IoT networks

An IoT (Internet of Things) network is composed of interconnected IoT nodes, which can include sensors, actuators, and fog nodes. Each IoT node typically comprises several key components: a power supply system, a processing unit (such as microprocessors, microcontrollers, or specialized hardware like digital signal processors), communication units (including radio, Ethernet, or optical interfaces), and additional electronic elements (e.g., sensors, actuators, and cooling mechanisms). These components work in unison to enable the node to collect, process, and transmit data effectively, supporting various IoT applications.

The architecture of a typical IoT network is structured into four main layers: the perception layer, the fog layer, the Internet core network (transport layer), and the cloud data centre (cite fig.). This multi-layered structure allows for scalability, efficiency, and optimized data processing.

Fig. here

• IoT network Layer: This foundational layer consists of IoT devices, such as sensors and actuators, that are responsible for collecting data from their surrounding environment. These devices can range from simple temperature and humidity sensors in smart homes to complex monitoring systems in industrial settings. Depending on their configuration, these devices may perform preliminary data processing to filter or compress data before transmission. For example, motion sensors in a security system might only transmit data when movement is detected, thereby conserving energy and bandwidth. This layer consists primarily of a network of IoT nodes connected directly to each other or an access point, depending on the network topology chosen for the given IoT network deployment scenario. The IoT nodes are connected directly to each other or an access point via low-power wireless communication technologies.

• Fog computing Layer: The fog computing layer acts as an intermediary between the IoT devices at the IoT network layer and the cloud. It provides localized, lightweight processing capabilities that help reduce latency and bandwidth usage. By processing data closer to the source, the fog layer can handle tasks such as real-time data analysis, decision-making, and local storage. This is particularly useful in applications where immediate responses are crucial, such as in autonomous vehicles, healthcare monitoring, and smart manufacturing systems. The use of fog computing enhances the network’s overall performance and reduces the burden on centralized cloud resources.

• Transport Layer (Internet Core Network): This layer is responsible for the transmission of data between the perception and fog layers and the cloud data centre. It serves as the backbone of IoT communication, leveraging a variety of networking technologies such as wireless networks (e.g., Wi-Fi, LTE, 5G), wired connections (e.g., Ethernet), and even optical networks for high-speed data transfer. The transport layer ensures reliable and secure data flow, using protocols that safeguard data integrity and reduce transmission errors. This layer's efficiency directly impacts the overall responsiveness and performance of the IoT network.

• Cloud Data Center layer: The cloud data centre layer represents the centralized processing hub where advanced data analytics, complex computation, and long-term data storage occur. It can handle vast amounts of data generated by IoT devices across the network. The cloud layer employs powerful data analytics tools, machine learning algorithms, and big data technologies to extract insights and generate actionable outcomes. For instance, data collected from smart grids can be analyzed to optimize energy distribution, while data from medical sensors can support remote patient monitoring and predictive healthcare interventions. The processed information is then sent back to users or devices to facilitate informed decision-making or automated physical responses (control of physical systems).

In an IoT network, the seamless integration of these layers enables efficient data collection, processing, and transmission. This layered approach supports diverse applications, ranging from smart homes equipped with automated climate control and security systems to large-scale industrial automation, smart cities, and agricultural monitoring. The robust structure of IoT networks allows for scalable solutions that can adapt to the needs of various industries, enhancing productivity, efficiency, and quality of life.

IoT network nodes are often connected directly with each other or an access point (which connects them to the internet) using low-power communication technologies (LPCT). These technologies are essential for enabling cost-effective connectivity among energy-constrained electronic devices. These technologies include wireless access technologies used at the physical layer to establish connectivity over physical mediums and communication protocols at the application layer to facilitate communication over IP networks.

Wireless Access Technologies Wireless access technologies are categorized into long-range, short-range, licensed, and unlicensed technologies, with the choice of technology depending on the specific application. For example, LoRaWAN (Low Power Wide Area Network) is preferred for open-field farming due to its long-range capabilities. Examples of short-range wireless access technologies include ZigBee, Bluetooth, Bluetooth Low Energy (BLE), Z-Wave, IEEE 802.15.4, and Near Field Communication (NFC). In contrast, examples of long-range technologies include LoRaWAN, Sigfox, Weightless-P, INGENU RPMA, TELENSA, NB-IoT, and LTE CAT-M.

Unlicensed technologies often prove more cost-effective in the long term compared to licensed technologies offered by cellular network providers. However, IoT operators must build and maintain their infrastructure for unlicensed technologies, which can involve significant initial costs.

Low Power Wide Area Networks (LPWAN) LPWAN technologies are pivotal for the broader adoption of IoT, as they maintain connectivity with battery-operated devices for up to ten years over distances spanning several kilometers. Key advantages of LPWAN technologies include:

• Unordered List ItemReliable wide-area coverage, enabling communication over long distances.
• Ultra-low power communication, ideal for battery-powered devices.
• Low-cost network connectivity, significantly reducing both capital expenditures (CAPEX) and operational expenditures (OPEX) for IoT operators.
• Support for scalable IoT solutions, allowing for the connection of vast numbers of sensors.
• Acceptable Quality of Service (QoS) for many IoT applications.

Well-established LPWAN communication protocols such as LoRaWAN, Sigfox, and NB-IoT are suitable for IoT systems designed to cover wide areas due to their low power consumption and reliable transmission over long distances. These protocols are optimized for transmitting text data; however, certain IoT applications, such as those in agriculture, such as crop and livestock monitoring, may require multimedia data transmission. In such cases, image and sound compression techniques must be applied, balancing the trade-off between data quality and bandwidth requirements.

Application Layer Communication Protocols Application layer communication protocols ensure reliable interaction between IoT devices and data analytics platforms, addressing the limitations of traditional HTTP protocols in constrained networks. The Constrained Application Protocol (CoAP) is a UDP-based request-response protocol standardized by the IETF (RFC 4944 and 6282) for use with resource-constrained devices. CoAP enables lightweight and efficient communication, making it suitable for IoT.

The MQTT protocol follows a publish-subscribe model, with a message broker distributing packets between entities. It uses TCP as the transport layer but also has an MQTT-SN (MQTT for Sensor Networks) specification that operates over UDP. Other notable communication protocols include the Advanced Message Queuing Protocol (AMQP), Lightweight Machine-to-Machine (LWM2M), and UltraLight 2.0, all designed to support efficient and reliable communication within IoT networks.

The Internet of Things (IoT) Gateway serves as a critical connection point that facilitates the interaction between sensors, actuators, and various other IoT devices with the broader Internet. This gateway plays an essential role by enabling communication not only between connected devices and the cloud but also by acting as a bridge for IoT nodes that cannot communicate directly with each other. Such gateways ensure seamless data transmission, device management, and integration into larger IoT networks, supporting both upstream and downstream data flow.

The type of wireless access technology employed influences the specific implementation of an IoT gateway. Different use cases and deployment scenarios may require specific types of gateways to ensure efficient connectivity and data handling. Several widely adopted IoT gateway solutions utilize LoRaWAN, Sigfox, WiFi, and NB-IoT technologies. Each of these protocols brings unique advantages tailored to distinct use cases. For instance, LoRaWAN and Sigfox are well-suited for long-range, low-power communication, which is essential for connecting dispersed agricultural sensors in rural areas. WiFi provides robust, high-speed connectivity for scenarios requiring larger data payloads. At the same time, NB-IoT offers cellular-based connectivity with low power consumption, ideal for areas where cellular infrastructure is present.

Resource-constrained computing devices such as Raspberry Pi, Orange Pi, and NVIDIA Jetson Nano Developer Kit can be utilized to handle networking and computational tasks at the edge. These devices, known for their affordability and energy efficiency, are capable of running lightweight algorithms that manage data preprocessing, real-time decision-making, and local storage. By leveraging these compact yet powerful computing nodes, organizations can implement IoT solutions that are scalable, cost-effective, and adaptable to various operational demands. The use of such technologies not only enhances connectivity but also paves the way for smart IoT solutions.

The concepts of fog computing and “edge” computing are frequently mentioned together and often used interchangeably. While they share a common goal of decentralizing computational resources and bringing them closer to the source of data generation, there are nuanced distinctions between the two. Fog computing, in particular, can be viewed as a broader system that encompasses edge computing within its scope, extending its capabilities across a wider network infrastructure. Both approaches represent an architectural design paradigm that moves computation, communication, control, and data storage closer to the end-users and data sources, enhancing overall system efficiency and responsiveness.

Traditional cloud computing models centralize data processing power in large data centres, which are often located at considerable distances from the IoT (Internet of Things) devices that generate data. While this centralized approach offers significant computational capacity and scalability, it introduces certain limitations, particularly for applications that require low latency and real-time data processing. The inherent latency in cloud computing arises from the physical distance between IoT devices and data centres, as well as potential network congestion. This latency can lead to delays that undermine the performance of critical applications, such as those in industrial automation, autonomous vehicles, healthcare monitoring systems, augmented reality, and smart city management. In these use cases, even slight delays can be detrimental, affecting decision-making processes and overall system effectiveness.

Cisco introduced fog computing to address these shortcomings by extending the cloud’s functionality closer to the data source, effectively forming an intermediary layer between IoT devices and centralized cloud data centres. This layer, often referred to as the “fog layer,” provides localized computing, storage, and networking capabilities, enabling data to be processed at or near the point of generation. By leveraging fog nodes, which can be routers, gateways, or other network devices with processing capabilities, fog computing supports data preprocessing, filtering, and real-time analysis before sending only relevant or summarized information to the cloud for further storage and processing. This approach reduces the amount of raw data transmitted over the network, thus minimizing bandwidth usage and enhancing overall system efficiency.

Edge computing, on the other hand, refers more specifically to processing that takes place directly on the devices at the network’s edge or very close to the data source. Edge devices, such as sensors, cameras, and IoT-enabled machinery, are equipped with sufficient processing power to handle basic data analysis and decision-making without the need to communicate with distant servers. This direct processing enables faster response times and reduces the dependency on continuous connectivity to a central cloud infrastructure.

Both fog and edge computing offer significant advantages over traditional cloud models by addressing latency and bandwidth limitations. They allow data to be processed, stored, and acted upon closer to where it is generated, which is particularly beneficial in scenarios involving massive data production and real-time decision-making. For instance, in an industrial setting with automated machinery, real-time data analysis can help identify and mitigate potential equipment failures before they escalate into major issues. In the realm of autonomous vehicles, local processing facilitated by edge computing ensures rapid response to dynamic road conditions and safety hazards, enhancing vehicle control and passenger safety.

Moreover, healthcare monitoring systems that rely on continuous data streams from patient devices, such as heart rate monitors and wearable sensors, benefit from the reduced latency and improved reliability offered by fog and edge computing. These technologies ensure that critical health data is analyzed promptly, enabling timely alerts and interventions that could be life-saving.

Smart cities represent another domain where the combination of fog and edge computing can play a transformative role. The vast array of sensors and IoT devices deployed for traffic management, energy distribution, public safety, and environmental monitoring produce an overwhelming amount of data. Processing this data locally through edge and fog nodes helps manage resources efficiently, reduce congestion, and respond to incidents in real-time.

The proximity enabled by fog and edge computing not only reduces latency but also enhances the security and privacy of data. Since data can be processed locally without needing to traverse long distances to central servers, there is a reduced risk of interception and unauthorized access. This local processing can comply better with data protection regulations that require sensitive data to remain within certain geographical boundaries.

Overall, fog and edge computing contribute to a more robust, adaptable, and scalable system architecture. They facilitate real-time analytics and empower IoT applications across multiple industries by delivering the responsiveness and efficiency needed in today’s data-driven world. By complementing traditional cloud services and addressing their inherent limitations, these technologies are poised to play an increasingly pivotal role in the future of distributed computing.