NB-IoT: concepts, applications, and deployment challenges

Narrowband Internet of Things (NB-IoT) is a type of low-power wide-area (LPWA) technology standardized by the 3 rd-Generation Partnership Project (3GPP) and based on long-term evolution (LTE) functionalities. NB-IoT has attracted significant interest from the research community due to its support for massive machine-type communication (mMTC) and various IoT use cases that have stringent specifications in terms of connectivity, energy efficiency, reachability, reliability, and latency. However, as the capacity requirements for different IoT use cases continue to grow, the various functionalities of the LTE evolved packet core (EPC) system may become overladen and inevitably suboptimal. Several research efforts are ongoing to meet these challenges; consequently, we present an overview of these efforts, mainly focusing on the Open System Interconnection (OSI) layer of the NB-IoT framework. We present an optimized architecture of the LTE EPC functionalities, as well as further discussion about the 3GPP NB-IoT standardization and its releases. Furthermore, the possible 5G architectural design for NB-IoT integration, the enabling technologies required for 5G NB-IoT, the 5G NR coexistence with NB-IoT, and the potential architectural deployment schemes of NB-IoT with cellular networks are introduced. In this article, a description of cloud-assisted relay with backscatter communication, a comprehensive review of the technical performance properties and channel communication characteristics from the perspective of the physical (PHY) and medium-access control (MAC) layer of NB-IoT, with a focus on 5G, are presented. The different limitations associated with simulating these systems are also discussed. The enabling market for NB-IoT, the benefits for a few use cases, and possible critical challenges related to their deployment are also included. Finally, present challenges and open research directions on the PHY and MAC properties, as well as the strengths, weaknesses, opportunities, and threats (SWOT) analysis of NB-IoT, are presented to foster the prospective research activities.

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“…-It can offer low-cost energy and power consumption in hardware [44], [146]. ensures free interference reconstruction at the receiver.…”

Section: Gas and Electricity Meteringmentioning

confidence: 99%

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

Hancke

Abu‐Mahfouz

2020

IEEE Access

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“…In order to complete the calibration process in various scenarios, various heterogeneous network nodes, such as NBIoT [ 17 ], LoRa [ 18 ], Bluetooth, Zigbee [ 19 ], etc., may be required. In scenarios that require real-time data transmission, Bluetooth is more suitable for sensor data transmission than LoRa in the lab because of its lowest transmission power consumption and low latency.…”

Section: Overall Functional Architecturementioning

confidence: 99%

A Remote Calibration Device Using Edge Intelligence

Wang

Hao

et al. 2022

Sensors

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Power system facility calibration is a compulsory task that requires in-site operations. In this work, we propose a remote calibration device that incorporates edge intelligence so that the required calibration can be accomplished with little human intervention. Our device entails a wireless serial port module, a Bluetooth module, a video acquisition module, a text recognition module, and a message transmission module. First, the wireless serial port is used to communicate with edge node, the Bluetooth is used to search for nearby Bluetooth devices to obtain their state information and the video is used to monitor the calibration process in the calibration lab. Second, to improve the intelligence, we propose a smart meter reading method in our device that is based on artificial intelligence to obtain information about calibration meters. We use a mini camera to capture images of calibration meters, then we adopt the Efficient and Accurate Scene Text Detector (EAST) to complete text detection, finally we built the Convolutional Recurrent Neural Network (CRNN) to complete the recognition of the meter data. Finally, the message transmission module is used to transmit the recognized data to the database through Extensible Messaging and Presence Protocol (XMPP). Our device solves the problem that some calibration meters cannot return information, thereby improving the remote calibration intelligence.

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“…IoV enables the vehicular road networks to interconnect with different wireless network technologies i.e., Wi-Fi and 4G/LTE for V2I, IEEE WAVE for V2V and V2R, MOST/Wi-Fi for V2S, and CarPlay NCF for V2P. It is useful to provide a comprehensive presentation to ML's concepts in IoV and explain the areas that could contribute to these networks' development [7].…”

Section: Background and Motivationmentioning

confidence: 99%

“…ese challenges are related to making particular decisions and forecasting different aspects of IoV, such as traffic monitoring and management, big data processing, energy and resource management, and intelligent interaction with users to provide high-quality services [6,7]. Several studies have been conducted on using artificial intelligence techniques such as machine learning to develop solutions to most of these challenges [8].…”

Section: Background and Motivationmentioning

confidence: 99%

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Ali

Hasan

Hassan

et al. 2021

Security and Communication Networks

Self Cite

132

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Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

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NB-IoT: concepts, applications, and deployment challenges

Cited by 37 publications

References 16 publications

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

A Remote Calibration Device Using Edge Intelligence

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

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