Cost-efficient deployment of multi-hop wireless networks over disaster areas using multi-objective meta-heuristics

Nowadays there is a global concern with the growing frequency and magnitude of natural disasters, many of them associated with climate change at a global scale. When tackled during a stringent economic era, the allocation of resources to efficiently deal with such disaster situations (e.g., brigades, vehicles and other support equipment for fire events) undergoes severe budgetary limitations which, in several proven cases, have lead to personal casualties due to a reduced support equipment. As such, the lack of enough communication resources to cover the disaster area at hand may cause a risky radio isolation of the deployed teams and ultimately fatal implications, as occurred in different recent episodes in Spain and USA during the last decade. This issue becomes even more dramatic when understood jointly with the strong budget cuts lately imposed by national authorities. In this context, this article postulates cost-efficient multi-hop communications as a technological solution to provide extended radio coverage to the deployed teams over disaster areas. Specifically, a Harmony Search (HS) based scheme is proposed to determine the optimal number, position and model of a set of wireless relays that must be deployed over a large-scale disaster area. The approach presented in this paper operates under a Pareto-optimal strategy, so a number of different deployments is then produced by balancing between redundant coverage and economical cost of the deployment. This information can assist authorities in their resource provisioning and/or operation duties. The performance of different heuristic operators to enhance the proposed HS algorithm are assessed and discussed by means of extensive simulations over synthetically generated scenarios, as well as over a more realistic, orography-aware setup constructed with LIDAR (Laser Imaging Detection and Ranging) data captured in the city center of Bilbao (Spain). (C) 2017 Elsevier B.V. All rights reserved.