Efficient Energy Management Assisted by Fog Computing

Now a day, every field is consuming services of Cloud Computing (CC) because everyone is not able to deploy his own data centers. Due to the large number of requests on central place concept of Fog for the distributed processing of requests. Fog is placed between user and cloud to reduce the Response Time (RT). Smart Grid (SG) is also integrated cloud architecture for efficient management of energy. In this paper Fog Based architecture for SG is presented. Due to this there is no chance of a peak time generation at any time. A three-tier architecture is used for request processing. In SG environment there is need of low latency and high RT for providing uninterpretable services to the end user. For this purpose, Fog layer is used between cloud and SM (Smart Meter) and it acts as intermediate layer. A model is presented in this paper for efficient distribution of load among all available Virtual Machines (VMs). A load balancing technique is implemented for load distribution. Two regions are taken into account for experimental results and each region is divided into six group/cluster of building. Three Fogs are placed in each region for better RT and it produce optimal results as shown in simulations and discussion section.

[1]  Weifa Liang,et al.  QoS-Aware Cloudlet Load Balancing in Wireless Metropolitan Area Networks , 2020, IEEE Transactions on Cloud Computing.

[2]  Mohammad Abdullah Al Faruque,et al.  Energy Management-as-a-Service Over Fog Computing Platform , 2016, IEEE Internet Things J..

[3]  Nirwan Ansari,et al.  Latency Aware Workload Offloading in the Cloudlet Network , 2017, IEEE Communications Letters.

[4]  Berthold Bitzer,et al.  Cloud computing framework for smart grid applications , 2013, 2013 48th International Universities' Power Engineering Conference (UPEC).

[5]  Nadeem Javaid,et al.  A Cloud-Fog-Based Smart Grid Model for Efficient Resource Utilization , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[6]  Tao Zhang,et al.  Fog Computing , 2017, IEEE Internet Comput..

[7]  Amir Safdarian,et al.  A Distributed Algorithm for Managing Residential Demand Response in Smart Grids , 2014, IEEE Transactions on Industrial Informatics.

[8]  Anjan Bose,et al.  GridCloud: Infrastructure for Cloud-Based Wide Area Monitoring of Bulk Electric Power Grids , 2019, IEEE Transactions on Smart Grid.

[9]  R. H. Goudar,et al.  Cloud Computing - Research Issues, Challenges, Architecture, Platforms and Applications: A Survey , 2012 .

[10]  Nirwan Ansari,et al.  Workload Allocation in Hierarchical Cloudlet Networks , 2018, IEEE Communications Letters.

[11]  Sudip Misra,et al.  Game-theoretic energy trading network topology control for electric vehicles in mobile smart grid , 2015, IET Networks.

[12]  Na Li,et al.  Optimal Residential Demand Response in Distribution Networks , 2014, IEEE Journal on Selected Areas in Communications.

[13]  Nadeem Javaid,et al.  Intelligent Resource Allocation in Residential Buildings Using Consumer to Fog to Cloud Based Framework , 2019 .

[14]  Nadeem Javaid,et al.  Cloud–Fog–Based Smart Grid Model for Efficient Resource Management , 2018, Sustainability.

[15]  Marimuthu Palaniswami,et al.  PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid , 2018, IEEE Transactions on Industrial Informatics.

[16]  MengChu Zhou,et al.  Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy , 2018, IEEE Transactions on Automation Science and Engineering.

[17]  Lei Zheng,et al.  A Distributed Demand Response Control Strategy Using Lyapunov Optimization , 2014, IEEE Transactions on Smart Grid.

[18]  Zion Hwang,et al.  An intelligent cloud-based energy management system using machine to machine communications in future energy environments , 2012, 2012 IEEE International Conference on Consumer Electronics (ICCE).

[19]  Nadeem Javaid,et al.  Integration of Cloud and Fog based Environment for Effective Resource Distribution in Smart Buildings , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[20]  Jiming Chen,et al.  Distributed Real-Time Demand Response in Multiseller–Multibuyer Smart Distribution Grid , 2015, IEEE Transactions on Power Systems.