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Decentralized greedy-based algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems

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IEEE ACCESS 2020年 8卷 75666-75681页

作者： Mehrabi, Abbas Nunna, H. S. V. S. Kumar Dadlani, Aresh Moon, Seungpil Kim, Kiseon Nottingham Trent Univ Dept Comp & Technol Nottingham NG11 8NS England Nazarbayev Univ Dept Elect & Comp Engn Astana 010000 Kazakhstan Korea Elect Power Res Inst KEPRI Deajeon 34056 South Korea Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea

Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management.

关键词： Vehicle-to-grid Cascading style sheets Optimal scheduling Batteries Scheduling Electric vehicle-to-grid (V2G) distributed energy management mixed integer non-linear programming greedy-based algorithm smart cities

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Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2019年第3期3卷 828-839页

作者： Mehrabi, Abbas Siekkinen, Matti Yla-Jaaski, Antti Aalto Univ Dept Comp Sci Espoo 02150 Finland

Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks.

关键词： Green mobile edge computing (GMEC) DASH quality of experience (QoE) fairness greedy-based algorithm

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Compressed channel estimation for FDD massive MIMO systems without prior knowledge of sparse channel model

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IET COMMUNICATIONS 2019年第6期13卷 657-663页

作者： Azizipour, Mohammad Javad Mohamed-Pour, Kamal KN Toosi Univ Technol Fac Elect Engn 470 Mirdamad Ave West Tehran 19697 Iran

In this study, a new greedy-based algorithm for sparse channel estimation in frequency-division duplexing massive multiple-input multiple-output (MIMO) systems is introduced. The proposed algorithm is able to acquire the channel with no information about the channel model such as the sparsity order, statistical bound on sparsity support, and special features of the channel. In other words, the authors assume that the massive channel is totally unknown and then exploit the inherent property of correlation between measurements and the sensing matrix to estimate the channel, which is available at the user side. By utilising this property, a halting parameter is defined as the halting threshold instead of known prior information assumed in the conventional greedy algorithms. Furthermore, the lower and upper bounds of the halting parameter are obtained that helps the algorithm to take the suitable values of this parameter and by considering the lower bound as the values of the halting parameter, the algorithm can prevent from missing the original channel bins. Simulation results demonstrate that the proposed algorithm outperforms its counterparts in terms of normalised mean square error while it faces a completely unknown massive MIMO channel.

关键词： frequency division multiplexing MIMO communication channel estimation mean square error methods greedy algorithms known prior information conventional greedy algorithms lower bounds upper bounds halting parameter original channel bins halting threshold utilising this property massive channel sparsity support sparsity order frequency-division duplexing massive multiple-input multiple-output systems sparse channel estimation greedy-based algorithm sparse channel model prior knowledge FDD massive MIMO systems compressed channel estimation completely unknown massive MIMO channel

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D2D-enabled Collaborative Edge Caching and Processing with Adaptive Mobile Video Streaming 20

D2D-enabled Collaborative Edge Caching and Processing with A...

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20th IEEE International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

作者： Mehrabi, Abbas Siekkinen, Matti Illahi, Gazi Yla-Jaaski, Antti Aalto Univ Dept Comp Sci Espoo Finland

ISBN: (纸本)9781728102702

Multi-access edge computing (MEC) enables placing video content at the edge of a mobile network with the aim of reducing data traffic in the backhaul network. Direct device-to-device (D2D) communication can further alleviate load from the backhaul network. Both MEC and D2D have already been examined by prior work, but their combination applied to adaptive video streaming have not yet been explored in detail. In this paper, we analyze how enabling D2D jointly with edge computing affects the quality of experience (QoE) of video streaming clients and contributes to reducing the backhaul traffic. To this end, we formulate the problem of jointly maximizing the QoE of the clients and minimizing the backhaul traffic and edge processing as an integer non-linear programming (INLP) optimization model and propose a low-complexity algorithm using self-parameterization technique to solve the problem. The main takeaway from simulation results is that enabling D2D with edge computing reduces the backhaul traffic by approximately 18% and edge processing by 30% on average while maintaining roughly the same average video bitrate per client compared to edge computing without D2D. Our results provide a guideline for system designers to judge the effectiveness of enabling D2D into MEC in the next generation of 5G mobile networks.

关键词： Multi-access edge computing (MEC) Device-to-Device communication DASH Quality of experience (QoE) Fairness greedy-based algorithm 5G networks

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Solving Priority-based Target Coverage Problem in Directional Sensor Networks with Adjustable Sensing Ranges

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WIRELESS PERSONAL COMMUNICATIONS 2017年第2期95卷 847-872页

作者： Razali, Mohd Norsyarizad Salleh, Shaharuddin Mohamadi, Hosein Univ Teknol Malaysia Fac Sci Dept Math Sci Johor Baharu 81310 Malaysia Univ Teknol Malaysia UTM Ctr Ind & Appl Math Johor Baharu 81310 Malaysia

The extensive applications of directional sensor networks (DSNs) in a wide range of situations have recently attracted a great deal of attention. DSNs primarily operate based on simultaneously observing a group of events (targets) occurring in a set area and maximizing network lifetime, as there are limitations to the directional sensors' sensing angle and battery power. The higher the number of sensing ranges of the sensors and the more different the coverage requirements for the targets, the more complex this issue will be. Also known as priority-based target coverage with adjustable sensing ranges (PTCASR), this issue, which has not yet been investigated in the field of study, is the highlight of this research. A potential solution to this problem, based on the fact that sensors are frequently densely deployed, would be to organize the sensors into a few cover sets. After that the cover sets needs to be successively activated-this process is referred to as the scheduling technique. This paper aims to resolve the issue of PTCASR with the proposal of two scheduling algorithms i.e. greedy-based and learning automata-based algorithms. These proposed algorithms were assessed for their performance via a number of experiments. Additionally, the effect of each algorithm on maximizing network lifetime was also investigated via a comparative study. Both algorithms were successful in solving the problem;however, the learning automata-based scheduling algorithm proved relatively superior to the greedy-based algorithm when it came to extending network lifetime.

关键词： Directional sensor networks Cover set formation Scheduling algorithms greedy-based algorithm Learning automata

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