Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Multi-Objective Cross-Layer Optimization for Selection of Cooperative Path Pairs in Multihop Wireless Ad hoc Networks

This paper focuses in the selection of an optimal path pair for cooperative diversity based on cross-layer optimization in multihop wireless ad hoc networks. Cross-layer performance indicators, including power consumption, signal-to-noise ratio, and load variance are optimized using multi-objective optimization (MOO) with Pareto method. Consequently, optimization can be performed simultaneously to obtain a compromise among three resources over all possible path pairs. The Pareto method is further compared to the scalarization method in achieving fairness to each resource. We examine the statistics of power consumption, SNR, and load variance for both methods through simulations. In addition, the complexity of the optimization of both methods is evaluated based on the required computing time

Décisions multicritères dans les réseaux de télécommunications autonomes

Les réseaux de données actuels sont des entités complexes qui opèrent dans des environnements dynamiques et hétérogènes. L'architecture et les protocoles de ces réseaux doivent faire face à plusieurs défis, notamment l'adaptation dynamique et la prise de décisions autonome en présence de plusieurs critères, souvent contradictoires, tels que le délai, le taux de perte, la gigue, l'énergie, etc. Cependant, les problèmes de décision multicritère ont généralement de multiples solutions. Ces problèmes sont résolus par des méthodes qui utilisent des paramètres dont les choix ont des conséquences difficiles à prévoir. De ce fait, la plupart des méthodes de décision multicritère proposées dans la littérature supposent la présence d'un décideur qui guide le processus de décision. Enfin, le choix des paramètres suppose souvent une interactivité avec le décideur, ce qui est difficile, voire impossible, à envisager dans un contexte autonome. Dans cette thèse, nous proposons une nouvelle méthode de décision multicritère adaptée aux systèmes autonomes en général et aux réseaux autonomes en particulier. La méthode de décision multicritère de type poupée russe'' que nous introduisons utilise un ensemble de boîtes de qualité englobantes, définies dans l'espace des critères, afin d'estimer une large gamme de fonctions d'utilité. D'une part, la méthode proposée s'adapte au caractère dynamique des réseaux autonomes, afin de maximiser la satisfaction des utilisateurs. D'autre part, elle utilise des paramètres qui sont soit directement déduits de faits objectifs, tels que des normes ou spécifications techniques, soit obtenus à l'aide d'une expérience de type MOS (Mean Opinion Score) au moyen d'une méthode de classification automatique. Nous avons testé les performances de la méthode de la poupée russe sur un cas pratique de routage dans les réseaux ad hoc sans fil. Les expérimentations ont montré que le routage réalisé avec la méthode de la poupée russe est toujours meilleur ou similaire à celui de la méthode de la somme pondérée qui est largement utilisée. Cela est dû à la capacité d'adaptation de la décision offerte par cette nouvelle méthode de décision multicritèreToday's data networks are complex entities that operate in dynamic and heterogeneous environments. The architecture and protocols of these networks have to face several challenges such as dynamic adaptation and autonomous decision-making in the presence of several, often conflicting, criteria such as delay, loss rate, jitter, energy, etc. However, multicriteria decision making problems usually have multiple solutions. These problems are solved by methods that use parameters whose choices have consequences difficult to predict. Thus, most multicriteria decision making methods proposed in the literature assume the presence of a decision maker who guides the decision process. Finally, the choice of parameter values often involves an interaction with the decision maker, which is difficult or impossible to do in an autonomous context. In this thesis, we propose a new multicriteria decision making method suitable for autonomous systems in general and autonomous networks in particular. The Russian doll like method we propose uses a set of nested quality boxes (like Russian dolls) defined in the criteria space, in order to approximate a wide range of utility functions. First, the proposed method adapts to the dynamic nature of autonomous networks in order to maximize user satisfaction. Second, it uses parameters that are directly deduced from objective facts such as technical standards or specifications, or obtained from a MOS (Mean Opinion Score) experiment using an automatic classification method. We tested the performance of the Russian doll like method in a case of routing in wireless ad hoc networks. Experiments have shown that routing done with the Russian doll like method is always better or similar to the one done by the weighted sum method which is widely used. This is due to the adaptation ability of the decision provided by this new multicriteria decision making methodEVRY-INT (912282302) / SudocSudocFranceF

Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009

Dieser Tagungsband enthält die Beiträge des 19. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft für Informatik (GI), der vom 2.-4. Dezember 2009 im Haus Bommerholz bei Dortmund stattfindet

Model-based design and operation of fuel cell systems

Fuel cells are a promising technology for the production of electricity from hydrogen or other fuels with high effi�ciency and low emissions. They are suitable for stationary, transportation and portable applications. However, they are still more expensive than existing technologies and there are technical challenges that need to be overcome for their commercialisation. Therefore, accurate and e�fficient design methodologies for fuel cell systems design are becoming increasingly important. Modelling and optimisation present a great potential to inform fuel cell systems design, which often results in savings in design cycle time and cost, and better design and operation. The purpose of this thesis is to investigate the applicability of model-based design approaches to fuel cell systems design when applied to a single-cell fuel cell, then a fuel cell stack and, ultimately, a system-level fuel cell system, such as a microcogeneration plant. The development of mathematical models for a single-cell fuel cell, a stack and a microcogeneration system is presented in detail. The use of these models in model-based design is then illustrated. For instance, the effectiveness of a conventional humidi�fication design is examined using the single-cell fuel cell model. The fuel cell stack model is used within a multi-objective optimisation framework to investigate how size trades for e�fficiency. Finally, the micro-cogeneration plant model is used to investigate the trade-off between fuel consumption and electrical power output, compare different micro-cogeneration operating strategies and examine the interaction between operating strategies and electricity network. Overall, when properly formulated and validated, modelling and optimisation are useful tools in fuel cell systems design as they provide means by which engineers can obtain valuable information about the behaviour of the system, make informed decisions, generate different design alternatives and identify good designs even before a prototype is fabricated