IoT UNIT - II PDF

Internet of Things UNIT-II IoT Architecture and Protocols: Taxonomy of IoT, Three-layer and five-layer architecture of IoT, cloud and fog based architecture, representative architecture, NFC, WSN, IoT Network Protocol Stack, Bluetooth, ZigBee and 6LowPAN. 3 layer IoT architecture IoT architecture i...

Internet of Things UNIT-II IoT Architecture and Protocols: Taxonomy of IoT, Three-layer and five-layer architecture of IoT, cloud and fog based architecture, representative architecture, NFC, WSN, IoT Network Protocol Stack, Bluetooth, ZigBee and 6LowPAN. 3 layer IoT architecture IoT architecture is a framework that specifies the physical elements, network technical arrangement and setup, operating procedures, and data formats to be used. IoT architecture can differ greatly based on execution; it must be flexible enough for open protocols to handle many network applications. A three-layer architecture is the common and generally known structure. It was first used in the initial phases of this IoT study. It indicates three levels: perception, network, and application. 1. Perception Layer : This perception layer is the IoT architecture’s physical layer. In these sensors and embedded systems are used mainly. These collect large amounts of data based on the requirements. This also includes edge devices, sensors, and actuators that communicate with the surroundings. It detects certain spatial parameters or detects other intelligent things /objects in the surroundings. 2. Network Layer : The data obtained by these devices must be distributed and stored. This is the responsibility of the network layer. It binds these intelligent objects to other intelligent/ smart objects. It is also in charge of data transfer. The network layer is in- charge of linking smart objects, network devices, and servers. It is also used to distribute and analyze sensor data. 3. Application Layer : The user communicates with this application layer. It is in-charge of providing the customer with software resources. Example: in smart home application, where users press a button in the app to switch on a coffee machine, for example. The application layer is in-charge of providing the customer with application-specific resources. It specifies different uses for the IoT, such as smart houses, smart cities, and smart health. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-1 5 Layer Architecture of Internet of Things Internet of Things (IoT) includes large number of smart devices connected to a broad internet network with the help of various networking technologies. Mostly these technologies are wireless in manner. This makes the structure more complex and difficult to manage. Therefore, architecture is required. An architecture is structure for specification of network’s physical components and their functional organization and configuration, its operational principles and procedures, as well as data formats used in its operation. The development of IoT depends on the technologies used, application areas, and business aspects. There are various IoT architectures are available for IoT devices. However, the “5 Layer Architecture is considered as the best-proposed architecture of IoT.” 5 Layer Architecture of IoT : When project work is done with various cutting edge technologies and broad application area, 5 layer architecture is considered as best. 5 Layer model can be considered as an extension to the basic architecture of IoT because it has two additional layers to the basic model. 5 Layer Architecture of Internet of Things  Perception Layer : This is the first layer of IoT architecture. In the perception layer, number of sensors and actuators are used to gather useful information like temperature, moisture ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-2  content, intruder detection, sounds, etc. The main function of this layer is to get information from surroundings and to pass data to another layer so that some actions can be done based on that information.  Network Layer : As the name suggests, it is the connecting layer between perception and middleware layer. It gets data from perception layer and passes data to middleware layer using networking technologies like 3G, 4G, UTMS, WiFI, infrared, etc. This is also called communication layer because it is responsible for communication between perception and middleware layer. All the transfer of data done securely keeping the obtained data confidential.  Middleware Layer : Middleware Layer has some advanced features like storage, computation, processing, action taking capabilities. It stores all data-set and based on the device address and name it gives appropriate data to that device. It can also take decisions based on calculations done on data-set obtained from sensors.  Application Layer : The application layer manages all application process based on information obtained from middleware layer. This application involves sending emails, activating alarm, security system, turn on or off a device, smartwatch, smart agriculture, etc.  Business Layer : The success of any device does not depend only on technologies used in it but also how it is being delivered to its consumers. Business layer does these tasks for the device. It involves making flowcharts, graphs, analysis of results, and how device can be improved, etc. Cloud Based Architecture of IoT Cloud architecture for IoT refers to the different modules that make up each organization's system for cloud computing and data processing. A strong cloud architecture helps ease the transition of data through new IoT technologies. To be successful, cloud architecture must remain fluid and flexible, ready to take on new softwares and platforms without friction. Any organization working with big data for an IoT application should be aware of how and why an IoT cloud architecture should be agile and how the different layers of that architecture work together. Cloud Architecture for IoT Must Be Agile Most people encounter the Internet of Things every day. For example, in retail, connected devices enable digital signage, mobile shopping options, and even item tracking. IoT cloud architecture keeps those features working and gives them the flexibility needed to keep improving. For those data processing functions to continue improving, an organization’s data platforms must be extremely agile. The greater the agility, the more an organization can utilize new and improved technologies. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-3 Cloud Architecture Layers In an effective IoT cloud architecture, data flows through different layers. Each layer makes the data more and more functional for analysis and insights. Cloud architecture will look different in each organization, but the bulk of any organization’s cloud architecture lies in the processing/reporting layer. Here is an example of a cloud architecture infrastructure that uses Talend’s services to support IoT. 1. Cloud Sources/Internal Aggregate Layer This is where all of the data getting ready to be processed comes from. It can be made up of any public information from SaaS platforms, IoT, and other cloud sources. This is the first layer within the cloud architecture, and it is where data processing begins. 2. Ingestion Framework Layer Talend’s ingestion framework allows for all unstructured, semi-structured, and structured data to flow from the cloud sources and other public platforms into the reporting layer. It acts as the middleman between original data and the processing/reporting layer. 3. Reporting Layer The makeup of the reporting layer varies based on the needs of each organization, but in most reporting layers, there will be different zones through which data flows. For example:  Raw Zone — Talend brings all of the information from the ingestion framework into the raw zone. The raw zone is simply a place for the raw data to land in the reporting layer. It is essentially the final step before the processing of data takes place.  Useable Zone — This zone uses various types of machine learning to help create visibility and pull out operational data. In this case, the useable zone is where data is sifted through and processed for generating insights in the final zone.  Final Zone — The final zone focuses on generating insights through the combination of historical data and real-time data. Data in the final zone is ready to be used for analysis and decision-making. 4. Outbound Services/Storage Layer Finally, Talend provides outbound services, such as APIs and managed access, so information can easily be shared with both internal and external parties. Fog Computing Fog computing is 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. Fog Computing Architecture ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-4  Fog Computing is the term coined by Cisco that refers to extending cloud computing to an edge of the enterprise’s network. Thus, it is also known as Edge Computing or Fogging. It facilitates the operation of computing, storage, and networking services between end devices and computing data centers. 1. The devices comprising the fog infrastructure are known as fog nodes. 2. In fog computing, all the storage capabilities, computation capabilities, data along with the applications are placed between the cloud and the physical host. 3. All these functionalities are placed more towards the host. This makes processing faster as it is done almost at the place where data is created. 4. It improves the efficiency of the system and is also used to ensure increased security. History of fog computing The term fog computing was coined by Cisco in January 2014. This was because fog is referred to as clouds that are close to the ground in the same way fog computing was related to the nodes which are present near the nodes somewhere in between the host and the cloud. It was intended to bring the computational capabilities of the system close to the host machine. After this gained a little popularity, IBM, in 2015, coined a similar term called “Edge Computing”. When to use fog computing? Fog Computing can be used in the following scenarios: 1. It is used when only selected data is required to send to the cloud. This selected data is chosen for long-term storage and is less frequently accessed by the host. 2. It is used when the data should be analyzed within a fraction of seconds i.e Latency should be low. 3. It is used whenever a large number of services need to be provided over a large area at different geographical locations. 4. Devices that are subjected to rigorous computations and processings must use fog computing. 5. Real-world examples where fog computing is used are in IoT devices (eg. Car-to-Car Consortium, Europe), Devices with Sensors, Cameras (IIoT-Industrial Internet of Things), etc. Advantages of fog computing  This approach reduces the amount of data that needs to be sent to the cloud.  Since the distance to be traveled by the data is reduced, it results in saving network bandwidth. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-5  Reduces the response time of the system.  It improves the overall security of the system as the data resides close to the host.  It provides better privacy as industries can perform analysis on their data locally. Disadvantages of fog computing  Congestion may occur between the host and the fog node due to increased traffic (heavy data flow).  Power consumption increases when another layer is placed between the host and the cloud.  Scheduling tasks between host and fog nodes along with fog nodes and the cloud is difficult.  Data management becomes tedious as along with the data stored and computed, the transmission of data involves encryption-decryption too which in turn release data. Applications of fog computing  It can be used to monitor and analyze the patients’ condition. In case of emergency, doctors can be alerted.  It can be used for real-time rail monitoring as for high-speed trains we want as little latency as possible.  It can be used for gas and oils pipeline optimization. It generates a huge amount of data and it is inefficient to store all data into the cloud for analysis. Difference Between Cloud Computing and Fog Computing Cloud Computing: The delivery of on-demand computing services is known as cloud computing. We can use applications to storage and processing power over the internet. It is a pay as you go service. Without owning any computing infrastructure or any data centers, anyone can rent access to anything from applications to storage from a cloud service provider. We can avoid the complexity of owning and maintaining infrastructure by using cloud computing services and pay for what we use. In turn, cloud computing services providers can benefit from significant economies of scale by delivering the same services to a wide range of customers. Fog Computing: Fog computing is a decentralized computing infrastructure or process in which computing resources are located between the data source and the cloud or any other data center. Fog computing is a paradigm that provides services to user requests at the edge networks. The devices at the fog layer usually perform operations related to networking such as routers, gateways, bridges, and hubs. Researchers envision these devices to be capable of performing both computational and networking operations, simultaneously. Although these devices are resource- constrained compared to the cloud servers, the geological spread and the decentralized nature help in offering reliable services with coverage over a wide area. Fog computing is the physical location of the devices, which are much closer to the users than the cloud servers. Why Is Fog Computing Used? There are several reasons why fog computing is used:  To improve latency and performance: Because fog nodes are often deployed at the network edge, closer to the IoT devices themselves, they can substantially reduce the processing time and enhance performance for applications that demand low latency.  To improve decision-making: It can help improve decision-making in real-time as fog computing allows for real-time data collection and analysis from IoT devices. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-6  To reduce costs: Fog computing can also help reduce costs associated with data storage and analysis. This is because, by bringing computation and data storage closer to the network edge, fog computing reduces the amount of data that needs to be transmitted back to a central location for processing. What Are the Four Types of Fog Computing? Fog computing is a term for technology that extends cloud computing and services to the edge of an enterprise’s network. It allows data, applications, and other resources to be moved closer to, or even on top of, end users. The four main types of fog computing are mentioned below.  Device-level fog computing runs on devices such as sensors, switches, routers, and other low-powered hardware. It can be used to gather data from these devices and send it to the cloud for analysis.  Edge-level fog computing runs on servers or appliances located at the edge of a network. These devices can be used to process data before it is sent to the cloud.  Gateway-level fog computing runs on devices that act as a gateway between the edge and the cloud. These devices can be used to manage traffic and ensure that only relevant data is sent to the cloud.  Cloud-level fog computing runs on servers or appliances located in the cloud. These devices can be used to process data before it is sent to end users. Where Is Fog Computing Needed? There are many potential applications for fog computing, including:  Connected cars — collecting and processing data from sensors in real-time to enable features such as autonomous driving and infotainment.  Smart cities — monitoring traffic flows, managing public transportation, optimizing energy use, and more.  Industrial IoT — enhancing efficiency and safety in factories, power plants, mines, and other industrial infrastructure.  Connected health — supporting remote patient monitoring, telemedicine, and other healthcare applications.  AR/VR — enabling low-latency, high-quality augmented and virtual reality experiences. Fog computing can be used to support a wide range of applications that require data to be processed at the edge of the network. In many cases, moving compute and storage resources closer to the data source improves performance and reduces costs. For example, connected cars generate a significant volume of data that needs to be analyzed in real-time to enable features such as autonomous driving. Who Uses Fog Computing? Fog computing is often used in cases where real-time response is needed, such as with industrial control systems, video surveillance, or autonomous vehicles. It can also be ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-7  used to offload computationally intensive tasks from centralized servers or to provide backup and redundancy in case of network failure. Components of Fog Computing Some of the key components of cloud fog computing include the following:  Edge devices: These are the devices located at the edge of the network, closest to the data source. Edge devices include sensors, PLCs (programmable logic controllers), and gateway routers.  Data processing: Data processing is done locally on edge devices rather than sent to a central location for processing. The result is improved performance and reduced latency.  Data storage: Edge devices can store data locally rather than sending it to a central location for storage. This improves security and privacy, as well as reduces latency.  Connectivity: Fog computing requires high-speed connectivity between edge devices and the rest of the network. This is achieved through wired or wireless means. Why Is Fog Computing Beneficial for IoT? The internet of things (IoT) is a system of interconnected devices, sensors, and software components that share data and information. The power of the IoT comes from its ability to collect and analyze massive volumes of data from various sources. This data can be used to improve efficiency, optimize operations and make better decisions. Fog computing in IoT is a decentralized computing model that brings computation and data storage closer to the edge of the network. In other words, fog computing moves processing power and data storage away from centralized server farms and into local networks where IoT devices are located. What Are the Advantages and Disadvantages of Fog Computing? There are several advantages to using a fog computing architecture: 1. Reduced latency: By processing data at or near the edge of the network, fog computing can help reduce latency. 2. Improved security and privacy: By keeping data and applications closer to the user, fog computing can help improve security and privacy. 3. Increased scalability: Fog computing can help increase scalability as more resources may be added at the edge of the network. There are also several disadvantages to using a fog computing architecture: 1. Limited resources: Because fog computing relies on devices at the edge of the network, there may be limited resources available. This can impact performance. 2. Complex architecture: Fog computing can be complex to implement and manage because of the distributed nature of the architecture. 3. Limited coverage: Because fog computing is still a relatively new technology, there may be limited coverage in terms of devices and locations that support it (HiTechWhizz, 2022). What Is Heavy.AI? Heavy.AI is a powerful artificial intelligence platform that enables businesses and developers to easily build and deploy AI-powered applications. Heavy.AI is built on top of the popular TensorFlow open-source library, making it easy to get started with deep learning and neural networks. With Heavy.AI, you can quickly train and deploy your ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-8 custom models or use one of the many pre-trained models available in the Heavy.AI marketplace. How Is Heavy.AI Related to a Fog Computing Solution? Heavy.AI also offers a fog computing solution that can be used to manage and process data from IoT devices at the edge of the network. This solution can improve the performance of IoT applications by reducing latency and ensuring data is processed locally. iFogSim is also an open-source fog computing simulator that can evaluate the performance of different fog computing architectures. iFogSim includes a library of modules that can simulate various aspects of fog computing, such as network topologies, device types, and application characteristics. Fog Computing Architecture The Fog computing architecture consists of physical and logical elements in the form of hardware and Software to implement IoT (Internet of Things) network. As shown in figure-2, it is composed of IoT devices, fog nodes, fog aggregation nodes with the help of fog data services, remote Cloud storage and local data storage server/cloud. Let us understand fog computing architecture components. IoT devices: These are devices connected on IoT network using various wired and wireless technologies. These devices produce data regularly in huge amount. There are numerous wireless technologies used in IoT which include Zigbee, Zwave, RFID, 6LoWPAN, HART, NFC, Bluetooth, BLE, NFC, ISA-100.11A etc. IoT protocols used include IPv4, IPv6, MQTT, CoAP, XMPP, AMQP etc. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-9  Fog Nodes: Any device with computing, storage and network connectivity is known as fog node. Multiple fog nodes are spread across larger region to provide support to end devices. Fog nodes are connected using different topologies. The fog nodes are installed at various locations as per different Applications such as on floor of a factory, on top of power pole, along side of railway track, in vehicles, on oil rig and so on. Examples of fog nodes are switches, embedded servers, controllers, routers, cameras etc. High sensitive data are processed at these fog nodes. Fog aggregate nodes: Each fog nodes have their aggregate fog node. It analyzes data in seconds to minutes. IoT data storage at these nodes can be of duration in hours or days. Its geographical coverage is wider. Fog data services are implemented to implement such aggregate node points. They are used to address average sensitive data. Remote Cloud: All the aggregate fog nodes are connected with The Cloud Cloud. Time insensitive data or less sensitive data are processed, analyzed and stored at The Cloud Cloud. Local server and cloud: Often fog computing architecture uses private server/cloud to store the confidential data of the firm. These local storage is also useful to provide data security and data privacy. 3 Layer Architecture of Fog Computing ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-10 Near field communication (NFC) Near Field Communication (NFC) technology allows users to make secure transactions, exchange digital content, and connect electronic devices with a touch. NFC transmissions are short range (from a touch to a few centimetres) and require the devices to be in close proximity. NFC is the technology in contactless cards, and the most common use of NFC technology in your smartphone is making easy payments with Samsung Pay. NFC can also be used to quickly connect with wireless devices and transfer data with Android Beam. Near Field Communication (NFC) is a set of short-range wireless technologies, typically requiring a distance of 4 cm or less to initiate a connection. NFC lets you share small payloads of data between an NFC tag and an Android-powered device, or between two Android-powered devices. Tags can range in complexity. Simple tags offer just read and write semantics, sometimes with one-time-programmable areas to make the card read-only. More complex tags offer math operations, and have cryptographic hardware to authenticate access to a sector. The most sophisticated tags contain operating environments, allowing complex interactions with code executing on the tag. The data stored in the tag ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-11  can also be written in a variety of formats, but many of the Android framework APIs are based around a NFC Forum standard called NDEF (NFC Data Exchange Format). Android-powered devices with NFC simultaneously support three main modes of operation: 1. Reader/writer mode, allowing the NFC device to read and write passive NFC tags and stickers. 2. P2P mode, allowing the NFC device to exchange data with other NFC peers. 3. Card emulation mode, allowing the NFC device itself to act as an NFC card. The emulated NFC card can then be accessed by an external NFC reader, such as an NFC point-of-sale terminal. NFC Basics This document describes how Android handles discovered NFC tags and how it notifies applications of data that is relevant to the application. It also goes over how to work with the NDEF data in your applications and gives an overview of the framework APIs that support the basic NFC feature set of Android. Advanced NFC This document goes over the APIs that enable use of the various tag technologies that Android supports. When you are not working with NDEF data, or when you are working with NDEF data that Android cannot fully understand, you have to manually read or write to the tag in raw bytes using your own protocol stack. In these cases, Android provides support to detect certain tag technologies and to open communication with the tag using your own protocol stack. Host-based Card Emulation This document describes how Android devices can perform as NFC cards without using a secure element, allowing any Android application to emulate a card and talk directly to the NFC reader. Wireless Sensor Network (WSN) Wireless Sensor Network (WSN), is an infrastructure-less wireless network that is deployed in a large number of wireless sensors in an ad-hoc manner that is used to monitor the system, physical, or environmental conditions. Sensor nodes are used in WSN with the onboard processor that manages and monitors the environment in a particular area. They are connected to the Base Station which acts as a processing unit in the WSN System. The base Station in a WSN System is connected through the Internet to share data. WSN can be used for processing, analysis, storage, and mining of the data. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-12 Wireless Sensor Network Architecture A Wireless Sensor Network (WSN) architecture is structured into three main layers:  Physical Layer: This layer connects sensor nodes to the base station using technologies like radio waves, infrared, or Bluetooth. It ensures the physical communication between nodes and the base station.  Data Link Layer: Responsible for establishing a reliable connection between sensor nodes and the base station. It uses protocols such as IEEE 802.15.4 to manage data transmission and ensure efficient communication within the network.  Application Layer: Enables sensor nodes to communicate specific data to the base station. It uses protocols like ZigBee to define how data is formatted, transmitted, and received, supporting various applications such as environmental monitoring or industrial control. These layers work together to facilitate the seamless operation and data flow within a Wireless Sensor Network, enabling efficient monitoring and data collection across diverse applications. WSN Network Topologies Wireless Sensor Networks (WSNs) can be organized into different network topologies based on their application and network type. Here are the most common types:  Bus Topology: In a Bus Topology, multiple nodes are connected to a single line or bus. Data travels along this bus from one node to the next. It’s a simple layout often used in smaller networks.  Star Topology: Star Topology have a central node, called the master node, which connects directly to multiple other nodes. Data flows from the master node to the connected nodes. This topology is efficient for centralized control.  Tree Topology: Tree Topology arrange nodes in a hierarchical structure resembling a tree. Data is transmitted from one node to another along the branches of the tree structure. It’s useful for expanding coverage in hierarchical deployments.  Mesh Topology: Mesh Topology feature nodes interconnected with one another, forming a mesh-like structure. Data can travel through multiple paths from one node to another until it reaches its destination. This topology offers robust coverage and redundancy. Each topology has its advantages and is chosen based on factors such as coverage area, scalability, and reliability requirements for the specific WSN application. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-13 Types of Wireless Sensor Networks (WSN) Terrestrial Wireless Sensor Networks  Used for efficient communication between base stations.  Consist of thousands of nodes placed in an ad hoc (random) or structured (planned) manner.  Nodes may use solar cells for energy efficiency.  Focus on low energy use and optimal routing for efficiency. Underground Wireless Sensor Networks  Nodes are buried underground to monitor underground conditions.  Require additional sink nodes above ground for data transmission.  Face challenges like high installation and maintenance costs.  Limited battery life and difficulty in recharging due to underground setup. Underwater Wireless Sensor Networks  Deployed in water environments using sensor nodes and autonomous underwater vehicles.  Face challenges like slow data transmission, bandwidth limitations, and signal attenuation.  Nodes have restricted and non-rechargeable power sources. Multimedia Wireless Sensor Networks  Used to monitor multimedia events such as video, audio, and images.  Nodes equipped with microphones and cameras for data capture.  Challenges include high power consumption, large bandwidth requirements, and complex data processing.  Designed for efficient wireless data compression and transmission. Mobile Wireless Sensor Networks (MWSNs)  Composed of mobile sensor nodes capable of independent movement.  Offer advantages like increased coverage area, energy efficiency, and channel capacity compared to static networks.  Nodes can sense, compute, and communicate while moving in the environment. Each type of Wireless Sensor Network is tailored to specific environmental conditions and applications, utilizing different technologies and strategies to achieve efficient data collection and communication. Applications of WSN  Internet of Things (IoT)  Surveillance and Monitoring for security, threat detection  Environmental temperature, humidity, and air pressure  Noise Level of the surrounding  Medical applications like patient monitoring  Agriculture  Landslide Detection Challenges of WSN  Quality of Service  Security Issue ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-14  Energy Efficiency  Network Throughput  Performance  Ability to cope with node failure  Cross layer optimization  Scalability to large scale of deployment A modern Wireless Sensor Network (WSN) faces several challenges, including:  Limited power and energy: WSNs are typically composed of battery-powered sensors that have limited energy resources. This makes it challenging to ensure that the network can function for long periods of time without the need for frequent battery replacements.  Limited processing and storage capabilities: Sensor nodes in a WSN are typically small and have limited processing and storage capabilities. This makes it difficult to perform complex tasks or store large amounts of data.  Heterogeneity: WSNs often consist of a variety of different sensor types and nodes with different capabilities. This makes it challenging to ensure that the network can function effectively and efficiently.  Security: WSNs are vulnerable to various types of attacks, such as eavesdropping, jamming, and spoofing. Ensuring the security of the network and the data it collects is a major challenge.  Scalability: WSNs often need to be able to support a large number of sensor nodes and handle large amounts of data. Ensuring that the network can scale to meet these demands is a significant challenge.  Interference: WSNs are often deployed in environments where there is a lot of interference from other wireless devices. This can make it difficult to ensure reliable communication between sensor nodes.  Reliability: WSNs are often used in critical applications, such as monitoring the environment or controlling industrial processes. Ensuring that the network is reliable and able to function correctly in all conditions is a major challenge. Components of WSN  Sensors: Sensors in WSN are used to capture the environmental variables and which is used for data acquisition. Sensor signals are converted into electrical signals.  Radio Nodes: It is used to receive the data produced by the Sensors and sends it to the WLAN access point. It consists of a microcontroller, transceiver, external memory, and power source.  WLAN Access Point: It receives the data which is sent by the Radio nodes wirelessly, generally through the internet.  Evaluation Software: The data received by the WLAN Access Point is processed by a software called as Evaluation Software for presenting the report to the users for further processing of the data which can be used for processing, analysis, storage, and mining of the data. Advantages  Low cost: WSNs consist of small, low-cost sensors that are easy to deploy, making them a cost-effective solution for many applications. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-15  Wireless communication: WSNs eliminate the need for wired connections, which can be costly and difficult to install. Wireless communication also enables flexible deployment and reconfiguration of the network.  Energy efficiency: WSNs use low-power devices and protocols to conserve energy, enabling long-term operation without the need for frequent battery replacements.  Scalability: WSNs can be scaled up or down easily by adding or removing sensors, making them suitable for a range of applications and environments.  Real-time monitoring: WSNs enable real-time monitoring of physical phenomena in the environment, providing timely information for decision making and control. Disadvantages  Limited range: The range of wireless communication in WSNs is limited, which can be a challenge for large-scale deployments or in environments with obstacles that obstruct radio signals.  Limited processing power: WSNs use low-power devices, which may have limited processing power and memory, making it difficult to perform complex computations or support advanced applications.  Data security: WSNs are vulnerable to security threats, such as eavesdropping, tampering, and denial of service attacks, which can compromise the confidentiality, integrity, and availability of data.  Interference: Wireless communication in WSNs can be susceptible to interference from other wireless devices or radio signals, which can degrade the quality of data transmission.  Deployment challenges: Deploying WSNs can be challenging due to the need for proper sensor placement, power management, and network configuration, which can require significant time and resources.  while WSNs offer many benefits, they also have limitations and challenges that must be considered when deploying and using them in real-world applications. IoT Network Protocols Before we dive into common IoT protocols, let's define the term "protocol" at a high level. Protocols are a set of rules for transmitting data between electronic devices according to a preset agreement regarding information structure and how each side will send and receive data. Correspondingly, IoT protocols are standards that enable the exchange and transmission of data between the Internet and devices at the edge. IoT protocols can be divided into two categories: IoT network protocols and IoT data protocols. Data protocols mainly focus on information exchange, while network protocols provide methods of connecting IoT edge devices with other edge devices or the Internet. Each category contains a number of protocols that each have their own unique features. We'll take a look at those next. List of Common IoT Protocols IoT Network Protocols  Wi-Fi ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-16   LTE CAT 1  LTE CAT M1  NB-IoT  Bluetooth  ZigBee  LoRaWAN IoT Data Protocols  AMQP  MQTT  HTTP  CoAP  DDS  LwM2M Layers of the IoT protocol stack "IoT protocol stack" refers to a hierarchy of software and hardware layers. As Particle's Sr. Solutions Architect Dan Kouba phrased it, "It is all the things that sit in between the data being produced at the edge to the data being received by your systems." The IoT network stack can be represented using the seven-layer OSI Network Model, starting from the physical layer at the bottom and ending with the application layer at the top. Specific protocols may represent only one layer or span many—regardless, they must be interoperable to ensure that the network functions as intended. Next, let's take a closer look at each layer and its related functions. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-17 Physical and Data Link Layers The first two layers from the bottom—the physical and data link layers—define the physical connection of end devices to the network. More specifically:  The physical layer receives unstructured raw data between devices and physical transmission media, then transmits the digital information into electrical, radio, or optical signals.  The data link layer catches the data and detects/corrects any errors that may have occurred. This layer also defines the protocol for flow control, as well as establishing and terminating connections between two physically connected devices. "The physical layer is the actual hardware that the electronics are on," explained Dan. "The data link layer represents how the modem negotiates with the cell tower—for example, to establish a communication channel between a device and the cell tower or other networking equipment." Network, Transport, and Session Layers The network, transport, and session layers facilitate data transfer over the connection, with a focus on logical addressing, traffic directing, error correction, flow control, congestion avoidance, session management, and reliability. "From the user’s perspective, these layers are the protocols that run on top of the tunnel to facilitate communication," Dan noted. "What does that message look like? How is it formulated? How do I put data in it? How do we get data out of it?" Presentation and Application Layers The two layers at the top—presentation and application—deal with data formatting and the boundary between the data coming from devices in the field and a business application or database.  The presentation layer transforms data into the form that is accepted by the application.  The application layer—the layer closest to the user—typically identifies communication partners, determines resource availability, and synchronizes communication. At this point in the process, all procedures are accomplished over an encrypted channel. Security applies to every layer in different ways and is often a function of the protocol being used. Once the data reaches the cloud, the systems will unpack it, analyze it, and make decisions accordingly before pushing each decision to the user's cloud platform. IoT network protocols: What are they and what do you need to know? Wi-Fi Wi-Fi is a ubiquitous protocol that can be found almost anywhere—industrial plants, homes, commercial buildings, and even your neighborhood restaurants. This widely favored technology is able to transmit large volumes of data over reasonable distances. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-18 However, many low-power or battery-powered IoT devices are unlikely to use Wi-Fi due to its high power consumption rate. Read our in-depth comparison of cellular vs. WiFi for IoT applications to learn more about when WiFi makes sense and when it doesn’t. LTE CAT 1 LTE CAT 1 is a communication standard specifically designed for servicing IoT applications. Compared with other standards, it scales down bandwidth and communication demand to save power and cost for large-scale and long-range IoT systems. Though LTE CAT 1 performs inferiorly to 3G networks, experts predict that it will replace 3G as major U.S. carriers sunset 3G in 2022. LTE CAT M1 LTE CAT M1—which can also be referred to as Cat-M—is a low-cost, low-power, wide- area network that specializes in transferring low to medium amounts of data. It was developed by the 3rd Generation Partnership Project as part of the 13th edition of LTE standard and is a core cellular IoT technology. Cat-M stands out as a protocol option because it is compatible with the prevailing LTE network, meaning major carriers pivoting to it will not have to invest in new antennas. Comparison: CAT M1 is considered a complementary technology to NB IoT. However, CAT M1 has a faster upload/download speed of 1 Mbps and a lower latency of 10 to 15 ms. As of 2024, LTE CAT M1 and NB-IoT are now widely deployed and supported by major carriers worldwide, as the rollout has significantly progressed since 2022. This widespread availability has enabled the growth of large-scale, low-power IoT deployments across various industries. NB-IoT While the protocols detailed previously have been in application for a long time, Narrow Band-IoT is a new, fast-growing, low-power, wide-area technology intended to specifically target the needs of battery-powered IoT devices. When compared to other cellular protocols, NB-IoT's advantages include improvements in power consumption, system capacity, and spectrum efficiency. For example, NB-IoT can connect huge fleets with up to 50,000 devices per network cell. However, NB-IoT doesn’t come without challenges. The protocol has very limited bandwidth, which can slow or limit data transmission capabilities and make essential features like over-the-air updates difficult or impossible to achieve. Also, the protocol has seen limited rollout and support in worldwide geographies. While support is growing, fragmented availability is a risk to any IoT deployment. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-19 Bluetooth Bluetooth focuses on point-to-point, short-range communication of a relatively small amount of data. In the IoT space, Bluetooth is commonly used to connect small, battery- powered sensors to IoT gateways or to facilitate communication with a smartphone, eBike, or other smart device. ZigBee Ratified in the early 2000s, ZigBee stands out as a low-cost, low-power, and reliable wireless network technology. The standard is adaptable and supports multiple network topologies, including mesh networks, point-to-multipoint, and point-to-point. ZigBee is most commonly used in home or building automation settings. LoRaWAN Long-range wide area network—also referred to as LoRa—is a long-range, radio-wide networking protocol with low power consumption. Normally, LoRaWAN wirelessly connects multiple battery-operated devices to the Internet within regional, national, or global networks. In the IoT field, LoRaWAN plays an important role in bidirectional communication, end- to-end security, localization, and mobility services. IoT data protocols: What are they and what do you need to know? AMQP Known for its reliability and interoperability, Advanced Message Queuing Protocol is an open messaging standard. This protocol utilizes queues of data, enabling connected systems to communicate asynchronously and better handle issues like traffic spikes and poor network conditions. Additional AMQP features include durable and persistent queues, federation and high- availability queues, clustering, and flexible routing. However, AMQP is known to be a verbose protocol in some circumstances. Comparison: Compared with MQTT (discussed next), AMQP is more reliable and secure. MQTT Message Queue Telemetry Transport is a lightweight pub/sub messaging protocol suitable for connecting small, low-power devices. This data protocol was designed specifically for IoT communication and requires minimal memory and processing power. On the wire, MQTT's bidirectional pub/sub architecture makes the protocol flexible and scalable for a wide variety of use cases and IoT system architectures. Additionally, the MQTT protocol is designed with reliability and scalability in mind— security is provided via Transport Layer Security, and persistent sessions allow the protocol to adapt to poor network conditions and reduce connection time overhead. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-20 HTTP You might recognize this acronym as appearing at the beginning of every website address you type, as Hypertext Transfer Protocol is the foundation of data communication for the World Wide Web. However, within the context of IoT applications, HTTP has many drawbacks. For instance, this protocol establishes a synchronous connection between two devices in order to transfer data—which presents a number of challenges for IoT deployments because devices and endpoints may not be online at the same time and connections may be unreliable due to network conditions. Additionally, HTTP relies on transferring data in ASCII, which is an inefficient way to transmit the small bits of data often exchanged by IoT systems and requires more processing power to encode and decode messages at both ends. Ultimately, while HTTP is a great choice for transferring website data, it is generally not a good choice for an IoT application. CoAP Constrained Application Protocol is used with constrained nodes and networks. This protocol is suited for IoT applications as it reduces the size of network packages, thereby decreasing network bandwidth overload. Other benefits of CoAP include improving the IoT life cycle, saving battery power and storage space, and reducing the amount of data required to operate. DDS Released in 2004, Data Distribution Service is a middleware architecture for real-time systems that focus on data communication between the nodes of a publication- or subscription-based messaging architecture. DDS is mainly used under circumstances that require real-time data exchange—for example, autonomous vehicles, power generation, and robotics. LwM2M Lightweight Machine-to-Machine protocol is designed for remote management of M2M devices and related services. LwM2M reduces costs associated with low-power module deployment and equipping devices with faster IoT solutions. Learn more about M2M vs IoT. ------------------------------------------------------------------------------------------------------------------------------- Internet of Things (Unit-II) Developed by Prof. Ratna Biswas Page-21