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37 results on '"Esseghir, Moez"'

15. A Game-Theoretic Multi-Level Energy Demand Management for Smart Buildings.

Author

Chouikhi, Samira, Merghem-Boulahia, Leila, Esseghir, Moez, and Snoussi, Hichem

Abstract

The advent of smart grids offers us the opportunity to better manage the electricity grids. One of the most interesting challenges in the modern grids is the consumer demand management. Indeed, the development in information and communication technologies encourages the development of demand-side management systems. This paper introduces two electricity management mechanisms in a smart buildings’ context. We first use negotiation and multi-leader–follower game techniques to model the interactions between the electricity providers and the buildings’ consumers. Then, we propose a distributed energy demand scheduling approach based on game theory with minimal interactions between consumers to optimize the energy demand cost. This approach aims to reduce the total energy cost and the peak to average consumption ratio, and to maximize the exploitation of renewable energy. We use a multi-agent system to model the system entities. The performance evaluation demonstrates that our distributed approach reduces the total consumption cost as well as each consumer bill. [ABSTRACT FROM AUTHOR]

Published
2019
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20. Energy saving mechanism for future HetNets

Author

Ameur Hocine, Esseghir Moez, Khoukhi Lyes, Environnement de Réseaux Autonomes (ERA), Institut Charles Delaunay (ICD), and Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Mathematical optimization, Queueing theory, business.industry, Computer science, Mobile computing, Energy consumption, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Capacity planning, Mobile telephony, business, ComputingMilieux_MISCELLANEOUS, 5G, Heterogeneous network, Energy (signal processing), Computer network
Abstract

In this paper, we study the energy consumption in heterogeneous networks (HetNets). HetNets are known to be one of the main characteristics of the future 5G networks. In this work we aim to minimize the global energy consumed within these networks by adapting the network density to the operating traffic load. We formulate this problem by expressing the energy gain (i.e., the energy saved) which should be maximized for an optimal network configuration, and then we show that finding an optimal solution for this problem is NP-Complete; thus we propose a feasible solution based on estimating the network state using queuing networks. Given that state, we proceed to adapt the network infrastructure by taking into account users required capacity. We study in our experimentations the performance of the proposed method according to different criteria; the results show significant improvements in terms of energy consumption while preserving as much as possible users' requirements in terms of capacity.

Published
2015

29. Learning Temporal–Spatial Spectrum Reuse.

Author

Zhang, Yi, Tay, Wee Peng, Li, Kwok Hung, Esseghir, Moez, and Gaiti, Dominique

Subjects
BANDWIDTH allocation, SPECTRUM allocation, INTERFERENCE (Telecommunication), LOGARITHMS, COMPUTATIONAL complexity, SIMULATION methods & models
Abstract

We formulate and study a multi-user multi-armed bandit problem that exploits the temporal–spatial opportunistic spectrum access (OSA) of primary user (PU) channels, so that secondary users (SUs) who do not interfere with each other can make use of the same PU channel. We first propose a centralized channel allocation policy that has logarithmic regret, but requires a central processor to solve an NP-complete optimization problem at exponentially increasing time intervals. To overcome the high computation complexity at the central processor, we also propose heuristic distributed policies that, however, have linear regrets. Our first distributed policy utilizes a distributed graph coloring and consensus algorithm to determine SUs’ channel access ranks, while our second distributed policy incorporates channel access rank learning in a local procedure at each SU at the cost of a higher regret. We compare the performance of our proposed policies with other distributed policies recently proposed for temporal (but not spatial) OSA. We show that all these policies have linear regrets in our temporal–spatial OSA framework. Simulations suggest that our proposed policies have significantly smaller regrets than the other policies when spectrum temporal–spatial reuse is allowed. [ABSTRACT FROM AUTHOR]

Published
2016
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