Energy efficient Nano Servers provisioning for Information Piece Delivery in a vehicular environment

In this paper, we propose energy efficient Information Piece Delivery (IPD) through Nano Servers (NSs) in a vehicular network. Information pieces may contain any data that needs to be communicated to a vehicle. The available power (renewable or non-renewable) for a NS is variable. As a result, the service rate of a NS varies linearly with the available energy within a given range. Our proposed system therefore exhibits energy aware rate adaptation (RA), which uses variable transmission energy. We have also developed another transmission energy saving method for comparison, where sleep cycles (SC) are employed. Both methods are compared against an acceptable download time. To reduce the operational energy, we first optimise the locations of the NSs by developing a mixed integer linear programming (MILP) model, which takes into account the hourly variation of the traffic. The model is validated through a Genetic Algorithm (GA1). Furthermore, to reduce the gross delay over the entire vehicular network, the available renewable energy (wind farm) is optimally allocated to each NS according to piece demand. This, in turn, also reduces the network carbon footprint. A Genetic Algorithm (GA2) is also developed to validate the MILP results associated with this system. Through transmission energy savings, RA and SC further reduce the NSs energy consumption by 19% and 18% respectively, however at the expense of higher download time. MILP model 4 (with RA) and model 5 (with SC) reduced the delay by 81% and 83% respectively, while minimising the carbon footprint by 96% and 98% respectively, compared to the initial MILP model.

[1]  Xu Yuan,et al.  A power-saving protocol for ad hoc networks , 2005, Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005..

[2]  Sven Petersson,et al.  Multi antenna techniques to improve energy efficiency in LTE radio access network , 2011, 2011 Future Network & Mobile Summit.

[3]  Zhenyu Shi,et al.  Millimeter-Wave Mobile Communications , 2017 .

[4]  Ivaylo Haratcherev,et al.  Low-Power Sleep Mode and Out-Of-Band Wake-Up for Indoor Access Points , 2009, 2009 IEEE Globecom Workshops.

[5]  Edgar H. Callaway.  Low Power Consumption Features of the IEEE 802.15.4 WPAN Standard , 2005, Handbook on Theoretical and Algorithmic Aspects of Sensor, Ad Hoc Wireless, and Peer-to-Peer Networks.

[6]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[7]  Martin Suchara,et al.  Greening backbone networks: reducing energy consumption by shutting off cables in bundled links , 2010, Green Networking '10.

[8]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[9]  Yin Zhang,et al.  Enabling high-bandwidth vehicular content distribution , 2010, CoNEXT.

[10]  Jaafar M. H. Elmirghani,et al.  Load Adaptive Caching Points for a Content Distribution Network , 2015, 2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies.

[11]  Song Guo,et al.  Minimum energy multicast routing for wireless ad-hoc networks with adaptive antennas , 2004, Proceedings of the 12th IEEE International Conference on Network Protocols, 2004. ICNP 2004..

[12]  Ming Li,et al.  CodeOn: Cooperative Popular Content Distribution for Vehicular Networks using Symbol Level Network Coding , 2011, IEEE Journal on Selected Areas in Communications.

[13]  P. Manzoni,et al.  CityMob: A Mobility Model Pattern Generator for VANETs , 2008, ICC Workshops - 2008 IEEE International Conference on Communications Workshops.

[14]  K. A. Stroud,et al.  Engineering Mathematics , 2020, Nature.

[15]  Jaafar M. H. Elmirghani,et al.  An energy efficient double cluster head routing scheme for motorway vehicular networks , 2012, 2012 IEEE International Conference on Communications (ICC).

[16]  Arvinder Kaur,et al.  A survey on Vehicular Cloud Computing and its security , 2015, 2015 1st International Conference on Next Generation Computing Technologies (NGCT).

[17]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[18]  Sergio Barbarossa,et al.  Small Cell Clustering for Efficient Distributed Fog Computing: A Multi-User Case , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[19]  Suresh Singh,et al.  Greening of the internet , 2003, SIGCOMM '03.

[20]  Donald F. Towsley,et al.  Proxy prefix caching for multimedia streams , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[21]  Enzo Baccarelli,et al.  Distributed and adaptive resource management in Cloud-assisted Cognitive Radio Vehicular Networks with hard reliability guarantees , 2015, Veh. Commun..

[22]  Vasilis Friderikos,et al.  Ultra Low Energy Store-Carry and Forward Relaying within the Cell , 2009, 2009 IEEE 70th Vehicular Technology Conference Fall.

[23]  Irena Bojanova.  IEEE Transactions on Cloud Computing: Entering Its Fourth Year with Confidence , 2016, IEEE Trans. Cloud Comput..

[24]  G. Fettweis,et al.  ICT ENERGY CONSUMPTION – TRENDS AND CHALLENGES , 2008 .

[25]  Ahmed Helmy,et al.  BEWARE: Background traffic-aware rate adaptation for IEEE 802.11 , 2008 .

[26]  Reza Curtmola,et al.  Avatar: Mobile Distributed Computing in the Cloud , 2015, 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering.

[27]  Chai Kiat Yeo,et al.  Enabling Efficient WiFi-Based Vehicular Content Distribution , 2013, IEEE Transactions on Parallel and Distributed Systems.

[28]  Yanghee Choi,et al.  REACT: Rate Adaptation using Coherence Time in 802.11 WLANs , 2011, Comput. Commun..

[29]  Yu-Chee Tseng,et al.  Cluster-based semi-asynchronous power-saving protocols for multi-hop ad hoc networks , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[30]  Chen Sun,et al.  Transmit Power Control for Cognitive Radio Over a Rayleigh Fading Channel , 2010, IEEE Transactions on Vehicular Technology.

[31]  Sangjin Kim,et al.  Rethinking Vehicular Communications: Merging VANET with cloud computing , 2012, 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings.

[32]  Kannan Ramchandran,et al.  Collaborative content distribution for vehicular ad hoc networks , 2006 .

[33]  Chen-Nee Chuah,et al.  Energy-aware node placement in wireless sensor networks , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[34]  Y.T. Hou,et al.  On energy provisioning and relay node placement for wireless sensor networks , 2005, IEEE Transactions on Wireless Communications.

[35]  Jaafar M. H. Elmirghani,et al.  A Vacation-Based Performance Analysis of an Energy-Efficient Motorway Vehicular Communication System , 2014, IEEE Transactions on Vehicular Technology.

[36]  Enzo Baccarelli,et al.  Energy-Efficient Adaptive Resource Management for Real-Time Vehicular Cloud Services , 2019, IEEE Transactions on Cloud Computing.