A theory on basics of edge computing notes

Edge computing notes

1. Edge Computing: Edge computing purpose and definition, Edge computing use cases, Edge computing hardware architectures, Edge platforms, Edge vs Fog Computing, Communication Models - Edge, Fog and M2M.

2. A Real-World Example shows why there is need of edge computing

3. Imagine an oil drilling platform in the middle of the North Sea. Operators collect data from sensors all over the platform as part of a daily routine, measuring things like pressure, temperature, wave height, and other factors that affect operating capacity. This kind of data comes fast, changes often and requires a real-time response. Suppose that the oil platform data is stored and processed in a cloud data center. The platform operators would have to send their data over the internet – and in the North Sea that means via satellite which is slow and expensive – just to evaluate their measurements. Now, imagine that a sensor on a critical component of the platform begins to detect signs of likely failure, a potential break down that could lead to a dangerous turn of events. It takes too much time to collect data points on the component, send them to the cloud for processing, and then wait for a recommended course of action. And if the connection slows – or falters for even a bit? Critical time is lost. By the time the platform operators receive a response from the cloud, it could be too late. Where seconds count and when the difference between uptime and downtime determines safety or disaster – depending on an unreliable internet connection isn’t an option. Enter edge computing. It’s a simple solution: eliminate the risks of a disaster by putting a data center on the oil drilling platform itself. When you move the processing of critical data to the place where it happens, you solve the problems of latency and downtime. Instead of sending data to the cloud, it’s processed in an edge data center – no more waiting on a slow connection for critical analysis.

4. Need for Edge computing

5. Edge computing purpose and definition

6. 5G technology has low latency 1ms as 4G have 200ms In 5G technology download speed is 1.4 times more than 4G 5G base station 5G cell density( 5G mobile broadband supports a far higher number of devices in a given area, enabling the connection of up to  1 million devices per square kilometer  (km 2 ))

7. Edge computing use cases

8. 1. Autonomous vehicles Autonomous platooning of truck convoys will likely be one of the first use cases for autonomous vehicles. Here, a group of truck travel close behind one another in a convoy, saving fuel costs and decreasing congestion. With edge computing, it will be possible to remove the need for drivers in all trucks except the front one, because the trucks will be able to communicate with each other with ultra-low latency.                 

9. 2. Remote monitoring of assets in the oil and gas industry Oil and gas failures can be disastrous. Their assets therefore need to be carefully monitored. However, oil and gas plants are often in remote locations. Edge computing enables real-time analytics with processing much closer to the asset, meaning there is less reliance on good quality connectivity to a centralised cloud.

10. 3. Predictive maintenance Manufacturers want to be able to analyse and detect changes in their production lines before a failure occurs. Edge computing helps by bringing the processing and storage of data closer to the equipment. This enables IoT sensors to monitor machine health with low latencies and perform analytics in real-time. 4. In-hospital patient monitoring Healthcare contains several edge opportunities. Currently, monitoring devices (e.g. glucose monitors, health tools and other sensors) are either not connected, or where they are, large amounts of unprocessed data from devices would need to be stored on a 3rd party cloud. This presents security concerns for healthcare providers. An edge on the hospital site could process data locally to maintain data privacy. Edge also enables right-time notifications to practitioners of unusual patient trends or behaviours (through analytics/AI), and creation of 360-degree view patient dashboards for full visibility.

11. 5. Cloud gaming Cloud gaming, a new kind of gaming which streams a live feed of the game directly to devices, (the game itself is processed and hosted in data centres ) is highly dependent on latency. Cloud gaming companies are looking to build edge servers as close to gamers as possible in order to reduce latency and provide a fully responsive and immersive gaming experience.

12. 6. Traffic management Edge computing can enable more effective city traffic management. Examples of this include optimising bus frequency given fluctuations in demand, managing the opening and closing of extra lanes, and, in future, managing autonomous car flows. With edge computing, there is no need to transport large volumes of traffic data to the centralised cloud, thus reducing the cost of bandwidth and latency.

13. 7. Smart homes Smart homes rely on IoT devices collecting and processing data from around the house. Often this data is sent to a centralised remote server, where it is processed and stored. However, this existing architecture has problems around backhaul cost, latency, and security. By using edge compute and bringing the processing and storage closer to the smart home, backhaul and roundtrip time is reduced, and sensitive information can be processed at the edge. As an example, the time taken for voice-based assistant devices such as Amazon’s Alexa to respond would be much faster.

14. Edge computing architecture

15. A Real-World Example of Edge Computing Let’s look at a concrete example. Imagine an oil drilling platform in the middle of the North Sea. Operators collect data from sensors all over the platform as part of a daily routine, measuring things like pressure, temperature, wave height, and other factors that affect operating capacity. This kind of data comes fast, changes often and requires a real-time response. Suppose that the oil platform data is stored and processed in a cloud data center. The platform operators would have to send their data over the internet – and in the North Sea that means via satellite which is slow and expensive – just to evaluate their measurements. Now, imagine that a sensor on a critical component of the platform begins to detect signs of likely failure, a potential break down that could lead to a dangerous turn of events. It takes too much time to collect data points on the component, send them to the cloud for processing, and then wait for a recommended course of action. And if the connection slows – or falters for even a bit? Critical time is lost. By the time the platform operators receive a response from the cloud, it could be too late. Where seconds count and when the difference between uptime and downtime determines safety or disaster – depending on an unreliable internet connection isn’t an option. Enter edge computing. It’s a simple solution: eliminate the risks of a disaster by putting a data center on the oil drilling platform itself. When you move the processing of critical data to the place where it happens, you solve the problems of latency and downtime. Instead of sending data to the cloud, it’s processed in an edge data center – no more waiting on a slow connection for critical analysis.

16. In the diagram above, the top layer represents cloud data centers, comprised of a central data center and interconnected regional data centers. The cloud data centers still serve a crucial role in an edge computing architecture because they’re the final repository of information. However, cloud data centers aren’t relied upon for local applications.

17. The next layer down is the edge layer. The edge could be an oil platform, as in our earlier example, but it could just as easily be a cruise liner, airplane, restaurant, retail shop or mobile medical clinic. The edge layer contains edge data centers and Internet of Things (IoT) gateways. These run on a local area network, which could be fiber, wireless, 5G or older networks such as 4G and earlier.

18. Within the edge layer, you see individual devices, smart phones, tablets and laptops carried by users, as well as IoT devices that all communicate with the edge data center. There is also communication between devices via a private area network such as RF or Bluetooth.

19. While this depiction shows a single edge data center for simplicity, there could be n number of additional edge data centers to facilitate computing across a business ecosystem. For example, you might power POS systems for a chain of retail stores using edge data centers in each city where stores are concentrated.

20. Edge Computing Architecture Data source/devices The data sources in an edge computing environment can be applications capturing data, sensors, appliances, or any data capturing device. Data generated by these devices is different depending upon the source. Data sources vary from one another depending upon their functionalities and locations. The various edge devices capture data and communicate via IoT protocols, sending data to the edge gateways. The protocols used for the data transfer can be Ethernet, Bluetooth, Wi-Fi, NFC, ZigBee, etc. In short every data generating device will be considered as an edge device.

21. Edge gateway An edge gateway acts as a node between edge devices and a core network . A core network comprises devices powerful enough to pre-process data. Edge gateways are employed to provide interfaces to wired and radio-based transmissions. The various standards used are: Z-Wave: Z-Wave is used for 30 meters point-to-point communication and is specified for applications that involve small transmissions like household appliance control applications. Bluetooth Low Energy: Bluetooth Low-Energy (BLE) or Bluetooth Smart makes use of radio signals with short-range and a minimum power requirement. It operates at a range that is nearly about ten times more than classic Bluetooth technology. Its latency factor compared to classic Bluetooth technology is 15 times less. The transmission power between 0.01 mW to 10 mW is feasible for its operation.

22. Protocols used The Various Protocols used in this Layer CoAP CoAP is an application layer protocol for edge devices and applications, created by IETF Constrained RESTful Environments ( CoRE ) working group. It is used in mobile-based social network applications and it makes complexity less by using HTTP methods(get, post, put, and delete). MQTT MQTT is built on top of the TCP protocol and is suitable for devices with low resource availability, unreliable or low bandwidth links. MQTT simply consists of three components, subscriber, publisher, and a broker.

23. Edge an edge-computing architecture simply means the edge of the network. The devices present at the edge of the network vary based upon the functionalities. A mobile phone can be employed at the edge. A router can be employed at the edge of the network etc. Edge comprises of those devices which can perform temporary data processing and temporary storage before sending the actual data to the cloud for further storage and processing. The communication between an edge device and an edge is facilitated by an edge gateway. Edge provides data computing capabilities nearer to the source of data.

24. Edge Computing Platforms in 2022 1. Alef Private Edge Platform Overview:  Alef is a New York-based edge computing company founded in 2009. It provides edge connectivity products for industrial sectors and healthcare, education, and governments.   2 Azure IoT Edge Overview:  Azure IoT Edge is part of Microsoft’s intelligent cloud-to-edge computing solutions suite. It primarily addresses IoT use cases.   3. ClearBlade Overview:  ClearBlade is an Austin-based company founded in 2007. It provides edge computing software for scalable IoT applications in industrial environments .  4. Eclipse ioFog Overview:  Eclipse is an integrated development environment built by the Eclipse Foundation, backed by IBM. Eclipse ioFog is the organization’s open-source edge computing platform.  5. ESF Edge Computing Platform Overview:  Everyware Software Framework (ESF) is an enterprise-grade IoT edge framework by Italian software company Eurotech. It is primarily meant for software vendors and developers. 

25. 6. Google Distributed Cloud Edge Overview: Google Distributed Cloud Edge was launched in October 2021, as part of the Google Distributed Cloud suite of hardware and software solutions . It equips enterprises and communication service providers to deliver edge-enabled apps.  7. HPE Edgeline Overview:  Edgeline is a converged edge system solution by Hewlett Packard Enterprise (HPE). Apart from the edge computing platform, it includes edge hardware systems and application services.  8. Infiot ZETO Overview: Founded in 2018, Infiot is a remote connectivity company based in India and the U.S. It specializes in cloud-native edge solutions and networks.  9. Mutable Public Edge Cloud Overview: Founded in 2015, Mutable uses edge architecture to power a very low latency connectivity environment. It raised $1.5 million in 2020 to develop its edge computing platform further.  10. Vapor IO Kinetic Grid Overview: Founded in 2015, Vapor IO is an IT and data center solutions company in Austin, Texas. It launched the edge-to-edge Kinetic Grid platform in June 2021. 

26. What Are the Top Computer Hardware Needs for Edge Computing? Edge computing hardware must be rugged, compact, have sufficient storage, have rich connectivity options, have a wide power range, and meet the performance requirements for the tasks they will perform. Edge computers must meet these requirements because they are often deployed in harsh environments where they must operate reliably and optimally. For example, if an edge computer is deployed in an oil production field, it must be able to handle exposure to extreme heat, dust, and debris( pieces from something that has been destroyed, ). We will now discuss the hardware requirements for edge computing in more detail below.

27. #1 Edge Computers Must be Rugged( strong )and Fanless #2 Edge Computers Must Meet Performance Requirements #3 Edge Computers Must Be Compact and Have Versatile Mounting Options #4 Edge PCs Must Be Equipped with Sufficient Rugged Storage #5 Rugged Edge Computers Must Have Rich I/O #6 Edge Computers Must Have Rich Wired and Wireless Connectivity Options

28. #7 Edge Computing Hardware Must Have a Wide Power Range #8 Edge Computers Must Be Secure #9 Edge Computers Need to support Performance Accelerators for Real-time Processing   #10 Edge Computers Must be Certified to Pass Telemetry ( Telemetry is the automatic measurement and  wireless  transmission of data from remote sources. In general, telemetry works in the following way: Sensors at the source measure either electrical data, such as  voltage  and  current , or physical data, such as temperature and pressure. Electronic devices then send this data to remote locations for monitoring and analysis.) to the Cloud  The final hardware requirement for edge computers is that they must be certified to pass data telemetry to the cloud.

29. Edge Computing Computation takes place at the edge of a device’s network, which is known as edge computing. That means a computer is connected with the network of the device, which processes the data and sends the data to the cloud in real-time. That computer is known as “edge computer” or “edge node”. With this technology, data is processed and transmitted to the devices instantly. Yet, edge nodes transmit all the data captured or generated by the device regardless of the importance of the data.   Example of Edge computing: Autonomous vehicle edge computing devices collect data from cameras and sensors on the vehicle, process it, and make decisions in milliseconds, such as self-parking cars. In order to accurately assess a patient’s condition and foresee treatments, data is processed from a variety of edge devices connected to sensors and monitors.

30. Fog Computing Fog computing is an extension of cloud computing . It is a layer in between the edge and the cloud. When edge computers send huge amounts of data to the cloud, fog nodes receive the data and analyze what’s important. Then the fog nodes transfer the important data to the cloud to be stored and delete the unimportant data or keep them with themselves for further analysis. In this way, fog computing saves a lot of space in the cloud and transfers important data quickly.

31. S.NO. EDGE COMPUTING FOG COMPUTING 01. Billions of nodes are present.  Millions of nodes are present.  02. Nodes are installed far away from the cloud.  Nodes in this computing are installed closer to the cloud(remote database where data is stored). 03. Edge computing is a subdivision of fog computing. Fog computing is a subdivision of cloud computing.  04. The bandwidth requirement is very low. Because data comes from the edge nodes themselves.  The bandwidth requirement is high. Data originating from edge nodes is transferred to the cloud.  

32. 05. Operational cost is higher. Operational cost is comparatively lower. 06. High privacy. Attacks on data are very low.  The probability of data attacks is higher.  07. Edge devices are the inclusion of the IoT devices or client’s network.  Fog is an extended layer of cloud.  08. The power consumption of nodes is low.  The power consumption of nodes filter important information from the massive amount of data collected from the device and saves it in the filter high.  09. Edge computing helps devices to get faster results by processing the data simultaneously received from the devices.  Fog computing helps in filtering important information from the massive amount of data collected from the device and saves it in the cloud by sending the filtered data. 

33. Communication Models - Edge, Fog and M2M. A communication model is  a pictorial representation of the communication process, ideas, thoughts, or concepts through diagrams, etc . They can be considered to be systematic representations of the process that help us understand how communication can be carried out.

34. Communicating machines The beginnings of the technology date back to the 1920s. At that time, M2M communication was used in telemetry. Weather balloons sent their data via radio waves to data processing systems for evaluation. With the advent of more and more new technologies and telecommunication networks, most recently 5G, the possibilities of M2M communication are astronomically expanding. Today, intelligently communicating machines –  smart  or  intelligent devices  – increasingly permeate all levels of human life: from the coordination of container ships to the fully automated smart factory to the smart home controlled via smartphone. M2M communication thus forms the basis for the so-called IoT – the  Internet of Things .

35. A universe of things What all M2M systems have in common is that they connect data endpoints to a central data integration point, a controlling server, where the data is evaluated and reactions are initiated. The result is a web of interconnected and interacting end devices called the Internet of Things. The IoT is not only the future, but already the present. Even in simple online shopping, automated requests and responses are constantly exchanged between client and server – and in the subsequent logistics, an order can be tracked precisely on the delivery route thanks to M2M. It is no coincidence that logistics is also at the forefront of current IoT trends. The technology has long since become indispensable in fleet management. The IoT promises far-reaching revolutions, especially in the medical field. Doctor visits for standard check-ups could soon be a thing of the past. There are already numerous wearables for monitoring vital functions. The trend is that in the future, doctors will be able to monitor the health of their patients in real time from a distance with the help of such and similar remote diagnostic systems. In addition, the IoT holds changes in store for the mobility sector in particular, where far-reaching changes in the architecture of the IoT are becoming visible.

36. Down to earth For a long time, the consensus was that every “thing” in the IoT could only communicate via an Internet-based cloud, but a paradigm shift has long been emerging for reasons of efficiency and feasibility. The magic word is “fog” or “edge” computing. It’s all about bringing data processing closer to the point of action. The goal is to make the systems “at the  edge ” smarter in order to enable their direct communication with each other. This practically means that an intermediate layer is installed between the application layer and the cloud. While the cloud floats somewhere far away in the data sky, the fog lies in a sense on the ground, referring to the immediate environment of operations. For example, the components of a smart factory form a fog and communicate directly with each other instead of first sending the data via the cloud. The fog can encompass the entire field level of the smart factory. As indicated, edge computing is currently being used in a particularly revolutionary way in the field of autonomous driving. Here, the technology ensures that vehicles can communicate directly with traffic signs or with each other without detouring via the cloud. The result of these experiments will be completely driverless, autonomous mobility in just a few years.

37. Interfacing with IoT Platforms: Basic hardware components like LED, Button, Camera, 8X8 LED Grid, Motor etc and interfacing them for input/output with IoT devices using PWM, UART, GPIO, I2C, SPI

38. The LED LED stands for Light Emitting Diode, and glows when electricity is passed through it. When you pick up the LED, you will notice that one leg is longer than the other. The longer leg (known as the „anode‟), is always connected to the positive supply of the circuit. The shorter leg (known as the „cathode‟) is connected to the negative side of the power supply, known as „ground‟. LEDs will only work if power is supplied the correct way round (i.e. if the „polarity‟ is correct). You will not break the LEDs if you connect them the wrong way round – they will just not light. If you find that they do not light in your circuit, it may be because they have been connected the wrong way round . Electroluminescence  ( EL ) is an  optical  and  electrical phenomenon , in which a material emits light in response to the passage of an  electric current  or to a strong  electric field . This is distinct from  black body   light emission  resulting from heat ( incandescence ), a chemical reaction ( chemiluminescence ), sound ( sonoluminescence ), or other mechanical action ( mechanoluminescence ). Incandescence  is the emission of  electromagnetic radiation  (including visible  light ) from a hot body as a result of its high temperature

39. Electroluminescence is the result of  radiative recombination  of  electrons  &  holes  in a material, usually a  semiconductor . The excited electrons release their energy as  photons  - light. Prior to recombination, electrons and holes may be separated either by  doping  the material to form a  p-n junction  (in semiconductor electroluminescent devices such as  light-emitting diodes ) or through excitation by impact of high-energy electrons accele In semiconductor production, doping is the intentional introduction of impurities into an intrinsic semiconductor for the purpose of modulating its electrical, optical and structural properties. The doped material is referred to as an extrinsic semiconductor.

40. Hardware buttons behave like keyboard keys. The user can press and immediately release a button, press and hold a button, or press two or more buttons simultaneously. The user can assign actions to a button press and release, and assign a separate action when the button is held down

41. The pi Camera module is a camera that can be used to take pictures and high definition video. Raspberry Pi Board has CSI (Camera Serial Interface) interface to which we can attach the PiCamera module directly. This Pi Camera module can attach to the Raspberry Pi’s CSI port using a 15-pin ribbon cable . Features of Pi Camera Here, we have used Pi camera v1.3. Its features are listed below, Resolution – 5 MP HD Video recording –     1080p @30fps, 720p @60fps, 960p @45fps and so on. It Can capture wide, still (motionless) images of a resolution 2592x1944 pixels CSI Interface enabled.  

42. 8x8 LED matrix Module: A LED-Matrix Display is a display device which contains light emitting diodes aligned in the form of matrix. This LED matrix displays are used in applications where Symbol, Graphic, Characters, Alphabets, Numerals are needed to be displayed together in static as well as Scrolling motion. LED Matrix Display is manufactured in various dimensions like 5x7,8x8,16x8,128x16, 128x32 and 128x64 where the numbers represent LED's in rows and columns, respectively. Also, these displays come in different colours such as Red, Green, Yellow, Blue, Orange, White. In LED matrix display, multiple LED's are wired together in rows and columns, to minimize the number of pins required to drive them. The matrix pattern is made either in row anode -column cathode or row cathode-column anode pattern. In row anode-column cathode pattern, the entire row is anode while all columns serve as cathode which is shown below and it is vice-versa in row cathode-column anode pattern.

43. 7219 Driver board: Before interfacing LED matrix with raspberry pi, we need to connect the Max7219 IC which is an Led driver to the LED matrix display. The reason behind using this led driver is that it drives the 64 Led's simultaneously which in turn reduces the number of wires so that the user will find it easy to connect the display to the raspberry pi. The MAX7219 has four wire SPI interface (we need only these four wires to interface it to the raspberry pi): 1. Din - MOSI - Master Output Serial Input. 2. Chip select - Load (CS) - active low Chip select. 3. Clock – SCK 4. Ground. And off course VCC (5V) is required.

44. Basic Working Princpal of Stepper Motor

45. What is GPIO? GPIO stands for General Purpose Input Output. The Raspberry Pi has two rows of GPIO pins, which are connections between the Raspberry Pi, and the real world. Output pins are like switches that the Raspberry Pi can turn on or off (like turning on/off a LED light). But it can also send a signal to another device. Input pins are like switches that you can turn on or off from the outside world (like a on/off light switch). But it can also be a data from a sensor, or a signal from another device. That means that you can interact with the real world, and control devices and electronics using the Raspberry PI and its GPIO pins!

46. Taking a Closer Look at the GPIO Pins Raspberry Pi 3 with GPIO This is an illustration of the Raspberry Pi 3. The GPIO pins are the small red squares in two rows on the right side of the Raspberry Pi, on the actual Raspberry Pi they are small metal pins. The Raspberry Pi 3 has 26 GPIO pins, the rest of the pins are power, ground or "other".

47. Physical Pin Number Power + Ground UART I2C SPI GPIO Do Not Connect Legend 3V3 1 2 5V GPIO 2 3 4 5V GPIO 3 5 6 GND GPIO 4 7 8 GPIO 14 GND 9 10 GPIO 15 GPIO 17 11 12 GPIO 18 GPIO 27 13 14 GND GPIO 22 15 16 GPIO 23 3V3 17 18 GPIO 24 GPIO 10 19 20 GND GPIO 9 21 22 GPIO 25 GPIO 11 23 24 GPIO 8 GND 25 26 GPIO 7 DNC 27 28 DNC GPIO 5 29 30 GND GPIO 6 31 32 GPIO 12 GPIO 13 33 34 GND GPIO 19 35 36 GPIO 16 GPIO 26 37 38 GPIO 20 GND 39 40 GPIO 21