Eyjólfur Ingi Ásgeirsson

On a game theoretic approach to capacity maximization in wireless networks

We consider the capacity problem (or, the single slot scheduling problem) in wireless networks. Our goal is to maximize the number of successful connections in arbitrary wirelessnetworks where a transmission is successful only if the signal-to-interference-plus-noise ratio at the receiver is greater than some threshold. We study a game theoretic approach towards capacity maximization introduced by Andrews and Dinitz (INFOCOM 2009) and Dinitz (INFOCOM 2010). We prove vastly improved bounds for the game theoretic algorithm. In doing so, we achieve the first distributed constant factor approximation algorithm for capacity maximization for the uniform power assignment. When compared to the optimum where links may use an arbitrary power assignment, we prove a O(log Δ) approximation, where Δ is the ratio between the largest and the smallest link in the network. This is an exponential improvement of the approximation factor compared to existing results for distributed algorithms. All our results work for links located in any metric space. In addition, we provide simulation studies clarifying the picture on distributed algorithms for capacity maximization.

Integrated Agent-based and System Dynamics Modelling for Simulation of Sustainable Mobility

In this article, a conceptual framework for a comprehensive evaluation of the diffusion process of alternative fuel vehicles is introduced. The framework takes into account the most influencing stakeholders, including car manufacturers, car dealers, consumers, energy supply system, fuel stations and government. The underlying mathematical models of different stakeholders are then integrated in one model of the whole energy and transport system. The hybrid modelling framework links the two powerful dynamic simulation approaches of system dynamics (SD) and agent-based (AB) modelling. Integrated modelling structure gives the potential of building more accurate and computationally efficient models for simulating the transition to sustainable mobility. We specify the integration process and the most important linking variables between various energy and transport components. Then the application of the integrated model is explained through a test case and, finally, the applicability of the hybrid AB and SD approach and its potential contribution to the models of transition to sustainable mobility will be concluded.

Bridging the gap between self schedules and feasible schedules in staff scheduling

Every company that has employees working on irregular schedules must deal with the difficult and time consuming problem of creating feasible schedules for the employees. We introduce an algorithm that takes a partial schedule created by requests from employees and creates feasible schedule where most of the employee’s requests are unchanged, while still making sure that rules and regulations are not violated. The algorithm is based on independent modules, which can be executed in any order, and each module tries to emulate some action taken by a staff manager.Our goal is to create a transparent and fair system that creates feasible schedules of high quality, but also a system where the employees can get an explanation and justification for every change that the algorithm makes to the employee requests. By emulating the actions of staff managers, the algorithm is easily understood by staff managers and, using detailed logs of any action, make any decision easy to explain to the employees.We will present the algorithm and show results from four real world companies and institutions. The results show that a simple module based heuristic can get good results and create fair and feasible schedules that encourage employees to participate in the self-scheduling process.

A fully distributed algorithm for throughput performance in wireless networks

We study link scheduling in wireless networks under stochastic arrival processes of packets, and give an algorithm that achieves stability in the physical (SINR) interference model. The efficiency of such an algorithm is the fraction of the maximum feasible traffic that the algorithm can handle without queues growing indefinitely. Our algorithm achieves two important goals: (i) efficiency is independent of the size of the network, and (ii) the algorithm is fully distributed, i.e., individual nodes need no information about the overall network topology, not even local information.

Decision Support Systems for the Food Industry

Applications of Decision Support Systems (DSSs) in the food industry, and in particular the seafood industry, are discussed. The amount of data recorded in the food industry has increased greatly in the last decade, parallel to descending cost of data recording through automatization and computer systems. The data can be used to fulfill the demands of consumers that want information on their food products, such as origin, impact on the environment and more. By using traceability this flow of data can be used for decision support. Many fields within food processing can gain from using DSS. Such fields include for example lowering environmental impact of food processing, safety management, processing management and stock management. Research and development projects that the authors have taken part in and the following implementations of software solutions are discussed and some examples given of practical usage of DSS in the food industry as a result of such work.

Vertex cover approximations on random graphs

The vertex cover problem is a classical NP-complete problem for which the best worst-case approximation ratio is 2-o(1). In this paper, we use a collection of simple graph transformations, each of which guarantees an approximation ratio of 3/2, to find approximate vertex covers for a large collection of randomly generated graphs. These reductions are extremely fast and even though they, by themselves are not guaranteed to find a vertex cover, we manage to find a 3/2-approximate vertex cover for almost every single random graph we generate.

Vertex cover approximations: experiments and observations

The vertex cover problem is a classic NP-complete problem for which the best worst-case approximation ratio is roughly 2. In this paper, we use a collection of simple reductions, each of which guarantees an approximation ratio of

Cost-effectiveness and Potential of Greenhouse Gas Mitigation through the Support of Renewable Transport Fuels in Iceland

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Market Penetration of Alternative Fuel Vehicles in Iceland: A Hybrid Modeling Approach

, to find approximate vertex covers for a large collection of test graphs from various sources. We explain these reductions and explore the interaction between them. These reductions are extremely fast and even though they, by themselves are not guaranteed to find a vertex cover, we manage to find a 3/2-approximate vertex cover for every single graph in our large collection of test examples.

Divide-and-Conquer Approximation Algorithm for Vertex Cover

The system dynamics model of Iceland’s energy systems (UniSyD_IS) is used to explore the potential transition paths towards renewable transport fuels with implications for greenhouse gas (GHG) emissions and mitigation costs. The study focuses on Iceland’s potential fuel pathways including renewable electricity, hydrogen from electrolysis, biogas from municipal wastes, bioethanol from lignocellulosic biomass, and biodiesel from oil seeds and waste oils.