Internet of Things
The Internet of Things (IoT), a current buzzword gaining traction in a number of industries. The IoT is not just one piece of technology, but an interrelated collection of hardware, software, services, and connectivity that must work together as a bigger solution.
IoT is a key enabler of digital transformation. It's an automation and analytics system that uses cloud-based IoT networks, sensing, big data, machine learning, and artificial intelligence to create complete systems for a product or service. When applied to any industry or sector, these systems provide greater accountability, control, and efficiency. The IoT can be viewed as a global infrastructure for the information society, enabling advanced services by interconnecting ‘things’ based on existing and evolving interoperable information and communication technologies.
The Internet of Things is connecting more and more hardware products being part of a larger network. Wireless networks, sensor technology, cloud-based and real-time computing make it possible to collate and analyze data across ‘things’ to make processes faster and more efficient. The actual value or importance of the internet of things comes from collecting valuable data from devices, communicating it, analyzing it, and using it to maximize the efficiency and the services offered by these IoT products.
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Building blocks of Internet of Things
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IoT is based on objects and devices which are known as “things” connected over internet that are equipped with sensors, software, and other technologies allowing to transmit and receive data to and from other things and systems. The main objective of IoT is to make things more dynamic and convenient. The common building blocks used for IoT system across industries and applications include sensors, wired and wireless solutions, antennas, batteries and few smaller connectors and passive components which fuels low power, interconnect, intelligent things of the eco system.
These sensors will collect extremely sensitive data and bridge the gap between the physical world and digital world. They are able to convert valuable information from the real world into digital data which is further processed to do some useful action, further analyzing it and using that data to enhance the products and services provided to users of IoT enabled equipment. They are capable of being identified and integrated into communication networks.
In any Smart application, sensors are very important. They detect any physical/chemical changes and after processing, the collected data sensors automate the application/devices to make it Smart. IoT integrates various types of sensors, devices and nodes having the capability to communicate with each other without human intervention. Things like Sensor and actuator modules are physically connected by common interfaces like USB, GPIO, I2C, SPI and UART.
The essence of IoT is the ‘things’ and ‘data’. The hardware utilized in IoT systems is equipped with electronic components, such as integrated sensors, smart sensors and actuators, connectivity/communication electronics and software to capture, filter and exchange data about themselves, their state and their environment.
‘Things’ have associated information, they may be static or dynamic and are embedded within a system. This enables many objects/devices to act as smart ‘things’. Objects enabled with IoT technology have been embedded with smart capabilities through the use of various tools and technologies. ‘Things’ have evolved due to the convergence of multiple technologies, real-time analytics, Artificial intelligence, machine learning, commodity sensors, and embedded systems. The other supporting skeleton systems that contribute to the enabling of IoT are traditional fields of embedded systems, wireless sensor networks, control systems, automation, and others.
Sensors are used in almost every area so as to create a Smart IoT environment, a few of the Smart environment applications include Smart parking, Smart traffic management, Smart lighting, Smart cities, Smart Metering and many more. There are different types of sensors which can range from very simple to complex. Some of the IoT sensors which are frequently seen are Proximity Sensors, Position Sensors, Occupancy Sensors, Motion Sensors, Velocity sensors, Temperature sensors, Pressure Sensors, Chemical Sensors, Humidity Sensors, Water Quality Sensors, Infrared Sensors, Gyroscope Sensors, Optical Sensors and many more. The classification of sensors can be based upon their specifications, its conversion method, type of material used, its sensing physical phenomenon, properties etc. This feature can be provided at different levels of integration, depending on how many different sensor types are included in a single package or sensor module.
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IoT applications vary greatly, but many of them need a large number of sensors spread out over a large area. There are many different of communication techniques for these sensors/devices to get connected. All of the different sensors and devices can use different transmission protocols, for example the rules and data format used for the information being transmitted. Sensors, gateways, routers, software, platforms, and other systems are all linked in the IoT ecosystem, the way they are connected with each other is referred to as IoT networking. It typically refers to various network solutions that vary in terms of power consumption, range, and bandwidth capability. Sensors and transducer devices are connected to networks using various networking devices such as hubs, gateways, routers, network bridges, and switches, depending upon the application. Selecting the right IoT connectivity or network protocol technology needs careful attention.
Connectivity solutions for IoT use digital message formats and some rules are required for exchanging data/messages between devices. These may be implemented using wireless or wired connectivity solutions. Wireless solutions have different standards for long-range and short-range connectivity. Long-range connectivity solutions may use either licensed (cellular) or unlicensed standards, known as LPWAN (Low Power Wide Area Networks). Short-range IoT networking solutions transmit data over short physical distances, with the distance between the data collector and the gateway which process the sensor data being typically less than 150 meters.
Gateways can communicate with sensors/devices over varying protocols and then translate that data into a standard protocol such as MQTT, they can pre-process and filter the data being generated in order to decrease transmission, processing, and storage requirements.
WiFi is the most used wireless technology for local area networks. They are used in a variety of IoT applications, especially in smart home and smart office settings. WiFi operates at around 2.4 GHz or 5 GHz frequencies. WiFi HaLow (802.11ah) and HEW (802.11ax) are two WiFi standards that have been developed specifically for IoT.
Bluetooth is also an important protocol for the Internet of Things, with its use for smart homes and industrial applications. This technology is growing considerably. It is a low-power, low-range, high-bandwidth choice for connectivity. Bluetooth V5 is the latest version that has been introduced and is specifically aimed at the Internet of Things. It boasts quadruple the range and double the speed.
LPWAN is a new global networking standard designed for smart networks with resource-constrained devices that are distributed across large areas and consume minimum amount of power. These networks are designed for IoT applications that have low data rates, low cost, require longer battery life and operate in remote and hard to reach locations.
The NarrowBand-Internet of Things (NB-IoT) is an LPWAN-based standard that enables a broad range of new IoT devices and services. They allow a large number of sensors/devices to collect and send data over large areas whilst preserving the battery life. It can last for years on a battery instead of weeks or months. IoT gateways are often required for these kinds of applications to work.
Cellular networks provide the backbone to access the internet. These networks focus on range and bandwidth at the expense of power consumption. It can send lots of data over long distances but it drains the battery rather quickly. Cellular offers solutions for IoT applications that involve long-distance data transfers with low latency. These are LTE-M, built on LTE evolutions, Cat-0 and also Cat-1, EC-GSM and NB-LTE. All these standards operate seamlessly on existing LTE or GSM networks. They support a wide array of IoT applications.
Some applications that require global coverage and/or mobility will use cellular technologies, but the majority of IoT devices will use non-cellular technologies' sharing frequencies in unlicensed bands to communicate with each other and with IoT applications in the cloud.
A wired connectivity solution uses Ethernet cable to connect to the network. Ethernet is a technology that connects wired local area networks (LANs) and allows devices to communicate with each other. The Ethernet cable is then linked to the network gateway via DSL or cable. Wired networks are a well-established infrastructure that is simple to connect to if you already have phone line.
The Internet of Things (IoT) is characterized as a paradigm in which objects equipped with sensors, actuators, and processors interact with one another to serve a meaningful purpose. Sensors, networking, data processing, and a user interface are the four main components of IoT. The sensor data could be sent for storing, processing, or further dissemination of information. In most cases, the process follows a loop that consists of three simple stages: input, processing, and output.
The first step of the data processing cycle is the input, for example sensing the critical and meaningful information from sensor data to perform decision-making. The various representations of sensor data includes types such as Boolean, binary, featured values, continuous data, and numeric values. The captured raw sensor data first needs to undergo data cleansing and processing. The processing is accomplished by the use of various data techniques such as data de-noising, data imputation, and data outlier detection, data aggregation and other various data manipulation techniques (Classification, Sorting and Calculation). Data integration or sensor fusion is the process of combining two or more data sources, which helps in generating more accurate and consistent dynamic system implied results in various applications.
IoT systems need special computing capabilities for data and storage, it is essential to store the data for performing data analysis and achieve the desired outcome. This aids in data collection, transmission, and analysis. In the meantime, the big data analytics, machine learning, Artificial Intelligence and deep learning techniques provide a promising solution towards the analysis of IoT sensor data. These technologies are game-changing, and they can be used to automate processes, predict equipment faults, and track security threats in real time. When solutions are completely autonomous, AI uses connected IoT network devices to help lead the way. By applying AI to IoT data management and analytics, organizations can quickly pull valuable information from these massive, heterogeneous data sets and respond to real-time conditions.
There is a need to integrate various emerging technologies such as edge computing, cloud computing and fog computing, towards achieving the efficient computation of data analytical models. Edge analytics analyses data at the network's edges rather than in a centralized location. Data can be analyzed in real time on the devices themselves or on a nearby gateway system that is connected to the IoT devices. Edge devices may function as gateways, allowing other devices on the network to communicate with another IoT Hub. An edge gateway is a network access point for applications that communicate with cloud-based services. In addition, they often provide network translation between networks that use different protocols.
Cloud computing uses big data and parallel distributed system technologies on the remote cloud server and it handles the huge volume of data generated by IoT sensors, this enables the system to provide efficient services for IoT applications. In fog-level based IoT sensor data processing, the sensor data features are extracted and processed to classify different the signal patterns by using a neural network. Based on the output of neural network classification, event identification and decision-making is performed at the fog level.
Avnet SmartEdge Industrial IoT Gateway (based on Raspberry Pi's Broadcom BCM2837 SoC, a 64-Bit quad-core Arm Cortex-A53 processor) is a good example. It delivers high-performance computing power which connects sensors and several other devices to the Cloud.
IoT edge processing and computing platforms based on single-board computers (SBCs) provide designers with a growing number of cost effective and well supported solutions. An SBC is a full-fledged computer built upon a single PCB with all the required basic components like CPU, Memory, Inputs/Outputs, and additional system components required for a computing system. These integrate a multi-core CPU architecture, 3D GPU for high-end graphics and general-purpose computing, with a Wi-Fi interface, High speed Ethernet interface (Gigabit), PCI express, LVDS display interface, HDMI, MIPI CSI-2 camera with serial interface, SATA interface and advanced graphics performance, all designed with a minimal power consumption. Raspberry Pi, Ultra96-V2, BeagleBone Black, BeagleBone AI are few of the commonly available SBCs.
There are various development kits and tools available for microcontroller units (MCUs), microprocessor units (MPUs), digital signal processors (DSPs) and field-programmable gate array (FPGA) based platforms which are the most efficient ways for designing and developing IoT based systems.
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Sensors and various communication technologies are extensively used in IoT systems to sense and transmit real-time data, which allows for fast computations and efficient decision-making. The role played by power management and how the remote devices and long-lasting devices can manage power efficiently, is important in a successful IoT implementation. Generally the IoT sensors have to be in active state all of the time (24/7) and they need power to perform their goal. As the number of sensors in one location grows from tens to hundreds, the energy requirements of sensors cannot be ignored. The best energy source is determined by the specific IoT application, whether it is battery-powered, energy scavenging, DC bus-powered, or mains-powered.
Considerations must also be made regarding power for the computing, networking capabilities, storage capacity and memory.
A large amount of energy is needed to operate the billions of IoT devices that are connected to the Internet. These could contribute to quite an amount of wasted electronic power. Since IoT is optimised for energy efficiency, a heterogeneous IoT network with various computational elements can be optimised for various work tasks, and can automatically respond to specific power demands for the applications. It needs to implement low standby currents and low-leakage in the circuits, and utilize clock throttling to achieve an efficient energy saving system.
The processing of large amounts of data and the use of intelligent algorithms for real-time data analysis will aid in the monitoring of energy consumption. For many IoT systems, the access to constant power is a recurring challenge, it may be the kind of implementation, or the cost of connecting the devices to a supply voltage that is the issue. Design considerations will include major system elements such as the selected microcontroller (MCU), wireless interface, and sensors, along with system power management. One important way to minimize the demand for power is to select a right controller/processor that can be more power efficient.
The appropriate network protocol is also essential; some protocols may consume more bandwidth than necessary, drawing excessive power to support it. Significant power savings can be achieved through autonomous handling of sensor interfaces and other peripheral functions. In a sensor node, the amount of data to be sent over the wireless link should be relatively small. As such, ZigBee provides an optimal mesh networking solution; Bluetooth Smart is an excellent choice for a standards-based, power-sensitive point-to-point configuration, and proprietary sub-GHz solutions provide maximum flexibility for network size, bandwidth and data payloads in star or point-to-point configurations.
Energy consumption in low-power and active modes, as well as the need to quickly wake up from low-power modes to full-speed operation, will make a significant difference in conserving battery power. A final design consideration for low-energy applications is about powering the system itself. Depending upon the type of battery used in the application, there is quite often a requirement for boost converters or boost-switching regulators.
There are many approaches to avoiding common IoT power consumption problems. To improve the power efficiency of IoT devices, multiple methods of energy reduction and energy harvesting can be combined. Energy harvesting can be done in a number of ways using piezoelectric materials, thermoelectric materials, solar energy, Wind energy etc. Energy-harvesting is one such system in which energy conversion is taken place from one energy form to another through its surroundings, this advanced technique has been significantly used in recent years and is a viable choice for some deployments. Energy reduction in a system is implemented on a hardware component level, by using advanced power management techniques and various power saving modes (sleep, active, low power mode)
For more complex systems, a power management integrated circuit (PMIC) gives more precise control over the whole system. From a single power source, you can generate multiple voltage rails to drive different elements of the embedded system.
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Almost all IoT applications that have been deployed or are in the process of being deployed need a high level of security. As IoT continues to take over many applications used in the world, there are many security challenges which are related to IoT, such as data Privacy, data Security and heterogeneity, are needed to ensure the safe working of the IoT system. Proper data management ensures safe data delivery within time as deployment within the consumer and enterprise spaces are emerging.
Any IoT application can be categorized into sensing layer, Network layer, middleware layer and application layer. Each of these layers uses diverse technologies that bring a number of issues and security threats. The sensing layer mainly deals with physical IoT sensors and actuators. Sensors detect certain physical phenomena happening around them and actuators perform a certain action on the physical environment, based on the sensed data. The network layer's primary purpose is to transmit the information from the sensing layer to the computing unit for processing.
The middleware layer will act as a bridge between the network and application layer. Middleware layer includes brokers, persistent data stores, queuing systems, machine learning, etc. Database security and cloud security are other main security challenges in the middleware layer.
In the application layer there are various IoT based end-to-end applications. Aside from these layers, there are a number of gateways that connects these layers and aids data flow. There are some security risks that are specific to these gateways. The gateway layer is a wide layer that connects multiple devices, people, objects, and cloud services. It helps in providing hardware and software solutions for IoT devices. The decrypting and encrypting of IoT data, as well as the translation of protocols for communication between layers, is handled by the gateways.
IoT security provides the technological space for protection of IoT connected objects and data. The key goal of IoT protection is to ensure data security, privacy, and confidentiality, as well as the security of infrastructures, devices, and services provided in an IoT environment. The security requirements such as Authentication, Authorization / Access Control, Availability, Confidentiality, Integrity, Privacy and Trust are needed from a technical point of view so as to maintain a secure implementation of the IoT.
The current and future solutions to the security of IoT threats contain various mechanisms like blockchain, edge computing, fog computing, and machine learning.
The blockchain may be a better solution for security and preserving the privacy of IoT data. It is the one solution for many security problems in the IoT environment which is decentralized in nature, so there is no need for any centralised authority to manage the transaction. A blockchain holds a strong defense against the tampering of the IoT device data, locking access and permitting the cooperated devices in IoT network.
IoT edge devices collect data from sensors and communicate with each other, the edge can be a convenient entry point to the network and core systems, making it vulnerable to cyber-attacks and physical security (tampering with a device). The threat of data and privacy violations, product tampering via remote control, and data attacks are all increased when a large amount of data is exchanged.
Machine learning (ML) appears to be a promising solution to protect IoT devices against cyber-attacks by providing a different approach for defending against attacks as compared to other traditional methods. Many domains are using ML for their development, and it is being used for IoT security as well. ML provides a promising platform to overcome the difficulties faced in securing IoT devices.
The semiconductor chipmaker Arm – who are the most prevalent architecture used in connected homes for security devices, light bulbs, appliances and more, have introduced a new security framework called Platform Security Architecture (PSA) to increase IoT security. This will help electronics designers to build security directly into the device firmware. PSA also provides IoT threat models, security assessment hardware and firmware architecture solutions based on a "best practice approach" for consumer devices.
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Sensors and actuators are connected either directly to edge devices, gateways or via low power radio technologies. Edge devices are the intelligence for computation on data received from the sensors, they are connected to the cloud either directly or through an edge gateway. These Gateways run complete operating systems and have more computing power, memory, and storage, and they are connected to the cloud which is a huge, interconnected network of powerful servers that performs services for businesses and for people. Edge computing is a model which aims to push some of the relevant data processing, logic decisions, data storage and storage attributes closer to the end devices instead of the gateway or cloud.
Cloud computing is based on resource sharing, which is an important requisite for IoT platforms. In the cloud computing process, a huge amount of data is collected from the IoT devices and is stored in external rented servers. The users can access the cloud services from any location via any device that has an internet connection. The cloud provides the application services with elasticity and scalable resources, which are readily accessible and available. The process of striking a balance between the storing and processing of the data on the edge or on the cloud is very important. Keeping too much data on the edge may also lead to the edge devices being overwhelmed and may impact the entire application.
The cloud lends the storage space and the computational capabilities of these IoT devices as ‘cloud services’. Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are the three types of cloud services available. Some of the popular IoT Cloud Platform solutions include Artik Cloud, Autodesk Fusion Connect, AWS IOT, GE Predix, Google Cloud IoT, Microsoft Azure IoT Suite, IBM Watson IoT, ThingWorx, Intel IoT Platform, Salesforce IoT Cloud, Telit DeviceWise, Zebra Zatar Cloud, macchina.io, ThingSpeak, and Particle Cloud.
4G of mobile telecommunication technology provides mobile broadband internet access to wireless modems, Smartphone’s and also to other mobile systems. 4G systems offer enhanced key services, such as HD video calling, higher bandwidth (BW), high data throughput, better QoS, and streaming online gaming services. It has a 40 MHz BW capacity and sets a 100 Mbps peak speed requirement.
For an instance, consider IoTConnect ® cloud platform supported by Avnet, this meets the unique needs of different industries such as ‘Smart City’, Manufacturing, Healthcare, Food processing (FMCG), Retail market, Construction, Environmental services and many more. The Highlighting features of the IoTConnect Platform are: easy configuration, notifications, real-time monitoring and analytics, multi-layer security, integration, connectivity, interoperability and edge software. A few of the services provided by IoTConnect include ‘Smart’ Rules, Device Management, Real Time Analytics, Remote Monitoring, Asset Tracking, and Data Infrastructure.
The IoTConnect Platform supports many interfacing protocols, including Bluetooth, 802.15.4/ZigBee or 6LoWPAN, ModBus, CAN bus, BACnet, CoAP, MQTTS, HTTPS, AMQP, and so on. The IoTConnect Platform can connect to almost any IoT device with the most powerful industry based protocols, to assist with the communication to the IoTConnect Platform cloud. It also allows you to connect your existing enterprise CRM and ERP systems so you can generate greater intelligence.
The IoTConnect Platform uses a software-defined infrastructure (SDI), which means it is easy to upgrade and independent of any hardware-specific dependencies. IoTConnect® can capture and analyze huge amounts of data by enabling enterprises to securely connect a wide range of data sources, devices, sensors, equipment and control systems. Once all the resources are connected, the data is aggregated, filtered, stored and analyzed. The data is then converted into easy-to-understand reports using data visualization tools and made available to the right people at the right time to improve decision-making.