireless sensor network (WSN) is always a very critical issue to deal with. Coming up with good coverage models implies more energy efficient networks. $K$-coverage is one model that ensures that every point in a given 3D Field of Interest (FoI) is guaranteed to be covered by $k$ sensors. When it comes to 3D, coming up with a deployment of sensors that gurantees $k$-coverage becomes much more complicated than in 2D. The basic idea is to come up with a geometrical shape that is guaranteed to be $k$-covered by taking a specific arrangement of sensors, and then fill the FoI will non-overlapping copies of this shape. In this work, we propose a new shape for the 3D scenario which we call a \textbf{Devilsoid}. Prior to this work, the shape which was proposed for coverage in 3D was the so called \textbf{Reuleaux Tetrahedron}. Our construction is motivated from a construction that can be applied to the 2D version of the problem \cite{MS} in which it imples better guarantees over the \textbf{Reuleaux Triangle}. Our contribution in this paper is twofold, firstly we show how Devilsoid gurantees more coverage volume over Reuleaux Tetrahedron, secondly we show how Devilsoid also guarantees simpler and more pragmatic deployment strategy for 3D wireless sensor networks. In this paper, we show the constuction of Devilsoid, calculate its volume and discuss its effect on the $k$-coverage in WSN

Coverage Optimization Strategy of Mobile Nodes in WSN Based on Nonlinear Sequence

Picking to meet certain condition with minimal coverage, sensor nodes to complete coverage and connectivity for wireless sensor networks has become a challenging one of core problems. For this reason, one kind based on the node mobile strategy optimal coverage algorithm was proposed. Firstly, the sensor node and the target node mapping relation model was established, which using geometric graphic in the square will target node planning to the inner square area, through to the mobile node scheduling strategy for the entire coverage area for effective coverage, achieving the goal node complete coverage of the objective. Secondly, probability expectation values were obtained through the algorithm to meet under the conditions with minimal sensor nodes, and the optimal coverage and connectivity probability models were given. Finally, the experimental results show that the algorithm can not only using the least nodes to complete the effective target area to be covered, but in reducing the network energy consumption has also greatly improved, simultaneously reducing the cyber source configuration and improving the network life cycle

A Novel Deployment Scheme Based on Three-Dimensional Coverage Model for Wireless Sensor Networks

Coverage pattern and deployment strategy are directly related to the optimum allocation of limited resources for wireless sensor networks, such as energy of nodes, communication bandwidth, and computing power, and quality improvement is largely determined by these for wireless sensor networks. A three-dimensional coverage pattern and deployment scheme are proposed in this paper. Firstly, by analyzing the regular polyhedron models in three-dimensional scene, a coverage pattern based on cuboids is proposed, and then relationship between coverage and sensor nodes’ radius is deduced; also the minimum number of sensor nodes to maintain network area’s full coverage is calculated. At last, sensor nodes are deployed according to the coverage pattern after the monitor area is subdivided into finite 3D grid. Experimental results show that, compared with traditional random method, sensor nodes number is reduced effectively while coverage rate of monitor area is ensured using our coverage pattern and deterministic deployment scheme

A Trapezoidal Fuzzy Membership Genetic Algorithm (TFMGA) for Energy and Network Lifetime Maximization under Coverage Constrained Problems in Heterogeneous Wireless Sensor Networks

Network lifetime maximization of Wireless Heterogeneous Wireless Sensor Networks (HWSNs) is a difficult problem. Though many methods have been introduced and developed in the recent works to solve network lifetime maximization. However, in HWSNs, the energy efficiency of sensor nodes becomes also a very difficult issue. On the other hand target coverage problem have been also becoming most important and difficult problem. In this paper, new Markov Chain Monte Carlo (MCMC) is introduced which solves the energy efficiency of sensor nodes in HWSN. At initially graph model is modeled to represent HWSNs with each vertex representing the assignment of a sensor nodes in a subset. At the same time, Trapezoidal Fuzzy Membership Genetic Algorithm (TFMGA) is proposed to maximize the number of Disjoint Connected Covers (DCC) and K-Coverage (KC) known as TFMGA-MDCCKC. Based on gene and chromosome information from the TFMGA, the gene seeks an optimal path on the construction graph model that maximizes the MDCCKC. In TFMGA gene thus focuses on finding one more connected covers and avoids creating subsets particularly. A local search procedure is designed to TFMGA thus increases the search efficiency. The proposed TFMGA-MDCCKC approach has been applied to a variety of HWSNs. The results show that the TFMGA-MDCCKC approach is efficient and successful in finding optimal results for maximizing the lifetime of HWSNs. Experimental results show that proposed TFMGA-MDCCKC approach performs better than Bacteria Foraging Optimization (BFO) based approach, Ant Colony Optimization (ACO) method and the performance of the TFMGA-MDCCKC approach is closer to the energy-conserving strategy

Target Tracking Using Wireless Sensor Networks

Tracking of targets in remote inaccessible areas is an important application of Wireless Sensor Networks (WSNs). The use of wired networks for detecting and tracking of intruders is not feasible in hard-to-reach areas. An alternate approach is the use of WSNs to detect and track targets. Furthermore, the requirements of the tracking problem may not necessarily be known at the time of deployment. However, issues such as low onboard power, lack of established network topology, and the inability to handle node failures have limited the use of WSNs in these applications. In this dissertation, the performance of WSNs in remote surveillance type of applications will be addressed through the development of distributed tracking algorithms. The algorithm will focus on identifying a minimal set of nodes to detect and track targets, estimating target location in the presence of measurement noise and uncertainty, and improving the performance of the WSN through distributed learning.The selection of a set of sensor nodes to detect and track a target is first studied. Inactive nodes are forced into `sleeping' mode to conserve power, and activated only when required to sense the target. The relative distance and angle of the target from sensor nodes are used to determine which of the sensors are needed to track the target.The effect of noisy measurements on the estimation of the position of the target is addressed through the implementation of a Kalman filter. Contrary to centralized Kalman filter implementations reported in the literature, implementation of the distributed Kalman filter is considered in the proposed solution.Distributed learning is implemented by passing on the knowledge of the target, i.e. the filter state and covariance matrix onto the subsequent node running the filter. The problem is mathematically formulated, and the stability and tracking error of the proposed strategy are rigorously examined. Numerical examples are then used to demonstrate the utility of the proposed technique.It will be shown by mathematical proofs and numerical simulation in this dissertation that distributed detection and tracking using a limited number of nodes can result in efficient tracking in the presence of measurement noise. Furthermore, minimizing the number of active sensors will reduce communication overhead and power consumption in networks, improve tracking efficiency, and increase the useful life span of WSNs