We propose a large neighborhood search (LNS) algorithm to solve the periodic location routing problem (PLRP). The PLRP combines location and routing decisions over a planning horizon in which customers require visits according to a given frequency and the specific visit days can be chosen. We use parallelization strategies that can exploit the availability of multiple processors. The computational results show that the algorithms obtain better results than previous solution methods on a set of standard benchmark instances from the literature.

In recent years, wireless mesh networking has attracted a growing interest due to its inherent flexibility, scalability, and reliability. The IEEE 802.16 standard, commonly known as worldwide interoperability for microwave access (WiMAX), is the latest technology that enables broadband wireless access over long distances. WiMAX, which emerges as a wireless alternative to cable and digital subscriber line (DSL), is an ideal candidate to serve as the infrastructure for large scale wireless mesh networks. This thesis focuses on the quality of service (QoS) provisioning techniques in WiMAX-based metropolitan area mesh networks. We study the connection admission control (CAC) and routing issues in the design and operation of wireless multihop mesh networks. We propose a joint CAC and routing scheme for multiple service classes with the objective to maximize the overall revenue from all carried connections. Connection-level QoS constraints such as handoff connection dropping probability can be guaranteed within a threshold. Multiple service classes can be prioritized by imposing different reward rates. We apply optimization techniques to obtain the optimal CAC policies. The optimality criterion is the long-run average reward. We demonstrate that the proposed scheme can the maximum revenue obtainable by the system under QoS constraints. We show that the optimal joint policy is a randomized policy, i.e., connections are admitted to the system with some probabilities when the system is in certain states. Simulation results illustrate that the proposed scheme meets our design goals and outperforms the existing scheme. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

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In the multiratecasting problem in wireless sensor networks, the source sensor is usually required to report to multiple destinations at dif- ferent rates for each of them. We present a MST-based rate-aware cost-efficient multiratecast routing protocol (MSTRC). The proposed MSTRC examines only one set partition of destinations at each for- warding step. A message split occurs when the locally-built minimum spanning tree (MST) over the current node and the set of destina- tions has multiple edges originated at the current node. Destinations spanned by each of these edges are grouped together, and for each of these subsets the best neighbor is selected as the next hop. We also suggested a novel face recovery mechanism to deal with void ar- eas, when no neighbor provides positive progress toward destinations. It constructs a MST of current node and destinations without the progress via neighbors, and for each set partition of destinations cor- responding to an edge e in MST, the face routing keeps going until a node that is closer to one of these destinations is found, allowing for greedy continuation, while the process repeats for the remaining desti- nations similarly. Our experimental results demonstrate that MSTRC is highly rate-efficient in all scenarios, and unlike existing solutions, it is adaptive to destination rate deviations.

In this thesis, we are concerned with multi-player games played on finite graphs. They are games in which the players interact trying to fulfil their own objectives which are not necessarily antagonistic to the others’. More particularly, we are interested in the study of dynamics which model the behaviour of the players when they repeatedly update their strategy in order to achieve a better outcome. A dynamics terminates when these updates converge to a state in which the players have no incentive to further update their respective strategies. We define several dynamics characterised by the type of updates made by the players.There are two types of contributions in this thesis. The first one is to draw a general framework to reason about the termination of dynamics in order to show its applicability to particular problems. In this abstract framework, we present several meta-theorems that make the links between games and dynamics explicit. For example, we introduce a notion of game minor that allows to induce simulations between the associated dynamics. The second type of contribution is the application of dynamics to a particular context, characterised by three parameters: the arena of the game, the conditions over the dynamics, and the payoff functions of the players. The first arena we deal with are sequential games (or games played on trees). Among other results, we prove that the acyclicity of the preferences is a necessary and sufficient condition to ensure the termination of dynamics that respect the Subgame Improvement Property (i.e. every update has to improve the payoff in the subgame of the change).Another studied arena is the so called One-player Games. We model BGP (Border Gateway Protocol, which is a standard interdomain routing protocol) into dynamics on graphs. We firstly revisit some classical results of network theory in our context, then we identify a theoretical and relevant framework regarding to the termination of the dynamics. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

An ad hoc mobile network is a collection of mobile nodes that are dynamically and arbitrarily located in such a manner that the interconnections between nodes are capable of changing on a continual basis. In order to simplify communication within the network, a routing protocol is used to discover routes between nodes. The primary goal of such an ad hoc network routing protocol is correct and efficient route establishment between a pair of nodes so that messages may be delivered on time. Route construction should be done with a minimum of overhead and bandwidth consumption. This document describes the Optimized Link State Routing (OLSR) protocol for mobile ad hoc networks. The key concept used in the protocol is that of multipoint relays (MPRs). MPRs provide an efficient mechanism for flooding control traffic by reducing the number of transmissions required.

Tato práce se zabývá návrhem architektury a implementací flexibilního, škálovatelného a bezpečného systému pro správu virtuálních privátních sítích, který by umožnil propojení jinak nedostupných routerů a zařízení v jejich lokálních sítích. Ačkoli je systém primárně určen pro použití s routery od výrobce Advantech, podpora jiných zařízení může být později přidána.

Anonymity on the internet is becoming a actuall issue nowadays. There are several tools, that can be used to monitor user's activity and it can lead to lose privacy of users. The aim of this master's thesis is to describe different ways of working anonymous systems, especially the method called Onion Routing. The introduction of this work is devoted to the description of this method together with asymmetric cryptosystem RSA. The second part belongs to basics of socket programming and to the implementation of anonymous system in programming language C++. The final part is focussed on analysis of system in terms of security and time complexity. The conditions of anonymity and decentralization are accomplished. There is no presence of central server in the system and the management is handled by signalling messages.

In the thesis of “Mohammed Aijaz Mohiuddin”, Engineering Nature-Inspired Heuristics for the Open Shortest Path First Weight Setting Problem, nature inspired heuristics were developed. Besides the existing two objectives, namely maximum utilization and the number of congested links, a third objective namely the number of unused links was used to formulate the fuzzy based objective function for the OSPFWS problem. The idea was to make use unused network links if any. Furthermore, a hybrid fuzzy based evolutionary Particle Swarm Optimization (FEPSO) algorithm was designed that harnessed evolutionary intelligence along with swarm intelligence. The proposed FEPSO algorithm was tested on different size test cases and its performance was mutually compared with other algorithms namely Simulated Annealing, Simulated Evolution, Particle Swarm Optimization, Weighted Aggregation Particle Swarm Optimization, Pareto-dominance Particle Swarm Optimization and Non-dominating Sorting Genetic Algorithm. Obtained results suggested the better performance of FEPSO among other algorithms over majority of test cases. / Thesis (PHD)--University of Pretoria, 2018. / Computer Science / PhD / Unrestricted

Driving can be a stressful task, especially under congestion conditions. Several studies have shown a positive correlation between stress and aggressive behaviour behind the wheel, leading to accidents. One common way to minimize stress while driving is to avoid highly congested roads. However, not all drivers show the same response towards high traffic situations or other road conditions. For instance, some drivers may prefer congested routes to longer ones to minimize travel time. Increasingly, drivers are employing Advanced Traveller Information Systems while commuting to both familiar and unfamiliar destinations, not just to obtain information on how to reach a certain endpoint, but to acquire real-time data on the state of the roads and avoid undesired traffic conditions. In this thesis, we propose an Advanced Traveller Information System that personalizes the driver’s route using their road preferences and measures their physiological signals during the trip to assess mental stress. The system then links road attributes, such as number of lanes, speed limit, and traffic severity, with the driver’s stress levels. Then, it uses machine learning to predict their stress levels on similar roads. Hence, routes that contribute to high-levels of stress can therefore be avoided in future trips. The average accuracy of the proposed stress level prediction model is 76.11%.