Energy consumption modeling in vehicular communication based on 4G

In recent years, wireless system has witnessed growing interest in vehicular networks and intelligent transport systems. In same time, due to the explosive growth of high-data-rate applications, more and more energy is consumed in order to guarantee best quality of service, to improve road safety, to enhance security and to reduce congestion. However, owing to energy resources limitation and to meet economic and environmental reasons, significant emphasis was placed on energy efficiency communications. In this light, this paper will provide energy consumption model based on Combinatorial optimization multi-objectives, assignment problem and game theory.

[1]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[2]  Hassan Ouahmane,et al.  Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory , 2017, Int. J. Commun. Networks Inf. Secur..

[3]  Terence D. Todd,et al.  Traffic Scheduling for Energy Sustainable Vehicular Infrastructure , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[4]  Terence D. Todd,et al.  Energy-Efficient Scheduling in Green Vehicular Infrastructure With Multiple Roadside Units , 2015, IEEE Transactions on Vehicular Technology.

[5]  Carlos A. Coello Coello,et al.  Multi-Objective Combinatorial Optimization: Problematic and Context , 2010, Advances in Multi-Objective Nature Inspired Computing.

[6]  Virginie E. Letschert.  Energy Efficiency Appliance Standards: Where do we stand, how far can we go and how do we get there? An analysis across several economies , 2014 .

[7]  Barbara M. Masini,et al.  A Survey on Infrastructure-Based Vehicular Networks , 2017, Mob. Inf. Syst..

[8]  Naveen Chilamkurti,et al.  Introduction: intelligent vehicular communications , 2017 .

[9]  Zhisheng Niu,et al.  On the design of relay caching in cellular networks for energy efficiency , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[10]  Zhu Han,et al.  Resource Allocation for Wireless Networks: Basics, Techniques, and Applications , 2008 .

[11]  Eranda C Ela,et al.  Assignment Problems , 1964, Comput. J..

[12]  Ching-Lai Hwang,et al.  Multiple Objective Decision Making , 1994 .

[13]  Jaafar M. H. Elmirghani,et al.  Energy and QoS — A new perspective in a city vehicular communication network , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[14]  Hao Wu,et al.  Analysis and Design of Vehicular Networks , 2005 .

[15]  Hidekazu Murata,et al.  Experimental study of minority game-based interference management for spectrum sharing , 2010, 2010 IEEE International Conference on Communication Systems.

[16]  Xavier Gandibleux,et al.  Multiple Objective Combinatorial Optimization — A Tutorial , 2003 .

[17]  David J. Goodman,et al.  Power control for wireless data based on utility and pricing , 1998, Ninth IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (Cat. No.98TH8361).

[18]  Cameron A. MacKenzie,et al.  Multiple objective decision-making , 2015 .

[19]  Zeeshan Hameed Mir,et al.  LTE and IEEE 802.11p for vehicular networking: a performance evaluation , 2014, EURASIP J. Wirel. Commun. Netw..

[20]  Hamdi Idjmayyel,et al.  Energy efficient vehicular networks in a city environment , 2013 .