Dr Kiran Kuchi,
Indian Institute of Technology Hyderabad
5G NB-IoT Technology co-developed by IIT-Hyderabad and WiSig Networks with support from Government of India, Department of Telecommunications & MEITY.
Authors: Dr Kiran Kuchi, Dr Praneeth Varma, Arvind Pai
The Internet of Things (IoT) refers to a network of interconnected physical devices, vehicles, appliances, and other objects embedded with sensors, software, and connectivity capabilities that enable them to collect and exchange data. These devices are typically equipped with unique identifiers and can communicate and interact with each other over the internet without the need for human intervention.
The concept behind the IoT is to create a seamless integration between the physical world and the digital world, allowing objects to be monitored, controlled, and optimized remotely. By connecting everyday objects and enabling them to share information, the IoT has the potential to revolutionize various aspects of our lives, including homes, industries, healthcare, transportation, and cities.
The key components of the IoT ecosystem are:
Devices and Sensors: These are physical objects embedded with sensors, actuators, and connectivity capabilities to collect data and interact with the environment.
Connectivity: IoT devices use various communication technologies such as Wi-Fi, Bluetooth, cellular networks, and low-power, wide-area networks (LPWANs) to connect and exchange data.
Data Processing and Analytics: Collected data from IoT devices is processed, analyzed, and
transformed into meaningful insights using cloud computing platforms, edge computing, and artificial intelligence (AI) algorithms.
Applications and Services: IoT applications and services leverage the data and insights generated by the IoT ecosystem to provide value-added solutions, automation, and improved decision-making.
Figure above depicts different wireless IoT technologies positioned depending upon the two key parameters viz. maximum range of connectivity Vs peak data rate offered.
NB-IoT (Narrowband Internet of Things) is a wireless communication technology specifically designed for connecting a large number of low-power devices and sensors in the Internet of Things (IoT) ecosystem. It is a Low-Power Wide-Area Network (LPWAN) technology that operates in licensed spectrum bands, providing efficient and reliable connectivity for IoT applications
Key features of NB-IoT include:
Low Power Consumption: NB-IoT devices are designed to operate with ultra-low power consumption, allowing them to have long battery life and enabling devices to last for years without frequent recharging or battery replacements. This is particularly beneficial for IoT devices deployed in remote or hard-to-reach areas.
Extended Coverage: NB-IoT offers excellent coverage range, allowing devices to communicate effectively even in challenging environments such as underground areas, basements, or rural regions. It achieves this through its ability to penetrate through walls and other obstacles, ensuring a reliable connection over long distances.
Cost Efficiency: NB-IoT leverages existing cellular infrastructure, which reduces deployment costs and makes it an economical solution for IoT connectivity. It operates in licensed spectrum bands, providing enhanced network security and minimizing interference from other wireless devices.
Scalability: NB-IoT networks can support a massive number of connected devices, enabling IoT deployments at a large scale. It uses narrowband technology, which allows for efficient use of network resources and accommodates a high density of connected devices within a coverage area.
Quality of Service: NB-IoT ensures reliable and robust communication with improved signal quality. It offers excellent signal penetration and coverage in both urban and rural areas, making it suitable for a wide range of IoT applications.
NB-IoT is suitable for various IoT applications, including:
Smart Metering: NB-IoT enables remote monitoring and management of utility meters, such as water, gas, and electricity meters, allowing for efficient and accurate meter reading and reducing the need for manual intervention.
Asset Tracking: NB-IoT can be utilized for tracking and monitoring assets such as containers, vehicles, equipment, and livestock. It enables real-time location monitoring, asset security, and optimization of supply chain management.
Environmental Monitoring: NB-IoT can facilitate environmental monitoring applications, such as air quality monitoring, pollution detection, and weather sensing. It helps in gathering data for better understanding and management of environmental conditions.
Smart Cities: NB-IoT plays a crucial role in various smart city applications, including smart lighting, waste management, parking management, and infrastructure monitoring. It enhances efficiency, reduces costs, and improves the overall quality of urban services.
Agriculture: NB-IoT enables precision farming by providing data on soil moisture, temperature, and other environmental parameters. It allows farmers to optimize irrigation, monitor crop health, and improve agricultural productivity.
5G NB-IoT (Narrowband Internet of Things) refers to the integration of NB-IoT technology with 5G networks. The integration of NB-IoT with 5G is part of the broader strategy to leverage the capabilities of 5G networks to support a wide range of IoT applications and bring following benefits and enhancements to IoT connectivity:
Increased Bandwidth: 5G NB-IoT leverages the higher bandwidth capabilities of 5G networks, allowing for faster and more efficient data transfer. This enables IoT devices to transmit larger amounts of data, facilitating more advanced applications and use cases.
Lower Latency: 5G NB-IoT reduces communication delays, or latency, between IoT devices and the network. This is crucial for time-sensitive applications, such as real-time monitoring and control systems, where immediate responsiveness is required.
Enhanced Capacity: 5G NB-IoT supports a higher number of connected devices within a given coverage area, improving the scalability of IoT deployments. This is especially important as the number of IoT devices continues to grow exponentially.
Improved Reliability: 5G NB-IoT offers improved network reliability, ensuring consistent and uninterrupted connectivity for IoT devices. This is vital for mission-critical applications that require a high level of availability and reliability, such as industrial automation or healthcare monitoring.
Network Slicing: 5G networks support network slicing, which allows dedicated virtual network
segments to be allocated for specific IoT applications. This enables optimized network resources and tailored connectivity, ensuring efficient data transmission and quality of service for diverse IoT use cases.
The latest IoT Analytics “State of IoT—Spring 2023” report shows that the number of global IoT
connections grew by 18% in 2022 to 14.3 billion active IoT endpoints. In 2023, IoT Analytics expects the global number of connected IoT devices to grow another 16%, to 16.7 billion active endpoints.
While 2023 growth is forecasted to be slightly lower than it was in 2022, IoT device connections are expected to continue to grow for many years to come.
Cellular IoT (2G, 3G, 4G, 5G, LTE-M, and NB-IoT) now makes up nearly 20% of global IoT connections. According to the Global Cellular IoT Connectivity Tracker & Forecast (Q1/2023 Update) by IoT Analytics, global cellular IoT connections grew 27% YoY in 2022, strongly surpassing the growth rate for global IoT connections. This growth is due to the adoption of newer technologies such as LTE-M, NB-IoT, LTE-Cat 1, and LTE Cat 1 bis, as older technologies such as 2G and 3G are being phased out. Although 5G module shipments also grew more than 100% YoY in 2022, the growth rate is still slower than many had expected. In 2023, the top five network operators—China Mobile, China Telecom, China Unicom, Vodafone, and AT&T—managed 84% of all global cellular IoT connections. In terms of IoT revenue, the top five network operators make up 64% of the IoT network operator market, with China Mobile, AT&T, Deutsche Telekom (including T-Mobile), China Unicom, and Verizon leading the market.
According to IoT Analytics, satellite IoT connections are expected to grow from six million to 22 million between 2022 to 2027, at a CAGR of 25%. While this growth is expected to have a minor effect on the overall market, the integration of satellite connectivity options into LPWA chipsets by companies like Qualcomm, SONY could accelerate adoption. This integration of satellite connectivity into LPWA chipsets is expected to drive further innovation and growth in the IoT market
LEO satellite connectivity for IoT is gaining popularity because it provides extensive coverage, minimal delays, and strong reliability. The technology is especially useful in the agriculture, maritime, and logistics industries. LEO satellites are closer to Earth than traditional satellites, resulting in reduced latency and faster data transmission, which are essential for real-time data processing. This type of connectivity is more resilient and reliable, ensuring consistent communication, even in challenging environments or during natural disasters. Advancements in LEO-based IoT satellite connectivity continue to optimize performance and enhance the user experience.
IIT Hyderabad & WiSig Networks co-developed a 5G NB-IOT System on Chip (SoC) named Koala with funding from Department of Telecommunications (DOT) and MEITY, Government of India. This SoC is currently under validation and application engineering phase. Chip will go into mass production during 2024.
Target Applications of this Koala SoC include:
- Utilities including Electricity, Gas, Water
- Asset, People, Animal Tracking
- Smart Buildings and Industry 4.0
Sensors or meters will be equipped with this Koala Soc that connects to cell towers offering
- Long range of 5-20 Km cell radius for terrestrial connectivity
- Battery life up to 10 years
- Cost lower than 4G LTE modems
Another unique feature of this SoC is L-Band Satellite connectivity. This functionality enables the sensors and devices equipped with this SoC to be tracked through LEO Satellites.
India Market Size for NB-IOT SoC would be about 1 Billion units in India over 5 Years with key applications like Smart Meters (300 Million) and Animal / Livestock Tracking (200 Million)
Low-end edge computing, powered by a combination of onboard computer and powerful Digital Signal Processing (DSP) processor, offers several advantages in the field of Artificial Intelligence (AI) and Machine Learning (ML).
Real-time Processing: By bringing AI/ML capabilities to the edge, low-end edge computing enables real-time processing of data. The onboard computer, coupled with a powerful DSP processor, allows for quick inference and decision-making directly at the edge devices. This is crucial for time-sensitive applications that require immediate responses, such as autonomous vehicles, industrial automation,
and critical surveillance systems.
Reduced Latency and Bandwidth Requirements: Performing AI/ML computations at the edge reduces the need to send data to the cloud or centralized servers for processing. By processing data locally, low-end edge computing minimizes latency, as there is no reliance on remote servers for analysis. Additionally, it reduces the strain on network bandwidth, as only relevant and actionable insights need to be transmitted, rather than raw data.
Enhanced Privacy and Security: With low-end edge computing, data remains local and is processed within the device itself. This approach minimizes the exposure of sensitive information to external networks or cloud servers, thereby enhancing data privacy and security. It is particularly crucial for applications involving personal data, video surveillance, or industrial trade secrets.
Improved Reliability and Availability: By performing AI/ML tasks at the edge, low-end edge computing ensures uninterrupted functionality even in situations where network connectivity may be limited or unreliable. The onboard computer and powerful DSP processor enable devices to continue processing and making decisions locally, reducing dependence on cloud connectivity. This is beneficial for use cases such as remote locations, harsh environments, or situations where intermittent network availability is expected.
Cost Optimization: Low-end edge computing eliminates the need for constant cloud connectivity and reduces data transmission costs. Processing AI/ML tasks at the edge helps to optimize resource usage and reduces cloud service expenses, especially in scenarios where a large number of edge devices are deployed. It also allows for efficient utilization of available computational resources by distributing the workload between the onboard computer and the DSP processor.
Customization and Flexibility: With low-end edge computing, developers have the flexibility to
customize AI/ML models and algorithms specific to their edge devices. This customization enables
tailoring AI/ML solutions to meet the unique requirements of different applications and industries. Developers can optimize the performance and resource utilization of the onboard computer and DSP processor, leading to more efficient and effective AI/ML implementations.
Offline Operation: Low-end edge computing allows edge devices to operate autonomously, even without an internet connection. This is beneficial in environments with limited or no network connectivity, such as remote locations or areas with intermittent coverage. Applications like edgebased AI/ML-powered drones, edge sensors in remote areas, or smart infrastructure in rural regions can continue to function and make decisions independently without relying on cloud connectivity.
TinyML, short for Tiny Machine Learning, refers to the field of deploying machine learning models on resource-constrained devices with limited computational power, memory, and energy resources. It focuses on running lightweight machine learning algorithms directly on edge devices, such as microcontrollers and Internet of Things (IoT) devices, enabling them to make intelligent decisions locally without relying on cloud connectivity.
The key characteristics of TinyML are:
Low Power Consumption: TinyML models are designed to operate with minimal power consumption, allowing them to run on battery-powered devices for extended periods. Power efficiency is a critical consideration in TinyML applications to ensure optimal device performance and maximize battery life.
Small Model Size: TinyML models are specifically optimized to have a small footprint in terms of memory and storage requirements. These models are designed to fit within the constraints of resource-constrained devices, where limited memory and storage are available.
Efficient Inference: TinyML models are typically optimized for efficient inference, focusing on lightweight algorithms that can execute quickly on low-power devices. The emphasis is on reducing the computational complexity and memory requirements to enable real-time or near real-time inferencing at the edge.
Edge Intelligence: The deployment of machine learning models on edge devices with TinyML enables local decision-making and intelligence. By performing inference directly on the device, TinyML reduces the need for frequent data transmission to the cloud, minimizing latency and preserving data privacy.
Scalability: TinyML models are designed to scale across a large number of deployed devices. The lightweight nature of these models allows for the deployment of machine learning capabilities in a distributed manner, enabling a network of intelligent edge devices.
TinyML has significant implications for a wide range of applications, including:
Smart Sensors and Wearables: TinyML enables smart sensors and wearable devices to perform real time analysis and interpretation of sensor data, enabling applications such as activity monitoring, health tracking, gesture recognition, and environmental sensing.
Industrial IoT: By deploying TinyML models on edge devices in industrial settings, real-time
monitoring, anomaly detection, predictive maintenance, and optimization of industrial processes can be achieved without relying on cloud connectivity.
Smart Home Automation: TinyML facilitates intelligent automation within smart homes, allowing devices to learn and adapt to user preferences and behaviours. This includes applications such as voice recognition, motion detection, energy management, and personalized user experiences.
Agriculture and Environmental Monitoring: TinyML can be utilized for real-time analysis of agricultural data, including soil moisture, weather conditions, crop health, and pest detection. It enables precision farming techniques and improves environmental monitoring in remote areas.
Autonomous Vehicles and Robotics: TinyML can power intelligent decision-making within autonomous vehicles, drones, and robots. It enables local perception, object detection, collision avoidance, and navigation without relying on constant connectivity.
Above figure shows an example application where the DSP, ARM controllers along with their dedicated
embedded code and data memories as well as external flash memory can be used to implement Tiny ML capabilities to build an edge computing device, which can work as an animal tracker.
In summary, IIT Hyderabad and WiSig Networks, enabled by funding support from Government of India, Department of Telecommunications and MEITY are building India’s first indigenously designed 5G NB-IoT SoC and cutting edge applications and use cases, that are expected assist the Indian and global ecosystems, enterprises and society as a whole.
The authors of this blog, the engineers and designers who worked on development of this Koala SoC, the institutions IIT Hyderabad and WiSig Networks, thank DCIS Scheme of Department of Telecommunications, Government of India for their continuous encouragement and support without which this could not have been achieved