Computación Natural en Redes Vehiculares

Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Ministerio de Economía y Competitividad (MINECO) del Gobierno de España y fondos FEDER con el proyecto roadME con TIN2011-28194 (http://roadme.lcc.uma.es). Beca de código AP2010-3108 del Ministerio de Educación, Cultura y Deporte del Gobierno Español

The Multi-agent Simulation-based Framework for Optimization of Detectors Layout in Public Crowded Places

AbstractIn this work the framework for detectors layout optimization based on a multi-agent simulation is proposed. Its main intention is to provide a decision support team with a tool for automatic design of social threat detection systems for public crowded places. Containing a number of distributed detectors, this system performs detection and an identification of threat carriers. The generic model of detector used in the framework allows to consider detection of various types of threats, e.g. infections, explosives, drugs, radiation. The underlying agent-based models provide data on social mobility, which is used along with a probability based quality assessment model within the optimization process. The implemented multi-criteria optimization scheme is based on a genetic algorithm. For experimental study the framework has been applied in order to get the optimal detectors’ layout in Pulkovo airport

Design of a WSN for the sampling of environmental variability in complex terrain

Las mediciones de parámetros ambientales in situ utilizando sistemas de sensores conectados a una red inalámbrica se han generalizado, pero el problema de monitorear áreas grandes y montañosas por medio de una red de sensores inalámbricos (WSN) no está bien resuelto. Las principales razones de esto son: (1) la distribución de la variabilidad ambiental es desconocida en el campo; (2) sin este conocimiento, sería necesario un gran número de sensores para asegurar la cobertura completa de la variabilidad ambiental y (3) los requisitos de diseño de WSN, por ejemplo, la conectividad efectiva (intervisibilidad), las distancias límite y la redundancia controlada por prueba y error. Utilizando la temperatura como variable ambiental objetivo, proponemos: (1) un método para determinar las clases ambientales homogéneas a muestrear utilizando el modelo de elevación digital (DEM) y simulaciones geométricas y (2) un procedimiento para determinar un diseño eficaz de WSN en complejos Terreno en términos de número de sensores, redundancia, coste y distribución espacial. La metodología propuesta, basada en sistemas de información geográfica y programación de números enteros binarios, se puede adaptar fácilmente a una amplia gama de aplicaciones que requieren un monitoreo ambiental exhaustivo y continuo con alta resolución espacial. Los resultados muestran que el diseño WSN es perfectamente adecuado para la topografía y las especificaciones técnicas de los sensores, y proporciona una cobertura completa de la variabilidad ambiental en términos de exposición al sol. Sin embargo, estos resultados aún deben ser validados en el campo y el procedimiento propuesto debe ser refinado.In-situ environmental parameter measurements using sensor systems connected to a wireless network have become widespread, but the problem of monitoring large and mountainous areas by means of a wireless sensor network (WSN) is not well resolved. The main reasons for this are: (1) the environmental variability distribution is unknown in the field; (2) without this knowledge, a huge number of sensors would be necessary to ensure the complete coverage of the environmental variability and (3) WSN design requirements, for example, effective connectivity (intervisibility), limiting distances and controlled redundancy, are usually solved by trial and error. Using temperature as the target environmental variable, we propose: (1) a method to determine the homogeneous environmental classes to be sampled using the digital elevation model (DEM) and geometric simulations and (2) a procedure to determine an effective WSN design in complex terrain in terms of the number of sensors, redundancy, cost and spatial distribution. The proposed methodology, based on geographic information systems and binary integer programming can be easily adapted to a wide range of applications that need exhaustive and continuous environmental monitoring with high spatial resolution. The results show that the WSN design is perfectly suited to the topography and the technical specifications of the sensors, and provides a complete coverage of the environmental variability in terms of Sun exposure. However these results still need be validated in the field and the proposed procedure must be refined.peerReviewe

Multi-Objective Evolving Neural Network supporting SDR Modulations Management

International audienceThis paper proposes a distributed Neural/Genetic algorithm able to compute both the more suitable positioning and transmission modulation schemes for fixed/mobile wireless nodes equipped with software defined radio abilities. Devices considered in this work are able to move towards new positions by applying the concept of controlled mobility. The selection of the more suitable modulation scheme is realized through the SDR (Software Defined Radio) paradigm. The synergistic combination of controlled mobility and SDR in a totally distributed way, allows to obtain a high degree of self-configurability; moreover, the extreme adaptability to the network conditions and application level constraints in terms of coverage and guaranteed connectivity, make the proposed approach well suited for quite different communication scenarios such as classical monitoring or disaster recovery. The obtained results, validated throughout an intensive simulation campaign, show how the controlled mobility paradigm applied to the wireless devices and the intrinsic re-configuring SDR capabilities, increase the performance of the network both in terms of coverage and connectivity respect to other algorithms

Optimal Sensor Network Layout Using Multi-Objective Metaheuristics

Wireless Sensor Networks (WSN) allow, thanks to the use of small wireless devices known as sensor nodes, the monitorization of wide and remote areas with precision and liveness unseen to the date without the intervention of a human operator. For many WSN applications it is fundamental to achieve full coverage of the terrain monitored, known as sensor field. The next major concerns are the energetic efficiency of the network, in order to increase its lifetime, and having the minimum possible number of sensor nodes, in order to reduce the network cost. The task of placing the sensor nodes while addressing these objectives is known as WSN layout problem. In this paper we address a WSN layout problem instance in which full coverage is treated as a constraint while the other two objectives are optimized using a multiobjective approach. We employ a set of multi-objective optimization algorithms for this problem where we define the energy efficiency and the number of nodes as the independent optimization objectives. Our results prove the efficiency of multi-objective metaheuristics to solve this kind of problem and encourage further research on more realistic instances and more constrained scenarios