Mobile Networks

The growth in the use of mobile networks has come mainly with the third generation systems and voice traffic. With the current third generation and the arrival of the 4G, the number of mobile users in the world will exceed the number of landlines users. Audio and video streaming have had a significant increase, parallel to the requirements of bandwidth and quality of service demanded by those applications. Mobile networks require that the applications and protocols that have worked successfully in fixed networks can be used with the same level of quality in mobile scenarios. Until the third generation of mobile networks, the need to ensure reliable handovers was still an important issue. On the eve of a new generation of access networks (4G) and increased connectivity between networks of different characteristics commonly called hybrid (satellite, ad-hoc, sensors, wired, WIMAX, LAN, etc.), it is necessary to transfer mechanisms of mobility to future generations of networks. In order to achieve this, it is essential to carry out a comprehensive evaluation of the performance of current protocols and the diverse topologies to suit the new mobility conditions

A Mixed-Integer Programming Approach for Jammer Placement Problems for Flow-Jamming Attacks on Wireless Communication Networks

In this dissertation, we study an important problem of security in wireless networks. We study different attacks and defense strategies in general and more specifically jamming attacks. We begin the dissertation by providing a tutorial introducing the operations research community to the various types of attacks and defense strategies in wireless networks. In this tutorial, we give examples of mathematical programming models to model jamming attacks and defense against jamming attacks in wireless networks. Later we provide a comprehensive taxonomic classification of the various types of jamming attacks and defense against jamming attacks. The classification scheme will provide a one stop location for future researchers on various jamming attack and defense strategies studied in literature. This classification scheme also highlights the areas of research in jamming attack and defense against jamming attacks which have received less attention and could be a good area of focus for future research. In the next chapter, we provide a bi-level mathematical programming model to study jamming attack and defense strategy. We solve this using a game-theoretic approach and also study the impact of power level, location of jamming device, and the number of transmission channels available to transmit data on the attack and defense against jamming attacks. We show that by increasing the number of jamming devices the throughput of the network drops by at least 7%. Finally we study a special type of jamming attack, flow-jamming attack. We provide a mathematical programming model to solve the location of jamming devices to increase the impact of flow-jamming attacks on wireless networks. We provide a Benders decomposition algorithm along with some acceleration techniques to solve large problem instances in reasonable amount of time. We draw some insights about the impact of power, location and size of the network on the impact of flow-jamming attacks in wireless networks

A Mixed-Integer Programming Approach for Jammer Placement Problems for Flow-Jamming Attacks on Wireless Communication Networks

In this dissertation, we study an important problem of security in wireless networks. We study different attacks and defense strategies in general and more specifically jamming attacks. We begin the dissertation by providing a tutorial introducing the operations research community to the various types of attacks and defense strategies in wireless networks. In this tutorial, we give examples of mathematical programming models to model jamming attacks and defense against jamming attacks in wireless networks. Later we provide a comprehensive taxonomic classification of the various types of jamming attacks and defense against jamming attacks. The classification scheme will provide a one stop location for future researchers on various jamming attack and defense strategies studied in literature. This classification scheme also highlights the areas of research in jamming attack and defense against jamming attacks which have received less attention and could be a good area of focus for future research. In the next chapter, we provide a bi-level mathematical programming model to study jamming attack and defense strategy. We solve this using a game-theoretic approach and also study the impact of power level, location of jamming device, and the number of transmission channels available to transmit data on the attack and defense against jamming attacks. We show that by increasing the number of jamming devices the throughput of the network drops by at least 7%. Finally we study a special type of jamming attack, flow-jamming attack. We provide a mathematical programming model to solve the location of jamming devices to increase the impact of flow-jamming attacks on wireless networks. We provide a Benders decomposition algorithm along with some acceleration techniques to solve large problem instances in reasonable amount of time. We draw some insights about the impact of power, location and size of the network on the impact of flow-jamming attacks in wireless networks

DR9.3 Final report of the JRRM and ASM activities

Deliverable del projecte europeu NEWCOM++This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.Postprint (published version

Mathematical optimization techniques for demand management in smart grids

The electricity supply industry has been facing significant challenges in terms of meeting the projected demand for energy, environmental issues, security, reliability and integration of renewable energy. Currently, most of the power grids are based on many decades old vertical hierarchical infrastructures where the electric power flows in one direction from the power generators to the consumer side and the grid monitoring information is handled only at the operation side. It is generally believed that a fundamental evolution in electric power generation and supply system is required to make the grids more reliable, secure and efficient. This is generally recognised as the development of smart grids. Demand management is the key to the operational efficiency and reliability of smart grids. Facilitated by the two-way information flow and various optimization mechanisms, operators benefit from real time dynamic load monitoring and control while consumers benefit from optimised use of energy. In this thesis, various mathematical optimization techniques and game theoretic frameworks have been proposed for demand management in order to achieve efficient home energy consumption scheduling and optimal electric vehicle (EV) charging. A consumption scheduling technique is proposed to minimise the peak consumption load. The proposed technique is able to schedule the optimal operation time for appliances according to the power consumption patterns of the individual appliances. A game theoretic consumption optimization framework is proposed to manage the scheduling of appliances of multiple residential consumers in a decentralised manner, with the aim of achieving minimum cost of energy for consumers. The optimization incorporates integration of locally generated and stored renewable energy in order to minimise dependency on conventional energy. In addition to the appliance scheduling, a mean field game theoretic optimization framework is proposed for electric vehicles to manage their charging. In particular, the optimization considers a charging station where a large number of EVs are charged simultaneously during a flexible period of time. The proposed technique provides the EVs an optimal charging strategy in order to minimise the cost of charging. The performances of all these new proposed techniques have been demonstrated using Matlab based simulation studies

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Green cooperative spectrum sensing and scheduling in heterogeneous cognitive radio networks

The motivation behind the cognitive radio networks (CRNs) is rooted in scarcity of the radio spectrum and inefficiency of its management to meet the ever increasing high quality of service demands. Furthermore, information and communication technologies have limited and/or expensive energy resources and contribute significantly to the global carbon footprint. To alleviate these issues, energy efficient and energy harvesting (EEH) CRNs can harvest the required energy from ambient renewable sources while collecting the necessary bandwidth by discovering free spectrum for a minimized energy cost. Therefore, EEH-CRNs have potential to achieve green communications by enabling spectrum and energy self-sustaining networks. In this thesis, green cooperative spectrum sensing (CSS) policies are considered for large scale heterogeneous CRNs which consist of multiple primary channels (PCs) and a large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. Firstly, a multi-objective clustering optimization (MOCO) problem is formulated from macro and micro perspectives; Macro perspective partitions SUs into clusters with the objectives: 1) Intra-cluster energy minimization of each cluster, 2) Intra-cluster throughput maximization of each cluster, and 3) Inter-cluster energy and throughput fairness. A multi-objective genetic algorithm, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is adopted and demonstrated how to solve the MOCO. The micro perspective, on the other hand, works as a sub-procedure on cluster formations given by macro perspective. For the micro perspective, a multihop reporting based CH selection procedure is proposed to find: 1) The best CH which gives the minimum total multi-hop error rate, and 2) the optimal routing paths from SUs to the CHs using Dijkstra\u27s algorithm. Using Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different levels of local detection performance. Then, a convex optimization framework is established to minimize the intra-cluster energy cost subject to collision and spectrum utilization constraints.Likewise, instead of a common fixed sample size test, a weighted sample size test is considered for quantized soft decision fusion to obtain a more EE regime under heterogeneity. Secondly, an energy and spectrum efficient CSS scheduling (CSSS) problem is investigated to minimize the energy cost per achieved data rate subject to collision and spectrum utilization constraints. The total energy cost is calculated as the sum of energy expenditures resulting from sensing, reporting and channel switching operations. Then, a mixed integer non-linear programming problem is formulated to determine: 1) The optimal scheduling subset of a large number of PCs which cannot be sensed at the same time, 2) The SU assignment set for each scheduled PC, and 3) Optimal sensing parameters of SUs on each PC. Thereafter, an equivalent convex framework is developed for specific instances of above combinatorial problem. For the comparison, optimal detection and sensing thresholds are also derived analytically under the homogeneity assumption. Based on these, a prioritized ordering heuristic is developed to order channels under the spectrum, energy and spectrum-energy limited regimes. After that, a scheduling and assignment heuristic is proposed and shown to have a very close performance to the exhaustive optimal solution. Finally, the behavior of the CRN is numerically analyzed under these regimes with respect to different numbers of SUs, PCs and sensing qualities. Lastly, a single channel energy harvesting CSS scheme is considered with SUs experiencing different energy arrival rates, sensing, and reporting qualities. In order to alleviate the half- duplex EH constraint, which precludes from charging and discharging at the same time, and to harvest energy from both renewable sources and ambient radio signals, a full-duplex hybrid energy harvesting (EH) model is developed. After formulating the energy state evolution of half and full duplex systems under stochastic energy arrivals, a convex optimization framework is established to jointly obtain the optimal harvesting ratio, sensing duration and detection threshold of each SU to find an optimal myopic EH policy subject to collision and energy- causality constraints