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Dangana, M. Suitability of NB-IoT. Encyclopedia. Available online: (accessed on 13 July 2024).
Dangana M. Suitability of NB-IoT. Encyclopedia. Available at: Accessed July 13, 2024.
Dangana, Muhammad. "Suitability of NB-IoT" Encyclopedia, (accessed July 13, 2024).
Dangana, M. (2021, August 18). Suitability of NB-IoT. In Encyclopedia.
Dangana, Muhammad. "Suitability of NB-IoT." Encyclopedia. Web. 18 August, 2021.
Suitability of NB-IoT

Narrow-Band Internet of Things (NB-IoT) shares among the challenges faced by Internet of Things (IoT) and its applications in industrial settings are set to bring in the fourth industrial revolution. The industrial environment consisting of high profile manufacturing plants and a variety of equipment is inherently characterized by high reflectiveness, causing significant multi-path components that affect the propagation of wireless communications—a challenge among others that needs to be resolved. The suitability of NB-IoT for industrial applications is therewith explained.

Narrow-Band IoT Internet of Things Industrial IoT wireless communication latency self-organizing network (SON) edge computing long term evolution (LTE)

1. Introduction

The development of ad hoc Wireless Sensor Networks (WSN) contributes to the paradigm of the Internet of Things (IoT). Many wireless technologies have emerged with this paradigm; among these are the Low Power Wide Area Network (LPWAN) such as Narrow-Band IoT (NB-IoT), Long Term Evolution for Machines (LTE-M), Long Range Wide Area Network (LoRaWAN), SigFox, etc. Since IoT involves the connectivity of massive objects to the internet for information or data gathering, monitoring, and control, a highly reliable platform is required. As such, the focus has been on the requirements that will establish this reliable connectivity. Wireless technologies are therefore geared towards meeting these requirements among which include: low power consumption, to enable longer duration of connectivity and energy savings; coverage range, for extended object reachability; reliability, for error-free data transmission and network availability; security, for data confidentiality, integrity, and availability; latency, for real-time data delivery.
The standardization of NB-IoT technology by the Third Generation Partnership Project (3GPP) in 2016 [1] has empowered NB-IoT with features that have enabled it to be suitable for wireless data communication. The expanded Discontinuous Reception (eDRx) and Power Saving Mode (PSM) techniques allow NB-IoT to implement its low power consumption scheme. For a more extended coverage range, the technology employs the re-transmission and low-frequency modulation mechanisms. For a low latency sensitivity level, 3GPP has prescribed a tolerable latency level of 10s. The superimposition of NB-IoT on Long Term Evolution (LTE) provides the support and reliability needed for its network availability.
These 3GPP standardization features have increased the presence of NB-IoT modules in the forecast of the IoT device market share by 2030 [2]. Many wireless vendors have entered into the business of IoT manufacturing. Since NB-IoT is integrated into the LTE infrastructure, this has resulted in its application in numerous industrial sectors, such as agriculture, transportation, automobile factories, logistics and manufacturing, while increasing the data transmission over LTE infrastructure. However, the structural settings of these industries differ and are comparatively unique. The uniqueness of these industries is characterized by heavy machinery and in some cases with metals that are highly reflective to wireless signals. These forms of settings present a harsh condition for wireless communications [3].
The deployment of NB-IoT in indoor industrial environments is faced with these harsh channel conditions, and as such, a more appropriate wireless propagation model is needed to describe the wireless system of NB-IoT communication in an indoor industrial environment. Attempts have been made to characterize the wireless propagation of narrow-band in industrial settings [3][4]. Reference [3] performed physical measurements of wood and metal processing factories for large-scale and temporal fading at frequencies of 900, 2400, and 5200 MHz. The authors identified and classified the features of the industrial environments that affect the large-scale and temporal fading. Of interest is the presentation made by [4] on its comparative analysis of indoor industrial environment propagation models with a generic representation of path-loss for 900 MHz.
This paper presents a survey on NB-IoT, and as a future direction, some research areas are discussed in a bid to bring forth an appropriate representation of the NB-IoT network. This is to aid in designing suitable wireless communication systems that can be used in developing a reliable scheme for the measurement of an acceptable threshold for NB-IoT wireless performance in industries, measuring the tolerable latency sensitivity level as described in 3GPP, and analyzing the effect of the propagation model on power consumption rates in NB-IoT.
Figure 1. A structural description of the paper layout.
Table 1. Highlights of some research work.
Category Discussion Ref Year
NB-IoT Survey the features of NB-IoT and its application in industries [5] 2019
Features of some LPWAN are compared for industrial applications [6] 2019
Overview of NB-IoT [1][7] 2017
Evaluates the coverage performance of NB-IoT [8] 2016
Presents NB-IoT architecture through the perspective of 3GPP [9] 2017
Explanations on the automation of Industries with Narrow-Band Internet of Things Technology [10] 2020
Did a comparative analysis between two LPWAN viz; NB-IoT and LoRa on Power Wireless Private Network (PWPN) [11] 2018
Studied the performance of NB-IoT and its latency-energy levels. [12] 2020
Evaluates the Power Saving Mode (PSM) and Expanded Discontinues Reception (eDRx) schemes of NB-IoT and a developed energy model [13] 2018
Surveyed the energy efficiency of NB-IoT, its applications and challenges [14] 2019
Highlights the applications and implementations of NB-IoT [15] 2017
NB-IoT, SigFox, Lora and GPRS coverage range were compared in an area of 7800 km2
  [16] 2017
A study of the technical representation of NB-IoT from physical to its media access control layers [17] 2019
Proposed an uplink adaptation scheme for uplink scheduling in NB-IoT [18] 2017
Studied the performance of NB-IoT and eMTC in the implementation of smart cities [19] 2018
IIoT Analytically proposes IIoT architecture [20] 2018
Presents the opportunities in IIoT, its features, and challenges [21] 2018
Review on IIoT [22][23] 2018
Optimized QoS through wireless transmission in IIoT [24] 2020
Demonstration of IIoT by the inclusion of gateway [25] 2019
Uses machine learning for IIoT [26] 2020
IoT Identify the evolution of radio frequency identification in IoT technology [27] 2008
Survey the impact of IoT on technology, social, and businesses [28] 2018
Survey the architecture and features of IoT [29][30][31][32][33][34] 2018, 2017, 2016, 2020, 2019, 2019
WSN WSN and its types surveyed [35] 2018
Survey WSN in Agriculture [36] 2016
Discuss the application of WSN in the monitoring of the environment [37] 2012
Presents the requirements and challenges in the application of WSN in oil and gas industries [38] 2018
Proposes the use of WSN and IoT in oil and gas industries [39] 2017
Described application of WSN in industrial automation [40] 2008
The standardization process in industrial WSN for industrial automation [41] 2010

Research Methodology

Among the contributions of this paper are, firstly, to review the state-of-the-art research in the field of NB-IoT technology and its applications in industrial environments. To review other IoT technologies (WSN, IoT, and IIoT) upon which NB-IoT is established. This is aimed at understanding the relationships and impact of these technologies with NB-IoT, especially in terms of technical features, applications, and challenges in industrial environments. Lastly, to highlight the challenges and future directions of wireless communication of NB-IoT in industries. This paper reviewed research carried out in recent years in the field of Industrial Internet of Things (IIoT) application with some notes on earlier contributions. The process of this review involved a thorough checking of the significant contributions made by journals, articles, conference papers, and report papers. The scholarly databases were systematically queried to extract important information from the literature identified. A total of 368 papers were pulled out of the databases, and 116 papers were selected and comprehensively read, analyzed, and categorized based on their areas of review. Figure 2 depicts the paper extraction procedures. The first process screened out or excluded papers that were in duplicates, non-English written, and had text wrongfully categorized concerning the area of review interest. The review process began with the inclusion of papers on the basis of titles, abstracts, keywords, and conclusions. A total number of 116 papers were finally selected by article type (survey or review, theoretical, practical, etc.). Data were also extracted at this stage to include both theoretical and experimental contents. The third process of the review focused on the challenges that are related to the communication characteristics as supported by the network layers.
Figure 2. Data extraction procedure.

2. Technical Background of NB-IoT

The evolution of the Internet of Things (IoT) has occurred in recent years, with a forecast on IoT device connectivity running into billions in 2021 [2]. The standardization and careful research into technology has also led to meaningful progress. This section, therefore, briefly describes some of the features of WSN, IoT, Industrial IoT (IIoT), and NB-IoT, as well as their technical challenges.

2.1. The Wireless Sensor Networks (WSN) Technology

WSN is a set of sensing nodes that are arranged in a manner that allows them to function together to monitor or read some physical quantity (such as temperature, humidity, pressure, etc.) and communicate to a central point or device wirelessly. The full structure of WSN comprises the sensors (nodes), the communication technique, and the nature of their inter-connectivity. This implies that WSN has the capabilities of sensing, processing, storing, and communicating with its data destination through the wireless medium.

2.1.1. Structure of a Typical Sensor Node

To understand the WSN technology, a study of a typical sensor node is important. Figure 3 shows the four major units of a node: power, sensing, processing, and transceiver units. The power unit provides the energy required by other units in the node. This power source in most cases is provided by a battery. The sensing unit monitors the physical quantities that are presented in analog form or signals. With the aid of an Analog-to-Digital converter (ADC), the analog signal is converted to a digital signal. Both the sensor and ADC form the sensing unit. The digital signal is then processed by a micro-controller or processor. The processed data are stored temporarily in the storage section of the processing unit. The transceiver unit enables the node to connect with other nodes or the gateway in the network [42].
Figure 3. A detailed representation of the four units of an IoT node.

2.1.2. WSN Network Architecture

The network architecture of WSN is considered to be of two types: structured and unstructured [43]. A structured WSN network design provides for a well-mannered approach to its planning and distribution of the nodes. With this type of deployment, the network maintenance rate is minimal, which leads to a low management expenditure as well. While in an unstructured network, the nodes’ distribution and placements are scattered without proper planning. This type of design features dense and random deployment of nodes. This will lead to uncovered areas of deployment in the field, which may in-turn need monitoring. Since the nodes are not carefully placed, node failure detection and connectivity management become difficult to handle. Sensor nodes are of different types, which are designed or described based on the type of physical quantity they monitor. As shown in Figure 4, sensors such as humidity, temperature, flow, vibration, power sensors, etc., are connected wirelessly to a base station that is capable of aggregating data gathered from the sensors to the network server. The user computer connects with the server to access the data and further process the data, which will be presented in a format that is more understandable by the decision making team. The wireless media used by these nodes enables a multi-hop communication. These communication links could be provided by radio or infrared technologies. Where a radio link is used, WSN often uses the unlicensed frequency band known as the Industrial, Scientific, and Medical (ISM) band [42]. These frequency bands are specified by the International Table of Frequency Allocations, and they also provide huge spectrum allocation and are unregulated but highly prone to interference.
Figure 4. A WSN network architecture showing data sources, aggregation, storage, and control points.

2.1.3. WSN Topology and Types

The description of WSN topology is associated with the expected capabilities of sensor nodes, which enable the deployment of several numbers of nodes in a sensor field [44]. While four major types of topologies can be adapted for field deployment, such as, star, tree, point-to-point, and mesh topologies [36], well-planned handling of these arrangements are necessary for maintenance purpose [42]. Without a proper topology arrangement, maintenance-related issues become difficult to handle. These issues usually arise in deployment phases: pre-deployment and deployment phase, post-deployment phase, and redeployment of more nodes phase [42]. In some cases, the types of WSN are classified based on the field of application, such as health, industry, military, agriculture, etc. [45]. However, WSN types can be identified as terrestrial, underground, underwater, mobile, and multimedia wireless sensor networks [46].

2.1.4. Challenges Facing WSN

Although WSN technology has had performance improvements over the years [47], with many research questions getting resolved, other challenges are still present. Some of these challenges arise from node structure, while others are posed by the environment in which they operate in. Reference [48] highlighted some of these design challenges encountered by WSN. These include the operational environments, where WSN are deployed in areas that affect its operation, such as the industries, underwater, underground, etc.; the power consumed by nodes to process its data, transmit it, or receive any data; the scalability of these nodes, since they are deployed in large numbers for a specific application; provision of good quality of service; fault tolerance in the network; limitation in computational and storage capabilities; security of data in terms of confidentiality, integrity, and availability.

2.2. The Internet of Things (IoT) and Industrial Internet of Things (IIoT) Technologies

The advancement in IoT has lessened direct human intervention with objects that are capable of sharing information among themselves and the environment. Designed with sensing and actuating capabilities, these objects have become smart, considering their ability to communicate and autonomously react to real-time or physical events through a unifying infrastructure [49][50]. The interactions of sensors with Radio Frequency Identification (RFID) and the introduction of Internet Protocol (IP), which enables the identification of nodes and their location as well as easy routing on the Internet [51], have transformed WSNs into IoT. Figure 5 shows the forecasted increase in the IoT devices by 2030, however, this is an estimate as signifies by the asterisk. As the number of IoT devices grows tremendously to about 50 billion worldwide [2], the IPv4 identification process will be exhausted, and IPv6 becomes useful for supporting this increase in the IoT paradigm.
Figure 5. Forecast of the number of IoT-connected devices by 2030 [2].
Industrial IoT is simply the introduction or application of IoT in industries, especially in manufacturing [20][50]. IoT connectivity is mostly consumer-grade and is used for building automation, human wearables, and messaging, etc., while in IIoT, the connectivity is secured and targeted at the automation of industrial processes such as in defense, aerospace industries, etc. [50]. Table 2 further shows the comparisons between IoT and IIoT based on their respective characteristics [21].
Table 2. Comparisons of IoT and IIoT characteristics.
Types Data Volume Connectivity Exchange of Information Market Segment Criticality Impact
IoT Big data Consumer grade, e.g., smart homes, entertainment, etc. Business to consumers, business to business to consumers Service providers, consumers, limited enterprises, and small businesses Not stringent (Excluding medical applications) Revolution  
IIoT Specific and limited data Secured, e.g., health care, energy, etc. Business to business only Enterprises and Industries Critical for timing, reliability, privacy, and security Evolution

2.2.1. IoT the Enabling Technology for IIoT

Cloud computing, Machine-to-Machine (M2M), Artificial Intelligent (AI), and Blockchain are among the large group of technologies that enables effective and reliable IIoT [52], with IoT forming the backbone. Figure 6 shows the structure of a typical IoT technology. The things here are regular objects, such as a VR headset, watch, electric cooker, parking lot or garage, security cameras, shopping cart, etc., that are made smart because of the sensor nodes embedded in them. This sensing ability enables these things to interact through a common infrastructure, the Internet, with a management center for decision making. The interaction is possible through the various wireless communication technologies available. For the protection of data, a firewall is designed to secure the information exchange between the objects and the center. Communication device manufacturers have provided solutions to protect this data. The network layer provides the platform on which different communication technologies can function. These include long-range licensed and unlicensed wireless bands. The licensed wireless technology, such as 2G/3G and LTE involves, the use of existing telecommunication infrastructures, while the unlicensed wireless (LoRa, ZigBee, etc.) is an ad hoc setup with Internet capability (through an Internet modem or a gateway). The smart objects, set up in this fashion, have various applications such as in homes, city environment, traffic, etc. Their management is also done through any internet-empowered device such as smartphones, laptops, tablets, etc. Figure 7 represents the application of IoT in the industrial environment, the IIoT. In industrial applications, the sensor nodes are embedded in the industrial equipment for monitoring, operations, and management. The communication technologies remain the same; however, the processes that are managed are of critical nature. Applications include oil and gas industries, chemical, automobile, food processing industries, etc. While IoT could be less stringent in terms of criticality, IIoT is critical with timing, reliability, privacy, and security of data being gathered in operation or monitoring.
Figure 6. A typical structure of consumer-based IoT showing basic layers.
Figure 7. A typical structure of Industrial IoT (IIoT) showing basic layers.

2.2.2. The IoT/IIoT Communication Technologies

IoT and IIoT are thought to have three or five architectural functionality layers [53][54]. Meanwhile, in [55], for a Man Like Nervous (MLN) system, other layers are involved. Three-layered IoT/IIoT architecture include perception, network, and application layers. While in five-layered architecture, in addition to the three layers are processing and business layers. However, in any type of architecture proposed or adopted, the network or transport [53] layer is responsible for the communication of devices among themselves and with the network infrastructure (e.g., gateway, server, etc.). IIoT allows the industries to meet their operational targets, among other things. However, most of the peculiar needs of the industries are centered on the capabilities of communication technologies as supported by the network layer. These designs need to include latency—time required for data propagation and processing, topology–device connection style, throughput—the volume of data that the network can support, scalability—number of supported devices in the network, the security of data and energy—IoT device operational time without power supply [56]. Table 3 lists the common types of communication technologies used by IoT/IIoT and the network-supported features.
Table 3. Common IoT/IIoT wireless communication features.
Type Latency Bandwidth Data Rate Coverage Range Energy Security Scalability Ref.
NB-IoT 1–10 s 200 KHz 200 kbps 1 km/10 km (Urban/Rural) 10 years battery life Yes 52,000 [5][6][9][57][58][59]
LoRaWAN 600 ms 250 KHz and 125 KHz 50 kbps 5 km/20 km (Urban/Rural) >10 years battery life Yes Approx. 104 nodes/BS [5][6][58][59][60]
SigFox 1–20 s 100 Hz 100 bps 10 km/40 km (Urban/Rural) >10 years battery life Yes Approx. 106 nodes/BS [5][6][58][59][60]
Bluetooth 200–500 ms 1 MHz 3 Mbps 10 m 72 microwatts Yes 8 [58][59][61][62][63][64]
Wifi 20–250 ms 22 MHz 11 Mbps–10 Gbps 100 m 0.2 watt Yes 2007 [58][59][61][62][63][64]
ZigBee 60–150 ms 0.3/0.6 MHz, 2 MHz 250 kbps 10–100 m 90 microwatts Yes 65,000 [58][59][61][62][63][65]
The network or transport layer handles the coverage range of IoT/IIoT. Each type of communication technology has an approximate range it can cover. Therefore, depending on the requirements needed for an application, appropriate technology can be employed. However, for IIoT where timing is critical, latency is an important feature that needs to be put into consideration to achieve reasonable reliability of deployment. To this end, a true picture or understanding of the industrial wireless communication for the application of IoT is necessary.

2.2.3. IIoT Challenges

Some of the challenges faced by Industrial IoT are the requirements needed for their deployment in various settings. Although different fields of application have their requirements [21], most of these requirements are expected or provided by the network layer of the IoT architecture. For example, in [66], the scalability, throughput, latency, energy consumption, topology, and security of data were explained. More so, the interoperability of devices to support the heterogeneous nature of IoT is presented as a future requirement for IoT in [67]. Standardization, architecture, and the need for an increase in storage are some other challenges that IIoT faces [68]. This research paper presents the challenges that are related to the network layer.
Scalability: The capacity provided by the global information infrastructure to connect a large number of common objects describes the scalability. This challenge can present itself on different levels [69]. The addressing and identification of devices pose a challenge; however, this issue is mitigated by the use of IPv6 and 6LoWPAN protocols [70]. Due to the large size of these connected objects, a high volume of data communication (big data) and networking capabilities are issues to be tackled. The application layer also contributes to the challenge posed by scalability as a result of the numerous services and service execution options that are generated by heterogeneous connected IoT devices. Therefore, one of the challenges in scalability is the ease of adding new devices to the network without major interruption.
Latency: Since the industrial application of IoT is time-critical and usually deployed in environments that are noisy and hard on the wireless communications, the Quality of Service (QoS) provided by IIoT is often characterized by the real-time achievement of the deadline set by an end-to-end communication task, from sensing to control execution in the system [71]. In industries, various communication traffic is generated, and depending on the type, the latency and reliability provided by IIoT can either be least critical or most critical. These types of traffic are explained in [72]. Table 4 presents a summary of the latency and reliability levels needed for the communication traffic.
Table 4. Latency and reliability summary for industrial communication traffic.
Industrial traffic (data) that requires fractions of seconds in latency in IIoT communications includes the emergency, critical control, and remote control traffics. This traffic is used for safety purposes, such as the leakage of radiation or poisonous gases in industries [72], continual flow of industrial processes such as automation processes, which require 1 ms latency rate [73], and control of unmanned vehicles, which require 50 ms latency and 1–108
reliability [74].
Energy consumption: The primary energy supply to IoT is batteries. The replacement of these batteries becomes difficult when they are fully drained. However, energy harvesting techniques are becoming promising in resolving this challenge. Ideas and research are already moving toward techniques where solar panels or piezoelectric material will be used to relieve IoT devices from the shackles of battery operations [69]. Meanwhile, efforts have been made to optimize the network protocols in the wireless communication systems. These protocols are optimized to help in reducing the amount of energy consumed by these devices. Examples are the implementation of idle time of the devices when transmission or reception of data is not taking place.
Security: Data generated by IIoT are important assets to the industry management, and while lack of proper security of these data is yet to be provided, this provides the reason why some industries are yet to fully deploy IoT. Although some traditional security mechanisms are in use, these are not sufficient enough to protect the complexity of the IIoT systems. These mechanisms include secure protocols in [75], privacy assurance in [76], and lightweight cryptography, as described in [77].

2.3. The Standardization and Technology of NB-IoT

NB-IoT is one of the licensed frequency band communication technologies that were standardized by the 3GPP [1]. This technology is fused into LTE infrastructure as it is also an LPWAN-based technology. Among its category of LPWAN but utilizing the unlicensed frequency bands are LoRaWAN, Ingenu, SigFox, etc. NB-IoT is characterized by low-cost deployment and long-range coverage [17]. The use of the already existing cellular infrastructure by NB-IoT makes it a good candidate for the deployment of IoT as it provides a standardized common platform for the connectivity of objects. Among the advantages of NB-IoT include support for an effective cellular communication network, wide radius coverage with bidirectional triggering between data and signaling planes, low power consumption rate, and capacity to support the massive connections of devices [15][78].

2.3.1. Standardization of NB-IoT

NB-IoT has passed through a series of standardization by 3GPP. Figure 8 describes a brief history of NB-IoT standardization from inception to its current freezing state. The diagram is a unique representation of the standardization history as it captures the various stages at a single glance. The standardization started in 2005 when 3GPP Release 8 (R8) was specified to cater for massive device connections, billing of usage, addressing, machine type communication mode, and security issues. All the standardization versions have a start date and end date with overlapping of some specific technological fields of concern. Examples are the versions R12 and R13 with various standardization numbers and specific areas of concern. In 2015, Version R13 was released with features that further reduced the energy consumption rate of NB-IoT through the implementation of eDRX and PSM mechanisms [15].
Figure 8. A unique representation of the NB-IoT standardization process.
Figure 9 shows the major technology companies involved in the standardization process [79]. These companies include Vodafone and Huawei; they paired together to work on NB-IoT M2M communication features and made their submission in May 2014; Qualcomm presented NB-IoT Orthogonal Frequency-Division Multiplexing (OFDM) in August 2014. The works of the above formed the NB Cloud IoT (NB CIoT); a year later, Ericsson presented the LTE features of NB. These works were fused by rapporteurs in September 2015 to form the NB-IoT 3GPP Version R13 work item with a standardization number of 45.820. In this version, the following technological features were addressed: reduced latency, improved indoor coverage, ability to be compatible with existing infrastructure (LTE), support for a low data and low cost terminal.
Figure 9. Participating companies in NB-IoT standardization.

2.3.2. NB-IoT and Its Features

Some of the main features of NB-IoT are enhanced coverage range, low energy consumption rate meaning long battery life, capacity to connect a massive number of devices per cell, increased reliability, low cost of the terminal and various deployment modes. These features are briefly explained below.
Coverage and Latency: NB-IoT gives better coverage as compared to the legacy LTE with 20dB performance. This allows it to deliver to areas that are difficult to reach, such as basements, making it suitable for use in underground car parks. NB-IoT can achieve this coverage either by in-band, stand-alone, or guard band deployment modes. The 20 dB coverage enhancement is supported by retransmission and low-frequency modulation mechanisms. With the two retransmission tones (3.7 and 15 kHz) available for use, NB-IoT can retransmit using up to 128 bits for uplink and 2048 bits for downlink. The latency budget of NB-IoT is 10 s, but a lower latency of 6 s can be achieved for maximal coupling losses as simulated and specified in TR45.820 [1][17].
The transmission tone feature, which enhances the coverage capability of NB-IoT is presented uniquely in a block diagram in Figure 10. The diagram presents the operational bandwidths of NB-IoT; 200 kHz for stand-alone deployment and 180 kHz for both in-bound and guard-band deployments. For the uplink transmission, NB-IoT uses a Single Carrier Frequency-Division Multiplexing Access (SC-FDMA) modulation scheme. With this modulation scheme, it operates at 3.75 or 15 kHz sub-carrier intervals. These single-tone carriers are at 32 and 8 ms, respectively. The transmission rates used are from 160 to 200 kb/s to achieve a large coverage range that is powered by its high spectral density. The downlink transmission, however, uses an OFDMA modulation scheme with only a 15 kHz sub-carrier interval. It also utilizes multi-tone carriers of 3, 6, and 12 ms that support 160 to 250 kb/s transmission rates. The coverage enhancement of NB-IoT to achieve a 20dB was evaluated in [80], and it was shown that this is true as compared to the legacy LTE.
Figure 10. Block diagram of transmission tone mechanism.
Low energy consumption: The standardization process of the NB-IoT introduced the PSM and eDRX mechanisms for low energy consumption. In the PSM, the device remains registered to the network of transmission but usually enters a deep sleep mode and completely switches off most of its circuitry; at this stage, the device is not reachable from the network. Meanwhile, it could wake up at any time to transmit data when necessary. The eDRX is a temporary idle state that does not listen to the radio channel but periodically becomes active to receive paging messages from the network for possible incoming data before switching to PSM. This periodic activeness is guided by a specified timer, and the paging system is the process of monitoring the control channel for downlink or uplink data indication. Without any activity on the control channel and the expiration of the specified timer, the device switches from the idle state to the PSM state. These two basic features allow for the energy saving of NB-IoT, which surpasses the legacy LTE. Figure 11 shows the idle and paging states [13]. Timer T3324 is the time required to enter into deep sleep mode.
Figure 11. The PSM and eDRX modes.
NB-IoT Deployment modes: NB-IoT is stipulated by RP-151621 to be deployed in FDD transmission [15] and has three types of deployment modes. These include the stand-alone, in-band, and guard-band modes. Figure 12 presents these modes. The stand-alone is an independent type of deployment that utilizes a bandwidth of 200 kHz and does not overlap with the LTE frequency band. The guard-band is a deployment type that utilizes the guard frequency of the LTE or the edge band. It has a 180 kHz bandwidth allocation. Lastly, the in-band deployment is also assigned a bandwidth of 180 kHz and utilizes one of the LTE frequency bands.
Figure 12. NB-IoT modes of deployment.

2.3.3. NB-IoT Comparison with Other IoT Technologies

In Table 5 [1][7][16][18], the comparison between NB-IoT, other licensed, and unlicensed band IoT is presented. The IoT operating in the licensed bands include eMTC, while LoRaWAN, SigFox, Ingenu, etc., are for the unlicensed band.
Table 5. Comparison between NB-IoT frequency band and other bands.
Parameters eMTC NB-IoT LoRa SigFox
Spectral Bandwidth 1.4 MHz 180 KHz 7.8–500 KHz 200 KHz
Spectral Frequency Band (MHz) 700–900 700–900 868 868
Cellular Cellular ISM ISM
Licensed Licensed Unlicensed Unlicensed
Spectral Efficiency High High Very Low Very Low
Data Rate <1 Mbps <50 kbps <10 kbps 100 bps
Coverage Area <10 km <15 km <10 km <12 km
Terminal Cost ($-2020) 3.3 2–3 2.64 2.64
Power Efficiency Medium-High Medium-High Very High High
Interference Immunity Medium Medium Very High Medium
Standard 3GPP Release 14 3 GPP Release 13 LoRaWAN2 ETSI
Comparisons between NB-IoT and other LPWAN have been carried out to evaluate some features. These features include the cost of deployment, coverage range, interference immunity, power, and spectral efficiencies [6][7][16][81][82]. However, the results obtained showed that these technologies have some distinctive features that are suitable for specific purposes or applications. In some cases, trade-offs are required by the user for their deployments. An example is the deployment of NB-IoT in industries. Such decisions would leverage the existing infrastructure, security, and coverage of the LTE. Meanwhile, a decision that chooses LoRa, would require additional network infrastructure for deployment.

2.3.4. NB-IoT Applications in Industries

The application of NB-IIoT spans many of the industrial sectors with varying degrees of deployments [20]. This usually depends on the criticality and level of confidence in NB-IoT technology. However, its application has been forecast to increase in the foreseeable future especially with the increase in the manufacturing of NB-IoT modules by various vendors [2]. NB-IoT is deployed for different purposes in industries, as shown in Figure 13. In the oil and gas industries, NB-IoT deployment includes refining, distribution, and monitoring of products. Applications are also found in food processing, agriculture, and water industries.
Figure 13. Areas of NB-IoT applications in industries.


  1. Chen, M.; Miao, Y.; Hao, Y.; Hwang, K. Narrow Band Internet of Things. IEEE Access 2017, 5, 20557–20577.
  2. Number of Internet of Things (IoT) Connected Devices Worldwide in 2018, 2025 and 2030 (In Billions). 2019. Available online: (accessed on 10 August 2020).
  3. Tanghe, E.; Joseph, W.; Verloock, L.; Martens, L.; Capoen, H.; Herwegen, K.V.; Vantomme, W. The industrial indoor channel: Large-scale and temporal fading at 900, 2400, and 5200 MHz. IEEE Trans. Wirel. Commun. 2008, 7, 2740–2751.
  4. Wang, W.; Capitaneanu, S.L.; Marinca, D.; Lohan, E.S. Comparative Analysis of Channel Models for Industrial IoT Wireless Communication. IEEE Access 2019, 7, 91627–91640.
  5. Al-Rubaye, S.; Rodriguez, J.; Fragonara, L.Z.; Theron, P.; Tsourdos, A. Unleash Narrowband Technologies for Industrial Internet of Things Services. IEEE Netw. 2019, 33, 16–22.
  6. Mekki, K.; Bajic, E.; Chaxel, F.; Meyer, F. A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express 2019, 5, 1–7.
  7. Sinha, R.S.; Wei, Y.; Hwang, S.H. A survey on LPWA technology: LoRa and NB-IoT. ICT Express 2017, 3, 14–21.
  8. Adhikary, A.; Lin, X.; Wang, Y.E. Performance Evaluation of NB-IoT Coverage. In Proceedings of the 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, QC, Canada, 18–21 September 2016; pp. 1–5.
  9. Zayas, A.D.; Merino, P. The 3GPP NB-IoT system architecture for the Internet of Things. In Proceedings of the 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, France, 21–25 May 2017.
  10. Imran, M.A.; Hussain, S.; Abbasi, Q.H. Narrowband Internet of Things (NB-IoT) for Industrial Automation. In Wireless Automation as an Enabler for the Next Industrial Revolution; Wiley: Hoboken, NJ, USA, 2020; pp. 65–87.
  11. Bao, L.; Wei, L.; Jiang, C.; Miao, W.; Guo, B.; Li, W.; Cheng, X.; Liu, R.; Zou, J. Coverage Analysis on NB-IoT and LoRa in Power Wireless Private Network. Procedia Comput. Sci. 2018, 131, 1032–1038.
  12. Azari, A.; Stefanović, D.; Popovski, P.; Cavdar, C. On the Latency-Energy Performance of NB-IoT Systems in Providing Wide-Area IoT Connectivity. IEEE Trans. Green Commun. Netw. 2020, 4, 57–68.
  13. Sultania, A.K.; Zand, P.; Blondia, C.; Famaey, J. Energy Modeling and Evaluation of NB-IoT with PSM and eDRX. In Proceedings of the 2018 IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, United Arab Emirates, 9–13 December 2018; pp. 1–7.
  14. Popli, S.; Jha, R.K.; Jain, S. A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges. IEEE Access 2019, 7, 16739–16776.
  15. Chen, J.; Hu, K.; Wang, Q.; Sun, Y.; Shi, Z.; He, S. Narrowband Internet of Things: Implementations and Applications. IEEE Internet Things J. 2017, 4, 2309–2314.
  16. Lauridsen, M.; Nguyen, H.; Vejlgaard, B.; Kovacs, I.Z.; Mogensen, P.; Sorensen, M. Coverage Comparison of GPRS, NB-IoT, LoRa, and SigFox in a 7800 km2 Area. In Proceedings of the 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, Australia, 4–7 June 2017; pp. 1–5.
  17. Mwakwata, C.B.; Malik, H.; Alam, M.M.; Moullec, Y.L.; Parand, S.; Mumtaz, S. Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives. Sensors 2019, 19, 2613.
  18. Yu, C.; Yu, L.; Wu, Y.; He, Y.; Lu, Q. Uplink scheduling and link adaptation for narrowband Internet of Things systems. IEEE Access 2017, 5, 1724–1734.
  19. Soussi, M.E.; Zand, P.; Pasveer, F.; Dolmans, G. Evaluating the Performance of eMTC and NB-IoT for Smart City Applications. In Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 20–24 May 2018; pp. 1–7.
  20. Boyes, H.; Hallaq, B.; Cunningham, J.; Watson, T. The industrial internet of things (IIoT): An analysis framework. Comput. Ind. 2018, 101, 1–12.
  21. Sisinni, E.; Saifullah, A.; Han, S.; Jennehag, U.; Gidlund, M. Industrial internet of things: Challenges, opportunities, and directions. IEEE Trans. Ind. Inform. 2018, 14, 4724–4734.
  22. Reddy, B.R.; Sujith, A. A comprehensive literature review on data analytics in IIoT (Industrial Internet of Things). HELIX 2018, 8, 2757–2764.
  23. Liao, Y.; Loures, E.d.F.R.; Deschamps, F. Industrial Internet of Things: A systematic literature review and insights. IEEE Internet Things J. 2018, 5, 4515–4525.
  24. Huang, X. Quality of service optimization in wireless transmission of industrial Internet of Things for intelligent manufacturing. Int. J. Adv. Manuf. Technol. 2019, 107, 1007–1016.
  25. Ferreira, I.V.; Bigheti, J.A.; Godoy, E.P. Development of a wireless gateway for industrial internet of things applications. IEEE Lat. Am. Trans. 2019, 17, 1637–1644.
  26. Khan, A.I.; Al-Badi, A. Open Source Machine Learning Frameworks for Industrial Internet of Things. Procedia Comput. Sci. 2020, 170, 571–577.
  27. Van Kranenburg, R. A Critique of Ambient Technology and the All-Seeing Network of RFID; The Netherlands Institute of Network Culture: Amsterdam, The Netherlands, 2008.
  28. Routh, K.; Pal, T. A survey on technological, business and societal aspects of Internet of Things by Q3, 2017. In Proceedings of the 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU), Bhimtal, India, 23–24 February 2018; pp. 1–4.
  29. Kaur, K. A Survey on internet of things—Architecture, applications, and future trends. In Proceedings of the 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India, 15–17 December 2018; pp. 581–583.
  30. Paul, P.V.; Saraswathi, R. The Internet of Things—A comprehensive survey. In Proceedings of the 2017 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC), Melmaruvathur, India, 22–23 March 2017; pp. 421–426.
  31. Solapure, S.S.; Kenchannavar, H. Internet of Things: A survey related to various recent architectures and platforms available. In Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 21–24 September 2016; pp. 2296–2301.
  32. Pico-Valencia, P.; Holgado-Terriza, J.A.; Quiñónez-Ku, X. A Brief Survey of the Main Internet-Based Approaches. An Outlook from the Internet of Things Perspective. In Proceedings of the 2020 3rd International Conference on Information and Computer Technologies (ICICT), San Jose, CA, USA, 9–12 March 2020; pp. 536–542.
  33. Sabry, S.S.; Qarabash, N.A.; Obaid, H.S. The Road to the Internet of Things: A Survey. In Proceedings of the 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON), Jaipur, India, 13–15 March 2019; pp. 290–296.
  34. Varshini, R.; Karthikeyan, A. Internet of Things–Evolution, Architecture and Real Time Application-Survey. In Proceedings of the 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), Vellore, India, 30–31 March 2019; pp. 1–4.
  35. Singh, M.K.; Amin, S.I.; Imam, S.A.; Sachan, V.K.; Choudhary, A. A Survey of Wireless Sensor Network and its types. In Proceedings of the 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), Greater Noida, India, 12–13 October 2018; pp. 326–330.
  36. Deepika, G.; Rajapirian, P. Wireless sensor network in precision agriculture: A survey. In Proceedings of the 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), Solan, India, 20–22 December 2016; pp. 1–4.
  37. Othman, M.F.; Shazali, K. Wireless sensor network applications: A study in environment monitoring system. Procedia Eng. 2012, 41, 1204–1210.
  38. Aalsalem, M.Y.; Khan, W.Z.; Gharibi, W.; Khan, M.K.; Arshad, Q. Wireless Sensor Networks in oil and gas industry: Recent advances, taxonomy, requirements, and open challenges. J. Netw. Comput. Appl. 2018, 113, 87–97.
  39. Aalsalem, M.Y.; Khan, W.Z.; Gharibi, W.; Armi, N. An intelligent oil and gas well monitoring system based on Internet of Things. In Proceedings of the 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), Jakarta, Indonesia, 23–24 October 2017; pp. 124–127.
  40. Khakpour, K.; Shenassa, M. Industrial control using wireless sensor networks. In Proceedings of the 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, Syria, 7–11 April 2008; pp. 1–5.
  41. Zheng, L. Industrial wireless sensor networks and standardizations: The trend of wireless sensor networks for process autometion. In Proceedings of the SICE Annual Conference 2010, Taipei, Taiwan, 18–21 August 2010; pp. 1187–1190.
  42. Akyildiz, I.F.; Su, W.; Sankarasubramaniam, Y.; Cayirci, E. A survey on sensor networks. IEEE Commun. Mag. 2002, 40, 102–114.
  43. Sachan, V.K.; Imam, S.A.; Beg, M. Energy-efficient communication methods in wireless sensor networks: A critical review. Int. J. Comput. Appl. 2012, 39, 35–48.
  44. Intanagonwiwat, C.; Govindan, R.; Estrin, D. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking, Boston, MA, USA, 1 August 2000; pp. 56–67.
  45. Singh, A.; Sharma, T. A survey on area coverage in wireless sensor networks. In Proceedings of the 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, India, 10–11 July 2014; pp. 829–836.
  46. Swetha, R.; Amarnath, S.; Sofia, A. Wireless Sensor Network: A Survey. IJARCCE Int. J. Adv. Res. Comput. Commun. Eng. 2018, 7, 11.
  47. Gungor, V.C.; Lu, B.; Hancke, G.P. Opportunities and challenges of wireless sensor networks in smart grid. IEEE Trans. Ind. Electron. 2010, 57, 3557–3564.
  48. Swati, A.J.; Priyanka, R. Wireless sensor network (WSN): Architectural design issues and challenges. Int. J. Comput. Sci. Eng. 2010, 2, 3089–3094.
  49. Schoder, D. Introduction to the Internet of Things. In Introduction to the Internet of Things. Internet of Things A to Z: Technologies and Applications; Wiley: Hoboken, NJ, USA, 2018; pp. 3–50.
  50. Mumtaz, S.; Alsohaily, A.; Pang, Z.; Rayes, A.; Tsang, K.F.; Rodriguez, J. Massive Internet of Things for industrial applications: Addressing wireless IIoT connectivity challenges and ecosystem fragmentation. IEEE Ind. Electron. Mag. 2017, 11, 28–33.
  51. Chaouchi, H. The Internet of Things: Connecting Objects to the Web; John Wiley & Sons: Hoboken, NJ, USA, 2013.
  52. Khan, W.; Rehman, M.; Zangoti, H.; Afzal, M.; Armi, N.; Salah, K. Industrial internet of things: Recent advances, enabling technologies and open challenges. Comput. Electr. Eng. 2020, 81, 106522.
  53. Jamali, J.; Bahrami, B.; Heidari, A.; Allahverdizadeh, P.; Norouzi, F. Towards the Internet of Things; Springer International Publishing AG: Cham, Switzerland, 2020.
  54. Ngu, A.H.; Gutierrez, M.; Metsis, V.; Nepal, S.; Sheng, Q.Z. IoT middleware: A survey on issues and enabling technologies. IEEE Internet Things J. 2016, 4, 1–20.
  55. Ning, H.; Wang, Z. Future internet of things architecture: Like mankind neural system or social organization framework? IEEE Commun. Lett. 2011, 15, 461–463.
  56. Flügel, C.; Gehrmann, V. Scientific workshop 4: Intelligent objects for the internet of things: Internet of things–application of sensor networks in logistics. In Constructing Ambient Intelligence (AmI 2008). Communications in Computer and Information Science; Gerhäuser, H., Hupp, J., Efstratiou, C., Heppner, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2008; Volume 32.
  57. Finnegan, J.; Brown, S. A comparative survey of LPWA networking. arXiv 2018, arXiv:1802.04222.
  58. Akpakwu, G.A.; Silva, B.J.; Hancke, G.P.; Abu-Mahfouz, A.M. A survey on 5G networks for the Internet of Things: Communication technologies and challenges. IEEE Access 2017, 6, 3619–3647.
  59. Raza, M.; Aslam, N.; Le-Minh, H.; Hussain, S.; Cao, Y.; Khan, N.M. A critical analysis of research potential, challenges, and future directives in industrial wireless sensor networks. IEEE Commun. Surv. Tutor. 2017, 20, 39–95.
  60. Centenaro, M.; Vangelista, L.; Zanella, A.; Zorzi, M. Long-range communications in unlicensed bands: The rising stars in the IoT and smart city scenarios. IEEE Wirel. Commun. 2016, 23, 60–67.
  61. Mecheraoui, C.; Cobb, J.; Swain, I. Evaluation of a wireless in-shoe sensor based on ZigBee used for drop foot stimulation. In Proceedings of the 2012 IEEE Radio and Wireless Symposium, Santa Clara, CA, USA, 15–18 January 2012; pp. 423–426.
  62. Treurniet, J.J.; Sarkar, C.; Prasad, R.V.; De Boer, W. Energy consumption and latency in BLE devices under mutual interference: An experimental study. In Proceedings of the 2015 3rd International Conference on Future Internet of Things and Cloud, Rome, Italy, 24–26 August 2015; pp. 333–340.
  63. Proskochylo, A.; Vorobyov, A.; Zriakhov, M.; Kravchuk, A.; Akulynichev, A.; Lukin, V. Overview of wireless technologies for organizing sensor networks. In Proceedings of the 2015 Second International Scientific-Practical Conference Problems of Infocommunications Science and Technology (PIC S&T), Kharkiv, Ukraine, 13–15 October 2015; pp. 39–41.
  64. Lee, J.; Su, Y.; Shen, C. A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi. In Proceedings of the IECON 2007-33rd Annual Conference of the IEEE Industrial Electronics Society, Taipei, Taiwan, 5–8 November 2007; pp. 46–51.
  65. Mulla, A.Y.; Baviskar, J.J.; Kazi, F.S.; Wagh, S.R. Implementation of ZigBee/802.15.4 in Smart Grid communication and analysis of power consumption: A case study. In Proceedings of the 2014 Annual IEEE India Conference (INDICON), Pune, India, 11–13 December 2014; pp. 1–7.
  66. Xu, L.D.; He, W.; Li, S. Internet of Things in Industries: A Survey. IEEE Trans. Ind. Inform. 2014, 10, 2233–2243.
  67. Lin, J.; Yu, W.; Zhang, N.; Yang, X.; Zhang, H.; Zhao, W. A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet Things J. 2017, 4, 1125–1142.
  68. Goswami, S.A.; Padhya, B.P.; Patel, K.D. Internet of Things: Applications, Challenges and Research Issues. In Proceedings of the 2019 Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 12–14 December 2019; pp. 47–50.
  69. Miorandi, D.; Sicari, S.; De Pellegrini, F.; Chlamtac, I. Internet of things: Vision, applications and research challenges. Ad Hoc Netw. 2012, 10, 1497–1516.
  70. Lee, S.K.; Bae, M.; Kim, H. Future of IoT Networks: A Survey. Appl. Sci. 2017, 7, 1072.
  71. Ferrari, P.; Flammini, A.; Sisinni, E.; Rinaldi, S.; Brandão, D.; Rocha, M.S. Delay Estimation of Industrial IoT Applications Based on Messaging Protocols. IEEE Trans. Instrum. Meas. 2018, 67, 2188–2199.
  72. Celebi, H.B.; Pitarokoilis, A.; Skoglund, M. Wireless Communication for the Industrial IoT. In Industrial IoT: Challenges, Design Principles, Applications, and Security; Butun, I., Ed.; Springer International Publishing: Cham, Switzerland, 2020; pp. 57–94.
  73. Voigtländer, F.; Ramadan, A.; Eichinger, J.; Lenz, C.; Pensky, D.; Knoll, A. 5G for Robotics: Ultra-Low Latency Control of Distributed Robotic Systems. In Proceedings of the 2017 International Symposium on Computer Science and Intelligent Controls (ISCSIC), Budapest, Hungary, 20–22 October 2017; pp. 69–72.
  74. Sasaki, K.; Makido, S.; Nakao, A. Vehicle Control System for Cooperative Driving Coordinated Multi -Layered Edge Servers. In Proceedings of the 2018 IEEE 7th International Conference on Cloud Networking (CloudNet), Tokyo, Japan, 22–24 October 2018; pp. 1–7.
  75. Gubbi, J.; Buyya, R.; Marusic, S.; Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Gener. Comput. Syst. 2013, 29, 1645–1660.
  76. Pöhls, H.C.; Angelakis, V.; Suppan, S.; Fischer, K.; Oikonomou, G.; Tragos, E.Z.; Rodriguez, R.D.; Mouroutis, T. RERUM: Building a reliable IoT upon privacy- and security- enabled smart objects. In Proceedings of the 2014 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Istanbul, Turkey, 6–9 April 2014; pp. 122–127.
  77. Peter, H.C.; Damith, C.R. Rerum: Building a reliable IoT upon privacy-and security-enabled smart objects. In Networked RFID Systems and Lightweight Cryptography; Peter, H.C., Damith, C.R., Eds.; Springer International Publishing: Cham, Switzerland, 2007; pp. 57–94.
  78. Hoymann, C.; Astely, D.; Stattin, M.; Wikstrom, G.; Cheng, J.; Hoglund, A.; Frenne, M.; Blasco, R.; Huschke, J.; Gunnarsson, F. LTE release 14 outlook. IEEE Commun. Mag. 2016, 54, 44–49.
  79. NarrowBand IoT Wide Range of Opportunities. 2020. Available online: (accessed on 17 June 2020).
  80. Andres-Maldonado, P.; Ameigeiras, P.; Prados-Garzon, J.; Navarro-Ortiz, J.; Lopez-Soler, J.M. An Analytical Performance Evaluation Framework for NB-IoT. IEEE Internet Things J. 2019, 6, 7232–7240.
  81. Ballerini, M.; Polonelli, T.; Brunelli, D.; Magno, M.; Benini, L. NB-IoT vs. LoRaWAN: An Experimental Evaluation for Industrial Applications. IEEE Trans. Ind. Inform. 2020, 16, 7802–7811.
  82. Ballerini, M.; Polonelli, T.; Brunelli, D.; Magno, M.; Benini, L. Experimental Evaluation on NB-IoT and LoRaWAN for Industrial and IoT Applications. In Proceedings of the 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland, 22–25 July 2019; Volume 1, pp. 1729–1732.
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