Pilar García-Díaz

A Coral Reefs Optimization algorithm for optimal mobile network deployment with electromagnetic pollution control criterion

Graphical abstractDisplay Omitted HighlightsA Mobile Network Deployment Problem is tackled using the CRO.The Coral Reefs Optimization is based on corals and corals reefs biology.A full description of the new algorithm is carried out.Experimental comparison with alternative soft-computing algorithms is done. In this paper we apply a novel meta-heuristic approach, the Coral Reefs Optimization (CRO) algorithm, to solve a Mobile Network Deployment Problem (MNDP), in which the control of the electromagnetic pollution plays an important role. The CRO is a new bio-inspired meta-heuristic algorithm based on the growing and evolution of coral reefs. The aim of this paper is therefore twofold: first of all, we study the performance of the CRO approach in a real hard optimization problem, and second, we solve an important problem in the field of telecommunications, including the minimization of electromagnetic pollution as a key concept in the problem. We show that the CRO is able to obtain excellent solutions to the MNDP in a real instance in Alcala de Henares (Madrid, Spain), improving the results obtained by alternative algorithms such as Evolutionary, Particle Swarm Optimization or Harmony Search algorithms.

An Agent System for Bandwidth Allocation in Reservation-Based Networks Using Evolutionary Computing and Vickrey Auctions

This paper presents an agent system for bandwidth allocation in reservation-based networks, using Vickrey auctions and an evolutionary algorithm. Our evolutionary approach performs the bandwidth allocation to agents which bid for networks resources by means of willingness-to-pay functions. It also calculates in a parallel way the price to be paid to the networks owner. We tackle the case of one-link, and its extension to a network. Simulations performed have shown the main advantages and problems inherent to this model

A novel Grouping Coral Reefs Optimization algorithm for optimal mobile network deployment problems under electromagnetic pollution and capacity control criteria

A new grouping Coral Reefs Optimization algorithm is presented.A problem of mobile network deployment with pollution control is tackled.A real case-study in Alcala de Henares (Madrid) is discussed.Comparison with state of the art algorithms shows excellent performance of the proposed algorithm. This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcala de Henares, Madrid, Spain.

Mobile network deployment under electromagnetic pollution control criterion: An evolutionary algorithm approach

Electromagnetic pollution due to mobile telephony is one of the most concerning problems arising since the spreading of this technology. Different studies have shown the relationship between continuous exposition to electromagnetic fields and different kinds of pathologies. Despite this, the electromagnetic danger for exposition is not taken into account in recent mobile network deployments. In this paper we propose a novel evolutionary algorithm for mobile networks deployment, which takes into account the control of the electromagnetic emission from the base stations as one of the key design parameters. The proposed evolutionary approach is a variable-length algorithm, able to produce solutions with different number of base stations. We detail the encoding, operators and a repairing procedure applied to obtain good solutions in terms of coverage, cost and electromagnetic pollution. The algorithm has been tested in a real problem of mobile network deployment in Alcala de Henares, Madrid, Spain, and compare with a greedy (constructive) approach and a meta-heuristic algorithm (Harmony Search), obtaining very good results.

Evolutionary bandwidth allocation in reservation-based networks with Vickrey auctions

This paper presents the description of a system for bandwidth allocation in reservation-based networks, using Vickrey auctions and an evolutionary algorithm. Our evolutionary approach performs the bandwidth allocation to agents which bid for networks resources by means of willingness-to-pay functions. It also calculates in a parallel way the price to be paid to the networks owner. We also tackle the case of the one-link model and its extension to a general and two types of special networks, ring-type and tree-type networks. Simulations performed have shown the main advantages and problems inherent to this model.

A discrete Particle Swarm Optimization Algorithm for Mobile Network Deployment Problems

In this paper we present a hybrid discrete Particle Swarm Optimization (PSO) algorithm for the Mobile Network Deployment Problem (MNDP). First, we fully describe the MNDP, including a term for taking into account the Electromagnetic field produced by new locations of BTSs. We also describe the proposed PSO algorithm and some variations incorporated in order to cope with discrete search spaces, such as the one in the MNDP. We finally show the performance of the proposed discrete PSO in a real MNDP in a Spanish city near Madrid: Alcalá de Henares.

GSMSIM: An Educational Simulation Tool for Teaching GSM-Based Mobile Communications in Laboratory Lectures

This paper describes an educational simulation tool, GSMSIM, which has been developed to teach GSM mobile communications in laboratory lectures. The tool has been programmed in OMNET++, a flexible environment which allows its extension to different aspects of GSM technology, such as the simulation of calls, traffic, handoffs, etc. This tool has been used in the courses ‘Communication Systems’ and ‘Telecommunication Networks I’, at the University of Alcalá, Madrid, Spain. We report some of the results obtained in three different scenarios simulating a small, medium and large city, with different characteristics of network coverage.