224 UNIT 11 FOG COMPUTING AND EDGE COMPUTING Structure 11.1 Introduction 11.2 Objectives 113. Fog Computing 11.3.1 Features of Fog Computing 114 Cloud Computing Vs Fog Computing 115 Fog Computing Architecture 11.6 Working of Fog Computing 11.7 Advantages of Fog Computing 11.8 Applications of Fog Computing 11.9 Challenges in Fog Computing 11.10 Edge Computing 11.11 Edge Computing Architecture 11.12. Cloud-Fog-Edge Architecture 11.13 Cloud Vs Fog Vs Edge 11.14 Applications of Edge Computing 11.15 Summary 11.16 Solutions/Answers 11,17 Further Readings 11.1 INTRODUCTION The utilization of burgeoning technologies such as IoT, online applications, and the widespread adoption of social networking platforms is driving a substantial surge in internet users. Consequently, the daily generation of data is skyrocketing, placing a significant burden on cloud infrastructure. Moreover, there is a mounting demand for escalated bandwidth and an imperative need for real-time applications and analytics, further amplifying the challenges faced in meeting these evolving requirements Fog computing represents a pioneering paradigm in the realm of computing, strategically positioned at the nexus between the cloud and edge devices. Developed to address the limitations of centralized cloud systems, this innovative technology, often attributed to the Cisco Group, endeavors to bring computational capabilities closer to where data is generated and utilized. By harnessing the power of distributed nodes at the edge of networks, fog computing optimizes the processing, storage, and networking. resources available, fostering quicker decision-making and enhanced efficiency for a myriad of Intemet of Things (IoT) devices.Its decentralized architecture (which we will be learning in this unit) offers a dynamic framework for handling the vast volumes of data generated by IoT devices, enabling real-time processing at the edge while selectively offloading complex tasks to the cloud for more extensive computations. This, convergence of edge and cloud resources marks a transformative shift in computing paradigms, promising to revolutionize how data is managed and services are delivered in an increasingly connected and data-driven world, In this unit, we will study the concept of Fog Computing, need for Fog computing, differences between Cloud Computing and Fog Computing, Fog Architecture, issues and challenges, concept of Edge Computing, Working of Edge computing, differences between Fog, Cloud and Edge and towards the end we will learn the applications and use cases. 11.2. OBJECTIVES After going through this unit, you should be able to: * describe Fog computing and the underlying working principle * identify the differences between fog and cloud computing; * discuss the fog architecture, advantages, applications and challenges associated; * define Edge computing + differentiate between Cloud, Fog and Edge computing «specify some applications and use cases of Edge computing 11.3 FOG COMPUTING The proliferation of Internet of Things (lo) devices and internet users has resulted in an exponential rise in network traffic, storage demands, and processing requirements. Cisco's 2020 estimates projected that by the close of 2023, there would be approximately 29.3 billion devices and 5.3 billion internet users, amplifying these trends, Cloud computing, a technology offering computation services over the internet on a pay-per-use basis, delivers dynamic provisioning of resources like storage, computation, and networking based on user demand, While it boasts advantages such as costeffeetiveness, rapid provisioning, robust computational capabilities, flexibility, automated updates, and user-side management, it also presents drawbacks. These include increased response times due to distant server locations, security concems over remotely stored and accessed resources via the internet, amplified bandwidth demands, and heightened network loads owing to escalating user numbers. In 2014, Cisco introduced ‘Fog Computing,’ a technology extending computing capabilities to the network's edge. The fog concept alludes to a cloud-like infrastructure situated close to the ground, akin to how fog gathers at the network's periphery. Fog Computing and Edge Computing 225Application Development, Fog Comps ‘Case Studies 226 ing and Fog computing strategically situates resources - compute, data, storage, and applications - between end-user layers where data originates and the cloud. Devices like gateways, routers, and base stations can be configured as fog devices. This approach brings cloud computing advantages closer to data generation sources, thereby reducing response times, _ bandwidth requirements, bolstering security, and offering other benefits. According to the OpenFog Consortium fog computing is "a horizontal system-level architecture that distributes computing, storage, control, and networking functions closer to users along a continuum from cloud to device." Fog computing doesn't seek to replace cloud computing but rather supplements it. Fog servers or devices provide limited resources compared to extensive cloud infrastructure, Therefore, while cloud computing operates as a centralized system for high processing power and storage, fog devices serve in the user's proximity for low-latency operations, balancing the computing load efficiently. 11.3.1 Features of Fog Computing Fog nodes(FN) spread close to IoT devices at the edge of the network, optimizing the services provided, and increasing the utilization of fog node resources such as storage, networking, and data transmission. The features of fog computing can be understood in contrast with the features of cloud computing and are listed as follows: © Location Awareness and Low Latency: FN provides IoT devices with s based on their locations relative to the region of that FN. Each device sends data and gets services from the closest FN, and receives responses within milliseconds. Therefore fog nodes services improve the efficiency of the real-time and decision making services. serv’ * Geographic Distribution: FNs are distributed at consecutive locations to guarantee highquality data transmission between themselves and [oT devices during their traverse of FNs’ regions. © Decentralization: Multiple FNs can cooperate to perform any heavy computing services from IoT devices. When computing is distributed between FNs’ resources, there is no need for centralized servers. ‘© Real-Time Services: FNs have the ability to provide the users with real- time services, with great value to many systems that do not tolerate ‘© Saving Utilization of Cloud Storage: The FNs collect a massive amount of user data, and onlyfiltered data is emigrated to be stored in the cloud, ‘+ Heterogeneity: Fog computing system consists of broad types of FNs that vary in the capabilities from one to another, due to different operating systems between high-capacity servers and limitedcapacity sensors, Communication in the fog computing systems also varies in speed and nature, and may be wired or wirelesses, depending on the requirements of serviced systems.* Mobility Support: FNs allow direct communication with mobile devices, enabling user identity and location to be distinguished using protocols such as Cisco’s locator/ID separation protocol. 11.4 CLOUD COMPUTING Vs FOG COMPUTING Cloud computing is defined as a model that allows ubiquitous access to shared resources on demand over the internet on a pay-per-use basis. Large pools of resources are maintained at data centers by the cloud service providers. Virtual resources from these pools are dynamically provisioned and allocated to users on demand. High performance can be achieved by using cloud resources but it may not be used for real time applications that demand higher response time due to the distant location of cloud servers. Fog computing is introduced to fill up the gap between the cloud servers and end devices. Fog servers like cloud servers can offer various resources — compute, storage, or network. Due to its proximity to end users, it allows computations to be done faster or near real time. Hence it is better suited for latency sensitive applications. Since fog computing makes use of devices like- switches, routers, gateways; it is generally limited by resources and hence offers less computation power as compared to cloud. Some of the differences between cloud computing and fog computing are given in Table Il Table 1: Differences between Cloud Computing and Fog Computing Criterion Cloud Computing Fog Computing ‘Architecture Architecture in | Architecture is centralized distributed Location Distant location from | In the proximity of end the end users users Resources Huge amount of | Limited amount of, resources resources Computational Higher computation | Lower computation Capabilities capabilities capabilities Response Time More response time Less response time Access, Can be accessed over |Can be accessed by intemet various protocols and standards Security Less security More security Geographical Centralized Localized Distribution Distance between the | Multiple Hop Single Hop Client and the Serving Node Service Provide delay in real- | Perfectly suited for real- time services time services. Mobility Support __| Limited support Fully support Fog Computing and Edge Computing 227Application Developme Fog Computing and ‘Case Studies 228 11.5 FOG COMPUTING ARCHITECTURE Fog architecture is illustrated as a three-tier architecture, comprising the edge, fog, and cloud levels, as shown in Figure 1. Researchers described the fog architecture as a network of IoT back-end systems, and also considered the fog level to be an extension of the cloud level, to provide services closer to the edge-based position of the IoT applications. In ascending order, the edge, fog, and cloud levels have increasing processing and storage capacities. In short, the level of fog is a decentralized level imposed between the cloud and the edge levels, to reduce the overhead and latency of applications in the cloud. Services will be sent to the cloud level when there are computations and data requiring more computing power than that available or expedient in the fog level. The fog level is made up of several geographically distributed Fog nodes(FN) represented by high computational devices (such as routers, bridges, gateways, switches, and local servers). These fog nodes are connected from one side to the Io devices (such as mobile phones, sensors, cameras, and wearable devices), and from the other side to the cloud servers. All of them are linked via wireless connections (e.g. Bluetooth and Zigbee ete.), wired connections (optical fiber, Ethernet, etc.), or both connection types can be used in combination. The level of the cloud is “used to support services such as Infrastructure as a Service (IaaS), Software as a Service (SaaS) and Platform as a Service (PaaS)” Three layer logical architecture of fog computing is given in Fig 1. The first layer represents the end devices layer, middle layer represents the fog devices, and the top most layer represents the cloud servers. pote ST) CLOUD LAYER Ln = ros Laven / i] P\N < Hoan END DEVICES Figure 1: Fog ArchitectureGeneral architecture of fog computing is composed of three layers (as shown, in Fig 1) i) End Devices Layer - Layer | is composed of end devices which can be mobile devices, IoT devices, computer systems, camera, etc. Data either captured or generated from these end devices is forwarded to a nearby fog server at Layer 2 for processing. ii) Fog Layer - Layer 2 is composed of multiple fog nodes or devices or servers. They are placed at the edge of a network, between layer 1 and cloud servers. They can be implemented in devices like — switches, routers, base stations, access points or can be specially configured fog servers. iii) Cloud Layer - Layer 3 is composed of Cloud data centers, They consist of huge infrastructure - high performance servers, massive storage devices, etc. They provide all cloud benefits like- high performance, automatic backup, agility. > Check Your Progress 1 1) What is Fog Computing? 2) Mention the differences between Cloud computing and Fog computing, 3) Explain the architecture of Fog Computing, 11.6 WORKING OF FOG COMPUTING Introducing a fog layer between the centralized cloud and end devices significantly enhances the system's overall performance. The collaboration between fog computing and cloud computing unfolds as follows: Fog Computing and Edge Computing 229Application Development, Fog Comps ‘Case Studies 230 ing and + A substantial volume of data originates from end devices and IoT gadgets such as mobile phones, cameras, and laptops. This data is then directed to the nearest fog server (in layer 2) for processing. + Data sensitive to latency or requiring real-time responses receives priority processing by the fog servers. Processed results or necessary actions are then relayed back to the end devices. Additionally, fog servers transmit condensed results to cloud servers in layer 3 for future analysis, allowing only refined data to be sent to the cloud layer. + Im instances where fog servers encounter resource unavailability or lack necessary information to fulfill requests, they can engage with neighboring servers or, based on the offloading strategy, forward the request to cloud servers at Layer 3. Moreover, data that is less time- sensitive is generally routed to cloud servers for processing and storage. Upon task completion, responses are delivered to users at layer 1 through fog servers. 11.7 ADVANTAGES OF FOG COMPUTING Fog computing emerged as a response to the evolving challenges posed by the surge in connected devices and the limitations of traditional cloud-centrie models. Its necessity stems from several key factors: ‘+ Low Lateney Requirements: Many applications, especially in IoT and real-time analytics, demand instantaneous processing and response times. Fog computing brings computational resources closer to the edge devices, minimizing latency by reducing the distance data needs to travel for processing. + Bandwidth Optimization: Transmitting enormous volumes of raw data from edge devices to centralized cloud servers can strain network bandwidth. Fog computing enables local processing and selective data transmission, optimizing bandwidth usage and reducing network congestion. * Enhanced Privacy and Security: Certain sensitive data might require immediate processing or secure handling without being transmitted to distant cloud servers. Fog computing allows for local data processing, improving privacy and security by keeping critical information closer to its source. * Scalability and Reliability: With the exponential growth of IoT devices, fog computing offers a scalable infrastructure, It distributes computing resources across the network, enhancing reliability by reducing dependency on a single centralized location. * Offline Operations: In scenarios where continuous connectivity isn’t ‘guaranteed, such as remote areas or unstable network environments, fog computing enables devices to function autonomously by processing data locally, ensuring continuity even without constant connectivity to the cloud.* Regulatory Compliance: Certain regulations or compliance standards may necessitate data processing or storage within specific geographical boundaries. Fog computing allows for compliance by processing data locally, adhering to regional regulations. These needs collectively drive the adoption and development of fog computing, offering a versatile and efficient solution to the challenges imposed by the burgeoning IoT landscape and the demand for real-time, 11.8 APPLICATIONS OF FOG COMPUTING Fog computing since its introduction, is gaining popularity due to its applications in various industries. Some of the applications are Smart Cities ‘Urban areas leveraging technology to enhance the quality of life and services offered to residents are often termed as smart cities. Fog computing stands as, a pivotal component in the construction of smart cities. Through the utilization of smart devices, IoT technologies, and fog devices, various tasks become achievable, These include the creation of smart homes and buildings through efficient energy management, bolstering security measures, and cultivating intelligent urban landscapes by establishing systems for smart parking, infrastructure development, traffic management, and environmental monitoring. Additionally, the integration of fog computing facilitates the enhancement of services in sectors such as hospitals, highways, factories, and other industrial domains (See Fig. 2). lousane ian ey *) aan ‘Series a foes Figure 2: Smart Cities Scenario (Source: www. OpenFogConsortium.org) Smart Car and Traffic Control System Utilizing IoT devices in conjunction with widespread fog devices enables the establishment of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V21) Fog Computing and Edge Computing 231Application Development, Fog Comps ‘Case Studies 232 ing and communication networks. Vehicles equipped with sensors can communicate both internally and externally, connecting with their surroundings. Furthermore, sensors and actuators can be affixed to infrastructure elements along roadways, such as traffic lights, street signs, and gates. These sensors collect data and transmit it to nearby or vehicle-attached fog devices. Subsequent computation by fog devices allows for the execution of actions by either the vehicle or infrastructure, employing various control mechanisms (actuators). For instance, upon detecting vehicles traveling in the wrong direction or pedestrians approaching, a traffic light might signal red to prevent collisions, or other vehicles could be automatically prompted to apply brakes for safety measures (See Fig. 3). ===" @) e acres Figure 3: Smart ear and Traffic control System (Source: www.OpenFogConsortium.org) Smart Grids The electrical grid functions as a network transmitting energy generated from diverse sources to consumers. Employing fog computing enables the efficient distribution of energy. IoT sensors have the capability to monitor energy produced by various sources, including wind energy farms, thermal plants, hydraulic plants, and more, Subsequently, this data is relayed to nearby fog servers, facilitating the identification of the most optimal energy source and the detection of potential issues, such as equipment malfunctions. Depending on the identified problems, these systems can also suggest alternative energy sources to maintain efficiency and uninterrupted energy supply (See Fig. 4).Fog Computing and Edge Computing Smart Healthcare Systems Fog computing finds relevance in healthcare systems as well. Various sensors can capture and transmit patients’ health reports to fog devices. These fog devices, upon conducting analyses such as diagnosing cardiac diseases, are capable of initiating required actions based on the insights gained. Surveillance Security and surveillance cameras are prevalent across numerous locations. Transmitting extensive data collected by these cameras to cloud servers faces, challenges due to bandwidth limitations. Consequently, the data gathered from these cameras can be directed to nearby fog servers. These fog servers can conduct video processing tasks to detect issues such as theft, kidnappings, or even aid in identifying missing individuals or resolving yes. Subsequently, necessary actions can be initiated through alert generation or reporting to law enforcement agencies. 11.9 CHALLENGES IN FOG COMPUTING Fog computing, despite its numerous advantages, encounters several challenges: + Complexity: Fog devices can be diverse in architecture and located at different locations. Fog devices further store and analyse their own data hence add more complexity to the network. + Power Consumption: Fog devices require high power consumption for proper functioning. Adding more fog devices increases energy consumption, which results in an increase of cost. + Data Management: Data is distributed across multiple fog devices hence data management and maintaining consistency is challenging. + Authentication: Establishing trust and authentication may raise issues. + Security Concerns: As data is processed closer to the edge, there are 033Application Development, Fog Computing and ‘Case Studies 234 heightened concerns regarding security vulnerabilities. Fog nodes and devices may be more susceptible to breaches, raising issues about data privacy and integrity. + Interoperability: The diversity in devices, protocols, and standards poses a challenge for seamless communication and interoperability between various fog devices and systems. Ensuring compatibility and smooth operations across heterogeneous platforms remains a challenge. + Scalability: Managing the scalability of fog computing infrastructure is a complex task. Accommodating the increasing number of devices, users, and data streams while maintaining performance and responsiveness demands sophisticated scalability mechanisms. + Resource Constraints: Fog devices typically have _ limited computational power, storage, and energy resources compared to cloud data centers. Optimizing resource usage and managing constraints effectively without compromising performance is a significant challenge + Reliability and Resilience: Ensuring the reliability and resilience of fog ‘computing systems is crucial, especially in dynamic environments where devices might intermittently connect or disconnect. Maintaining continuous service availability becomes challenging in such scenarios. + Data Management and Analytics: Handling vast amounts of data generated by ToT devices demands efficient data management and analytics. Processing and analyzing this data at the edge require sophisticated algorithms and strategies to extract meaningful insights, + Standardization and Governance: Lack of standardized protocols and governance frameworks in fog computing can hinder its widespread adoption. Establishing unified standards and governance _ models becomes essential for seamless integration and operation. Navigating these challenges is crucial for the successful adoption and implementation of fog computing across various industries and applications. Addressing these obstacles will enable the realization of its full potential in enhancing efficiency, reducing latency, and enabling real-time processing, © Check Your Progress 2 1) Explain advantages and challenges associated with fog computing.2) Whatare the various application areas of fog computing. 11.10 EDGE COMPUTING Edge computing is a distributed computing paradigm that brings computation, and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. It is a technology which offers data processing on the same layer where data is generated by making use of edge devices having computation capabilities. This allows data to be processed faster than processing at fog devices at no or a very low cost. This also increases utilization of edge devices. Bdge or end devices found today are smarter with various advanced features like artificial intelligence enabled in them. Edge computing takes advantage of this intelligence to reduce load on network or cloud servers. Also edge devices when used for computation offers hardware security along with low power consumption. It can improve security by encrypting data closer to the network core, Edge computing is often seen as similar to fog computing but there are several differences, Edge computing devices are limited in their resource capabilities and therefore cannot replace existing Cloud or Fog computing technology. But edge computing when added with these technologies can offer numerous advantages and applications Edge computing revolyes around storing and processing data in closer proximity to users and the applications they utilize. Ultimately, this approach diminishes latency and provides a safeguard against internet disruptions. Architectures in edge computing hold the potential to drive advancements in: + Smart homes + Autonomous vehicles + Surgical roboties * Advanced real-time gaming experiences ‘As an alternative architecture to cloud computing, edge computing caters to applications requiring high-speed and continuous availability. The reliance of applications solely on cloud-based data storage and processing creates dependency on internet connectivity, making them susceptible to its inherent vulnerabilities. Instances where internet connectivity slows down or becomes unavailable result in a slowdown or failure of the entire application. Edge computing circumvents these intemet dependencies by situating data as close as possible to its origin and consumption points. This proximity Fog Computing and Edge Computing 235Application Development, Fog Comps ‘Case Studies 236 ing and accelerates application performance and enhances their continuous availability. Fig 5 shows the Cloud-Fog-Edge collaboration scenario, 11.11 EDGE COMPUTING ARCHITECTURE Following are the fundamental components constituting an edge ecosystem: + Edge Devices: Specialized equipment designed for specific purposes, equipped with limited computational capabilities + Edge Nodes: Devices, servers, or gateways responsible for executing edge computing tasks. + Edge Servers: Computers situated in facilities near edge devices. These systems manage application workloads and shared services, necessitating higher computational power compared to edge devices. + Edge Gateways: Edge servers tasked with network operations such as tunneling, firewall management, protocol translation, and wircless connections. These gateways can also support application workloads, * Cloud: A public or private cloud serving as a storage repository for containerized workloads, encompassing applications and machine leaming models. Additionally, the cloud hosts and operates applications responsible for managing edge nodes. Edge computing has three primary nodes: the device edge, local edge, and the cloud as shown in Fig 5: DEVICE EDGE LOCAL EDGE THE CLOUD + U@VERe + Basic anoyties > - Bininess onl | on = Dotawarehousing Figure $: Edge Computing Architecture Device edge refers to the physical site where edge devices operate on-site, encompassing cameras, sensors, industrial machinery, and similar apparatus. These devices possess the computational capability necessary to collect and transmit data,Local edge denotes a system that supports applications and network workloads, comprising two distinet layers: + An application layer responsible for executing applications that exceed the processing capacity of edge devices due to their substantial footprint This layer manages tasks such as intricate video analytics or complex processing for Internet of Things (Io) applications. + A network layer responsible for managing physical or virtualized network components like routers and switches. ‘The cloud or the nexus, hosts application and network workloads responsible for handling processing tasks beyond the capability of other edge nodes. Despite its name, this edge layer operates either within an on-site data center or within the cloud infrastructure, The Fig 6 below offers a comprehensive architectural depiction, highlighting components pertinent to each individual edge node. DEVICE EDGE LOCAL EDGE THE CLOUD cron oun Industry sohatons elite Rekbood topes onappe 4 4 T Petes direc Edge —_‘Mult-cloud > Bw cee cence Becoes ea Cy classaevice Wt Soren networks Figure 6: Edge Computing Architecture Showing all of its Components ‘As shown in Fig 6, industry solutions and applications may reside across various nodes, with particular workloads better suited for either the device or the local edge. Additionally, certain workloads have the flexibility to transition dynamically between nodes based on specific conditions, either through manual intervention or automated processes. Virtualization stands as a critical component within an expansive edge computing infrastructure. This technology simplifies the deployment and operation of numerous applications on edge servers. 11.12 CLOUD-FOG-EDGE ARCHITECTURE Edge computing facilitates data processing at the network's edge, offering several benefits such as decreased latency, reduced data transmission to cloud or fog, lowered bandwidth costs, and minimized energy consumption. Fog Computing and Edge Computing 237Application Developme Fog Computing and ‘Case Studies 238 Edge computing can work in collaboration with cloud computing only or ean be either implemented with Cloud —Fog collaboration environment. As shown in Fig 7, instead of sending all the data directly to the cloud or fog layer from the edge devices, data is first processed at the edge layer. Processing data at the edge layer gives near real time response due to physical proximity of edge devices. As data generated at the edge layer is huge, it cannot be handled entirely at the edge layer. Hence it is offloaded to the Cloud or Fog layer. In Cloud-Fog-Edge collaboration scenario, data from edge layer is first offloaded to fog servers over a localized network, which in tum can offload it to cloud servers for updates or further processing needs. In Cloud-Edge scenarios, data after processing on the edge layer, can be offloaded to the cloud layer as resources available at the edge layer are insufficient to handle large amounts of data, Here the edge layer can decide what is relevant and what is not before sending to further layers, hence reducing load on cloud and fog servers. CLOUD SERVERS AAA a CLOUD LAYER Cloud offloading FOG SERVERS FOG SERVERS FOG LAYER EDGE LAYER Figure 7: Cloud ~ Fog - Edge Computing Architecture 11.13 CLOUD Vs FOG Vs EDGE Cloud, fog and edge computing all are concepts of distributed computing. All of them perform computation but at different proximity levels and with different resource capacities. Adding Edge and fog layer to the cloud reduces, the amount of storage needed at cloud. It allows data to be transferred at a faster data rate because of transferring relevant data. Also the cloud would store and process only relevant data resulting in cost reduction, Edge computing devices are located at the closest proximity to users. Fog computing devices are located at intermediate proximity. Cloud computing devices are at distant and remote locations from users. Fog computing generally makes use of a centralized system which interacts with gatewaysand computer systems on LAN. Edge computing makes use of embedded systems directly interfacing with sensors and controllers. But this distinction does not always exist. Some of the common differences between Cloud, Fog and Edge computing are shown in Table 2. Table : Differences between Cloud, Fog and Edge Computing Cloud Computing _| Fog Computing Edge Comput Centralized approach | Distributed approach _| Distributed approach Large amount of | Intermediate amount of | | resources resources High latency Medium latency Low latency Low data rate Medium data rate High data rate Globally distributed | Regionally distributed _| Locally distributed Non-teal time response | Near real time response | Real time response Can be accessed with Can be accessed with | 5 thout {Cb accessed internet — without internet In the next section, let us study some applications of edge computing. 11.14 APPLICATIONS OF EDGE COMPUTING Edge computing has applications similar to fog computing due to its close proximity. Some of the applications are listed below: + Smart Manufacturing: Edge computing facilitates _ predictive maintenance in manufacturing plants by analyzing real-time data from machinery sensors. It enables proactive equipment maintenance, minimizing downtime and improving overall operational efficiency. + Telemedicine and Healtheare: In healthcare, edge computing supports remote patient monitoring and wearable medical devices. It enables real- time analysis of patient data, facilitating prompt medical interventions and personalized healthcare services. + Autonomous Vehicles: Edge computing processes data from sensors in autonomous vehicles, enabling quick decision-making for nay collision avoidance, and ensuring safety on the roads. + Retail and Inventory Management: In retail, edge computing assists in inventory management by analyzing data from sensors and cameras in stores. It enables real-time inventory tracking, minimizing stockouts and optimizing supply chain operations. + Smart Cities: Edge computing powers smart city initiatives by enabling real-time data processing for traffic management, waste management, environmental monitoring, and public safety systems Fog Computing and Edge Computing 239Application Development, Fog Comps ‘Case Studies 240 ing and + Content Delivery and Streaming: Edge computing enhances content delivery networks (CDNs) by caching and delivering content from edge servers closer to users. It reduces latency for streaming services, ensuring faster and smoother content delivery. + Energy Management: In the energy sector, edge computing supports the optimization of energy grids and renewable energy sources. It enables real-time monitoring and control of energy distribution, improving efficiency and reliability. + Edge AI and Surveillance: Edge computing facilitates Al-powered video analytics for surveillance cameras. It allows real-time object detection, facial recognition, and threat identification, enhancing security systems’ efficiency. + Fleet Management: For logistics and transportation, edge computing assists in fleet management by analyzing vehicle data in real time. It helps optimize routes, monitor driver behavior, and ensure efficient logistics operations. + Gaming and AR/VR: Edge computing reduces latency in cloud gaming and augmented/virtual reality applications. It ensures smoother and more responsive gaming experiences by processing data closer to the user. + Patient monitoring:Edge devices present on the hospital site can process data generated from various monitoring devices like- temperature sensors, glucose monitors ete, Notifications can be generated to depict unusual trends and behaviours. + Monitoring Within Oil and Gas Industries: Edge computing can help prevent oil and gas failures. These plants typically operate in remote locations, so an edge center is a much better option than a distant server or cloud. Devices can use real-time analytics to monitor the system and shut down machines before a disaster occurs. These use cases illustrate how edge computing addresses specific needs across industries, leveraging its ability to process data closer to the source, reduce latency, and enable real-time decision-mal © Check Your Progress 3 1) What Edge Computing? 2) Mention the differences between Cloud, Fog and Edge Computing.2) State some applications of Edge Computing 11.15 SUMMARY In this unit two emerging technologies — Fog computing and Edge computing, are discussed. Cisco introduced Fog Computing as a technology which extends computing to the edge of the network. Fog computing systems are modem decentralized architectures that extend cloud storage, networking, and computing capabilities to the edge of the network to support IoT applications on a large scale. The location awareness of the fog computing system, geographic distribution, and other features provide new security challenges that should be taken into account, Edge computing refers to a decentralized computing paradigm where data processing and storage are performed closer to the data source rather than relying solely on centralized cloud servers. In this model, data processing ‘occurs at or near the “edge” of the network, closer to where data is generated or consumed, such as IoT devices, sensors, or user devices. Rather than sending all data to a centralized cloud for processing, edge computing distributes computational tasks to localized servers or devices situated closer to where the data is created. This proximity reduces latency, enhances real- time processing capabilities, and decreases the volume of data that needs to be transmitted to remote servers. It allows for quicker decision-making, especially for time-sensitive applications.Edge computing architectures involve a network of edge devices, gateways, and servers that collectively manage and process data. These edge devices can include sensors, routers, IoT devices, and servers deployed in various locations, such as factories, smart cities, vehicles, or retail stores. 11.16 SOLUTIONS / ANSWERS. Cheek Your Progress 1 1. Cisco in 2014 introduced a term called ‘Fog Computing’ to a technology which extends computing to the edge of the network. Fog computing is a technology in which resources like - compute, data, storage and applications are located in-between the end user layer (where data is generated) and the cloud. Devices like gateways, routers, base stations can be configured as fog devices. It can bring all the advantages offered by cloud computing closer to the location where data is generated; hence leading to reduced response time, reduced bandwidth requirements, enhanced security and other benefits. Fog Computing and Edge Computing 241Application Development, Fog Computing and ‘Case Studies 242 2. Some of the differences between cloud computing and fog computing are! Cloud Computing Fog Computing Architecture in centralized Architecture is distributed Distant location from the end users In the proximity of end users Huge amount of resources Limited amount of resources Higher computation capabilities Lower computation capabilities More response time Less response time Less security More security 3. Architecture of fog computing is composed of three layers =~ 1, End Devices Layer — It is composed of end devices which can be mobile devices, IoT devices, computer systems, camera, etc. Data either captured or generated from these end devices is forwarded to a nearby fog server at Layer 2 for processing, 2. Fog Layer—It is composed of multiple fog devices or servers. They are placed at the edge of a network, between layer 1 and cloud servers. They can be implemented in devices like — switches, routers, base stations, access points or can be specially configured fog servers, 3. Cloud Layer — It is composed of Cloud data centers. They consist of huge infrastructure - high performance servers, massive storage devices, ete. They provide all cloud benefits like- high performance, automatic backup, agility. Cheek Your Progress 2 Various advantages associated with fog computing are: a) Low latency b) Reduced bandwidth ©) Reduced cost 4) Mobility Various challenges associated with fog are: a) Complexity b) Maintaining security ©) Authenticating 4) Additional power consumption Various application areas of fog computing are — a) Smart Cities Fog computing can play a vital role in building smart cities. With the help of smart devices, IoT devices and fog devices, it is possible to do tasks like - creating smart homes and buildings by energy managementof buildings, maintaining security, etc. b) Smart Car and Traffic Control System By making use of IoT devices and ubiquitous fog devices, vehicle-to- vehicle (V2V) and vehicle-to-infrastructure (V2) communication is possible. Fog devices afier computation can direct the vehicle or infrastructure to take action with the help of various controls (actuators). c) Surveillance Security and Surveillance cameras are deployed in many areas. Data collected from these can be forwarded to nearby fog servers. Fog servers in tum can perform video processing to find out problems like theft, kidnapping, murders, ete. Check your progress 3 1, Edge computing is a technology which offers data processing on the same layer where data is generated by making use of edge devices having computation capabilities. This allows data to be processed even faster than processing at fog devices at no or a very low cost. This also increases utilization of edge devices. 2. Various differences between cloud, fog and edge computing are : Cloud Computing | Fog Computing Edge Computing lize Comiplized Distributed approach _| Distributed approach approach Large amount of | Intermediate amount of Limited resources resources resources High latency Medium latene; Low latency Low data edium data rate High data rate Globally : Regionally distributed | Locally distribute< distributed ew y di | Non-eal time | Near realtime Real time response response response Can be act with Can be accessed | Can be accessed intemet or without with internet . without internet internet 3. Some applications of edge computing are: a) Gaming Gamings which require live streaming feed of the game depends upon latency. In this, edge servers are placed closed to the gamers to reduce latency. b) Content Delivery It allows caching of data like- web pages, videos near users in order to improve performance by delivering content fastly. Fog Computing and Edge Computing 243Application ‘Case Studies 244 ) Smart Homes IoT devices can collect data from around the house and process it. Response generated is secure and in real time as round-trip time is reduced. For example, response generated by Amazon’s Alexa. 11.17 FURTHER READINGS 1, Fog Computing-Theory and Practice, Assad Abbas, Samee U, Khan, Albert Y. Zomaya, Wiley, 2020. 2. Fog and Edge Computing: Principles and Paradigms, Rajkumar Buyya, Satish Narayana Srirama, John Wiley & Sons, 2019 3. Multidisciplinary Applications of Fog Computing : Responsiveness in Real-Time, Debi Prasanna Acharjya, Kauser Ahmed P, IGI Global, 2023. 4, Fog Computing - Concepts, Frameworks, and Applications (eBook), Ravi Tomar, Avita Katal, Sus! Choudhary,CRC Press, 2022. 5, Edge Computing — Fundamentals, Advances and Applications, K. Anitha Kumari, G. Sudha Sadasivam, D. Dharani, CRC Press, 2022 6. Edge Computing, Ajit Singh, SPD, 2019. 1a Dahiya, Niharika Singh, Tanupriya