548 research outputs found

    A biased random-key genetic algorithm for the capacitated minimum spanning tree problem

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    This paper focuses on the capacitated minimum spanning tree(CMST)problem.Given a central processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals,the goal is to design a minimum cost network to carry this demand. Potential links exist between any pair of terminals and between the central processor and the terminals. Each potential link can be included in the design at a given cost.The CMST problem is to design a minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-keygenetic algorithm(BRKGA)is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem.This paper explores several solution encodings as well as different strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem. Computational experiments are presented showing the effectivenes sof the approach:Seven newbest- known solutions are presented for the set of benchmark instances used in the experiments.Peer ReviewedPostprint (author’s final draft

    Analysis and optimization of highly reliable systems

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    In the field of network design, the survivability property enables the network to maintain a certain level of network connectivity and quality of service under failure conditions. In this thesis, survivability aspects of communication systems are studied. Aspects of reliability and vulnerability of network design are also addressed. The contributions are three-fold. First, a Hop Constrained node Survivable Network Design Problem (HCSNDP) with optional (Steiner) nodes is modelled. This kind of problems are N P-Hard. An exact integer linear model is built, focused on networks represented by graphs without rooted demands, considering costs in arcs and in Steiner nodes. In addition to the exact model, the calculation of lower and upper bounds to the optimal solution is included. Models were tested over several graphs and instances, in order to validate it in cases with known solution. An Approximation Algorithm is also developed in order to address a particular case of SNDP: the Two Node Survivable Star Problem (2NCSP) with optional nodes. This problem belongs to the class of N P-Hard computational problems too. Second, the research is focused on cascading failures and target/random attacks. The Graph Fragmentation Problem (GFP) is the result of a worst case analysis of a random attack. A fixed number of individuals for protection can be chosen, and a non-protected target node immediately destroys all reachable nodes. The goal is to minimize the expected number of destroyed nodes in the network. This problem belongs to the N P-Hard class. A mathematical programming formulation is introduced and exact resolution for small instances as well as lower and upper bounds to the optimal solution. In addition to exact methods, we address the GFP by several approaches: metaheuristics, approximation algorithms, polytime methods for specific instances and exact methods in exponential time. Finally, the concept of separability in stochastic binary systems is here introduced. Stochastic Binary Systems (SBS) represent a mathematical model of a multi-component on-off system subject to independent failures. The reliability evaluation of an SBS belongs to the N P-Hard class. Therefore, we fully characterize separable systems using Han-Banach separation theorem for convex sets. Using this new concept of separable systems and Markov inequality, reliability bounds are provided for arbitrary SBS

    GRASP/VND Optimization Algorithms for Hard Combinatorial Problems

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    Two hard combinatorial problems are addressed in this thesis. The first one is known as the ”Max CutClique”, a combinatorial problem introduced by P. Martins in 2012. Given a simple graph, the goal is to find a clique C such that the number of links shared between C and its complement C C is maximum. In a first contribution, a GRASP/VND methodology is proposed to tackle the problem. In a second one, the N P-Completeness of the problem is mathematically proved. Finally, a further generalization with weighted links is formally presented with a mathematical programming formulation, and the previous GRASP is adapted to the new problem. The second problem under study is a celebrated optimization problem coming from network reliability analysis. We assume a graph G with perfect nodes and imperfect links, that fail independently with identical probability ρ ∈ [0,1]. The reliability RG(ρ), is the probability that the resulting subgraph has some spanning tree. Given a number of nodes and links, p and q, the goal is to find the (p,q)-graph that has the maximum reliability RG(ρ), uniformly in the compact set ρ ∈ [0,1]. In a first contribution, we exploit properties shared by all uniformly most-reliable graphs such as maximum connectivity and maximum Kirchhoff number, in order to build a novel GRASP/VND methodology. Our proposal finds the globally optimum solution under small cases, and it returns novel candidates of uniformly most-reliable graphs, such as Kantor-Mobius and Heawood graphs. We also offer a literature review, š and a mathematical proof that the bipartite graph K4,4 is uniformly most-reliable. Finally, an abstract mathematical model of Stochastic Binary Systems (SBS) is also studied. It is a further generalization of network reliability models, where failures are modelled by a general logical function. A geometrical approximation of a logical function is offered, as well as a novel method to find reliability bounds for general SBS. This bounding method combines an algebraic duality, Markov inequality and Hahn-Banach separation theorem between convex and compact sets

    Routin in wireless sensor networks

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    Internet of Things (IoT) paradigm envisages to expand the current Internet witha huge number of intelligent communicating devices. Wireless Sensor Networks(WSN) deploy the devices running on meagre energy supplies and measuring environmental phenomena (like temperature, radioactivity, or CO 2 ). WSN popularapplications include monitoring, telemetry, and natural disaster prevention. Major WSN challenges are energy efficiency, overcome impairments of wireless medium, and operate in the self-organisation. The WSN integrating IoT will rely on a set of the open standards striving to offer scalability and reliability in a variety of the operating scenarios and conditions. Nevertheless, the current state of the standards have interoperability issues and can benefit from further improvements. The contributions of the thesis work are:We performed an extensive study of Bloom Filters and their use in storing nodetext-based elements in IP address. Different techniques of compression andvariants of filters allowed us to develop an efficient system closing the gapbetween feature-routing and classic approach compatible with IPv6 networks.We propose Featurecast, a routing protocol/naming service for WSN. It allowsto query sensor networks using a set of characteristics while fitting in anIPv6 packet header. We integrate our protocol in RPL and introduce a newmetric, which increase the routing efficiency. We check its performance inboth extensive simulations and experimentations on real sensors in a large-scale Senslab testbed. Large-scale simulations demonstrate the advantagesof our protocol in terms of memory usage, control overhead, packet deliveryrate and energy consumption.We introduce WEAVE - a routing protocol for networks with geolocation. Our so-lution does not use any control message and learn its paths only by observingthe traffic. Several mechanisms are introduce to keep a fixed-size header andbypass both small as well as large obstacles and provide an efficient communication between nodes. We performed simulations on large scale involvingmore than 19000 nodes and real-sensor experimentations on IoT-lab testbed. Our results show that we achieve much better performance especially in large and dynamic networks without introducing any control overhead.Le paradigme d’Internet des objets (IoT) envisage d’élargir Internet actuelle avec un grand nombre de dispositifs intelligents. RĂ©seaux de Capteurs sans Fil (WSN) dĂ©ploie les dispositifs fonctionnant sur des approvisionnements Ă©nergĂ©tiques maigres et mesurant de phĂ©nomĂšnes environnementaux (comme la tempĂ©rature, la radioactivitĂ©, ou CO 2). Des applications populaires de WSN comprennent la surveillance, le tĂ©lĂ©mĂ©trie, et la prĂ©vention des catastrophes naturelles. Des dĂ©fis majeurs de WSN sont comment permettre Ă  l’efficacitĂ© Ă©nergĂ©tique, surmonter les dĂ©ficiences de support sans fil, et d’opĂ©rer dans Ă  la maniĂšre auto-organisĂ©e. L’intĂ©gration de WSN dans IoT se posera sur des standards ouvertes efforçant d’offrir Ă©volutivitĂ© et de fiabilitĂ© dans une variĂ©tĂ© de scĂ©narios et conditions de fonctionnement. NĂ©anmoins, l’état actuel des standards a les problĂšmes d’interopĂ©rabilitĂ© et peuvent bĂ©nĂ©ficier de certaines amĂ©liorations. Les contributions de la thĂšse sont :Nous avons effectuĂ© une Ă©tude approfondie des filtres de Bloom et de leur utilisation dans le stockage de caractĂ©ristiques de nƓud dans l’adresse IP. DiffĂ©rentes techniques de compression et de variantes de filtres nous ont permisde dĂ©velopper un systĂšme efficace qui comble l’écart entre le routage de caractĂ©ristiques et l’approche classique compatible avec les rĂ©seaux IPv6.Nous proposons Featurecast, un protocole de routage / service de nommage pourWSN. Il permet d’interroger les rĂ©seaux de capteurs en utilisant un ensemble de caractĂ©ristiques tout raccord en entĂȘte de paquet IPv6. Nous intĂ©grons notre protocole dans RPL et introduisons une nouvelle mesure, qui augmentent l’efficacitĂ© de routage. Nous vĂ©rifions sa performance contre dans des simulations approfondies et des test sur des capteurs rĂ©els dans un bancd’essai Ă  grande Ă©chelle. Simulations approfondies dĂ©montrent les avantagesde notre protocole en termes d’utilisation de la mĂ©moire, le surcharge de con-trĂŽle, le taux de livraison de paquets et la consommation d’énergie.Nous introduisons WEAVE - un protocole de routage pour les rĂ©seaux avec gĂ©olo-calisation. Notre solution n’utilise pas de message de contrĂŽle et apprend sesvoies seulement en observant le trafic. Plusieurs mĂ©canismes sont introduitspour garder un en-tĂȘte de taille fixe, contourner Ă  la fois les petits commeles grands obstacles et fournir une communication efficace entre les nƓuds.Nous avons effectuĂ© des simulations Ă  grande Ă©chelle impliquant plus de 19000noeuds et des expĂ©riences avec des capteurs rĂ©els sur banc d’essai IoT-lab.Nos rĂ©sultats montrent que nous atteignons bien meilleures performances enparticulier dans les rĂ©seaux grands et dynamiques sans introduire de surcharg

    Distributed optimization algorithms for multihop wireless networks

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    Recent technological advances in low-cost computing and communication hardware design have led to the feasibility of large-scale deployments of wireless ad hoc and sensor networks. Due to their wireless and decentralized nature, multihop wireless networks are attractive for a variety of applications. However, these properties also pose significant challenges to their developers and therefore require new types of algorithms. In cases where traditional wired networks usually rely on some kind of centralized entity, in multihop wireless networks nodes have to cooperate in a distributed and self-organizing manner. Additional side constraints, such as energy consumption, have to be taken into account as well. This thesis addresses practical problems from the domain of multihop wireless networks and investigates the application of mathematically justified distributed algorithms for solving them. Algorithms that are based on a mathematical model of an underlying optimization problem support a clear understanding of the assumptions and restrictions that are necessary in order to apply the algorithm to the problem at hand. Yet, the algorithms proposed in this thesis are simple enough to be formulated as a set of rules for each node to cooperate with other nodes in the network in computing optimal or approximate solutions. Nodes communicate with their neighbors by sending messages via wireless transmissions. Neither the size nor the number of messages grows rapidly with the size of the network. The thesis represents a step towards a unified understanding of the application of distributed optimization algorithms to problems from the domain of multihop wireless networks. The problems considered serve as examples for related problems and demonstrate the design methodology of obtaining distributed algorithms from mathematical optimization methods

    Energy efficient broadcasting in wireless ad hoc networks

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    In recent years wireless multi-hop networks have attracted significant attention due to their wide range of potential civil and military applications. Broadcasting is a funda- mental data dissemination scheme for these networks. The transmission power control is an important issue in wireless ad hoc networks and still has no satisfactory solution methods. The wireless networking environment presents formidable challenges to the study of broadcasting problems. In particular, the properties of the wireless medium and the presence of battery-powered devices require novel modeling and algorithmic approaches concentrating on judicious use of limited energy resources in wireless net- works. In addition, networks are often required to provide certain quality of service (QoS) guarantees in terms of the end-to-end delay along the individual paths from the source to each of the destination nodes. Moreover, the received signal at each receiv- ing node must be strong enough to be successfully decoded. In this study we address the minimum-energy broadcast problem in multi-hop wireless networks with respect to two different constraints: (i) each node must receive broadcast message within a given delay bound Δ, and (ii) signal-to-interference-plus-noise ratio (SINR) of the received signal must be above a given threshold [y] so that the received signal can be successfully decoded at the receiving node. We propose two distinct algorithms Distributed Tree Expansion (DTE) and SINR-BIP which aim to generate minimum power broadcast tree with respect to constraint (i) and (ii), respectively and exclusively. DTE is based on an implementation of a distributed minimum spanning tree algorithm in which the tree grows at each iteration by adding a node that can cover the maximum number of currently uncovered nodes in the network with minimum incremental transmission power and without violating the delay constraint. In SINR-BIP, we apply the similar idea of well-known Broadcast Incremental Power (BIP) algorithm while considering the SINR values of received powers. In addition, we use an embedded pruning procedure in SINR-BIP, so that the myopic effect of the algorithm is mitigated. Both the algo- rithms DTE and SINR-BIP are constructive in nature since the broadcast tree grows at each iteration. We observed that the DTE outperforms the existing algorithms and the total energy consumptions of the generated broadcast trees by DTE is within 20% percent of the solutions obtained by Integer Programming

    Positioning and Scheduling of Wireless Sensor Networks - Models, Complexity, and Scalable Algorithms

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    Unified Role Assignment Framework For Wireless Sensor Networks

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    Wireless sensor networks are made possible by the continuing improvements in embedded sensor, VLSI, and wireless radio technologies. Currently, one of the important challenges in sensor networks is the design of a systematic network management framework that allows localized and collaborative resource control uniformly across all application services such as sensing, monitoring, tracking, data aggregation, and routing. The research in wireless sensor networks is currently oriented toward a cross-layer network abstraction that supports appropriate fine or course grained resource controls for energy efficiency. In that regard, we have designed a unified role-based service paradigm for wireless sensor networks. We pursue this by first developing a Role-based Hierarchical Self-Organization (RBSHO) protocol that organizes a connected dominating set (CDS) of nodes called dominators. This is done by hierarchically selecting nodes that possess cumulatively high energy, connectivity, and sensing capabilities in their local neighborhood. The RBHSO protocol then assigns specific tasks such as sensing, coordination, and routing to appropriate dominators that end up playing a certain role in the network. Roles, though abstract and implicit, expose role-specific resource controls by way of role assignment and scheduling. Based on this concept, we have designed a Unified Role-Assignment Framework (URAF) to model application services as roles played by local in-network sensor nodes with sensor capabilities used as rules for role identification. The URAF abstracts domain specific role attributes by three models: the role energy model, the role execution time model, and the role service utility model. The framework then generalizes resource management for services by providing abstractions for controlling the composition of a service in terms of roles, its assignment, reassignment, and scheduling. To the best of our knowledge, a generic role-based framework that provides a simple and unified network management solution for wireless sensor networks has not been proposed previously

    Geometric optimization and querying : exact & approximate

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    This thesis has two main parts. The first part deals with the stage illumination problem. Given a stage represented by a line segment L and a set of lightsources represented by a set of points S in the plane, assign powers to the lightsources such that every point on the stage receives a sufficient amount, e.g. one unit, of light while minimizing the overall power consumption. By assuming that the amount of light arriving from a fixed lightsource decreases rapidly with the distance from the lightsource, this becomes an interesting geometric optimization problem. We present different solutions, based on convex optimization, discretization and linear programming, as well as a purely combinatorial approximation algorithm. Some experimental results are also provided. In the second part of this thesis, we are concerned with two different geometric problems whose solutions are based on the construction of a data structure that would allow for efficient queries. The central idea of our data structures is the well-separated pair decomposition. The first problem we address is the k-hop restricted shortest path under the power-euclidean distance function. Given a set P of n points in the plane and the distance function jpqjd +Cp for some constant d > 1, nonnegative offset cost Cp and p;q 2 P, where jpqj denotes the Euclidean distance between p and q, we consider the problem of finding paths between any pair of points that minimize the lenght of the path and do not use more than some constant number k of hops. Known exact algorithms for this problem required W(nlogn) per query pair (p;q). We relax the exactness requirement and only require approximate (1+e) solutions which allows us to derive schemes which guarantee constant query time using linear space and O(nlogn) preprocessing time. The dependence on e is polynomial in 1=e. We also develop a tool that might be of independent interest: For any pair of points p;q 2 P report in constant time the cluster pair (A;B) representing (p;q) in a well-separated pair decomposition of P. The second problem in this part is so-called cone-restricted nearest neighbor. For a given point set in Euclidean space we consider the problem of finding (approximate) nearest neighbors of a query point but restricting only to points that lie within a fixed cone with apex at the query point. We investigate the structure of the Voronoi diagram induced by this notion of proximity and present approximate and exact data structures for answering cone-restricted nearest neighbor queries. In particular, we develop an approximate Voronoi diagram of size O((n=ed) log(1=e)) that can be used to answer cone-restricted nearest neighbor queries in O(log(n=e)) time.Diese Arbeit besteht aus zwei Teilen. Der erste Teil behandelt das Stage Illumination Problem. Hierbei möchte man eine BĂŒhne, die durch ein GeradenstĂŒck reprĂ€sentiert ist, durch Lichtquellen, die durch Punkte in der Ebene reprĂ€sentiert sind, so beleuchten, dass jeder Punkt der BĂŒhne genĂŒgend Licht erhĂ€lt und dabei möglichst wenig Energie verbrauchen. Wenn man annimmt, dass die LichtintensitĂ€t stark mit der Entfernung zur Lichtquelle abnimmt, so stellt dies ein interesanntes geometrisches Optimierungsproblem dar. Wir geben verschiedene Lösungen an, die sowohl auf konvexer Optimierung, Diskretisierung und Linearer Programmierung basieren, als auch einen kombinatorischen Approximationsalgorithmus. Es werden auch experimentelle Resultate angegeben. Im zweiten Teil dieser Arbeit behandeln wir zwei verschiedene geometrische Probleme, deren Lösungen auf einer Datenstruktur basieren, die effiziente Anfragen beantworten kann. Die zentrale Idee unserer Datenstruktur ist die well-separated pair decomposition WSPD. Das erste Problem, das wir ansprechen ist das k-hop restricted shortest path under the power-euclidean distance function. FĂŒr n Punkte in der Ebene möchte man den kĂŒrzesten Pfad zwischen zwei beliebigen Punkten finden, der nicht mehr als k Kanten benötigt. Bekannte exakte Algorithmen fĂŒr dieses Problem benötigen W(nlogn) Zeit pro Anfrage (p;q). Wir lockern die Exaktheitsforderung und verlangen nur eine (1+e)-Approximation. Dies erlaubt uns eine Methode zu entwickeln, die konstante Zeit pro Anfrage garaniert und nur linearen Platz benötigt bei einer Vorverarbeitungszeit von O(nlogn). Die AbhĂ€ngigkeit von e ist polynomiell in 1=e. Außerdem entwickeln wir eine Methode, die davon unabhĂ€ngig von Interesse ist. FĂŒr ein Punktepaar p;q 2 P bestimmen wir in konstanter Zeit das Cluster-paar (A;B), das (p;q) in einer WSPD von P bestimmt. Das zweite Problem in diesem Teil ist das sogenannte cone-restricted nearest neighbor problem. FĂŒr eine gegebene Menge von Punkten im Euklidischen Raum betrachten wir das Problem den nĂ€chsten Nachbarpunkt zu bestimmen, der in einem Kegel liegt, dessen Spitze ein beliebiger Anfragepunkt ist. Wir untersuchen das dazugehörige Voronoi- Diagramm und entwickeln effiziente Datenstrukturen sowohl fĂŒr exakte als auch fĂŒr approximative cone-restricted nearest neighbor-Anfragen. Im speziellen entwickeln wir ein approximatives Voronoi-Diagramm der GrĂ¶ĂŸe O((n=ed) log(1=e)), das dazu benutzt werden kann, Anfragen in der Zeit O(log(n=e)) zu beantworten
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