Self-Configuration and Self-Optimization Process in Heterogeneous Wireless Networks

Self-organization in Wireless Mesh Networks (WMN) is an emergent research area, which is becoming important due to the increasing number of nodes in a network. Consequently, the manual configuration of nodes is either impossible or highly costly. So it is desirable for the nodes to be able to configure themselves. In this paper, we propose an alternative architecture for self-organization of WMN based on Optimized Link State Routing Protocol (OLSR) and the ad hoc on demand distance vector (AODV) routing protocols as well as using the technology of software agents. We argue that the proposed self-optimization and self-configuration modules increase the throughput of network, reduces delay transmission and network load, decreases the traffic of HELLO messages according to network’s scalability. By simulation analysis, we conclude that the self-optimization and self-configuration mechanisms can significantly improve the performance of OLSR and AODV protocols in comparison to the baseline protocols analyzed

Pervasive handheld computing systems

The technological role of handheld devices is fundamentally changing. Portable computers were traditionally application specific. They were designed and optimised to deliver a specific task. However, it is now commonly acknowledged that future handheld devices need to be multi-functional and need to be capable of executing a range of high-performance applications. This thesis has coined the term pervasive handheld computing systems to refer to this type of mobile device. Portable computers are faced with a number of constraints in trying to meet these objectives. They are physically constrained by their size, their computational power, their memory resources, their power usage, and their networking ability. These constraints challenge pervasive handheld computing systems in achieving their multi-functional and high-performance requirements. This thesis proposes a two-pronged methodology to enable pervasive handheld computing systems meet their future objectives. The methodology is a fusion of two independent and yet complementary concepts. The first step utilises reconfigurable technology to enhance the physical hardware resources within the environment of a handheld device. This approach recognises that reconfigurable computing has the potential to dynamically increase the system functionality and versatility of a handheld device without major loss in performance. The second step of the methodology incorporates agent-based middleware protocols to support handheld devices to effectively manage and utilise these reconfigurable hardware resources within their environment. The thesis asserts the combined characteristics of reconfigurable computing and agent technology can meet the objectives of pervasive handheld computing systems

Heurísticas para determinação do itinerário de agentes móveis sob restrições temporais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduação em Engenharia ElétricaO presente trabalho aborda o desenvolvimento de um modelo computacional para aplicações baseadas em agentes móveis imprecisos com restrição temporal, considerando a definição dinâmica do itinerário e seu impacto no tempo de resposta da missão. Neste modelo computacional cada agente possui certa flexibilidade na definição de seu itinerário. Esta flexibilidade está relacionada com características dos recursos. Para auxiliar o agente na definição de seu itinerário são propostas heurísticas. Cada heurística confere ao agente um comportamento distinto que, baseado nas diferentes características de cada recurso, é utilizado pelo agente móvel na definição do itinerário. Essas heurísticas podem ser utilizadas individualmente ou em pares/trios (através do uso de clones). Heurísticas mais elaboradas também foram propostas, capazes de escolher seu comportamento considerando um histórico de benefícios conseguidos em execuções passadas do agente móvel. O agente usa adaptação na partida. Uma vez escolhido o comportamento para a missão em questão, ele prossegue com este comportamento até o final da missão. Para realizar a escolha do comportamento foi utilizada probabilidade condicional baseada nas características do ambiente (sistema distribuído) e nos últimos eventos (histórico). As heurísticas propostas foram avaliadas através de simulações