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)

A Grouping Harmony Search Algorithm for Assigning Resources to Users in WCDMA Mobile Networks

This paper explores the feasibility of a particular implemen- tation of a Grouping Harmony Search (GHS) algorithm to assign re- sources (codes, aggregate capacity, power) to users in Wide-band Code Division Multiple Access (WCDMA) networks. We use a problem for- mulation that takes into account a detailed modeling of loads factors, including all the interference terms, which strongly depend on the as- signment to be done. The GHS algorithm aims at minimizing a weighted cost function, which is composed of not only the detailed load factors but also resource utilization ratios (for aggregate capacity, codes, power), and the fraction of users without service. The proposed GHS is based on a particular encoding scheme (suitable for the problem formulation) and tailored Harmony Memory Considering Rate and Pitch Adjusting Rate processes. The experimental work shows that the proposed GHS algorithm exhibits a superior performance than that of the conventional approach, which minimizes only the load factors

Spatial regression analysis of NO_(x) and O_(3) concentrations in Madrid urban area using Radial Basis Function networks

This paper discusses the performance of Radial Basis Function networks (RBF) in a problem of spatial regression of pollutants in Madrid. Specifically, the spatial regression of NO_(x) and O_(3) is considered, in such a way that, starting from a set of measuring points provided by the air quality monitoring network of Madrid, the complete surface of the pollutants in the city is obtained. This pollutant surface can be used as an initial step for modeling intra-urban pollution using land-use regression techniques for example. Also, different works has used a pollutant surface to study the patterns of pollution in different cities in the world and also to establish their air monitoring networks under mathematical criteria. The paper is focussed in analyzing the performance of RBF networks to obtain this first pollutant surface, so different RBF training algorithms are tested in this paper. Specifically, evolutionary-based RBF training algorithms are described, and compared with classical training algorithms for RBF networks with Gaussian kernels. The inclusion of meteorological variables in the RBF networks are also discussed in the paper. The experimental part of the article studies real results of the application of RBF networks to obtain a first pollutant surface of NO_(x) and O_(3), using the data of the air pollution monitoring network of Madrid and the meteorological network of the city

Hybridizing logistic regression with product unit and RBF networks for accurate detection and prediction of banking crises

As the current crisis has painfully proved, the financial system plays a crucial role in economic development. Although the current crisis is being of an exceptional magnitude, financial crises are recurrent phenomena in modern financial systems. The literature offers several definitions of financial instability, but for our purposes we identity financial crisis with banking crisis as the most common example of financial instability. In this paper we introduce a novel model for detection and prediction of crises, based on the hybridization of a standard logistic regression with product unit (PU) neural networks and radial basis function (RBF) networks. These hybrid approaches are fully described in the paper, and applied to the detection and prediction of banking crises by using a large database of countries in the period 1981-1999. The proposed techniques are shown to perform better than other existing statistical and artificial intelligence methods in this problem.Banking crises prediction Product unit neural networks Radial basis function neural networks Logistic regression Hybrid methods