Search Results (95)

With the growth of Internet of Things (IoT) business demands, NB-IoT integrating low earth orbit (LEO) satellite communication systems is considered a crucial component for achieving global coverage of IoT networks in the future. However, the long propagation delay and significant Doppler frequency shift of the satellite-to-ground link pose substantial challenges to the uplink and downlink synchronization in LEO satellite-based NB-IoT networks. To address this challenge, we first propose a Multiple Segment Auto-correlation (MSA) algorithm to detect the downlink Narrow-band Primary Synchronization Signal (NPSS), specifically tailored for the large Doppler frequency shift of LEO satellites. After detection, downlink synchronization can be realized by determining the arrival time and frequency of the NPSS. Then, to complete the uplink synchronization, we propose a position-based scheme to obtain the Timing Advance (TA) values and pre-compensated Doppler shift value. In this scheme, we formulate a time difference of arrival (TDOA) equation using the arrival times of NPSSs from different satellites or at different times as observations. After solving the TDOA equation using the Chan method, the uplink synchronization is completed by obtaining the TA values and pre-compensated Doppler shift value from the terminal position combined with satellite ephemeris. Finally, the feasibility of the proposed scheme is verified in an Iridium satellite constellation. Compared to conventional GNSS-assisted methods, the approach proposed in this paper reduces terminal power consumption by 15–40%. Moreover, it achieves an uplink synchronization success rate of over 98% under negative SNR conditions. Full article

Despite significant progress in fall detection systems, many of the proposed algorithms remain difficult to implement in real-world applications. A common limitation is the lack of location awareness, especially in outdoor scenarios where accurately determining the fall location is crucial for a timely emergency response. Moreover, the complexity of many existing algorithms poses a challenge for deployment on edge devices, such as wearable systems, which are constrained by limited computational resources and battery life. As a result, these solutions are often impractical for long-term, continuous use in practical settings. To address the aforementioned issues, we developed a portable, wearable device that integrates a microcontroller (MCU), an inertial sensor, and a chip module featuring Global Positioning System (GPS) and Narrowband Internet of Things (NB-IoT) technologies. A low-complexity algorithm based on a finite-state machine was employed to detect fall events, enabling the module to meet the requirements for long-term outdoor use. The proposed algorithm is capable of filtering out eight types of daily activities—running, walking, sitting, ascending stairs, descending stairs, stepping, jumping, and rapid sitting—while detecting four types of falls: forward, backward, left, and right. In case a fall event is detected, the device immediately transmits a fall alert and GPS coordinates to a designated server via NB-IoT. The server then forwards the alert to a specified communication application. Experimental tests demonstrated the system’s effectiveness in outdoor environments. A total of 6750 samples were collected from fifteen test participants, including 6000 daily activity samples and 750 fall events. The system achieved an average sensitivity of 97.9%, an average specificity of 99.9%, and an overall accuracy of 99.7%. The implementation of this system provides enhanced safety assurance for elderly individuals during outdoor activities. Full article

This study investigates the optimization of satellite constellations for Low-Power, Wide-Area Network (LPWAN)-based Internet of Things (IoT) communications in Very Low Earth Orbit (VLEO) at 200 km and 300 km altitudes and Low Earth Orbit (LEO) at 600km using a Genetic Algorithm (GA). Focusing on three LPWAN technologies—LoRa, Narrowband IoT (NB-IoT), and Mioty—we evaluate their performance in terms of revisit time, data transmission volume, and economic efficiency. Results indicate that a 300 km VLEO constellation with LoRa achieves the shortest average revisit time and requires the fewest satellites, offering notable cost benefits. NB-IoT provides the highest data transmission volume. Mioty demonstrates strong scalability but necessitates a larger satellite count. These findings highlight the potential of VLEO satellites, particularly at 300 km, combined with LPWAN solutions for efficient and scalable IoT Non-Terrestrial Network (IoT-NTN) applications. Future work will explore multi-altitude simulations and hybrid LPWAN integration for further optimization. Full article

This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and 5G, where NB-IoT is utilized for low-power, reliable data transmission from environmental sensors, such as soil information and weather monitoring, while 5G supports high-bandwidth, low-latency tasks like task scheduling and path tracking to effectively address the diverse communication requirements of modern complex agricultural scenarios. The cloud platform improves operational efficiency and resource utilization through real-time task scheduling, dynamic optimization, and seamless coordination between devices. To accommodate the diverse operational demands of agricultural environments, the system incorporates a real-time data feedback mechanism leveraging sensor data for path tracking and adjustment, enhancing adaptability and stability. Furthermore, a multi-machine collaborative scheduling strategy combining Dijkstra’s algorithm and an improved Harris hawk optimization (IHHO) algorithm, along with a multi-objective optimized path tracking method, is introduced to further improve scheduling efficiency and resource utilization while improving path tracking accuracy and smoothness and reducing external interferences, including environmental fluctuations and sensor inaccuracies. Experimental results demonstrate that the IoT-based cloud platform excels in data transmission reliability, path tracking accuracy, and resource optimization, validating its feasibility in smart agriculture and providing an efficient and scalable solution for large-scale agricultural operations. Full article

While the market for massive machine type communications (mMTC) is evolving at an unprecedented pace, the standardization bodies, including 3GPP, are lagging behind with standardization of truly 5G-grade cellular Internet-of-Things (CIoT) systems. As an intermediate solution, an early data transmission mechanisms encapsulating the data into the preambles has been recently proposed for 4G/5G Narrowband IoT (NB-IoT) technology. This mechanism is also expected to become a part of future CIoT systems. The aim of this paper is to propose a model for CIoT systems with and without early transmission functionality and assess the optimal distribution of resources at the random access and data transmission phases. To this end, the developed model captures both phases explicitly as well as different traffic composition in downlink and uplink directions. Our numerical results demonstrate that the use of early transmission functionality allows one to drastically decrease the delay of uplink packets by up to 20–40%, even in presence of downlink traffic sharing the same set of resources. However, it also affects the optimal share of resources allocated for random access and data transmission phases. As a result, the optimal performance of 5G mMTC technologies with or without early transmission mode can only be attained if the dynamic resource allocation is implemented. Full article

Soil environmental monitoring is crucial for ensuring the sustainability and productivity of agriculture. This study aims to develop a wireless soil monitoring system that utilizes Narrowband Internet of Things (NB-IoT), solar energy, and Global Positioning System (GPS) technologies to address the issues of high labor demand, high costs, and delayed feedback in traditional soil monitoring methods. This system can collect soil temperature, humidity, and meteorological data in real time, transmit them to a cloud platform for analysis and visualization, and predict future soil data. It employs multiple learning algorithms to build models and uses the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization. Field stability experiments were conducted on the system, and the performance of the soil moisture prediction model was evaluated. During the 84-day stability experiment, the system operated stably for 80 days, with a data collection success rate of 95.87%. In the performance evaluation of the soil moisture model, the GBDT model achieved a coefficient of determination (R²) of 0.9838 on the validation set and a root-mean-square error (RMSE) of 0.0013, with an RMSE of 0.0013 on the test set as well. The experimental results demonstrate that the system is stable and reliable, featuring low power consumption, wide coverage, and high accuracy. It can effectively predict soil moisture, providing timely and accurate support for irrigation and farming decisions. Full article

In this paper, we propose an underlay cognitive radio (CR) system that includes subscribers, termed secondary users (SUs), which are designed to coexist with the spectrum owners, termed primary users (PUs). The suggested network includes the PUs system and the SUs system. The coexistence between them is achieved by using a novel dynamic spectrum co-access multicarrier-based cognitive radio (DSCA-MC-CR) technique. The proposal uses a quadrature phase shift keying (QPSK) modulation technique within the orthogonal frequency-division multiplexing (OFDM) scheme that maximizes the system data rate and prevents data inter-symbol interference (ISI). The proposed CR transmitter station (TX) and the CR receiver node (RX) can use an advanced smart antenna system, i.e., a multiple-input and multiple-output (MIMO) system that provides high immunity against channel impairments and provides a high data rate through its different combining techniques. The proposed CR system is applicable to coexist within different existing communication applications like fifth-generation (5G) applications, emergence applications like the Internet of Things (IoT), narrow-band (NB) applications, and wide-band (WB) applications. The coexistence between the PUs system and the SUs system is based on using power donation from the SUs system to improve the quality of the PU signal-to-interference-and-noise ratios (SINRs). The green communication concept achieved in this proposal is compared with similar DSCA proposals from the literature. The simulations of the proposed technique show enhancement in the PUs system throughput and data rate along with the better performance of the SUs system. Full article

This study proposes a low-cost, automatic, wide-area real-time water pipeline monitoring model based on Narrowband Internet of Things (NB-IoT) technology, aiming to solve the challenges faced in the context of global water pipeline management. This model focuses on real-time monitoring of pipeline operations to reduce water waste and improve management efficiency, directly contributing to the achievement of the sustainable development goals (SDGs). Water resource management faces several significant global challenges, including water scarcity, inefficient resource utilization, and infrastructure degradation. Traditional water pipeline monitoring systems are often manual, time-consuming, and unable to detect leaks or failures in real time, leading to significant water loss and financial costs. In response to these issues, NB-IoT technology offers a promising solution with its advantages of low power consumption, long-range communication, and cost-effectiveness. The development of an NB-IoT-based smart water pipeline monitoring system is therefore essential for enhancing the efficiency and sustainability of water resource management. Through enabling real-time monitoring and data collection, this system can address critical issues in global water management, reducing waste and supporting the sustainable development goals (SDGs). This model utilizes Low-Power Wide-Area Network (LPWAN) technology, combined with an LTE mobile network and ARM Cortex-M4 microcontroller, to achieve long-distance multi-sensor data collection and monitoring. The research results show that NB-IoT technology can effectively improve water resource management efficiency, reduce water waste, and is of great significance for the digital transformation of infrastructure and the development of smart cities. This technical solution not only supports “Goal 6: Clean Drinking Water and Sanitation” in the United Nations’ sustainable development goals (SDGs) but also promotes the realization of low-cost teaching aids related to engineering education-related information and communication technologies (ICTs). This study demonstrates the key role of ICTs in promoting sustainable development and provides a concrete practical example for smart water resource management. Full article

Deploying Cellular Internet of Things (CIoT) devices in urban and remote areas faces significant energy efficiency challenges. This is especially true for Narrowband IoT (NB-IoT) devices, which are expected to function on a single charge for up to 10 years while transmitting small amounts of data daily. The 3rd Generation Partnership Project (3GPP) has introduced energy-saving mechanisms in Releases 13 to 16, including Early Data Transmission (EDT) and Preconfigured Uplink Resources (PURs). These mechanisms extend battery life and reduce latency by enabling data transmission without an active Radio Resource Control (RRC) connection or Random Access Procedure (RAP). This paper examines these mechanisms using the LENA-NB simulator in the ns-3 environment, which is a comprehensive framework for studying various aspects of NB-IoT. The LENA-NB has been extended with PURs, and our analysis shows that PURs significantly enhance battery life and latency efficiency, particularly in high-density environments. Compared to the default RAP method, PURs reduce energy consumption by more than 2.5 times and increases battery life by 1.6 times. Additionally, PURs achieve latency reductions of 2.5–3.5 times. The improvements with PURs are most notable for packets up to 125 bytes. Our findings highlight PURs’ potential to enable more efficient and effective CIoT deployments across various scenarios. This study represents a detailed analysis of latency and energy consumption in a simulated environment, advancing the understanding of PURs’ benefits. Full article

An ultra-low quiescent current output-capacitor-less low dropout (OCL-LDO) regulator for power-sensitive applications is proposed in this paper. To improve the gain of the OCL-LDO feedback loop, the error amplifier employs a combination of a cross-coupled input stage for boosting the equivalent input transconductance and a negative resistance technique to improve the gain. Meanwhile, in order to address the issue of transient response of the ultra-low quiescent current OCL-LDO, a sub-threshold slew-rate enhancement circuit is proposed in this paper, which consists of a transient signal input stage and a slew-rate current increase branch. The proposed OCL-LDO is fabricated in a 0.18 $\mu$m CMOS process with an effective area of 0.049 mm². According to the measurement results, the proposed OCL-LDO has a maximum load current of 100 mA and a minimum quiescent current of 640 nA at an input voltage of 1.2 V and an output voltage of 1 V. The overshoot and undershoot voltages are 197 mV and 201 mV, respectively, and the PSR of the OCL-LDO is −72.4 dB at 1 kHz when the load current is 100 $\mu$A. In addition, the OCL-LDO has a load regulation of 7.6 $\mu$V/mA and a line regulation of 0.87 mV/V. Full article

This paper proposes a novel multi-band textile monopole antenna for patient tracking applications. The designed antenna has compact footprints (0.13λ₀²) and works in the narrow band-internet of things (NB-IoT) 1.8 GHz, radio frequency identification (RFID), and industrial, scientific, and medical (ISM) 2.45 GHz and 5.8 GHz bands. The impedance bandwidths and gain of the antenna at 1.8 GHz, 2.45 GHz, and 5.8 GHz are 310 MHz, 960 MHz, and 1140 MHz; 3.7 dBi, 5.3 dBi, and 9.6 dBi, respectively. Also, the antenna’s behavior is checked on different body parts of the human body in various bending scenarios. As per the evaluated link budget, the designed antenna can easily communicate up to 100 m of distance. The specific absorption rate values of the designed antenna are also within acceptable limits as per the (FCC/ICNIRP) standards at the reported frequency bands. Unlike traditional rigid antennas, the proposed textile antenna is non-intrusive, enhancing user safety and comfort. The denim material makes it comfortable for extended wear, reducing the risk of skin irritation. It can also withstand regular wear and tear, including stretching and bending. The presented denim-based antenna can be seamlessly integrated into clothing and accessories, making it less obtrusive and more aesthetically pleasing. Full article

With the increase in elevator usage, more and more elevator real-time monitoring equipment is being applied to the operation of elevators. Traditional elevator monitoring equipment adopts a multi-sensor decentralized installation and layout, and the monitoring accuracy is low, which directly affects the effective alarm of the monitoring system; however, existing online monitoring systems cannot quickly alarm for faults. Aiming to solve the above problems, an elevator online monitoring system based on narrow-band Internet of Things (NB-IoT) is designed. The system is highly integrated with an STM32 main control chip, a six-axis acceleration gyroscope sensor, and an air pressure sensor to realize the edge calculation of the monitoring system. At the same time, this paper eliminates the temperature drift of the pressure sensor by using a temperature compensation algorithm and inputs the extracted characteristic parameters into the BP neural network for training to eliminate the zero drift so as to obtain the real-time height data of the elevator. The six-axis acceleration gyroscope sensor is used to calculate the posture so as to avoid the problem that a three-axis acceleration sensor or a three-axis gyroscope sensor alone cannot obtain accurate posture data. In order to further improve the monitoring accuracy, the peak-to-peak value of the signal is calculated by using a 95% confidence interval algorithm to reduce the suppression of the high-frequency components of the signal by noise and ensure that the signal has a large signal-to-noise ratio so that the obtained elevator car posture and vibration operation data are more accurate. Finally, the effectiveness of the proposed method is verified by experiments. Full article

With the development of IoT technology and 5G massive machine-type communication, the 3GPP standardization body considered as viable the integration of Narrowband Internet of Things (NB-IoT) in low Earth orbit (LEO) satellite-based architectures. However, the presence of the LEO satellite channel comes up with new challenges for the NB-IoT random access procedures and coverage enhancement mechanism. In this paper, an Adaptive Coverage Enhancement (ACE) method is proposed to meet the requirement of random access parameter configurations for diverse applications. Based on stochastic geometry theory, an expression of random access channel (RACH) success probability is derived for LEO satellite-based NB-IoT networks. On the basis of a power consumption model of the NB-IoT terminal, a multi-objective optimization problem is formulated to trade-off RACH success probability and power consumption. To solve this multi-objective optimization problem, we employ the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) method to obtain the Pareto-front solution set. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraints of RACH success probability requirements. Simulation results show that the maximum number of repetitions and back-off window size have a great influence on the system performance and their value ranges should be set within [4, 18] and [0, 2048]. The power consumption of coverage enhancement with ACE is about 58% lower than that of the 3GPP proposed model. All this research together provides good reference for the scale deployment of NB-IoT in LEO satellite networks. Full article

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks. Full article

With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor’s processing time in key block generations, non-repeating checks, and data block transfers, respectively. Full article