Big Data Analytics Platforms for Electric Vehicle Integration in Transport Oriented Smart Cities: Computing Platforms for Platforms for Electric Vehicle Integration in Smart Cities

Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and carbon footprint. In this article, the authors analyze different use-cases to show how big data analytics (BDA) can play vital role for successful electric vehicle (EV) to smart grid (SG) integration. Followed by this, this article presents an edge computing model and highlights the advantages of employing such distributed edge paradigms towards satisfying the store, compute and networking (SCN) requirements of smart EV applications in TOSCs. This article also highlights the distinguishing features of the edge paradigm, towards supporting BDA activities in EV to SG integration in TOSCs. Finally, the authors provide a detailed overview of opportunities, trends, and challenges of both these computing techniques. In particular, this article discusses the deployment challenges and state-of-the-art solutions in edge privacy and edge forensics.

[1]  Mohammad Saad Alam,et al.  Fog Computing Model for Evolving Smart Transportation Applications , 2019, Fog and Edge Computing.

[2]  M. M. Sufyan Beg,et al.  Fog Computing for Next Generation Transport- a Battery Swapping System Case Study , 2018 .

[3]  Mohammad Saad Alam,et al.  Feasibility of Fog Computing in Smart Grid Architectures , 2018, Proceedings of 2nd International Conference on Communication, Computing and Networking.

[4]  Samee Ullah Khan,et al.  Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers , 2018, Comput. Networks.

[5]  Martin Reisslein,et al.  Guest Editorial Special Section on Smart Grid and Renewable Energy Resources: Information and Communication Technologies With Industry Perspective , 2017, IEEE Trans. Ind. Informatics.

[6]  Boyang Li,et al.  Big Data Analytics for Electric Vehicle Integration in Green Smart Cities , 2017, IEEE Communications Magazine.

[7]  Cheng Huang,et al.  Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges , 2017, IEEE Communications Magazine.

[8]  Leandros Maglaras,et al.  Security and Privacy in Fog Computing: Challenges , 2017, IEEE Access.

[9]  Hongnian Yu,et al.  Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond , 2017, IEEE Access.

[10]  F. Richard Yu,et al.  Fog Vehicular Computing: Augmentation of Fog Computing Using Vehicular Cloud Computing , 2017, IEEE Vehicular Technology Magazine.

[11]  Mohammad Saad Alam,et al.  Plug-in Electric Vehicle to Cloud Data Analytics for Charging Management , 2017 .

[12]  Lyes Khoukhi,et al.  Smart Grid Solution for Charging and Discharging Services Based on Cloud Computing Scheduling , 2017, IEEE Transactions on Industrial Informatics.

[13]  Ke Zhang,et al.  Predictive Offloading in Cloud-Driven Vehicles: Using Mobile-Edge Computing for a Promising Network Paradigm , 2017, IEEE Vehicular Technology Magazine.

[14]  Ke Zhang,et al.  Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading , 2017, IEEE Veh. Technol. Mag..

[15]  Rongxing Lu,et al.  From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework , 2017, IEEE Access.

[16]  Vacius Jusas,et al.  Methods and Tools of Digital Triage in Forensic Context: Survey and Future Directions , 2017, Symmetry.

[17]  Athanasios V. Vasilakos,et al.  Fog Computing for Sustainable Smart Cities , 2017, ArXiv.

[18]  Sherali Zeadally,et al.  Cloud-Assisted Context-Aware Vehicular Cyber-Physical System for PHEVs in Smart Grid , 2017, IEEE Systems Journal.

[19]  Zhenyu Wen,et al.  Fog Orchestration for Internet of Things Services , 2017, IEEE Internet Computing.

[20]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[21]  Julian de Hoog,et al.  Interconnecting Fog computing and microgrids for greening IoT , 2016, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia).

[22]  Wendy K Bernstein,et al.  Calm Before the Storm: An Uncomplicated Epicardial Lead Extraction Resulting in DIC and Patient Demise. , 2016, A & A case reports.

[23]  Tao Zhang,et al.  Fog and IoT: An Overview of Research Opportunities , 2016, IEEE Internet of Things Journal.

[24]  Qing Yang,et al.  An Architecture of Cloud-Assisted Information Dissemination in Vehicular Networks , 2016, IEEE Access.

[25]  M. Erol-Kantarci,et al.  Toward Low-Carbon Economy and Green Smart Grid through Pervasive Demand Management , 2016 .

[26]  Sheng Chen,et al.  Modeling the Impact of Mobility on the Connectivity of Vehicular Networks in Large-Scale Urban Environments , 2016, IEEE Transactions on Vehicular Technology.

[27]  Depeng Jin,et al.  Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures , 2016, IEEE Transactions on Vehicular Technology.

[28]  Biswanath Mukherjee,et al.  A Survey on Resiliency Techniques in Cloud Computing Infrastructures and Applications , 2016, IEEE Communications Surveys & Tutorials.

[29]  Rodrigo Roman,et al.  Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges , 2016, Future Gener. Comput. Syst..

[30]  Rose Qingyang Hu,et al.  On Reliability of Smart Grid Neighborhood Area Networks , 2015, IEEE Access.

[31]  Robert Shorten,et al.  On the Design of Campus Parking Systems With QoS Guarantees , 2015, IEEE Transactions on Intelligent Transportation Systems.

[32]  Stefan Schmid,et al.  Distributed Cloud Computing: Applications, Status Quo, and Challenges , 2015, CCRV.

[33]  Carol L. Stimmel,et al.  Big Data Analytics Strategies for the Smart Grid , 2014 .

[34]  Giovanni Pau,et al.  Internet of Vehicles: From intelligent grid to autonomous cars and vehicular fogs , 2014, 2014 IEEE World Forum on Internet of Things (WF-IoT).

[35]  P. L. So,et al.  V2G Capacity Estimation Using Dynamic EV Scheduling , 2014, IEEE Transactions on Smart Grid.

[36]  Ragib Hasan,et al.  Cloud Forensics: A Meta-Study of Challenges, Approaches, and Open Problems , 2013, ArXiv.

[37]  Taskin Koçak,et al.  A Survey on Smart Grid Potential Applications and Communication Requirements , 2013, IEEE Transactions on Industrial Informatics.

[38]  Abdelhamid Mellouk,et al.  ITS-cloud: Cloud computing for Intelligent transportation system , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[39]  Ling Guan,et al.  Optimal Scheduling for Charging and Discharging of Electric Vehicles , 2012, IEEE Transactions on Smart Grid.

[40]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[41]  David Walker,et al.  Frenetic: a network programming language , 2011, ICFP.

[42]  Nei Kato,et al.  Toward intelligent machine-to-machine communications in smart grid , 2011, IEEE Communications Magazine.

[43]  Gillian Fletcher,et al.  The calm before the storm. , 2005, The practising midwife.

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

[45]  Mohammad Saad Alam,et al.  A Risk Averse Business Model for Smart Charging of Electric Vehicles , 2018 .

[46]  Bin Zhao,et al.  Survey of Digital Forensics Technologies and Tools for Android based Intelligent Devices , 2015, Int. J. Digit. Crime Forensics.

[47]  Azah Mohamed,et al.  Hybrid electric vehicles and their challenges: A review , 2014 .

[48]  David Costenaro,et al.  The Megawatts behind Your Megabytes: Going from Data-Center to Desktop , 2012 .

[49]  Henry Leung,et al.  Data fusion in intelligent transportation systems: Progress and challenges - A survey , 2011, Inf. Fusion.

[50]  R. V. Renesse,et al.  Running Smart Grid Control Software on Cloud Computing Architectures , 2011 .

[51]  Martin S. Olivier,et al.  Isolating a cloud instance for a digital forensic investigation , 2011, ISSA.