A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network likemobilemultihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic

Intelligent search in social communities of smartphone users

Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain,however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft’s GeoLife project, DBLP and Pics ‘n’ Trails but also using our real Android SmartP2P3 system deployed over our SmartLab4 testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors

Security risk assessment in cloud computing domains

Cyber security is one of the primary concerns persistent across any computing platform. While addressing the apprehensions about security risks, an infinite amount of resources cannot be invested in mitigation measures since organizations operate under budgetary constraints. Therefore the task of performing security risk assessment is imperative to designing optimal mitigation measures, as it provides insight about the strengths and weaknesses of different assets affiliated to a computing platform. The objective of the research presented in this dissertation is to improve upon existing risk assessment frameworks and guidelines associated to different key assets of Cloud computing domains - infrastructure, applications, and users. The dissertation presents various informal approaches of performing security risk assessment which will help to identify the security risks confronted by the aforementioned assets, and utilize the results to carry out the required cost-benefit tradeoff analyses. This will be beneficial to organizations by aiding them in better comprehending the security risks their assets are exposed to and thereafter secure them by designing cost-optimal mitigation measures --Abstract, page iv

Differential Evolution-based 3D Directional Wireless Sensor Network Deployment Optimization

Wireless sensor networks (WSNs) are applied more and more widely in real life. In actual scenarios, 3D directional wireless sensors (DWSs) are constantly employed, thus, research on the real-time deployment optimization problem of 3D directional wireless sensor networks (DWSNs) based on terrain big data has more practical significance. Based on this, we study the deployment optimization problem of DWSNs in the 3D terrain through comprehensive consideration of coverage, lifetime, connectivity of sensor nodes, connectivity of cluster headers and reliability of DWSNs. We propose a modified differential evolution (DE) algorithm by adopting CR-sort and polynomial-based mutation on the basis of the cooperative coevolutionary (CC) framework, and apply it to address deployment problem of 3D DWSNs. In addition, to reduce computation time, we realize implementation of message passing interface (MPI) parallelism. As is revealed by the experimentation results, the modified algorithm proposed in this paper achieves satisfying performance with respect to either optimization results or operation time

Natural computing for vehicular networks

La presente tesis aborda el diseño inteligente de soluciones para el despliegue de redes vehiculares ad-hoc (vehicular ad hoc networks, VANETs). Estas son redes de comunicación inalámbrica formada principalmente por vehículos y elementos de infraestructura vial. Las VANETs ofrecen la oportunidad para desarrollar aplicaciones revolucionarias en el ámbito de la seguridad y eficiencia vial. Al ser un dominio tan novedoso, existe una serie de cuestiones abiertas, como el diseño de la infraestructura de estaciones base necesaria y el encaminamiento (routing) y difusión (broadcasting) de paquetes de datos, que todavía no han podido resolverse empleando estrategias clásicas. Es por tanto necesario crear y estudiar nuevas técnicas que permitan de forma eficiente, eficaz, robusta y flexible resolver dichos problemas. Este trabajo de tesis doctoral propone el uso de computación inspirada en la naturaleza o Computación Natural (CN) para tratar algunos de los problemas más importantes en el ámbito de las VANETs, porque representan una serie de algoritmos versátiles, flexibles y eficientes para resolver problemas complejos. Además de resolver los problemas VANET en los que nos enfocamos, se han realizado avances en el uso de estas técnicas para que traten estos problemas de forma más eficiente y eficaz. Por último, se han llevado a cabo pruebas reales de concepto empleando vehículos y dispositivos de comunicación reales en la ciudad de Málaga (España). La tesis se ha estructurado en cuatro grandes fases. En la primera fase, se han estudiado los principales fundamentos en los que se basa esta tesis. Para ello se hizo un estudio exhaustivo sobre las tecnologías que emplean las redes vehiculares, para así, identificar sus principales debilidades. A su vez, se ha profundizado en el análisis de la CN como herramienta eficiente para resolver problemas de optimización complejos, y de cómo utilizarla en la resolución de los problemas en VANETs. En la segunda fase, se han abordado cuatro problemas de optimización en redes vehiculares: la transferencia de archivos, el encaminamiento (routing) de paquetes, la difusión (broadcasting) de mensajes y el diseño de la infraestructura de estaciones base necesaria para desplegar redes vehiculares. Para la resolución de dichos problemas se han propuesto diferentes algoritmos CN que se clasifican en algoritmos evolutivos (evolutionary algorithms, EAs), métodos de inteligencia de enjambre (swarm intelligence, SI) y enfriamiento simulado (simulated annealing, SA). Los resultados obtenidos han proporcionado protocolos de han mejorado de forma significativa las comunicaciones en VANETs. En la tercera y última fase, se han realizado experimentos empleando vehículos reales circulando por las carreteras de Málaga y que se comunicaban entre sí. El principal objetivo de estas pruebas ha sido el validar las mejoras que presentan los protocolos que se han optimizado empleando CN. Los resultados obtenidos de las fases segunda y tercera confirman la hipótesis de trabajo, que la CN es una herramienta eficiente para tratar el diseño inteligente en redes vehiculares

SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) are very useful in many application areas including battlefield surveillance, environment monitoring and target tracking, industrial processes and health monitoring and control. The classical WSNs are composed of large number of densely deployed sensors, where sensors are battery-powered devices with limited signal processing capabilities. In the crowdsourcing based WSNs, users who carry devices with built-in sensors are recruited as sensors. In both WSNs, the sensors send their observations regarding the target to a central node called the fusion center for final inference. With limited resources, such as limited communication bandwidth among the WSNs and limited sensor battery power, it is important to investigate algorithms which consider the trade-off between system performance and energy cost in the WSNs. The goal of this thesis is to study the sensor management problems in resource limited WSNs while performing target localization or tracking tasks. Most research on sensor management problems in classical WSNs assumes that the number of sensors to be selected is given a priori, which is often not true in practice. Moreover, sensor network design usually involves consideration of multiple conflicting objectives, such as maximization of the lifetime of the network or the inference performance, while minimizing the cost of resources such as energy, communication or deployment costs. Thus, in this thesis, we formulate the sensor management problem in a classical resource limited WSN as a multi-objective optimization problem (MOP), whose goal is to find a set of sensor selection strategies which re- veal the trade-off between the target tracking performance and the number of selected sensors to perform the task. In this part of the thesis, we propose a novel mutual information upper bound (MIUB) based sensor selection scheme, which has low computational complexity, same as the Fisher information (FI) based sensor selection scheme, and gives estimation performance similar to the mutual information (MI) based sensor selection scheme. Without knowing the number of sensors to be selected a priori, the MOP gives a set of sensor selection strategies that reveal different trade-offs between two conflicting objectives: minimization of the number of selected sensors and minimization of the gap between the performance metric (MIUB and FI) when all the sensors transmit measurements and when only the selected sensors transmit their measurements based on the sensor selection strategy. Crowdsourcing has been applied to sensing applications recently where users carrying devices with built-in sensors are allowed or even encouraged to contribute toward the inference tasks. Crowdsourcing based WSNs provide cost effectiveness since a dedicated sensing infrastructure is no longer needed for different inference tasks, also, such architectures allow ubiquitous coverage. Most sensing applications and systems assume voluntary participation of users. However, users consume their resources while participating in a sensing task, and they may also have concerns regarding their privacy. At the same time, the limitation on communication bandwidth requires proper management of the participating users. Thus, there is a need to design optimal mechanisms which perform selection of the sensors in an efficient manner as well as providing appropriate incentives to the users to motivate their participation. In this thesis, optimal mechanisms are designed for sensor management problems in crowdsourcing based WSNs where the fusion center (FC) con- ducts auctions by soliciting bids from the selfish sensors, which reflect how much they value their energy cost. Furthermore, the rationality and truthfulness of the sensors are guaranteed in our model. Moreover, different considerations are included in the mechanism design approaches: 1) the sensors send analog bids to the FC, 2) the sensors are only allowed to send quantized bids to the FC because of communication limitations or some privacy issues, 3) the state of charge (SOC) of the sensors affects the energy consumption of the sensors in the mechanism, and, 4) the FC and the sensors communicate in a two-sided market