A potential field approach to finding minimum-exposure paths in wireless sensor networks

2013

IEEE Sensors J.

Deriving the critical density to achieve region coverage for random sensor deployment is a fundamentally important problem in the area of Internet of Things (IoT). Most of the existing works on sensor coverage mainly concentrate on the full coverage models, which ensure that all points in the deployment region are covered at the expense of high complexity and cost. In contrast, many applications of IoT focus on the exposure-path prevention, which does not require full coverage sensor deployment, and instead it only needs the partial coverage, because the exposure paths are prevented as long as no moving objects or phenomena can go through a deployment region without being detected. Finally, we focus on the partial coverage by applying the percolation theory to solve the exposure path problem for the network of sensors in IoT. Most of the existing percolationbased schemes apply the continuum-percolation theory, which, however, suffers from the loose lower and upper bounds on the critical density. To overcome this problem, we propose a bond-percolation theory based scheme by mapping the exposure path problem into a bond percolation model. Using this model, we derive the critical densities for both omnidirectional sensor networks and directional sensor networks under random sensor deployment where sensors are deployed according to a twodimensional Poisson process. The rigorous modeling/analyses and extensive simulations show that our proposed scheme can yield much tighter upper and lower bounds on the critical densities as compared with those generated by continuum-percolation. IndexTerms-Wireless sensor networks, coverage, exposure-path prevention, internet of things, percolation theory, directional sensing model.

Section: A Path Coveragementioning

confidence: 99%

Percolation Theory-Based Exposure-Path Prevention for Wireless Sensor Networks Coverage in Internet of Things

2013

IEEE Sensors J.

“…The author also described a framework for a faster implementation of the algorithm. The authors of presented an artificial‐potential approach for planning the minimum‐exposure paths of multiple vehicles in a dynamic environment containing multiple mobile sensors and multiple fixed obstacles. Their approach presents the ability of computing multiple minimum‐exposure paths online, while avoiding mutual collisions as well as collisions with obstacles sensed during the motion.…”

Section: Related Workmentioning

confidence: 99%

Minimal exposure path algorithms for directional sensor networks

Wirel. Commun. Mob. Comput.

2012

As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst‐case coverage plays an important role in the applications for detecting intrusions. However, most existing works about minimal exposure path are based on omnidirectional sensors. In contrast, this paper studies the minimal exposure path problem for two different types of directional sensing models: the binary sector model and directional sensitivity model. For the binary sector model, we construct a special Voronoi diagram, called sector centroids‐based Voronoi diagram, to transform the minimal exposure path problem from a continuous geometric problem into a discrete geometric problem. By using the sector centroids‐based Voronoi diagram, we develop an approximate algorithm to find the minimal exposure path in the sensors deployment field. For the directional sensitive model, we formulate the minimal exposure path problem by using two sensing intensity functions: all‐sensor intensity function and maximum‐sensor intensity function and then generate two weighted grids to convert the minimal exposure path problem into two discrete geometric problems. On the basis of the aforementioned weighted grids, we also develop two approximation algorithms to find the minimal exposure path for the directional sensitive model. We conduct extensive experiment simulations to validate and evaluate our proposed models and algorithms. Copyright © 2012 John Wiley & Sons, Ltd.

“…The author also described a framework for a faster implementation of the algorithm. The authors of [10] presented an artificial-potential approach for planning the minimum-exposure paths of multiple vehicles in a dynamic environment containing multiple mobile sensors and multiple fixed obstacles. Their approach presents the ability of computing multiple minimum-exposure paths online, while avoiding mutual collisions as well as collisions with obstacles sensed during the motion.…”

Section: Related Workmentioning

confidence: 99%

Minimal Exposure Path Algorithms for Directional Sensor Networks

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

2009

As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst-case coverage plays an important role in the applications for detecting intrusions. However, most existing works about minimal exposure path are based on omnidirectional sensors. In contrast, this paper studies the minimal exposure path problem for directional sensor networks. We first develop a directional sensing model: directional sensitivity model. Then, we formulate the minimal exposure path problem by using two sensing intensity functions: all-sensor intensity function and maximum-sensor intensity function, and generate two weighted grids to convert the minimal exposure path problem into two discrete geometric problems. Based on the above weighted grids, we also develop two approximation algorithms to find the minimal exposure path for the directional sensitive model. We conduct extensive simulations to validate and evaluate our proposed models and algorithms.Index Terms-Coverage, minimal exposure path, wireless sensor networks, directional sensing model.