The fast-paced development of power systems necessitates the smart grid (SG) to facilitate real-time control and monitoring with bidirectional communication and electricity flows. In order to meet the computational requirements for SG applications, cloud computing (CC) provides flexible resources and services shared in network, parallel processing, and omnipresent access. Even though CC model is considered to be efficient for SG, it fails to guarantee the Quality-of-Experience (QoE) requirements for the SG services, viz. latency, bandwidth, energy consumption, and network cost. Fog Computing (FC) extends CC by deploying localized computing and processing facilities into the edge of the network, offering location-awareness, low latency, and latency-sensitive analytics for mission critical requirements of SG applications. By deploying localized computing facilities at the premise of users, it pre-stores the cloud data and distributes to SG users with fast-rate local connections. In this paper, we first examine the current state of cloud based SG architectures and highlight the motivation(s) for adopting FC as a technology enabler for real-time SG analytics. We also present a three layer FC-based SG architecture, characterizing its features towards integrating massive number of Internet of Things (IoT) devices into future SG. We then propose a cost optimization model for FC that jointly investigates data consumer association, workload distribution, virtual machine placement and Quality-of-Service (QoS) constraints. The formulated model is a Mixed-Integer Nonlinear Programming (MINLP) problem which is solved using Modified Differential Evolution (MDE) algorithm. We evaluate the proposed framework on real world parameters and show that for a network with approximately 50% time critical applications, the overall service latency for FC is nearly half to that of cloud paradigm. We also observed that the FC lowers the aggregated power consumption of the generic CC model by more than 44%.

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The advent of intelligent vehicular applications and IoT technologies gives rise to data-intensive challenges across different architectural layers of an intelligent transportation system (ITS). Without powerful communication and computational infrastructure, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in daily life. The current cloud computing and cellular set-ups are far from perfect because they are highly dependent on, and bear the cost of additional infrastructure deployment. Thus, the geo-distributed ITS components require a paradigm shift from centralized cloud-scale processing to edge centered fog computing (FC) paradigms. FC outspreads the computing facilities into the edge of a network, offering location-awareness, latency-sensitive monitoring, and intelligent control. In this article, the authors identify the mission-critical computing needs of the next generation ITS applications and highlight the scopes of FC based solutions towards addressing them. Then, the authors discuss the scenarios where the underutilized communication and computational resources available in connected vehicles can be brought in to perform the role of FC infrastructures. Then the authors present a service-oriented software architecture (SOA) for FC-based Big Data Analytics in ITS applications. The authors also provide a detailed analysis of the potential challenges of using connected vehicles as FC infrastructures along with future research directions.

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Contemporary Smart Grid (SG) systems are enticed by smart devices and entities due to unfolded developments in intelligent transportation technologies (ITT). The SG ecosystem, when introduced to Internet of Things (IoT) makes every object active and brings them online. However, the traditional cloud deployments look puerile to meet the analytics and computational exigencies for such dynamic subsystems. Starting with highlighting the mission critical requirements of an idealized SG infrastructure, this work proposes an edge centered FOG (From cOre to edGe) computing model primarily focused to realize the processing and computational objectives of SG. The motive of the work is to comprehend the applicability of fog computing algorithms to interplay with the core centered cloud computing support, thus enabling to come up with a new breed of real-time and latency free utilities. Further, for demonstrating the feasibility of the proposed framework, a comparative optimization framework is proposed that captures the monetary expenses due to the power consumption, latency and emission issues in both cloud based as well as fog commuting frameworks. Finally, the suitability and viability of fog computing approaches are demonstrated through its comparative results of the metrics with that of traditional data center or cloud computing approach. Results clearly demonstrate the superiority of FOG computing over its cloud counterpart.