Network Emergent Coverage Patterns from Stochastic Dynamic Models
TL;DR
This work investigates the influence of the movement patterns (mobility) of network nodes on the overall performance of the network in terms of the area that can be covered by nodes' communications in terms of the area that can be covered by nodes' communications.
Abstract
This work investigates the influence of the movement patterns (mobility) of network nodes on the overall performance of the network in terms of the area that can be covered by nodes' communications. This is important because, in urban areas, there are still areas that cannot be covered due to a lack of communication range of network nodes (sensors). Usually covering all areas may lead to a heavy cost due to the number of static sensors that might be implanted in particular areas. Therefore, this research comes to overcome this issue by testing a variety of mobility models and seeing which of them lead to a better performance. This work is based on simulations performed on a wide range of settings and variables such as mobility models, number of nodes within the network, network distribution of nodes, communication range of nodes, and other settings. The evaluation is based on the amount of area that is covered under particular settings. The findings show that the Exponential and Levy Flight models reflect the highest exploration efficiency (MNVL = 96.39 and 95.59 respectively). The Rayleigh Flight model show the largest average coverage area with alomost 36.3%. However, it shows high variability of CV=5.04%. The Levy with Exponential Cutoff model reflects high stability rate with CV=0.16% and moderate coverage of 31.7%.