Fog Computing for Realizing Smart Neighborhoods in Smart Grids
- Real-time control and monitoring of smart grid operations.
- Scalable and distributed smart grid architecture.
- Security and privacy-preserving smart grid operations.
- Network bandwidth and traffic management.
- Reliable smart grid operations and power outage prevention.
- Effective distributed power generation and RES integration.
- Optimized smart grid operations and minimized losses.
- Effective MicroGrid operations and V2G integration.
- The need for the geographical distribution of resources rather than a centralized one as in the Cloud;
- The need to incorporate large networks of sensors communicating, usually by wireless access, Zigbee, Bluetooth and related communication technologies;
- The need to support real-time communication with IoT sensor networks and mobile devices;
- The need to support heterogeneous devices and interoperability with different service providers;
- The need for on-line analytic and interplay with the Cloud.
- We present an extensive literature study of Fog Computing and its fundamentals along with discussing various technologies which led to the emergence and foundation of Fog Computing and, how Fog Computing is different and better than the previous technologies. We present various application domains where Fog Computing finds application like smart grids, smart healthcare, smart manufacturing, smart connected vehicles along with describing the use-cases.
- We describe smart grid principles and their challenges. We define and formalize the problem of smart neighborhoods and show the applicability of Fog Computing to solve these challenges. We propose a potential Fog Computing architecture to address these challenges.
- We propose, based on the literature study, the potential solution techniques which should be used to realize smart neighborhoods. We discuss with the help of a use case scenario of using artificial intelligence-based predictive techniques towards addressing the challenges of smart neighborhoods.
- We discuss the challenges in Fog Computing and smart grid integration.
2. Related Works
- Fog architectures, frameworks and programming models related research.
- Resource management, provisioning and control related research.
- Fog application domains related research.
- Security and privacy related research.
2.1. Fog Architectures, Frameworks and Programming Models
2.2. Resource Management, Provisioning and Control
2.3. Fog Application Domains
2.4. Security and Privacy in Fog Computing
3. Related Technologies and Their Characteristics
3.1. Mobile Computing (MC)
3.2. Mobile Cloud Computing (MCC)
3.3. Edge Computing (EC)
3.4. Multi-Access Edge Computing (MEC)
3.5. How Fog Computing Is Reliable than Other Computing Paradigms
4. Applications of Fog Computing and Use Cases
4.1. Smart Grids
4.2. Smart Healthcare
4.3. Smart Manufacturing
4.4. Smart Connected Vehicles
4.5. Smart Cities
5. Smart Grids towards Smart Neighborhoods
5.1. Smart Grids Concepts
5.2. Advanced Metering Infrastructure
5.3. Vehicle to Grid (V2G) Integration
- Store power;
- Load shifting by peak shaving and valley filling;
- Renewable energy resource integration;
- Frequency and voltage regulation;
5.4. Renewable Energy Source Integration
- Reliable MicroGrid operation;
- Clean environment friendly energy availability;
- Secure and privacy preserving MicroGrid operation;
- Energy supply to store in EVs;
- Energy cost reduction;
- Frequency and voltage regulation;
5.5. Community MicroGrid
5.6. Demand Response Management
6. Smart Grid Challenges and Problem Formalization for Smart Neighborhoods
6.1. Real-Time Control of Monitoring of Grid Operations
6.2. Scalable and Distributed Smart Grid Architecture
6.3. Reliable Power Supply
6.4. Security and Privacy Preserving Grid Operations
6.5. Effective RES and V2G Integration
6.6. Optimize Power Production and Consumption and Minimize Loses
7. Fog Computing Based Architecture towards Smart Neighborhoods and Potential Solutions
- Data storage
- Distributed and scalable Fog Computing architecture
- Real-time service
- Resource management, provisioning and control
- Resilient and Reliable Grid operations
- Security and Privacy preserving architecture
- Coordination between Cloud and grid sensor networks
7.1. Identifying Potential Solutions
- Artificial Intelligence based prediction methods: Prediction is the most effective method to ensure power outages are minimized, and demand response management is achieved. Predictions using machine learning, deep learning and swarm intelligence techniques are used in various domains of study in many fields [96,97,98,99,100]. In our case of community MicroGrids, as discussed in Section 7, consumers need a reliable power supply, and any anomaly in the power consumption should be quickly detected. Here predictions techniques are the most effective. Since minute-sampled power consumption data at the appliance level is available through smart meters, predictions of power consumption for the next couple of days in every smart home can be made. In this manner, the power supplier/service provider will be able to know how much power should be to be generated and help them in making decisions. At the same time, forecasting the generated power by the power producers will help utility providers become aware of the power production in coming days and therefore, inform consumers about the costs based on the production. This will do the scheduling of appliances with customer preferences and cost minimization. With this, demand-side management with minimum energy loss will be effectively achieved through predictions.For reliability issues or detecting faults like sensor faults, malfunctioned devices or security threats also, forecasting is the most effective tool. We can analyse and forecast future power consumption values from the previous month’s data. Large deviations from the predicted consumption could be a potential fault, and customers and service provider will be alerted the customer about the possible faults (after eliminating false alarms). They will be able to locate faults at the appliance level because granular data analysis and real-time fault detection are done on Fog nodes.Potentially high number of use cases can be handled with forecasting techniques as sensor data, and computing resources are available near the sensors. Another use case is predicting power produced by wind turbines and PV panels. With weather data of past weeks and months, we can predict how much power will be produced in the coming weeks, months. This will allow consumers to make informed decisions like, planning how much power they need to buy and decision about selling power to other users at maximum profit. Importantly, renewable sources of energy effectively reduce the carbon footprint by the neighborhood and contribute to the environment.To do effective resource management and load balancing, we can use prediction techniques. We can predict how much application processing load fog network of a particular region will have. Suppose one region having a fog network have a higher load then QoS will be compromised. In this case, to balance the load and transfer the processing request to a less loaded neighboring fog network will be an effective solution. It will also help in deciding what appropriate fog nodes locations based on the workload of the area are.
- Blockchain for secure fog platform: To address the security and privacy challenges mentioned above and secure trading of excess power among households, we need a secure platform trusted by the users. Blockchain can be used effectively to trade electricity among users in a network. Blockchain is a shared and distributed ledger used to record transactions by multiple untrusting nodes in a network. Blockchain is gaining popularity because applications can be operated securely in a distributed manner, which previously had to go through a third party intermediary which could not be trusted . The transaction is made secure through validation by all the distributed fog nodes. A literature review on applications of blockchain technology is presented in Reference , describing the usability of blockchain in IoT sensor domains. Each user can manage their own data, and each fog node stores only encrypted fragments of user energy data . Therefore, it is possible to achieve complete privacy of user data and secure trading without any third party having access to and control of the data.
Use Case of Using Artificial Intelligence Based Methods towards Realizing Smart Neighborhoods
7.2. Challenges in Fog Computing and Smart Grid Integration
- Security and Privacy: Security and privacy issues in Fog and Smart Grid integration are still a challenge. As discussed, Blockchain is a promising solution to address security and privacy challenges, but the research of their integration is still at the initial stages, and it is not tested by integrating with whole smart grid infrastructure . Smart Grid is a complex architecture with the integration of AMI, RES and EVs. These components form an infrastructure which is distributed, heterogeneous and operates at the edge of the network. Sharing information on the distributed network, migrating services, load balancing and resource management between heterogeneous fog nodes at neighborhood level and, across the distributed transmission, distribution power channel is susceptible to malicious attacks.
- Fog Programming models and interoperability issues: There is no fixed set of rules and methods that describe the functionality, organization, and implementation of the Fog Computing based smart grid system. Fog Computing itself has no standardized programming models and frameworks, though there are several works in this direction . Fog nodes need to do a lot of coordination among different stakeholders in the power infrastructure. The geographical distribution of sensors and uncertain network connectivity and interoperability with Cloud server is a challenge for reliable service .
- Reliability and scalability: Networks play an essential role in reliable communications in smart grid infrastructure. However, the traditional networks are not designed for high scalability of fog nodes as the number of smart meters, EVs are increasing continuously. SDNs are a promising solution [107,108] but they should be integrated and tested on whole power distribution network for network reliability. Another challenge is that fog nodes are not as resource-rich as Cloud servers and therefore, service provisioning and load balancing could delay the real-time performance of the smart grid services. AI integration on Fog devices is promising but at the same time is challenging. Artificial intelligence algorithms like neural networks give accurate predictions but require more processing capacity and data to give prediction results. Running these algorithms can affect the real-time performance of Demand response management and other smart grid services. Similarly, delay in accurate prediction of power produced by RES will cause reliability and performance issues.
7.3. Simulation Platforms for Fog Computing
Conflicts of Interest
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|Attributes||Cloud Computing||Fog Computing|
|Definition||On demand compute, storage and networking services on centralized Cloud servers located far from IoT devices and users accessed via core network||Compute, storage, and networking services in continuum between IoT sensor networks and Cloud servers|
|Applications||Applications which are not latency sensitive like software services, platform services, e-commerce applications, web applications, and so forth||IoT applications which are latency-sensitive like medical services, smart healthcare, smart grid, smart connected vehicles, augmented reality, and so forth.|
|Architecture||Centralized||Decentralized and hierarchical|
|Programming||Distributed programming||Distributed and parallel programming|
|Storage capacity||High storage data servers||Data hubs near IoT sensor networks|
|Reliability||Less reliable in terms of connectivity, performance and security||More reliable in terms of connectivity, performance, and security|
|Security & Privacy||Low||High. Provided on Fog networks through Blockchain and other technologies and along cloud-to-things continuum|
|Communication mode||IP networks||WLAN, WiFi, LAN, WAN, ZigBee, wired communication, cellular networks|
|Computing capacity||High computing capacity||Moderate computing capacity|
|Geographical distribution||Global, centralized||Less global, geographically distributed|
|Fog architectures, Frameworks and Programming Models||[12,13,14,15,16,17,18,19,20,21,22,23,24]|
|Resource management, Provisioning and Control||[25,26,27,28,29,30,31,32,33,34,35,36]|
|Fog Applications in healthcare, smart grids, smart vehicles and other domains||[8,37,38,39,40,41,42,43,44,45,46,47,48]|
|Security and Privacy in Fog Computing||[49,50,51,52,53,54,55,56]|
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Jaiswal, R.; Davidrajuh, R.; Rong, C. Fog Computing for Realizing Smart Neighborhoods in Smart Grids. Computers 2020, 9, 76. https://doi.org/10.3390/computers9030076
Jaiswal R, Davidrajuh R, Rong C. Fog Computing for Realizing Smart Neighborhoods in Smart Grids. Computers. 2020; 9(3):76. https://doi.org/10.3390/computers9030076Chicago/Turabian Style
Jaiswal, Rituka, Reggie Davidrajuh, and Chunming Rong. 2020. "Fog Computing for Realizing Smart Neighborhoods in Smart Grids" Computers 9, no. 3: 76. https://doi.org/10.3390/computers9030076