Fog Computing for Internet of Things (IoT)-Aided Smart Grid Architectures

Hussain, Md. Muzakkir; Beg, M. M. Sufyan

doi:10.3390/bdcc3010008

Cited by 60 publications

(25 citation statements)

References 41 publications

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“…Fog computing is located near the devices producing data such as sensors as opposed to cloud computing, which is far away from the data resources, and processes data in a shared centralized server. Due to the scalable characteristic of cloud computing, it can assist the supervisory level of the system, namely TSO/DSO dealing with massive data computation, whereas, applying fog computing near to the agents leads to an increase in the speed of processing data and improve privacy [91,92]. There is a lack of IoT protocol and integrated standard applied to the system, associating fog and cloud computing.…”

Section: Iot Protocols Application Roadmap and Future Trends For Smart Gridmentioning

confidence: 99%

A Comprehensive Review on IoT Protocols’ Features in Smart Grid Communication

2020

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Since the smart grid deals with a large mass of data and critical missions, it requires ubiquitous, reliable, and real-time communication. The Internet of Things (IoT) technology, which has the potential of connecting all objects over the globe through the Internet, excels in providing robust information transmission infrastructure in the smart grid. There are a multitude of possible protocols, standards, and configurations for communication in the smart grid. A commonly applied communication standard IEC 61850 recommends the use of Manufacturing Message Specification (MMS) protocol for communication in Local Area Network (LAN) and eXtensible Messaging and Presence Protocol (XMPP) in Wide Area Network (WAN). However, a plethora of research on this topic compares the behavior of other IoT protocols and standard recommendations in the smart grid. On the other hand, the sky-rocketing penetration of Renewable Energy Sources (RES), especially in the form of micro grid, transformed the central control structure of the smart grid into a distributed style called Multi-Agent Systems (MAS). This new approach defined new communication requirements and more particular IoT protocol characteristic requirements. However, a limited number of the existing studies have considered IoT protocol characteristic requirements of the smart grid and its new control structures. In this paper, we initially investigate the communication requirements of the smart grid and introduce all IoT protocols and their specifications. We analyze IoT protocol characteristics and performances in the smart grid through literature review based on the smart grid communication requirements. In this approach, we highlight weak points of these practices making them fail to acquire the holistic guidelines in utilizing proper IoT protocol that can meet the smart grid environment interaction requirements. Using the existing facilities, the public Internet, we follow the arrangement of cost-effective high penetration communication requirements for new structures of the smart grid, i.e., the MAS and multi-micro grid. In this case, we consider IoT protocol Quality of Services (QoS) requirements, especially in the case of security and reliability, to satisfy stakeholders, namely utilities and prosumers. Addressing effective elements in applying IoT in the smart grid’s future trends is another contribution to this paper.

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Section: Iot Protocols Application Roadmap and Future Trends For Smart Gridmentioning

confidence: 99%

A Comprehensive Review on IoT Protocols’ Features in Smart Grid Communication

2020

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“…According to [8], the researchers have proposed an RL based on the offload code mechanism to ensure low-latency services for mobile service users. Based on [10,14,25], the offloading-based scheduling algorithm designed for it sets a threshold value so that if there are not enough resources to execute the request, the requests are directed upward through the middle layer devices to the cloud. Based on [26], ECIF, an offloading method, could lead to the integration of IoT and CC applications.…”

Section: Ml-based Scheduling In Fogmentioning

confidence: 99%

“…• Processing data and decision making must be done in less than a second. Based on [10], CC fails to guarantee the Quality-of-Experience (QoE) requirements for some services like the smart grids (SG) services such as latency, bandwidth, energy consumption, and network cost. FC extends CC into the edge of the network by deploying limited resource, localized computing, and processing facilities.…”

Section: Introductionmentioning

confidence: 99%

Energy saving scheduling in a fog-based IoT application by Bayesian taskclassification approach

Heydari¹,

Rahbari²,

Nickray³

2019

Turk J Elec Eng & Comp Sci

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The Internet of things increases information volume in computer networks and the concept of fog will help us to control this volume more efficiently. Scheduling resources in such an environment would be an NP-Hard problem. This article has studied the concept of scheduling in fog with Bayesian classification which could be applied to gain the task requirements like the processing ones. After classification, virtual machines will be created in accordance with the predicted requirements. The ifogsim simulator has been applied to study our fog-based Bayesian classification scheduling (FBCS) method performance in an EEG tractor application. Algorithms have been evaluated on a practical application of brain signal tracking system. According to the results, the FBCS method, compared with other methods, has reduced the energy consumption in the cloud and the executing task cost in cloud; and also the average of energy consuming in mobiles has been decreased by smart decision making.

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“…Edge computing, which aims to displace data science analytics in the cloud as close as possible to the edge of the network [ 27 , 37 ], can prevent network latency for latency-sensitive IoT applications (where network connectivity is not always available) [ 38 , 39 ] and fulfill the lack of location awareness as well as data mobility for IoT end devices [ 36 ] deployed in practical fields of interest (i.e., real-time responsiveness from heterogeneous sensor data for local interpretable and actionable data insights can be guaranteed). Edge computing has been foreseen as a remedy to alleviate the various issues of cloud computing [ 40 , 41 ]. The contributions of this study are summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

Chen

Chang³

et al. 2021

Sensors

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Electricity is a vital resource for various human activities, supporting customers’ lifestyles in today’s modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium’s Niagara Framework® has been established over fog (edge)-cloud computing, where NIALM as an IoT application in energy management has also been investigated in the framework. The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive deployment of plug-load power meters (smart plugs), where a two-stage NIALM approach is completed. The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart building for residential DSM. As demonstrated and reported in this study, the established SHEMS prototype using the investigated two-stage NIALM approach is feasible and usable.

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Fog Computing for Internet of Things (IoT)-Aided Smart Grid Architectures

Cited by 60 publications

References 41 publications

A Comprehensive Review on IoT Protocols’ Features in Smart Grid Communication

A Comprehensive Review on IoT Protocols’ Features in Smart Grid Communication

Energy saving scheduling in a fog-based IoT application by Bayesian taskclassification approach

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

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