IoT-enabling technologies consist of big data, digital twins, cloud computing, sensors, communications protocols, analytics software, edge devices, etc.
IoT Enabling Technologies
Smart objects are connected to the IoT, and more data is collected from them in order to perform analytics to regulate trends and associations that lead to insights. For example, a jetliner with 6,000 sensors generates 2.5 terabytes of data per day, In such a way, “big data” refers to these large data sets that need to be collected, stored, queried, analyzed, and generally managed in order to deliver on the promise of the IoT – insight.
The technical challenge of big data is that the IoT system must deal with not only the data collected from smart objects but also additional data that is needed to execute such analytics for an eg- public and private data sets related to weather, GIS, financial, seismic, map, GPS, crime, etc.
Furthermore, today smart objects come online, so IoT operators used three metrics to explain the big data: volume (ie., the amount of data they collect from their IoT sensors measured in gigabytes, terabytes, and petabytes); velocity (i.e., the speed at which data is collected from the sensors); and variety (types of structured and unstructured data collected, especially when compared to video and picture files.
Big Data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions, Analysts working with Big Data typically want the knowledge that comes from analyzing the data.
Some examples of big data generated by IoT systems are described as follows:
- Sensor data is generated by IoT systems such as weather monitoring stations.
- Machine sensor data is collected from sensors embedded in industrial and energy systems for monitoring their health and detecting Failures.
- Health and fitness data generated by IoT devices such as wearable fitness bands
- Data generated by IoT systems for the location and tracking of vehicles
- Data generated by retail inventory monitoring systems.
Characteristics of Big Data
Big data can be described by the following characteristics:
- Volume – The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not.
- Variety – The type and nature of the data. This helps people who analyze it to effectively use the resulting insight. Big data draws from text, images, audio, and video; plus it completes missing pieces through data fusion.
- Velocity – In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time. Compared to small data, big data are produced more continually. Two kinds of velocity related to Big Data are the frequency of generation and the frequency of handling, and recording. and publishing.
- Veracity – It is the extended definition for big data, which refers to the data quality and the data value. The data quality of captured data can vary greatly, affecting the accurate analysis.
John Vickers, manager of NASA’s National Center for Advanced Manufacturing. introduced the concept of Digital TWIN in 2003. This concept determines the digital copy of a physical asset that grows in a virtual environment over the physical asset’s lifetime. We know that sensors within the object collect real-time data, and a set of models forming the digital twin is updated with all of the same information.
Hence, an inspection of the digital twin would reveal the same information as a physical inspection of the smart object itself, remotely. The digital twin of the smart object is used to not only optimize operations of the smart object through reduced maintenance costs and downtime but to improve the next generation of its design.
Cloud computing is like a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.
Cloud computing provides 3 service models. They are very essential for the IoT because it allows any user with a browser and an internet connection to transform smart object data into actionable intelligence.
So cloud computing provides “the virtual infrastructure for utility computing integrating applications, monitoring devices, storage devices, analytics tools, visualization platforms, and client delivery to enable businesses and users to access IoT-enabled Applications on demand anytime, anyplace and anywhere,
Cloud computing resources can be provisioned on demand by the users, without requiring interactions with the cloud service provider. The process of provisioning resources is automated. Cloud computing resources can be accessed over The network using standard access mechanisms that provide platform-independent access through the use of heterogeneous client platforms such as workstations, laptops, tablets, and smartphones.
Sensors are proficient in detecting events or changes in a specific quantity (e.g pressure) communicating the event or change data to the cloud (directly or via a gateway) and, in some circumstances, receiving data back from the cloud (e.g., a control command) or communicating with other smart objects.
Wired and wireless communication technologies have also improved and nearly every type of electronic device should be connected to sensors embedded in smart objects to send and receive data over the cloud for collection, storage, and eventual analysis.
The protocols for allowing IoT sensors to relay data include wireless technologies such as RFID, NFC, Wi-Fi, Bluetooth, Bluetooth Low Energy (BLE), XBee, ZigBee, Z-Wave, Wireless M-Bus, SIGFOX, and NuelNET, as well as satellite connections and mobile networks using GSM, GPRS, 3G LTE, or WiMAX.
Communication protocols form the backbone of IoT systems and enable network connectivity and coupling to applications. Communication protocols allow devices to exchange data over the network. Multiple protocols often describe different aspects of a single communication. A group of protocols designed to work together is known as a protocol suite; when implemented in software they are a protocol stack.
Internet communication protocols are published by the Internet Engineering Task Force (IETF). The IEEE handles wired and wireless networking, and the International Organization for Standardization (ISO) handles other types. The ITU-T handles telecommunication protocols and formats for the public switched telephone network (PSTN). As the PSTN and Internet converge, the standards are also being driven towards convergence.
Within the IoT ecosystem, Application Service Providers (ASPS) – which may or may not differ from the companies who sell and service the smart objects – provide software to companies that can transform “raw” machine (big) data collected from smart objects into actionable intelligence.
The software performs data mining and employs mathematical models and statistical techniques to provide insight to users. That is, events, trends, and patterns are extracted from big data sets in order to present the software’s end-users with insight in the form of portfolio analysis, predictions, risk analysis, automation, and corrective, maintenance, and optimization recommendations.
Smart objects embedded with sensors connect via the Internet to various service provider systems. Because of these edge devices. It can be any device like a router, routing switch, Integrated Access Device (IAD), Multiplexer, or Metropolitan Area Network (MAN) and Wide Area Network (WAN) access device which provides an entry point from the global, public Internet into an ASP’s or other enterprise’s private network.
For example, edge devices may translate between different network protocols, and provide first-hop security, initial Quality of Service (QoS), and access/ distribution policy functionality.
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