Fog computing is a distributed computing paradigm that performs storage and processing of data close to IoT devices to help address issues of latency, bandwidth, and scalability. It extends cloud computing and services to the edge of the network by leveraging edge devices such as routers, switches, and access points. Fog computing helps overcome limitations of cloud and edge computing alone by avoiding resource contention at the edge through use of geographically distributed edge devices and cloud resources. It supports real-time analytics through dynamic task placement across edge and cloud resources to minimize latency. Challenges to realizing fog computing's potential include enabling real-time analytics, developing programming models for dynamic environments, and addressing security, reliability, and power consumption issues in distributed fog
A Review: The Internet of Things Using Fog ComputingIRJET Journal
Fog computing is a new computing paradigm that processes data and analytics at the edge of the network, rather than sending all data to a centralized cloud. This helps address issues with the cloud-based Internet of Things (IoT) model, such as high latency, bandwidth constraints, location awareness, and mobility. Fog computing brings computing resources closer to IoT devices and end users by using edge devices like routers, switches, and access points as "fog nodes" that can perform analytics and decision making. This allows time-sensitive IoT applications to function more efficiently. Fog computing also helps optimize resource usage by balancing processing between the edge and cloud.
In the Field of Internet of Things IOT the devices by themselves can recognize the environment and conduct a certain functions by itself. IOT Devices majorly consist with sensors. Cloud Computing which is based in sensor networks manages huge amount of data which includes transferring and processing which takes delayed in service response time. As the growth of sensor network is increased, the demand to control and process the data on IOT devices is also increasing. Vikas Vashisth | Harshit Gupta | Dr. Deepak Chahal "Fog Computing: An Empirical Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30675.pdf Paper Url :https://www.ijtsrd.com/computer-science/realtime-computing/30675/fog-computing-an-empirical-study/vikas-vashisth
This document discusses fog computing as an extension of cloud computing that moves some computing and storage to the edge of the network. It begins with an abstract that outlines fog computing and its advantages over cloud, such as lower latency. The introduction discusses Cisco's vision for fog computing and bringing applications to billions of connected devices at the network edge. It then discusses how fog computing addresses the issues of slow response times and scalability that cloud computing faces for machine-to-machine communication. The document provides examples of how fog computing could be applied in smart traffic lights, wireless sensor networks, and the internet of things.
Report-Fog Based Emergency System For Smart Enhanced Living EnvironmentKEERTHANA M
Report-An ambient assisted-living emergency system exploits cloud and fog computing, an outdoor positioning mechanism, and emergency and communication protocols to locate activity-challenged individuals.
Topic: Moving from Cloud Computing to Fog Computing: How the “Internet of Things will Change the Way We Live and Work
Speaker: Jeff Hagins, Co-founder & CTO, SmartThings
Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks,
Fog computing is a term created by Cisco that refers to extending cloud computing to the edge of an enterprise's network.
Cisco introduced its fog computing vision in January 2014 as a way of bringing cloud computing capabilities to the edge of the network .
As the result, closer to the rapidly growing number of connected devices and applications that consume cloud services and generate increasingly massive amounts of data.
Fog computing is a distributed computing paradigm that processes data closer to IoT devices rather than sending all data to centralized cloud servers. This helps address issues like high latency, bandwidth constraints, and scalability challenges. Fog computing deploys compute and storage resources between end devices and cloud data centers. It can perform tasks like data aggregation, analytics, and decision making near devices to enable low-latency applications. Coordinating fog and cloud resources requires addressing challenges regarding resource management, load balancing, APIs, security, and fault tolerance.
The term “fog computing” or “edge computing” means that rather than hosting and working from a centralized cloud, fog systems operate on network ends. It is a term for placing some processes and resources at the edge of the cloud, instead of establishing channels for cloud storage and utilization.
A Review: The Internet of Things Using Fog ComputingIRJET Journal
Fog computing is a new computing paradigm that processes data and analytics at the edge of the network, rather than sending all data to a centralized cloud. This helps address issues with the cloud-based Internet of Things (IoT) model, such as high latency, bandwidth constraints, location awareness, and mobility. Fog computing brings computing resources closer to IoT devices and end users by using edge devices like routers, switches, and access points as "fog nodes" that can perform analytics and decision making. This allows time-sensitive IoT applications to function more efficiently. Fog computing also helps optimize resource usage by balancing processing between the edge and cloud.
In the Field of Internet of Things IOT the devices by themselves can recognize the environment and conduct a certain functions by itself. IOT Devices majorly consist with sensors. Cloud Computing which is based in sensor networks manages huge amount of data which includes transferring and processing which takes delayed in service response time. As the growth of sensor network is increased, the demand to control and process the data on IOT devices is also increasing. Vikas Vashisth | Harshit Gupta | Dr. Deepak Chahal "Fog Computing: An Empirical Study" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30675.pdf Paper Url :https://www.ijtsrd.com/computer-science/realtime-computing/30675/fog-computing-an-empirical-study/vikas-vashisth
This document discusses fog computing as an extension of cloud computing that moves some computing and storage to the edge of the network. It begins with an abstract that outlines fog computing and its advantages over cloud, such as lower latency. The introduction discusses Cisco's vision for fog computing and bringing applications to billions of connected devices at the network edge. It then discusses how fog computing addresses the issues of slow response times and scalability that cloud computing faces for machine-to-machine communication. The document provides examples of how fog computing could be applied in smart traffic lights, wireless sensor networks, and the internet of things.
Report-Fog Based Emergency System For Smart Enhanced Living EnvironmentKEERTHANA M
Report-An ambient assisted-living emergency system exploits cloud and fog computing, an outdoor positioning mechanism, and emergency and communication protocols to locate activity-challenged individuals.
Topic: Moving from Cloud Computing to Fog Computing: How the “Internet of Things will Change the Way We Live and Work
Speaker: Jeff Hagins, Co-founder & CTO, SmartThings
Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks,
Fog computing is a term created by Cisco that refers to extending cloud computing to the edge of an enterprise's network.
Cisco introduced its fog computing vision in January 2014 as a way of bringing cloud computing capabilities to the edge of the network .
As the result, closer to the rapidly growing number of connected devices and applications that consume cloud services and generate increasingly massive amounts of data.
Fog computing is a distributed computing paradigm that processes data closer to IoT devices rather than sending all data to centralized cloud servers. This helps address issues like high latency, bandwidth constraints, and scalability challenges. Fog computing deploys compute and storage resources between end devices and cloud data centers. It can perform tasks like data aggregation, analytics, and decision making near devices to enable low-latency applications. Coordinating fog and cloud resources requires addressing challenges regarding resource management, load balancing, APIs, security, and fault tolerance.
The term “fog computing” or “edge computing” means that rather than hosting and working from a centralized cloud, fog systems operate on network ends. It is a term for placing some processes and resources at the edge of the cloud, instead of establishing channels for cloud storage and utilization.
IOT in Electrical & Electronics EngineeringLokesh K N
The evaluation of the IOT in the electrical power industry transformed the way things performed in usual manner. IOT increased the use of wireless technology to connect power industry assets and infrastructure in order to lower the power consumption and cost. The implementation of IoT in power system must rely on the line monitoring and real-time control in all aspects of the grid operating parameters, and the basic characteristics are grid information, communication, and automation.
IoT ( M2M) - Big Data - Analytics: Emulation and DemonstrationCHAKER ALLAOUI
The document discusses Internet of Things (IoT) concepts including emulation and demonstration of IoT platforms, architectures, and technologies. It provides examples of using sensors, MQTT brokers, Node-Red, and IBM IoT platforms to collect, transmit, and analyze IoT data from devices. Sections include presentations on IoT history and monitoring, as well as emulations of Philips Hue lighting and sensors to demonstrate IoT data collection and control capabilities.
Cloud computing involves clusters of servers connected over a network that allow users to access computational resources and pay only for what they use. While cloud computing provides advantages like flexibility and cost savings, security is a main concern as user data is stored remotely. Fog computing is a new technique that extends cloud computing by providing additional security measures and isolating user data at the network edge to enhance privacy. It aims to place data closer to end users to improve security in cloud environments.
Industrial Internet of things has continued to create the buzz, one can’t deny that it is the one of the fastest emerging technology with humpty amount of Data getting captured daily and with every industry demanding optimisation, analytics, automations and valuable insights Industrial Internet of things is something which the need of the day.
The Smart City concept operates in a complex urban environment, incorporating several complex systems of infrastructure, human behavior, technology, social and political structures and the economy. A Smart City provides an intelligent way to manage components such as transport, health, energy, education and the environment.
In this presentation, Praneeth introduces IoT and associated trends. Praneeth is interested in IoT applications in home automation space and he also has several ideas WRT to water management and transport management using IoT applications.
The document discusses applying autonomic computing principles to wireless sensor networks (WSNs). It introduces WSNs and their design goals/challenges, which include fault tolerance, power management, efficient routing and data aggregation. Autonomic computing aims to make systems self-configuring, self-healing, self-optimizing and self-protecting. The document argues that autonomic computing is well-suited for addressing WSN challenges by allowing for self-configuration, recovery from failures, optimized resource usage, and protection. It outlines architectures like MANNA that apply a service-oriented approach to autonomically manage WSNs.
This report describes how things get connected via internet.It also describes how actually iot architecture looks like.The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.In short, the Internet of Things refers to the rapidly growing network of connected objects that are able to collect and exchange data using embedded sensors. Thermostats, cars, lights, refrigerators, and more appliances can all be connected to the IoT.
IRJET- A Review Paper on Internet of Things based Remotely Monitoring of ...IRJET Journal
This document provides a review of internet of things (IoT) based remote monitoring of temperature and humidity. It discusses wireless sensor networks (WSNs) that can gather temperature and humidity data from remote locations and transmit it to a central station. The document reviews existing research on WSN systems for environmental monitoring and identifies gaps such as high costs due to technologies like Xbee connectivity, large microcontroller sizes, and lack of smart power utilization and low power systems. It proposes future research objectives like developing a low-cost system using WiFi technology, implementing mobile alerts and live data portals, and improving power management to increase battery life. The document concludes there is significant scope for future work to address issues in previous approaches and develop more cost
1) Fog computing is an extension of cloud computing that processes data closer to the edge of the network, such as at factory equipment, power poles, or vehicles. It aims to improve efficiency and reduce data transportation costs compared to cloud computing alone.
2) Fog computing involves fog nodes that are located between end devices and the cloud. Fog nodes can perform tasks like data analysis, storage, and sharing results with the cloud and other nodes. This helps process time-sensitive data locally for applications involving the internet of things.
3) Fog computing provides advantages over cloud computing like lower latency, better support for mobility and real-time interactions, local data processing for privacy and efficiency, and ability to handle
Summer Internship report on IOT RTTC TrivandrumFathimaCec
This document provides an overview of Internet of Things (IoT) and the evolution of mobile communication networks from 1G to 5G. It discusses key concepts such as practical IoT applications, IP addressing, and the features and limitations of each generation of mobile networks. The document aims to educate readers on the basics of IoT and how wireless technology has advanced to support increasing demands.
ABSTRACT
Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. For securing user data from such attacks a new paradigm called fog computing can be used. Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined network .This technique can monitor the user activity to identify the legitimacy and prevent from any unauthorized user access. Here we have discussed this paradigm for preventing misuse of user data and securing information.
This document discusses a wireless home automation system using the Internet of Things (IoT). It begins with an abstract that defines IoT as connecting physical devices to the internet to collect and share data. It then discusses how home automation is gaining popularity due to advances in automation technology and the widespread use of the internet. A wireless home automation system using IoT allows users to control home functions and appliances remotely using computers or smartphones. The system aims to reduce energy usage and human effort. Key advantages of a wireless system over wired include lower cost, easier expansion, and the ability to integrate mobile devices.
The seminar presentation introduced fog computing, which extends cloud computing and services to the edge of the network. Fog computing provides data, compute, and application services to end-users. It was developed to address limitations of cloud computing like high latency and lack of location awareness. Fog computing improves efficiency, latency, security, and supports real-time interactions through geographical distribution of resources at the edge of the network. The presentation covered fog computing characteristics, architecture, applications in areas like smart grids and vehicle networks, and concluded that fog computing will grow in helping network paradigms requiring fast processing.
The slides defines IoT and show the differnce between M2M and IoT vision. It then describes the different layers that depicts the functional architecture of IoT, standard organizations and bodies and other IoT technology alliances, low power IoT protocols, IoT Platform components, and finally gives a short description to one of IoT low power application protocols (MQTT).
This document discusses fog computing. Fog computing extends cloud computing by providing data, compute, storage, and application services closer to the edge of the network. It was introduced by Cisco to efficiently share and store data between distributed devices in the Internet of Things. Fog computing helps address issues with cloud computing like high latency by processing data locally at edge devices instead of sending all data to a centralized cloud. It provides advantages like improved security, reduced data transfers across networks, and better support for real-time applications. The document compares fog and cloud computing and concludes that fog computing will grow in helping network paradigms that require fast processing.
Fog Computing Reality Check: Real World Applications and ArchitecturesBiren Gandhi
Is Fog Computing just a buzz or a real business?
The IoT is flooded with a variety of platforms and solutions. Fog Computing has been notably appearing as an evolving term in the context of IoT software. There is skepticism that Fog Computing is just another buzzword destined to disappear in the dust of time. Get insight from concrete business cases in a variety of IoT verticals – Agriculture, Industrial Manufacturing, Transportation, Smart & Connected Communities etc. and learn how Fog Computing can play a substantial role in each one of these verticals. Develop a judicious point of view with respect to the future of Fog Computing through market research, technology disruption vectors and ROI use cases presented in this session.
oT applications usually rely on cloud computing services to perform data analysis such as filtering,
aggregation, classification, pattern detection, and prediction. When applied to specific domains, the IoT
needs to deal with unique constraints. Besides the hostile environment such as vibration and electric-
magnetic interference, resulting in malfunction, noise, and data loss, industrial plants often have Internet
access restricted or unavailable, forcing us to design stand-alone fog and edge computing solutions.
Michael Dell predicts that by 2025, 75% of data will be processed outside traditional datacenters and clouds, pointing to huge growth in edge computing. Edge computing is being accelerated by advances in 5G technology and lower costs of intelligent devices. For edge computing to grow rapidly, various stakeholders like tower companies, network operators, manufacturers, and hyperscalers must collaborate and ensure technologies are integrated, consistent globally, and securely connect diverse edge devices using different protocols. Success at the edge will depend on 5G integration, open collaboration, global consistency, flexible connectivity, and strong security.
Internet of Things (IoT) represents a remarkable transformation of the way in which our world will soon interact. Much like the World Wide Web connected computers to networks, and the next evolution connected people to the Internet and other people, IoT looks poised to interconnect devices, people, environments, virtual objects and machines in ways that only science fiction writers could have imagined.
The Internet of Things (IoT) is one of the hottest mega-trends in technology – and for good reason , IoT deals with all the components of what we consider web 3.0 including Big Data Analytics, Cloud Computing and Mobile Computing .
IOT in Electrical & Electronics EngineeringLokesh K N
The evaluation of the IOT in the electrical power industry transformed the way things performed in usual manner. IOT increased the use of wireless technology to connect power industry assets and infrastructure in order to lower the power consumption and cost. The implementation of IoT in power system must rely on the line monitoring and real-time control in all aspects of the grid operating parameters, and the basic characteristics are grid information, communication, and automation.
IoT ( M2M) - Big Data - Analytics: Emulation and DemonstrationCHAKER ALLAOUI
The document discusses Internet of Things (IoT) concepts including emulation and demonstration of IoT platforms, architectures, and technologies. It provides examples of using sensors, MQTT brokers, Node-Red, and IBM IoT platforms to collect, transmit, and analyze IoT data from devices. Sections include presentations on IoT history and monitoring, as well as emulations of Philips Hue lighting and sensors to demonstrate IoT data collection and control capabilities.
Cloud computing involves clusters of servers connected over a network that allow users to access computational resources and pay only for what they use. While cloud computing provides advantages like flexibility and cost savings, security is a main concern as user data is stored remotely. Fog computing is a new technique that extends cloud computing by providing additional security measures and isolating user data at the network edge to enhance privacy. It aims to place data closer to end users to improve security in cloud environments.
Industrial Internet of things has continued to create the buzz, one can’t deny that it is the one of the fastest emerging technology with humpty amount of Data getting captured daily and with every industry demanding optimisation, analytics, automations and valuable insights Industrial Internet of things is something which the need of the day.
The Smart City concept operates in a complex urban environment, incorporating several complex systems of infrastructure, human behavior, technology, social and political structures and the economy. A Smart City provides an intelligent way to manage components such as transport, health, energy, education and the environment.
In this presentation, Praneeth introduces IoT and associated trends. Praneeth is interested in IoT applications in home automation space and he also has several ideas WRT to water management and transport management using IoT applications.
The document discusses applying autonomic computing principles to wireless sensor networks (WSNs). It introduces WSNs and their design goals/challenges, which include fault tolerance, power management, efficient routing and data aggregation. Autonomic computing aims to make systems self-configuring, self-healing, self-optimizing and self-protecting. The document argues that autonomic computing is well-suited for addressing WSN challenges by allowing for self-configuration, recovery from failures, optimized resource usage, and protection. It outlines architectures like MANNA that apply a service-oriented approach to autonomically manage WSNs.
This report describes how things get connected via internet.It also describes how actually iot architecture looks like.The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.In short, the Internet of Things refers to the rapidly growing network of connected objects that are able to collect and exchange data using embedded sensors. Thermostats, cars, lights, refrigerators, and more appliances can all be connected to the IoT.
IRJET- A Review Paper on Internet of Things based Remotely Monitoring of ...IRJET Journal
This document provides a review of internet of things (IoT) based remote monitoring of temperature and humidity. It discusses wireless sensor networks (WSNs) that can gather temperature and humidity data from remote locations and transmit it to a central station. The document reviews existing research on WSN systems for environmental monitoring and identifies gaps such as high costs due to technologies like Xbee connectivity, large microcontroller sizes, and lack of smart power utilization and low power systems. It proposes future research objectives like developing a low-cost system using WiFi technology, implementing mobile alerts and live data portals, and improving power management to increase battery life. The document concludes there is significant scope for future work to address issues in previous approaches and develop more cost
1) Fog computing is an extension of cloud computing that processes data closer to the edge of the network, such as at factory equipment, power poles, or vehicles. It aims to improve efficiency and reduce data transportation costs compared to cloud computing alone.
2) Fog computing involves fog nodes that are located between end devices and the cloud. Fog nodes can perform tasks like data analysis, storage, and sharing results with the cloud and other nodes. This helps process time-sensitive data locally for applications involving the internet of things.
3) Fog computing provides advantages over cloud computing like lower latency, better support for mobility and real-time interactions, local data processing for privacy and efficiency, and ability to handle
Summer Internship report on IOT RTTC TrivandrumFathimaCec
This document provides an overview of Internet of Things (IoT) and the evolution of mobile communication networks from 1G to 5G. It discusses key concepts such as practical IoT applications, IP addressing, and the features and limitations of each generation of mobile networks. The document aims to educate readers on the basics of IoT and how wireless technology has advanced to support increasing demands.
ABSTRACT
Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider. For securing user data from such attacks a new paradigm called fog computing can be used. Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined network .This technique can monitor the user activity to identify the legitimacy and prevent from any unauthorized user access. Here we have discussed this paradigm for preventing misuse of user data and securing information.
This document discusses a wireless home automation system using the Internet of Things (IoT). It begins with an abstract that defines IoT as connecting physical devices to the internet to collect and share data. It then discusses how home automation is gaining popularity due to advances in automation technology and the widespread use of the internet. A wireless home automation system using IoT allows users to control home functions and appliances remotely using computers or smartphones. The system aims to reduce energy usage and human effort. Key advantages of a wireless system over wired include lower cost, easier expansion, and the ability to integrate mobile devices.
The seminar presentation introduced fog computing, which extends cloud computing and services to the edge of the network. Fog computing provides data, compute, and application services to end-users. It was developed to address limitations of cloud computing like high latency and lack of location awareness. Fog computing improves efficiency, latency, security, and supports real-time interactions through geographical distribution of resources at the edge of the network. The presentation covered fog computing characteristics, architecture, applications in areas like smart grids and vehicle networks, and concluded that fog computing will grow in helping network paradigms requiring fast processing.
The slides defines IoT and show the differnce between M2M and IoT vision. It then describes the different layers that depicts the functional architecture of IoT, standard organizations and bodies and other IoT technology alliances, low power IoT protocols, IoT Platform components, and finally gives a short description to one of IoT low power application protocols (MQTT).
This document discusses fog computing. Fog computing extends cloud computing by providing data, compute, storage, and application services closer to the edge of the network. It was introduced by Cisco to efficiently share and store data between distributed devices in the Internet of Things. Fog computing helps address issues with cloud computing like high latency by processing data locally at edge devices instead of sending all data to a centralized cloud. It provides advantages like improved security, reduced data transfers across networks, and better support for real-time applications. The document compares fog and cloud computing and concludes that fog computing will grow in helping network paradigms that require fast processing.
Fog Computing Reality Check: Real World Applications and ArchitecturesBiren Gandhi
Is Fog Computing just a buzz or a real business?
The IoT is flooded with a variety of platforms and solutions. Fog Computing has been notably appearing as an evolving term in the context of IoT software. There is skepticism that Fog Computing is just another buzzword destined to disappear in the dust of time. Get insight from concrete business cases in a variety of IoT verticals – Agriculture, Industrial Manufacturing, Transportation, Smart & Connected Communities etc. and learn how Fog Computing can play a substantial role in each one of these verticals. Develop a judicious point of view with respect to the future of Fog Computing through market research, technology disruption vectors and ROI use cases presented in this session.
oT applications usually rely on cloud computing services to perform data analysis such as filtering,
aggregation, classification, pattern detection, and prediction. When applied to specific domains, the IoT
needs to deal with unique constraints. Besides the hostile environment such as vibration and electric-
magnetic interference, resulting in malfunction, noise, and data loss, industrial plants often have Internet
access restricted or unavailable, forcing us to design stand-alone fog and edge computing solutions.
Michael Dell predicts that by 2025, 75% of data will be processed outside traditional datacenters and clouds, pointing to huge growth in edge computing. Edge computing is being accelerated by advances in 5G technology and lower costs of intelligent devices. For edge computing to grow rapidly, various stakeholders like tower companies, network operators, manufacturers, and hyperscalers must collaborate and ensure technologies are integrated, consistent globally, and securely connect diverse edge devices using different protocols. Success at the edge will depend on 5G integration, open collaboration, global consistency, flexible connectivity, and strong security.
Internet of Things (IoT) represents a remarkable transformation of the way in which our world will soon interact. Much like the World Wide Web connected computers to networks, and the next evolution connected people to the Internet and other people, IoT looks poised to interconnect devices, people, environments, virtual objects and machines in ways that only science fiction writers could have imagined.
The Internet of Things (IoT) is one of the hottest mega-trends in technology – and for good reason , IoT deals with all the components of what we consider web 3.0 including Big Data Analytics, Cloud Computing and Mobile Computing .
IRJET- An Overview of Slum Rehabilitation by IN-SITU TechniqueIRJET Journal
This document discusses integrating wireless sensor networks with cloud computing using middleware services. It begins by providing background on cloud computing and wireless sensor networks, and describes how merging the two can allow for easy management of remotely connected sensor nodes and generated data. A model is proposed that uses middleware between the wireless sensor network and cloud for data compatibility, bandwidth management, security and connectivity. Networked control systems are presented as an example middleware that can collect sensor data, utilize cloud services for processing, and provide information to different types of users.
IRJET- Integrating Wireless Sensor Networks with Cloud Computing and Emerging...IRJET Journal
This document discusses integrating wireless sensor networks with cloud computing through the use of middleware services. It proposes a model that combines wireless sensor networks and cloud computing, allowing for easy management of remotely connected sensor nodes and the data they generate. The model uses middleware as an intermediary layer between the wireless sensor networks and cloud to provide data compatibility, bandwidth management, security, and connectivity. It describes how sensor data can be collected via heterogeneous wireless networks, additional computational capabilities provided through cloud services, and information delivered to different types of end users through a networked control system. Load balancing of the cloud computing environment is achieved using a honey bee foraging strategy algorithm.
Fog computing is defined as a decentralized infrastructure that places storage and processing components at the edge of the cloud, where data sources such as application users and sensors exist.It is an architecture that uses edge devices to carry out a substantial amount of computation (edge computing), storage, and communication locally and routed over the Internet backbone.To achieve real-time automation, data capture and analysis has to be done in real-time without having to deal with the high latency and low bandwidth issues that occur during the processing of network data In 2012, Cisco introduced the term fog computing for dispersed cloud infrastructures.. In 2015, Cisco partnered with Microsoft, Dell, Intel, Arm and Princeton University to form the OpenFog Consortium.The consortium's primary goals were to both promote and standardize fog computing. These concepts brought computing resources closer to data sources.Fog computing also differentiates between relevant and irrelevant data. While relevant data is sent to the cloud for storage, irrelevant data is either deleted or transmitted to the appropriate local platform. As such, edge computing and fog computing work in unison to minimize latency and maximize the efficiency associated with cloud-enabled enterprise systemsFog computing consists of various componets such as fog nodes.Fog nodes are independent devices that pick up the generated information. Fog nodes fall under three categories: fog devices, fog servers, and gateways. These devices store necessary data while fog servers also compute this data to decide the course of action. Fog devices are usually linked to fog servers. Fog gateways redirect the information between the various fog devices and servers. With Fog computing, local data storage and scrutiny of time-sensitive data become easier. With this the amount and the distance of passing data to the cloud is reduced, therefore reducing the security challenges.Fog computing enables data processing based on application demands, available networking and computing resources. This reduces the amount of data required to be transferred to the cloud, ultimately saving network bandwidth.Fog computing can run independently and ensure uninterrupted services even with fluctuating network connectivity to the cloud. It performs all time-sensitive actions close to end users which meets latency constraints of IoT applications.
IoT applications where data is generated in terabytes or more, where a quick and large amount of data processing is required and sending data to the cloud back and forth is not feasible, are good candidates for fog computing. Fog computing provides real-time processing and event responses which are critical in healthcare. Besides, it also addresses issues regarding network connectivity and traffic required for remote storage, processing and medical record retrieval from the cloud.
Extends cloud computing services to the edge of the network.
Similar to cloud, Fog provides:
Data
Computation
Storage
Application Services to end users.
Motivations for Fog Computing:
Smart Grid, Smart Traffic Lights in vehicular networks and Software Defined Networks.
A review on orchestration distributed systems for IoT smart services in fog c...IJECEIAES
This paper provides a review of orchestration distributed systems for IoT smart services in fog computing. The cloud infrastructure alone cannot handle the flow of information with the abundance of data, devices and interactions. Thus, fog computing becomes a new paradigm to overcome the problem. One of the first challenges was to build the orchestration systems to activate the clouds and to execute tasks throughout the whole system that has to be considered to the situation in the large scale of geographical distance, heterogeneity and low latency to support the limitation of cloud computing. Some problems exist for orchestration distributed in fog computing are to fulfil with high reliability and low-delay requirements in the IoT applications system and to form a larger computer network like a fog network, at different geographic sites. This paper reviewed approximately 68 articles on orchestration distributed system for fog computing. The result shows the orchestration distribute system and some of the evaluation criteria for fog computing that have been compared in terms of Borg, Kubernetes, Swarm, Mesos, Aurora, heterogeneity, QoS management, scalability, mobility, federation, and interoperability. The significance of this study is to support the researcher in developing orchestration distributed systems for IoT smart services in fog computing focus on IR4.0 national agenda.
The document discusses the Cortex-A11 multicore processor and its components. It describes the processor's architecture including the snoop control unit, accelerator coherence port, generic interrupt controller, advanced bus interface unit, floating point unit, NEON media processing engine, L2 cache controller, program trace macrocell, and memory management unit. The purpose of these components is to provide efficient performance, low power consumption, and scalability for applications such as mobile devices and infotainment systems.
The Future of Fog Computing and IoT: Revolutionizing Data ProcessingFredReynolds2
Sending a business e-mail, watching a YouTube video, making an online video call meeting, or playing a video game online requires considerable data flow. It necessitates such massive data flow in the direction of servers in data centers. Cloud computing prefers remote data processing and substantial storage systems to develop online apps we use daily. But we must know that other decentralized cloud computing systems exist. Fog computing technology is growing wildly in popularity. As per fog technology experts, the global fog technology market will reach nearly $2.3 billion at the end of 2032. The market for fog technology was $196.7 million at the end of 2022.
Making Actionable Decisions at the Network's EdgeCognizant
With the vast analytical power unleashed by the Internet of Things (IoT) ecosystem, IT organizations must be able to apply both cloud analytics and edge analytics - cloud for strategic decision-making and edge for more instantaneous response based on local sensors and other technology.
The document provides an overview of the Internet of Things (IoT). It defines IoT as the network of physical devices embedded with electronics, software, sensors and connectivity that allows these devices to collect and exchange data. It describes how IoT devices can be remotely monitored and controlled via existing network infrastructure. The document also outlines several key components of IoT including hardware, software, communication technologies, applications in different industries, and major players. It provides examples of large-scale IoT deployments and a glossary of common IoT terms.
Fog computing or fog networking, also known as fogging, is an architecture that uses edge devices to carry out a substantial amount of computation, storage, and communication locally and routed over the internet backbone.
Internet of Things is an idea under development. It is the future connecting the Smart devices to the Internet. Interested to know more about the current developments and the future road map of this project then this presentation is for you.
A Comprehensive Exploration of Fog Computing.pdfEnterprise Wired
This article delves into the intricacies of Fog computing, exploring its definition, key components, benefits, and its transformative impact on various industries.
Fog computing is a distributed computing paradigm that extends cloud computing and services to the edge of the network. It facilitates efficient local data processing, storage, and analysis to reduce latency. The architecture of fog computing includes devices at the edge that communicate peer-to-peer to process and manage data locally rather than routing it through centralized cloud data centers. Common applications of fog computing include connected vehicles, smart grids, smart cities, and healthcare devices.
STEAM++ AN EXTENSIBLE END-TO-END FRAMEWORK FOR DEVELOPING IOT DATA PROCESSING...ijcsit
IoT applications usually rely on cloud computing services to perform data analysis such as filtering, aggregation, classification, pattern detection, and prediction. When applied to specific domains, the IoT needs to deal with unique constraints. Besides the hostile environment such as vibration and electricmagnetic interference, resulting in malfunction, noise, and data loss, industrial plants often have Internet access restricted or unavailable, forcing us to design stand-alone fog and edge computing solutions. In this context, we present STEAM++, a lightweight and extensible framework for real-time data stream processing and decision-making in the network edge, targeting hardware-limited devices, besides proposing a micro-benchmark methodology for assessing embedded IoT applications. In real-case experiments in a semiconductor industry, we processed an entire data flow, from values sensing, processing and analysing data, detecting relevant events, and finally, publishing results to a dashboard. On average, the application consumed less than 500kb RAM and 1.0% of CPU usage, processing up to 239 data packets per second and reducing the output data size to 14% of the input raw data size when notifying events.
IRJET- Earthquake Early Warning System for AndroidIRJET Journal
This document describes the development of an earthquake early warning system for Android devices. The system uses sensors in smartphones to detect earthquake vibrations and differentiate them from human motion. When an earthquake is detected, a warning is sent to other smartphones via a network. The system aims to enhance current warning systems by leveraging rising mobile and sensor technologies. It discusses developing smartphone software to capture sensor data and perform analytics to identify earthquakes. Evaluation shows smartphones can accurately detect earthquakes and create a new type of seismic network. This would improve safety for communities vulnerable to earthquakes worldwide.
The document provides an overview of the Internet of Things (IoT). It defines IoT as the network of physical objects embedded with sensors that can collect and exchange data. It describes how IoT works using technologies like RFID sensors, smart technologies, and nanotechnologies to identify things, collect data, and enhance network power. It also discusses current and future applications of IoT in various fields, technological challenges, and criticisms of IoT regarding privacy, security, and control issues.
Similar to Fog Computing: Helping the Internet of Things Realize its Potential (20)
Chances are you have a Wi-Fi network at home, or live close to one (or more) that tantalizingly pops up in a list whenever you boot up the laptop.
The problem is, if there's a lock next to the network name (AKA the SSID, or service set identifier), that indicates security is activated. Without the password or passphrase, you're not going to get access to that network, or the sweet, sweet internet that goes with it.
A distributed denial-of-service (DDoS) attack is a malicious attempt to disrupt normal traffic of a targeted server, service or network by overwhelming the target or its surrounding infrastructure with a flood of Internet traffic. DDoS attacks achieve effectiveness by utilizing multiple compromised computer systems as sources of attack traffic. Exploited machines can include computers and other networked resources such as IoT devices. From a high level, a DDoS attack is like a traffic jam clogging up with highway, preventing regular traffic from arriving at its desired destination.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together.
There are two methods for interfacing memory and I/O devices with a microprocessor: I/O mapped I/O and memory mapped I/O. I/O mapped I/O treats I/O devices and memory separately, while memory mapped I/O treats I/O devices as memory. I/O mapped I/O can use either 8 or 16 address lines, allowing connection of up to 256 fixed I/O devices or 65,536 variable I/O devices. Specific instructions like IN, OUT, and MOV are used to access I/O ports depending on whether it is fixed or variable addressing.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together.
This document discusses procedures, macros, and stack operations in assembly language. It explains that procedures allow repetitive code to be written once and called multiple times to save memory. Procedures use stack operations to push return addresses and data onto the stack. Macros simplify programming by reducing repetitive code. Procedures are called at runtime, while macro calls are replaced with their body at assembly time.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together.
Procedures and macros allow code to be reused in assembly language programs. Procedures are subroutines that are called using CALL and RET instructions. Macros allow short, repetitive code sequences to be defined once and reused by replacing the macro call with its body code. Some key differences are that procedures occupy less memory than macros since macro code is generated each time, while procedures' code is only stored once. Procedures are accessed using CALL while macros are accessed by name.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together. Some microprocessors in the 20th century required several chips. Microprocessors help to do everything from controlling elevators to searching the Web. Everything a computer does is described by instructions of computer programs, and microprocessors carry out these instructions many millions of times a second. [1]
Microprocessors were invented in the 1970s for use in embedded systems. The majority are still used that way, in such things as mobile phones, cars, military weapons, and home appliances. Some microprocessors are microcontrollers, so small and inexpensive that they are used to control very simple products like flashlights and greeting cards that play music when you open them. A few especially powerful microprocessors are used in personal computers.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together. Some microprocessors in the 20th century required several chips. Microprocessors help to do everything from controlling elevators to searching the Web. Everything a computer does is described by instructions of computer programs, and microprocessors carry out these instructions many millions of times a second. [1]
Microprocessors were invented in the 1970s for use in embedded systems. The majority are still used that way, in such things as mobile phones, cars, military weapons, and home appliances. Some microprocessors are microcontrollers, so small and inexpensive that they are used to control very simple products like flashlights and greeting cards that play music when you open them. A few especially powerful microprocessors are used in personal computers.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together. Some microprocessors in the 20th century required several chips. Microprocessors help to do everything from controlling elevators to searching the Web. Everything a computer does is described by instructions of computer programs, and microprocessors carry out these instructions many millions of times a second. [1]
Microprocessors were invented in the 1970s for use in embedded systems. The majority are still used that way, in such things as mobile phones, cars, military weapons, and home appliances. Some microprocessors are microcontrollers, so small and inexpensive that they are used to control very simple products like flashlights and greeting cards that play music when you open them. A few especially powerful microprocessors are used in personal computers.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together. Some microprocessors in the 20th century required several chips. Microprocessors help to do everything from controlling elevators to searching the Web. Everything a computer does is described by instructions of computer programs, and microprocessors carry out these instructions many millions of times a second. [1]
Microprocessors were invented in the 1970s for use in embedded systems. The majority are still used that way, in such things as mobile phones, cars, military weapons, and home appliances. Some microprocessors are microcontrollers, so small and inexpensive that they are used to control very simple products like flashlights and greeting cards that play music when you open them. A few especially powerful microprocessors are used in personal computers.
This document describes the Jcc family of conditional jump instructions in x86 assembly language. It provides the instruction name, description of the condition tested, and any alternative mnemonics or opposite instructions. The instructions test various CPU flags like carry, zero, sign, overflow, parity, and compare values based on signed or unsigned arithmetic.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together. Some microprocessors in the 20th century required several chips. Microprocessors help to do everything from controlling elevators to searching the Web. Everything a computer does is described by instructions of computer programs, and microprocessors carry out these instructions many millions of times a second. [1]
Microprocessors were invented in the 1970s for use in embedded systems. The majority are still used that way, in such things as mobile phones, cars, military weapons, and home appliances. Some microprocessors are microcontrollers, so small and inexpensive that they are used to control very simple products like flashlights and greeting cards that play music when you open them. A few especially powerful microprocessors are used in personal computers.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together. Some microprocessors in the 20th century required several chips. Microprocessors help to do everything from controlling elevators to searching the Web. Everything a computer does is described by instructions of computer programs, and microprocessors carry out these instructions many millions of times a second. [1]
Microprocessors were invented in the 1970s for use in embedded systems. The majority are still used that way, in such things as mobile phones, cars, military weapons, and home appliances. Some microprocessors are microcontrollers, so small and inexpensive that they are used to control very simple products like flashlights and greeting cards that play music when you open them. A few especially powerful microprocessors are used in personal computers.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together. Some microprocessors in the 20th century required several chips. Microprocessors help to do everything from controlling elevators to searching the Web. Everything a computer does is described by instructions of computer programs, and microprocessors carry out these instructions many millions of times a second. [1]
Microprocessors were invented in the 1970s for use in embedded systems. The majority are still used that way, in such things as mobile phones, cars, military weapons, and home appliances. Some microprocessors are microcontrollers, so small and inexpensive that they are used to control very simple products like flashlights and greeting cards that play music when you open them. A few especially powerful microprocessors are used in personal computers.
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together. Some microprocessors in the 20th century required several chips. Microprocessors help to do everything from controlling elevators to searching the Web. Everything a computer does is described by instructions of computer programs, and microprocessors carry out these instructions many millions of times a second. [1]
Microprocessors were invented in the 1970s for use in embedded systems. The majority are still used that way, in such things as mobile phones, cars, military weapons, and home appliances. Some microprocessors are microcontrollers, so small and inexpensive that they are used to control very simple products like flashlights and greeting cards that play music when you open them. A few especially powerful microprocessors are used in personal computers.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
2. AU G U S T 2 0 1 6 41
EDITOR SAN MURUGESAN
BRITE Professional Services;
san@computer.org
many more data sources could easily
reach millions of tuples per second.
To address these issues, edge com-
puting4 was proposed to use comput-
ing resources near IoT sensors for local
storage and preliminary data process-
ing. This would decrease network con-
gestion, as well as accelerate analysis
and the resulting decision making.
However, edge devices can’t handle
multiple IoT applications competing
for their limited resources, which re-
sults in resource contention and in-
creases processing latency.
Fog computing—which seamlessly
integrates edge devices and cloud re-
sources—helps overcome these lim-
itations. It avoids resource conten-
tion at the edge by leveraging cloud
resources and coordinating the use
of geographically distributed edge
devices.
FOG COMPUTING
CHARACTERISTICS
Fog computing is a distributed para-
digm that provides cloud-like services
to the network edge. It leverages cloud
and edge resources along with its own
infrastructure, as Figure 1 shows. In
essence, the technology deals with
IoT data locally by utilizing clients or
edge devices near users to carry out a
substantial amount of storage, com-
munication, control, configuration,
and management. The approach bene-
fits from edge devices’ close proximity
to sensors, while leveraging the on-
demand scalability of cloud resources.
Fog computing involves the compo-
nents of data-processing or analytics
applications running in distributed
cloud and edge devices. It also facili-
tates the management and program-
ming of computing, networking, and
storage services between datacen-
ters and end devices. In addition, it
supports user mobility, resource and
interface heterogeneity, and distrib-
uted data analytics to address the
requirements of widely distributed ap-
plications that need low latency.
FOG-COMPUTING
COMPONENTS
Figure 2 presents a fog-computing
reference architecture. Fog systems
generally use the sense-process-actu-
ate and stream-processing program-
ming models. Sensors stream data to
IoT networks, applications running on
fog devices subscribe to and process
the information, and the obtained in-
sights are translated into actions sent
to actuators.
Fog systems dynamically dis-
cover and use APIs to build com-
plex functionalities. Components
at the resource-management layer
use information from the resource-
monitoring service to track the state
of available cloud, fog, and network
resources and identify the best can-
didates to process incoming tasks.
With multitenant applications, the
resource-management components
prioritize the tasks of the various par-
ticipating users or programs.
Edge and cloud resources commu-
nicate using machine-to-machine
(M2M) standards such as MQTT (for-
merly MQ Telemetry Transport) and
the Constrained Application Protocol
(CoAP). Software-defined networking
(SDN) helps with the efficient manage-
ment of heterogeneous fog networks.
FOG-COMPUTING
SOFTWARE SYSTEMS
There are four prominent software
systems for building fog computing
environments and applications.
Provider A Provider B
Public cloud
Private cloudPrivate cloud
IoT sensors ...
Fog computing
Figure 1. Distributed data processing in a fog-computing environment. Based on the
desired functionality of a system, users can deploy Internet of Things sensors in differ-
ent environments including roads, medical centers, and farms. Once the system collects
information from the sensors, fog devices—including nearby gateways and private
clouds—dynamically conduct data analytics.
3. 42 CO M PUTE R W W W. C O M P U T E R . O R G / C O M P U T E R
CLOUD COVER
Cisco IOx provides device manage-
ment and enables M2M services in fog
environments.5 Using device abstrac-
tions provided by Cisco IOx APIs, ap-
plications running on fog devices can
communicate with other IoT devices
via M2M protocols.
Cisco Data in Motion (DMo) enables
data management and analysis at the
network edge and is built into prod-
ucts that Cisco Systems and its part-
ners provide.
LocalGrid’s fog-computing plat-
form is software installed on network
devices in smart grids. It provides re-
liable M2M communication between
devices and data-processing services
without going through the cloud.
Cisco ParStream’s fog-computing
platformenablesreal-timeIoTanalytics.
FOG-COMPUTING
APPLICATIONS
Various applications could benefit
from fog computing.
Healthcare and activity tracking
Fog computing could be useful in
healthcare, in which real-time process-
ing and event response are critical. One
proposed system utilizes fog comput-
ing to detect, predict, and prevent falls
by stroke patients.6 The fall-detection
learning algorithms are dynamically
deployed across edge devices and cloud
resources. Experiments concluded that
this system had a lower response time
and consumed less energy than cloud-
only approaches.
A proposed fog computing–based
smart-healthcare system enables low
latency, mobility support, and location
and privacy awareness.7
Smart utility services
Fog computing can be used with smart
utility services,8 whose focus is im-
proving energy generation, delivery,
and billing. In such environments,
edge devices can report more fine-
grained energy-consumption details
(for example, hourly and daily, rather
than monthly, readings) to users’ mo-
bile devices than traditional smart
utility services. These edge devices
can also calculate the cost of power
consumption throughout the day and
suggest which energy source is most
economical at any given time or when
home appliances should be turned on
to minimize utility use.
Augmented reality, cognitive
systems, and gaming
Fog computing plays a major role
in augmented-reality applications,
which are latency sensitive. For exam-
ple, the EEG Tractor Beam augmented
multiplayer, online brain–computer-
interaction game performs continu-
ous real-time brain-state classification
on fog devices and then tunes classifi-
cation models on cloud servers, based
on electroencephalogram readings
that sensors collect.9
A wearable cognitive-assistance
system that uses Google Glass devices
helps people with reduced mental
acuity perform various tasks, includ-
ing telling them the names of people
they meet but don’t remember.10 In
this application, devices communicate
with the cloud for delay-tolerant jobs
such as error reporting and logging.
For time-sensitive tasks, the system
streams video from the Glass cam-
era to the fog devices for processing.
The system demonstrates how using
nearby fog devices greatly decreases
end-to-end latency.
MODELING AND SIMULATION
To enable real-time analytics in fog
computing,wemustinvestigatevarious
resource-management and scheduling
Applications
Edge cloud
resources
Software-
defined
networking
Machine-to-
machine
IoT sensors
actuators
Programming
models
Sense-process-actuate Stream processing
Multitenant resource
management
Operator flow
placement and resource
scheduling
Raw data
management
Monitoring
profiling
API service
management
API discovery
Authorization
authentication
API composition
Figure 2. Fog-computing architecture. In the bottom layer are end devices—including
sensors and actuators—along with applications that enhance their functionality. These
elements use the next layer, the network, for communicating with edge devices, such as
gateways, and then with cloud services. The resource-management layer runs the entire
infrastructure and enables quality-of-service enforcement. Finally, applications leverage
fog-computing programming models to deliver intelligent services to users.
4. AU G U S T 2 0 1 6 43
techniques including the placement,
migration, and consolidation of
stream-processing operators, applica-
tion modules, and tasks. This signifi-
cantly impacts processing latency and
decision-making times.
However, constructing a real IoT en-
vironment as a testbed for evaluating
such techniques is costly and doesn’t
provide a controllable environment for
conducting repeatable experiments.
To overcome this limitation, we devel-
oped an open source simulator called
iFogSim.11 iFogSim enables the model-
ing and simulation of fog-computing
environments for the evaluation of re-
source-management and scheduling
policies across edge and cloud resources
undermultiplescenarios,basedontheir
impactonlatency,energyconsumption,
network congestion, and operational
costs. It measures performance met-
rics and simulates edge devices, cloud
datacenters, sensors, network links,
data streams, and stream-processing
applications.
CHALLENGES
Realizing fog computing’s full poten-
tial presents several challenges in-
cluding balancing load distribution
between edge and cloud resources, API
and service management and sharing,
and SDN communications. There are
several other important examples.
Enabling real-time analytics
In fog environments, resource man-
agement systems should be able to dy-
namically determine which analytics
tasks are being pushed to which cloud-
or edge-based resource to minimize
latency and maximize throughput.
Thesesystemsalsomustconsiderother
criteria such as various countries’ data
privacy laws involving, for example,
medical and financial information.
Programming models
and architectures
Most stream- and data-processing
frameworks, including Apache Storm
and S4, don’t provide enough scal-
ability and flexibility for fog and IoT
environments because their architec-
ture is based on static configurations.
Fog environments require the ability
to add and remove resources dynam-
ically because processing nodes are
generally mobile devices that fre-
quently join and leave networks.
Security, reliability,
and fault tolerance
Enforcing security in fog
environments—which have multiple
service providers and users, as well as
distributed resources—is a key chal-
lenge. Designing and implementing
authentication and authorization tech-
niques that can work with multiple fog
nodes that have different computing
capacities is difficult. Public-key infra-
structures and trusted execution envi-
ronments are potential solutions.12
Users of fog deployments also must
plan for the failure of individual sen-
sors, networks, service platforms, and
applications. To help with this, they
could apply standards, such as the
Stream Control Transmission Proto-
col, that deal with packet and event re-
liability in wireless sensor networks.13
Power consumption
Fog environments consist of many
nodes. Thus, the computation is dis-
tributed and can be less energy effi-
cient than in centralized cloud sys-
tems. Using efficient communications
protocols such as CoAP, effective fil-
tering and sampling techniques, and
joint computing and network resource
optimization can minimize energy
consumption in fog environments.
F
og computing enables the seam-
less integration of edge and
cloud resources. It supports the
decentralized and intelligent process-
ing of unprecedented data volumes
generated by IoT sensors deployed for
smooth integration of physical and cy-
ber environments.
This could generate many benefits
to society by, for example, enabling
smart healthcare applications. The
further development of fog comput-
ing could thus help the IoT reach its
vast potential.
REFERENCES
1. R. Buyya and A. Dastjerdi, Internet
of Things: Principles and Paradigms,
Morgan Kaufmann, 2016.
2. J. Manyika et al., Unlocking the Poten-
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Company, 2015.
3. R. Cortés et al., “Stream Processing
of Healthcare Sensor Data: Studying
User Traces to Identify Challenges
from a Big Data Perspective,” Proce-
dia Computer Science, vol. 52, 2015,
pp. 1004–1009.
4. W. Shi and S. Dustdar, “The Promise
of Edge Computing,” Computer, vol.
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5. F. Bonomi et al., “Fog Computing: A
Platform for Internet of Things and
Analytics,” Big Data and Internet of
Things: A Roadmap for Smart Environ-
ments, N. Bessis and C. Dobre, eds.,
Springer, 2014, pp. 169–186.
6. Y. Cao et al., “FAST: A Fog Comput-
ing Assisted Distributed Analytics
System to Monitor Fall for Stroke
Mitigation,” Proc. 10th IEEE Int’l Conf.
Networking, Architecture and Storage
(NAS 15), 2015, pp. 2–11.
7. V. Stantchev et al., “Smart Items, Fog
and Cloud Computing as Enablers
of Servitization in Healthcare,” J.
Sensors Transducers, vol. 185, no. 2,
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8. J. Li et al., “EHOPES: Data-Centered
Fog Platform for Smart Living,” Proc.
2015 Int’l Telecommunication Networks
and Applications Conf. (ITNAC 15),
2015, pp. 308–313.
9. J. Zao et al., “Augmented Brain
Computer Interaction Based on Fog
Computing and Linked Data,” Proc.
10th IEEE Int’l Conf. Intelligent Envi-
ronments (IE 14), 2014, pp. 374–377.
10. K. Ha et al., “Towards Wearable
Cognitive Assistance,” Proc. 12th Int’l
Conf. Mobile Systems, Applications, and
Services (MobiSys 14), 2014, pp. 68–81.
11. H. Gupta et al., iFogSim: A Tool-
kit for Modeling and Simulation of
Resource Management Techniques
5. 44 CO M PUTE R W W W. C O M P U T E R . O R G / C O M P U T E R
CLOUD COVER
in Internet of Things, Edge and Fog
Computing Environments, tech. report
CLOUDS-TR-2016-2, Cloud Comput-
ing and Distributed Systems Labora-
tory, Univ. of Melbourne, 2016; http://
cloudbus.org/tech_reports.html.
12. I. Stojmenovic and S. Wen, “The Fog
Computing Paradigm: Scenarios and
Security Issues,” Proc. 2014 Federated
Conf. Comp. Sci. and Info. Sys. (FedCSIS
14), 2014, pp. 1–8.
13. H. Madsen et al., “Reliability in the
Utility Computing Era: Towards Reli-
able Fog Computing,” Proc. 20th Int’l
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AMIR VAHID DASTJERDI is a
Research Fellow with the University
of Melbourne’s Cloud Computing
and Distributed Systems (CLOUDS)
Laboratory. Contact him at amir
.vahid@unimelb.edu.au.
RAJKUMAR BUYYA is director of
the CLOUDS Laboratory and CEO
of Manjrasoft Pty Ltd., a university
spin-off company that commercial-
izes cloud-computing innovations.
Contact him at rbuyya@unimelb
.edu.au.
Selected CS articles and
columns are also available for
free at http://ComputingNow
.computer.org.
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