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Evolved Packet Core (EPC) architecture with CIoT (Cellular IoT) enhancements for NB-IoT. This is an enhancement of the existing LTE cellular architecture. SCEF: Service Capability Exposure Function; ePDG: Enhanced Packet Data Gateway; PGW: Packet Data Node Gateway; S-GW: Serving Gateway; HSS: Home Subscriber Server; MME: Mobility Management Entity.
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Previous survey articles on Low-Powered Wide-Area Networks (LPWANs) lack a systematic analysis of the design goals of LPWAN and the design decisions adopted by various commercially available and emerging LPWAN technologies, and no study has analysed how their design decisions impact their ability to meet design goals. Assessing a technology’s abili...
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... These devices offer numerous benefits, including the ability to collect data about their surroundings and the people and objects within them, as shown in Fig. 3. This data, with the realization of 6G, can further improve the efficiency of various processes, such as manufacturing, logistics, and healthcare (Popli et al., 2018;Wan et al., 2020a;Chaudhari et al., 2020;Ismail et al., 2018;Buurman et al., 2020). ...
The Cellular Internet of Things (CIoT) has seen significant growth in recent years. With the deployment of 5G, it has become essential to reduce the power consumption of these devices for long-term sustainability. The upcoming 6G cellular network introduces the concept of zero-energy CIoT devices, which do not require batteries or manual charging. This paper focuses on these devices, providing insight into their feasibility and practical implementation. The paper examines how CIoT devices use simultaneous wireless information and power transfer, beamforming, and backscatter communication techniques. It also analyzes the potential use of energy harvesting and power management in zero-energy CIoT devices. Furthermore, the paper explores how low-power transceivers can lower energy usage while maintaining dependable communication functions.
... The applications of these devices can be seen in just a few fields such as environment monitoring and control, smart cities, agriculture, navigation system, fleet management, Medical diagnostics, among others [1][2][3][4][5][6]. These devices can communicate through IoT supportive network such as Long Range Wide Area Network (LoRaWAN), Sigfox, and NarrowBand IoT (NB-IoT) [7]. The LoRaWAN, Sigfox, and NB-IoT are popular Low Power Wide Area Networks (LPWAN) technologies. ...
... The usage of these technologies to estimate the position of IoT end terminals are growing in unprecedented pattern. Reasons being that such network standards are scalable, consumes low power and can be used to locate radio terminal in an environment with poor visibility of the sky [6][7][8]. ...
An attempt to further enhance the accuracy and reliability of M-iWMLR localization algorithm using a new weight matrix that was formulated with two-tier RSS data normalization is presented. The two-tier normal-ization: data clipping and z-score normalization were applied to form a new weight matrix in this work. Data clipping was first applied to reduce significantly the effects of outliers on the RSS data while z-score normalization provides data consistency. The new localization algorithm herein, referred to as Ext.M-iWMLR algorithm is carefully evaluated by the use of location accuracy (location error), root mean square error (RMSE), range of error, and R 2 score metrics. This algorithm is validated with the Modified Improved Weighted Multiple Linear Regression (M-iWMLR). The simulation results generated with MATLAB show that the Ext.M-iWMLR algorithm, at 95 percentile reduced the mean location error by 19.45%. The range of error and RMSE are reduced by 11.08% and 17.95%, respectively. Furthermore, the respective R 2 scores were increased by 5.71% and 17.17% for the latitude and longitude coordinates. It was established that the new weight matrix formulated through two-step normalization enhanced all the considered metrics.
... Sigfox is an open-source technology and the first of its kind. Sigfox was designed to focus on an even longer range and lower data rate compared to LoRaWAN, in which it utilizes ultra-narrow-band (UNB) frequency [43]. It was designed using a variety of low-power IoT devices, including several sensors and M2M applications [13]. ...
... And so, the scalability factor of an IoT environment is very important to keep in mind. The scalability of IoT systems can be considered from two perspectives [43]. One is load scalability, which refers to how scalable the traffic amount is for each device in a system. ...
The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the device that is suitable for use. Thus far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. This paper summarizes the network architectures of some of the most popular IoT wireless communications protocols. It also presents a comparative analysis of some of the critical features, including power consumption, coverage, data rate, security, cost, and quality of service (QoS). This comparative study shows that low-power wide area network (LPWAN)-based IoT protocols (LoRa, Sigfox, NB-IoT, LTE-M) are more suitable for future industrial applications because of their energy efficiency, high coverage, and cost efficiency. In addition, the study also presents an Industrial Internet of Things (IIoT) application perspective on the suitability of LPWAN protocols in a particular scenario and addresses some open issues that need to be researched. Thus, this study can assist in deciding the most suitable IoT communication protocol for an industrial and production field.
... Recently, the reliability of LoRaWAN communication has been studied from different perspectives by researchers. Some have covered the strengths and weaknesses of LoRaWAN communication [6,7], while others have considered the design goals of various use cases and analysed LoRaWAN's suitability [8]. Some of the research work has focused on its optimisation for urban environments [7], while other work has hinted at its reliability for industrial setups in general [9][10][11] and yet other researchers have considered specific industrial setups, such as farming [12] and health [13]. ...
... There are a considerable number of use cases where LoRaWAN communication has proved reliable, as already highlighted in the record of reliability. However, from the literature surveyed, it is clear that it has not yet been deployed deep underground, underwater, in space [8] or even in mining. It is therefore important to consider the reliability of LoRaWAN communication in mining. ...
... In particular, it has been deployed in waste management in North Korea, solar power plant management in the United States of America, power usage monitoring in France, smart meters in Germany, a smart golf course in Canada and smart islands in Spain [6]. However, as observed by Filho et al. [2] and Buurman et al. [8], it has yet to be deployed in mining, deep underground, deep underwater and in space. Also noteworthy is what the investigation by Sundaram et al. [6] brings out, which is that the technology still has challenges in four areas; namely, link coordination, resource allocation, reliable transmission and security. ...
While a robust and reliable communication network for monitoring the mining environment in a timely manner to take care of people, the planet Earth and profits is key, the mining environment is very challenging in terms of achieving reliable wireless transmission. This survey therefore investigates the reliability of LoRaWAN communication in the mining environment, identifying the challenges and design requirements. Bearing in mind that LoRaWAN is an IoT communication technology that has not yet been fully deployed in mining, the survey incorporates an investigation of LoRaWAN and other mining IoT communication technologies to determine their records of reliability, strengths and weaknesses and applications in mining. This aspect of the survey gives insight into the requirements of future mining IoT communication technologies and where LoRaWAN can be deployed in both underground and surface mining. Specific questions that the survey addresses are: (1) What is the record of reliability of LoRaWAN in mining environments? (2) What contributions have been made with regard to LoRa/LoRaWAN communication in general towards improving reliability? (3) What are the challenges and design requirements of LoRaWAN reliability in mining environments? (4) What research opportunities exist for achieving LoRaWAN communication in mining environments? In addition to recommending open research opportunities, the lessons learnt from the survey are also outlined.
... This is relevant if the RSS is reported for the antenna port of the receiver, whereas the signal is processed at the chipset port. The path loss depends on various factors such as the channel frequency, distance between the transmitter and receiver, and random shadow fading variations caused by the environment [20][21][22][23][24][25][26][27][28][29]. In most of the models published to date, the path loss can be modeled as follows: ...
Multiple Gateways (GWs) provide network connectivity to Internet of Things (IoT) sensors in a Wide Area Network (WAN). The End Nodes (ENs) can connect to any GW by discovering and acquiring its periodic beacons. This provides GW diversity, improving coverage area. However, simultaneous periodic beacon transmissions among nearby GWs lead to interference and collisions. In this study, the impact of such intra-network interference is analyzed to determine the maximum number of GWs that can coexist. The paper presents a new collision model that considers the combined effects of the Medium Access Control (MAC) and Physical (PHY) layers. The model takes into account the partial overlap durations and relative power of all colliding events. It also illustrates the relationship between the collisions and the resulting packet loss rates. A performance evaluation is presented using a combination of analytical and simulation methods, with the former validating the simulation results. The system models are developed from experimental data obtained from field measurements. Numerical results are provided with Gaussian Frequency Shift Keying (GFSK) modulation. This paper provides guidance on selecting GFSK modulation parameters for low bit-rate and narrow-bandwidth IoT applications. The analysis and simulation results show that larger beacon intervals and frequency hopping help in reducing beacon loss rates, at the cost of larger beacon acquisition latency. On the flip side, the gateway discovery latency reduces with increasing GW density, thanks to an abundance of beacons.
... This is relevant if the RSS is reported for the antenna port of the receiver, whereas the signal is processed at the chipset port. The path loss depends on various factors such as the channel frequency, distance in between the transmitter and receiver, and random shadow fading variations caused by the environment [15][16][17][18][19][20][21][22][23][24]. In most of the models published to date, the path loss can be modeled as follows: ...
Multiple Gateways (GWs) provide network connectivity to Internet of Things (IoT) sensors in a Wide Area Network (WAN). The End Nodes (ENs) can connect to any GW by discovering and acquiring its periodic beacons. This provides GW diversity and hence improves coverage. If the GWs are within vicinity to each other, and they keep sending periodic beacons to be discovered, the result is interference and collisions. In this study, the impact of such intra-network interference is analyzed to determine the maximum number of GWs that can co-exist within a given urban neighborhood. The paper shows the relationship between beacon collisions and the resulting packet loss rates in a given urban environment. The expected collision rates are calculated for the Medium Access Control (MAC) layer. The eventual packet loss rates are calculated with the MAC and Physical (PHY) layers combined. For this purpose, a new co-channel interference model is presented. The model calculates the average Carrier to Interference ratio (C/I) averaged across all the colliding events. It takes into account the partial overlap durations and the relative power of each colliding signal. The model shows that a collision does not always cause a packet loss. Packet losses occur if there is a collision and if the C/I is below a threshold. The performance evaluation is presented using a combination of analytical and simulation methods. The analytical methods provide the theoretical foundation, and have been used to validate the simulation results. Furthermore, the system models are developed from experimental data obtained from measurements with real hardware. Numerical results are provided with a low bit rate Gaussian Frequency Shift Keying (GFSK) modulation at the PHY layer, which is derived from the IEEE 802.15.4 standard specification. This paper provides guidance on selecting the right GFSK modulation parameters for such low bit-rate and narrow bandwidth IoT applications. The analysis and simulation results show that larger beacon intervals help in reducing beacon loss rates, at the cost of larger beacon acquisition latency. On the flip side, the gateway discovery latency reduces with increasing GW density thanks to abundance of beacons.
... Low-power wide-area networks (LPWANs) [1] are an important part of the Internet of Things, which, according to forecasts, will serve approximately 14% of all Internet connections [2] by 2023. NB-Fi (Narrow Band Fidelity) [3,4] is a rather new and not so well studied LPWAN protocol widely deployed in several countries for environmental monitoring, data collection for smart homes and utilities, urban planning, and infrastructure management. ...
NB-Fi (Narrow Band Fidelity) is a promising protocol for low-power wide-area networks. NB-Fi networks use license-exempt Industrial, Scientific, and Medical (ISM) bands and, thus, NB-Fi devices can work in two modes: with and without Listen Before Talk (LBT). This paper compares these modes with different implementations of LBT in terms of packet loss rate (PLR), delay, energy consumption, and throughput. Interestingly, in some scenarios, the results contradict expectations from the classic papers on channel access because of the peculiarities of the NB-Fi technology. These contradictions are explained in the paper. The results show that LBT can significantly improve all the considered performance indicators when the network load exceeds 40 packets per second. With extensive simulation, we show that in a small NB-Fi network, the optimal PLR, delay, and energy consumption are obtained with the implementation of LBT that corresponds to non-persistent CSMA. In a large NB-Fi network, where some devices can be hidden from others, the best strategy to improve PLR, delay, throughput, and energy consumption is to use the implementation of LBT that corresponds to p-persistent CSMA.
... However, depending upon the user requirement, the adaptive scheme can be adopted. 77 ML schemes can be used in this regard to learn the data pattern to decide the data rate requirement for IoT devices. [78][79][80] ...
The Internet of Things (IoT) applications have grown in exorbitant numbers,
generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e. cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. Deep learning and other machine learning approaches are deployed to many systems related to Internet of Things or IoT. However, it faces challenges that adversaries can take loopholes to hack these systems through tampering history data. Throughout this chapter, the authors shall review on the studies made in the application of machine learning in IoT. The major issues and challenges faced during the application is focused. In the final part of the chapter, the future prospects are discussed.
... LoRaWAN is an efficient communication technology that scales greatly in vast deployments and is able to save end-node energy, as compared with its cellular counter part NB-IoT, for instance [4]. However, it depends on several configuration parameters, such as sleeping mode and data rate (DR), that highly determine the expected performance [5,6]. ...
... Discussing the application of fourth (4G) and fifth (5G) wireless network technologies in a review involving the Industry 4.0 and PLF, Morrone et al. (2022) points out that both network approaches are not widely implemented in certain areas, which makes new PLF projects dependent on the usage of satellite services or implementation of other network types. In order to assess this problem, one proposed approach is the use of LPWAN (Low-Power Wide Area Network) devices, as they associate wireless technology with low-power consumption, at the same time that allows long-range coverage (Buurman et al., 2020). Unlike other wireless network options, such as Wi-Fi, Bluetooth and the mobile "G" options (from 3 to 5), developed to offer high-speed over short distances at the cost of having a higher power consumption (Pasolini, 2022), the LPWAN model is able to cover large areas such as pastures, and thus it is frequently adopted for projects that connect devices designed to generate limited volume of data associated with a low transfer rate over longer distances. ...
Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.
