Blog 47: Latest advancement in Internet of Things (IOT)


The Internet of Things (IoT) is a rapidly growing technology that connects physical objects to the internet, allowing them to communicate with each other and with other systems in real time. This technology has the potential to greatly improve efficiency, automate processes, and provide new insights by gathering and analyzing data from a wide variety of sources. IoT devices can include anything from smartphones and laptops to home appliances, vehicles, and industrial equipment, and they can be found in various industries such as healthcare, transportation, manufacturing, agriculture, and smart cities among others. These devices are equipped with sensors and actuators that allow them to collect and transmit data, and communicate with other devices and the cloud. The data collected can be used to make decisions, automate tasks, monitor the performance of the system and improve its overall efficiency. IoT is considered to be one of the most disruptive technologies in recent history and is expected to have a significant impact on our daily lives.

Latest Technological advancement in IOT

The Internet of Things (IoT) technology is constantly evolving and new advancements are being made all the time, here are some examples:

Low-power IoT Devices:

Low-power IoT devices are devices that are designed to consume minimal power while connected to the internet. These devices are typically battery-powered and are designed to last for long periods of time without needing to be recharged or replaced. Some examples of low-power IoT devices include:

  • Smart sensors: These devices are used to measure and transmit environmental data, such as temperature, humidity, and light levels.
  • Wearables: Wearable devices such as smartwatches, fitness trackers, and medical monitoring devices are designed to be small, lightweight and consume minimal power.
  • Smart home devices: These devices, such as smart thermostats, security cameras, and smart plugs, are designed to consume minimal power while connected to the internet.
  • Industrial devices: IoT-enabled industrial equipment and devices, such as sensors and actuators, are designed to operate for long periods of time and consume minimal power.
  • Low-power wireless networks: To support low-power IoT devices, low-power wireless networks such as Zigbee, Z-Wave, and BLE are used for communication between the devices and the gateway or the cloud. These networks are designed to consume minimal power and are ideal for devices that need to transmit small amounts of data over short distances.

These devices are usually designed to operate on batteries and they typically use a low-power wireless protocol to communicate, such as Zigbee, Z-Wave, and BLE. These protocols use less power than traditional Wi-Fi or cellular, which is ideal for devices that are running on battery. Additionally, these devices can also include energy harvesting techniques that allow them to generate energy from the environment and thus reducing the need for batteries replacement.


6LoWPAN (IPv6 over Low power Wireless Personal Area Networks) is a standard that enables internet protocol (IP) communications over low-power wireless personal area networks (WPANs). It is an extension of the IPv6 protocol, which is designed to support a larger number of devices and longer addresses than IPv4, to be used in low-power, low-data rate networks. 6LoWPAN allows the use of IPv6 over IEEE 802.15.4 wireless networks, which are commonly used in low-power and low-data rate devices, such as IoT devices and sensor networks.

6LoWPAN allows for the integration of IPv6-enabled devices into low-power wireless networks and enables the use of the same protocol stack and routing protocols as in traditional IP networks. This allows for devices with different network technologies to communicate with each other, and eliminates the need for different protocols to be used for different types of networks.

6LoWPAN also provides a set of adaptation layers that enable IPv6 to be carried over IEEE 802.15.4 links, which are designed to support low-power and low-data rate communications. The layers include the header compression and fragmentation mechanisms that are needed to support the transmission of IP packets over these links.

This standard is particularly useful in IoT scenarios where devices are required to transmit small amounts of data and use a minimal amount of power. Additionally, it allows for IP-based communication and provides better scalability, security and reduces the complexities of other networking technologies.

IoT Analytics:

IoT analytics refers to the process of collecting, analyzing, and making sense of the large amount of data generated by IoT devices. IoT analytics allows organizations to extract insights, make decisions and improve the performance of their IoT systems, by identifying patterns and trends in the data, and providing real-time visibility into the operations of their devices and systems.

There are several ways in which IoT analytics can be implemented, these include:

  • Real-time analytics: This allows organizations to analyze data in real-time, which can be used to identify issues, make decisions and take actions to improve the performance of the system.
  • Predictive analytics: This uses historical data and machine learning algorithms to predict future events and trends. This can be used to optimize operations, reduce downtime, and improve efficiency.
  • Prescriptive analytics: This is a step further than predictive analytics, it gives actionable insights and recommendations to decision-makers.
  • Big data analytics: With the large amount of data generated by IoT devices, big data analytics can be used to process, store, and analyze the data at scale.
  • Edge analytics: This is the processing of data at the edge of the network, this means near the source, instead of sending all the data to the cloud, this reduces latency and saves bandwidth.

IoT analytics can be used in various industries such as healthcare, manufacturing, transportation, and smart cities among others. It enables organizations to improve their operations, make informed decisions and increase efficiency. Additionally, the use of advanced analytics, such as machine learning, deep learning and AI, has greatly expanded the capabilities of IoT analytics, making it possible to extract more insights, predict more accurate outcomes and make more precise decisions.

Self-Healing IoT Networks:

Self-healing IoT networks refer to the ability of an IoT network to detect and recover from failures or issues without human intervention. The goal of self-healing networks is to minimize downtime and ensure that the network is always operational.

Self-healing IoT networks typically use a combination of software and hardware solutions to monitor the network, detect issues, and take appropriate actions to recover from them. These solutions can include:

  • Network monitoring: This involves continuously monitoring the network for issues, such as high network traffic or poor signal quality.
  • Fault detection: Once an issue has been identified, the network can use algorithms to detect the fault and isolate it from the rest of the network.
  • Automatic recovery: Once the fault has been identified, the network can automatically trigger a recovery process, such as switching to a backup network or rerouting traffic.
  • Predictive maintenance: This uses analytics, Machine Learning and AI to analyze the data collected by IoT devices, identifying patterns and predict when equipment will fail. This allows to take preventative actions before the failure happens.
  • Network configuration management: This allows to ensure the network is configured in a consistent way and to make automatic adjustments to the network configuration in response to changing conditions.
  • Software-defined networking (SDN): This allows the network to be controlled by software instead of physical switches, routers and other devices, this increases the ability to monitor, manage and reconfigure the network.

Self-healing networks are an important aspect of IoT technology, as they ensure that the network is always available, even in the event of failures or issues. This is particularly important in mission-critical applications, such as industrial automation and healthcare, where downtime can have a significant impact on operations and result in significant costs.

Multi-access Edge Computing:

Multi-access Edge Computing (MEC) is a technology that enables the processing of data closer to the edge of the network, rather than in a centralized location such as a data center or the cloud. The idea behind MEC is to move computational resources closer to the point of data generation, reducing the need to transmit large amounts of data to a central location for processing. This reduces latency, improves the responsiveness of applications, and reduces the burden on the network.

MEC architecture typically includes several components:

  • Edge computing devices: These are small, low-powered computing devices that are placed at the edge of the network, near the source of data generation. They are designed to process and analyze data locally, and can be used to host applications and services.
  • Edge gateways: These devices act as a bridge between the edge computing devices and the core network. They are responsible for routing data between the edge devices and the core network, as well as performing security and other functions.
  • Cloud or data center: The cloud or data center is where data is stored and processed. The edge devices send the data that requires further processing or storage to the data center.
  • Network functions virtualization (NFV): this technology allows network functions such as routing, firewall and VPN to be implemented as software, running on commodity hardware. This allows the functions to be moved to the edge devices, reducing the need for specialized devices at the edge.

MEC is particularly useful for applications that require low latency, such as augmented reality, virtual reality, and real-time video streaming. It also enables new use cases for IoT devices by allowing to process data closer to the device. Additionally, it provides a way for organizations to comply with data privacy regulations by keeping data within the geographic boundaries. The integration of MEC with 5G networks enables the creation of private, low-latency network, for specific industries or applications


Edge-AI refers to the integration of artificial intelligence (AI) capabilities into edge devices, allowing them to perform AI computations and decision-making locally, rather than relying on a central server or the cloud. Edge-AI enables edge devices to process and analyze data in real-time, making decisions and taking actions based on the data without the need to transmit it to a central location for processing.

The concept of Edge-AI includes several key elements:

  • Edge devices: These are IoT devices, such as sensors and cameras, that are located at the edge of the network, near the point of data generation. Edge devices are typically small, low-powered devices that are designed to operate in harsh environments and on batteries.
  • AI algorithms: These are the software and models that enable the edge device to perform AI computations, such as image and speech recognition, object detection, and natural language processing.
  • Low-power hardware: Edge-AI devices typically use low-power hardware such as embedded processors, graphics processing units (GPUs) and other specialized chips that are designed to run AI algorithms while consuming minimal power.

Connectivity: Edge-AI devices use various connectivity options such as Wi-Fi, Cellular, Bluetooth, Zigbee, Z-Wave among others to communicate with the cloud and other edge devices

Edge-AI has a wide range of potential applications, such as:

  • Industrial automation: edge-AI devices can be used in industrial plants to monitor equipment, predict failures, and optimize operations.
  • Smart cities: Edge-AI devices can be used in smart cities to monitor traffic, predict accidents, and optimize traffic flow.
  • Healthcare: Edge-AI devices can be used in hospitals and clinics to monitor patients and predict potential health issues.
  • Surveillance and security: Edge-AI devices can be used to analyze video footage in real-time to detect suspicious activities, identify individuals, and alert security personnel.

Edge-AI enables devices to make real-time decisions, reduce the amount of data that needs to be transmitted to the cloud, improves security by avoiding sending sensitive data to the cloud, and allows for low-latency and responsive applications. Additionally, it makes it possible to have intelligence and automation on devices where it would be impractical or impossible to have a connection to the cloud or a data center.

 Industrial IoT (IIoT)

Industrial Internet of Things (IIoT) refers to the integration of IoT technology into industrial systems and processes. It involves connecting a wide variety of devices and equipment, such as sensors, actuators, and industrial control systems, to the internet to enable the collection and sharing of data in real-time. The goal of IIoT is to improve industrial efficiency, productivity, and safety by automating processes, reducing downtime, and providing real-time visibility into the operations of industrial systems.

IIoT typically includes several key components:

  • Sensors and devices: These are used to collect data from industrial systems and equipment, such as temperature, pressure, vibration, and flow.
  • Edge gateways: These devices act as a bridge between the sensors and devices, and the core network. They are responsible for collecting data from the sensors and devices, and forwarding it to the cloud or data center.
  • Cloud or data center: This is where data is stored, analyzed and processed. Cloud platforms provide a centralized location for data processing and analytics, and can be used to host industrial applications and services.
  • Network and communication: IIoT systems often use specialized networking protocols and technologies such as MQTT, LWM2M, and OPC-UA, to support low-power, low-data rate devices.
  • Analytics and Machine learning: This is used to extract insights from the data and make decisions, such as identifying patterns, predicting equipment failures, and optimizing processes.

IIoT has a wide range of potential applications, such as manufacturing, power generation, oil and gas, transportation, agriculture, and many more. Additionally, it allows for intelligent automation, maintenance optimization, and improved safety and security in industrial environments. With the use of data analytics, machine learning and AI, IIoT has the potential to revolutionize industrial processes, making them more efficient, safe, and profitable.

 5G and NB-IoT

5G is the fifth generation of cellular network technology, and is designed to offer faster speeds, lower latency, and greater capacity than previous generations. 5G networks are optimized for a wide range of use cases, including enhanced mobile broadband, massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC).

NB-IoT (Narrowband IoT) is a communication technology that is designed for IoT devices that require low data rates, long battery life and deep penetration in buildings. It uses a narrowband radio frequency, which allows for a large number of devices to be connected to the network, and supports low-power devices.

5G networks include NB-IoT as one of its radio access technologies, this means that NB-IoT devices can connect to the 5G network and leverage its higher bandwidth and low latency. Additionally, 5G networks are designed to support both cellular and non-cellular IoT devices, providing a unified platform for IoT communications.

NB-IoT’s low-power consumption and high penetration capabilities make it a great fit for many IoT use cases, such as smart metering, building automation, and industrial IoT. Furthermore, 5G’s high bandwidth capabilities and low-latency features make it ideal for supporting high-bandwidth IoT use cases such as augmented and virtual reality, autonomous cars and drones.

The combination of 5G and NB-IoT provides a comprehensive solution for a wide range of IoT use cases, enabling the deployment of low-power and low-data rate devices while providing high-bandwidth and low-latency capabilities for high-data rate devices and applications.

Please note that this is not an exhaustive list, the IoT technology is constantly evolving, and new developments are being made all the time.

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