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

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 percolation-based 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 two-dimensional 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.