NextGEN Digital [IoT,Bot]

Microsoft, Google, Amazon Stack

The most important benefit of the IoT will be the data generated by billions of new smart sensors and devices. Many IoT systems would generate substantial amounts of data at the edge of the architecture. It's often unnecessary, and sometimes impractical, to send all this data to a central server or a cloud system because of the volume or velocity of information. In an Edge computing or “fog computing,” model, sensors and connected devices transmit data to a nearby edge computing device, such as a gateway device that processes or analyzes the data, instead of sending it back to the cloud or a remote data center. Moving the data computation and service supply from the cloud to the edge enables the possibility of meeting application delay requirements, improves the scalability and energy efficiency of lightweight IoT devices, provides contextual information processing, and mitigates the traffic burdens of the backbone network.This will result in a growing number of IoT systems that use various forms of edge computing, that is, processing information near to its source. It can reduce the amount of raw data transmitted to the IoT server. Edge analytics could be implemented even in simple hardware embedded systems, such as an Arduino.

Analytics, from the simple to the complex, will play a critical role in deriving value from IoT data. There are four main types of analytics which can be conducted on IoT data: real-time, batch, predictive, and interactive analytics The large volume and potential velocity of data streams from sensors and other IoT devices will necessitate that analytics, such as predictive analytics, be used to both manage and analyze this data. IoT analytics is a spectrum ranging from simple logic through predictive analytics that refers to a range of technologies that can analyze data streams to identify patterns and make predictions about future events. Telematics is revolutionizing the auto insurance industry as devices are placed in cars which actually record the behavior of the driver. Advanced predictive analytics tools not only process sensor data, but can incorporate external data sources such as weather forecasts or social media activity.

The IoT is an environment where an object that can represent itself becomes greater by connecting to surrounding objects and the extensive data flowing around it. The industry needs to learn from its mistakes as it innovates and builds devices to function interconnected with the Internet. IoT security is the area of endeavour concerned with safeguarding connected devices and networks in the Internet of things. IoT security must address the challenges of millions of devices with the ability to automatically transfer data over a network; very diverse networks, platforms and protocols; and a wide range of threats, both traditional (such as hacking) and new (such as sensor spoofing or trash can attacks). It will need upgrading of established technologies such as encryption and firewalls, and newer solutions such as blockchain for things and network analytics using machine intelligence looking for anomalous traffic patterns. However, most of the low-cost, low-powered devices involved in modern IoT software platforms cannot support such advanced access control measures .Therefore the IoT software platform itself needs to implement alternative measures to handle such device level issues. For example strong information security at the cloud application level, requiring regular password updates and supporting updateable firmware by way of authentication.

Stream processing is a technology that enables its applications to act-on (collect, integrate, visualize, and analyze) real-time streaming data, while the data is being produced. In simpler words, Stream Processing gives us the ability to quickly process large amounts of data from multiple sources, in real-time. Most IoT systems involve some form of stream processing technology, for example, to ingest and process data from sensors. Stream processing architectures are very diverse as data streams can be processed in many places, for example, in things themselves that may perform simple data filtering, in gateways, in an IoT platform or in central systems. The most obvious benefit of stream processing is its ability to treat data not as static tables but as an infinite stream that goes from what happened in the past into what will happen in the future. Stream processing in some form is likely to be a feature of most IoT systems .However, it's likely to play a more important role in sophisticated systems supporting business function optimization, operating model transformation and business model reinvention.

An IoT platform is a software suite that provides a range of functions required by typical IoT systems. Developing for the Internet of Things is a complex endeavour and IOT platforms offer a starting point by combining many of the tools needed to manage a deployment from device management to data prediction and insights into one service. These may include provisioning and management of endpoints, protocol conversion, application development, data ingestion and management, event stream processing, analytics, visualization, cybersecurity, networking, communications, workflow and business process management, a developer studio, and integration adapters to connect to enterprise systems The gap between the device sensors and data networks is filled by an IoT Platform. IOT Platforms connects the data network to the sensor arrangement and provides insights using backend applications from data generated by hundreds of sensors.

Industrial Internet of Things, also known as Industry 4.0 refers to the usage of data and connectivity-focused technologies to streamline and automate manufacturing operations. It is the concept of connected devices same as IoT , with the difference being IoT is consumer centric and IIoT focuses on industries and it’s an integrated system of industrial application. The IIoT helps in improving efficiency, defects, scalability and cost savings for Industries. It helps companies save cost by using predictive maintenance, improved safety, and other operational efficiencies.
Industrial 4.0 brings a lot of advantages, the advantages of the frameworks and systems that the Industrial Internet of Things refers to, is that they can operate semi-independently or with very minimal human intervention. some are listed below:

• 1. Optimization – Better utilization of available resources
• 2. Use of Predictive and proactive decision making – by leveraging data from integrated sources
• 3. Remote Diagnosis and Prevention - remote monitoring with integrated systems
• 4. Real-Time Monitoring – With integrated systems