1,225 research outputs found
Enhancing Received Signal Strength-Based Localization through Coverage Hole Detection and Recovery
In wireless sensor networks (WSNs), Radio Signal Strength Indicator (RSSI)-based localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of WSNs. Insufficient coverage will significantly reduce the effectiveness of the applications. However, most existing studies on coverage assume that the sensing range of a sensor node is a disk, and the disk coverage model is too simplistic for many localization techniques. Moreover, there are some localization techniques of WSNs whose coverage model is non-disk, such as RSSI-based localization techniques. In this paper, we focus on detecting and recovering coverage holes of WSNs to enhance RSSI-based localization techniques whose coverage model is an ellipse. We propose an algorithm inspired by Voronoi tessellation and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes and can reach any set coverage rate, up to 100% coverage
A mobile assisted coverage hole patching scheme based on particle swarm optimization for WSNs
Wireless sensor networks (WSNs) have drawn much research attention in recent years due to the superior performance in multiple applications, such as military and industrial monitoring, smart home, disaster restoration etc. In such applications, massive sensor nodes are randomly deployed and they remain static after the deployment, to fully cover the target sensing area. This will usually cause coverage redundancy or coverage hole problem. In order to effectively deploy sensors to cover whole area, we present a novel node deployment algorithm based on mobile sensors. First, sensor nodes are randomly deployed in target area, and they remain static or switch to the sleep mode after deployment. Second, we partition the network into grids and calculate the coverage rate of each grid. We select grids with lower coverage rate as candidate grids. Finally, we awake mobile sensors from sleep mode to fix coverage hole, particle swarm optimization (PSO) algorithm is used to calculate moving position of mobile sensors. Simulation results show that our algorithm can effectively improve the coverage rate of WSNs
Reliable Communication using Path Recovering in Wireless Sensor Network
Sensor technology is one in every of the quick growing technologies within the current scenario. And it's big selection of application additionally. The power of sensors to figure while not being monitored by someone is its distinctive quality. Wireless device network comprise of little sensors that have minimum communicatory and procedure power. Several anomalies square measure gift in WSNs. One such drawback may be a hole. Space barren of any node will be brought up as a hole. This degrades the performance of the full network. It affects the routing capability of the network terribly badly. The formation of holes in an exceedingly WSN is unavoidable thanks to the inner nature of the network. This paper deals with detective work and healing such holes in associate on demand basis
Evasion Paths in Mobile Sensor Networks
Suppose that ball-shaped sensors wander in a bounded domain. A sensor doesn't
know its location but does know when it overlaps a nearby sensor. We say that
an evasion path exists in this sensor network if a moving intruder can avoid
detection. In "Coordinate-free coverage in sensor networks with controlled
boundaries via homology", Vin deSilva and Robert Ghrist give a necessary
condition, depending only on the time-varying connectivity data of the sensors,
for an evasion path to exist. Using zigzag persistent homology, we provide an
equivalent condition that moreover can be computed in a streaming fashion.
However, no method with time-varying connectivity data as input can give
necessary and sufficient conditions for the existence of an evasion path.
Indeed, we show that the existence of an evasion path depends not only on the
fibrewise homotopy type of the region covered by sensors but also on its
embedding in spacetime. For planar sensors that also measure weak rotation and
distance information, we provide necessary and sufficient conditions for the
existence of an evasion path
Coverage and Energy Analysis of Mobile Sensor Nodes in Obstructed Noisy Indoor Environment: A Voronoi Approach
The rapid deployment of wireless sensor network (WSN) poses the challenge of
finding optimal locations for the network nodes, especially so in (i) unknown
and (ii) obstacle-rich environments. This paper addresses this challenge with
BISON (Bio-Inspired Self-Organizing Network), a variant of the Voronoi
algorithm. In line with the scenario challenges, BISON nodes are restricted to
(i) locally sensed as well as (ii) noisy information on the basis of which they
move, avoid obstacles and connect with neighboring nodes. Performance is
measured as (i) the percentage of area covered, (ii) the total distance
traveled by the nodes, (iii) the cumulative energy consumption and (iv) the
uniformity of nodes distribution. Obstacle constellations and noise levels are
studied systematically and a collision-free recovery strategy for failing nodes
is proposed. Results obtained from extensive simulations show the algorithm
outperforming previously reported approaches in both, convergence speed, as
well as deployment cost.Comment: 17 pages, 24 figures, 1 tabl
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