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LoRa (long range) is a communication technology that employs chirp spread spectrum modulation. Among various low-power wide area network (LPWAN) technologies, LoRa offers unique advantages, including low power consumption, long transmission distance, strong anti-interference capability, and high network capacity. Addressing the issue of power-constrained end devices in IoT application scenarios, this paper proposes an adaptive data rate (ADR) algorithm for LoRa networks designed for power-constrained end devices (EDs). The algorithm evaluates the uplink communication link state between the EDs and the gateway (GW) by using a combined weighting method to comprehensively assess the signal-to-noise ratio (SNR), received signal strength indication (RSSI), and packet reception rate (PRR), and calculates a list of transmission power and data rates that ensure stable and reliable communication between the EDs and the GW. By using ED power consumption models, network throughput models, and ED latency models to evaluate network performance, the Zebra optimization algorithm is employed to find the optimal data rate for each ED under power-constrained conditions while maximizing network performance. Test results show that, in a single ED scenario, the average PRR achieved by the proposed ADR algorithm for power-constrained EDs in LoRa networks is 14% higher than that of the standard LoRaWAN ADR algorithm. In a multi-ED link scenario (50 end devices), the proposed method reduces the average power consumption of EDs by 10% compared to LoRaWAN ADR, achieves a network throughput of 6683 bps, and an average latency of 2.10 s, demonstrating superior performance overall. The proposed method shows unique advantages in LoRa networks with power-constrained EDs and a large number of EDs, as it not only reduces the average power consumption of the EDs but also optimizes network throughput and average latency. [ABSTRACT FROM AUTHOR]

The management of book preservation, back volumes, and other publications within a library predominantly falls under the responsibility of library staff. The application of IoT technology in this context lacks a systematic approach to monitoring and controlling hygrothermal factors. Presently, IoT utilization is confined to a passive mode of monitoring, recording, and logging. This concise study aims to strategize and recommend the optimal utilization of IoT platforms, transitioning from a passive observational mode to a more proactive and autonomous operational mode. A qualitative methodology was employed to devise and execute the integration of IoT technology, focusing on communication through the LoRa platform and data sharing via the Wi-Fi network. Upon thorough examination of the integrated IoT platform, opportunities for implementation and enhancement become apparent. Through meticulous analysis and execution of the planned IoT platform with a scope of improvement by IIoT and battery-free sensors, a viable solution is identified, and a comprehensive evaluation of its attributes is performed. [ABSTRACT FROM AUTHOR]

Low-power wide area network (LPWAN) technologies as part of IoT are gaining a lot of attention as they provide affordable communication over large areas. LoRa and Sigfox as part of LPWAN have emerged as highly effective and promising non-3GPP unlicensed band IoT technologies while challenging the supremacy of cellular technologies for machine-to-machine-(M2M)-based use cases. This paper presents the design goals of LoRa and Sigfox while throwing light on their suitability in congested environments. A practical traffic generator of both LoRa and Sigfox is introduced and further interpolated for understanding simultaneous operation of 100 to 10,000 such nodes in close vicinity while establishing deep understanding on effects of collision, re-transmissions, and link behaviour. Previous work in this field have overlooked simultaneous deployment, collision issues, effects of re-transmission, and propagation profile while arriving at a number of successful receptions. This work uses packet error rate (PER) and delivery ratio, which are correct metrics to calculate successful transmissions. The obtained results show that a maximum of 100 LoRa and 200 Sigfox nodes can be deployed in a fixed transmission use case over an area of up to 1 km. As part of the future scope, solutions have been suggested to increase the effectiveness of LoRa and Sigfox networks. [ABSTRACT FROM AUTHOR]

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms—Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applying image-based tampered frequency techniques with these algorithms, offering a new perspective on securing LoRa transmissions. We generated a dataset of over 26,000 images derived from real-world experiments with both normal and manipulated frequency signals by splitting video recordings of LoRa transmissions into frames to thoroughly assess the performance of each algorithm. Our results demonstrate that Local Outlier Factor achieved the highest accuracy of 97.78%, followed by Variational Autoencoder, traditional Autoencoder and Principal Component Analysis at 97.27%, and Isolation Forest at 84.49%. These findings highlight the effectiveness of these methods in detecting tampered frequencies, underscoring their potential for enhancing the reliability and security of LoRa networks. [ABSTRACT FROM AUTHOR]

In recent years, LoRa technology has emerged as a solution for wide-area coverage IoT applications. Deploying a LoRa single-hop network on applications may be challenging in cases of network deployments that require the installation of linear sensor network topologies covering very large distances over unpopulated areas with limited access to cellular networks and energy grids. In such cases, multi-hop communication may provide better alternative solutions to support these challenges. This research aims to study the deployment of multi-hop linear sensor networks that are energy efficient. The focus will be on assessing the coverage, throughput, and energy consumption benefits that can be achieved and the related tradeoffs that have to be considered when using multi-hop solutions. Since monitoring systems in long-distance infrastructures may benefit from solutions based on multi-hop communication, we consider oil pipeline infrastructures in the Saudi Arabian desert as a case study. An analytical model is considered for estimating the above-stated parameters and evaluating the performance of the multi-hop LoRa WSN (MHWSN) against the single-hop LoRa WSN (SHWSN). In addition, the model is used to study the tradeoffs between throughput and energy consumption in different settings of MHWSNs. Scenarios of oil pipeline monitoring systems in Saudi Arabia are specified for studying the proposed multi-hop system's performance. The obtained results show that when we have a large-scale network, such as an oil pipeline with medium traffic load requirements, multi-hop topologies may be an efficient deployment solution. [ABSTRACT FROM AUTHOR]

This paper reviews the potential of low-power wireless networks to improve road safety. The authors characterized this type of network and its application in road transport. They also presented the available technologies, highlighting one that was considered the most promising for transport applications. The study includes an innovative and proprietary concept of area-based vehicle speed monitoring using this technology and describes its potential for enhancing road safety. Assumptions and a model for the deployment of network equipment within the planned implementation area were developed. Using radio coverage planning software, the authors conducted a series of simulations to assess the radio coverage of the proposed solution. The results were used to evaluate the feasibility of deployment and to select system operating parameters. It was also noted that the proposed solution could be applied to traffic monitoring. The main objective of this paper is to present a new solution for improving road safety and to assess its feasibility for practical implementation. To achieve this, the authors conducted and presented the results of a series of simulations using radio coverage planning software. The key contribution of this research is the authors′ proposal to implement simultaneous vehicle speed control across the entire monitored area, rather than limiting it to specific, designated points. The simulation results, primarily related to the deployment and selection of operating parameters for wireless sensor network devices, as well as the type and height of antenna placement, suggest that the practical implementation of the proposed solution is feasible. This approach has the potential to significantly improve road safety and alter drivers′ perceptions of speed control. Additionally, the positive outcomes of the research could serve as a foundation for changing the selection of speed control sites, focusing on areas with the highest road safety risk at any given time. [ABSTRACT FROM AUTHOR]

The population increases at an exponential rate as human society advances, and pollution is increasingly depleting the availability of resources such as water and land. All these problems are thought to require the use of smart agriculture. By reducing use of chemical fertilizers and pesticides, smart agriculture could mitigate land pollution and increase the sustainability of agricultural practices while also greatly enhancing the agro-ecological environment, yield, and quality of crops. The steps to make agriculture smart are made possible through data and communication technology, which helps with automatic operation and cultivation. Moreover, advances in wireless communication protocols will bring agriculture to a more intelligent stage. This study provides an overview of IoT technology and its application in the smart agriculture industry to make crop production automatic and intelligent by assessing their architecture (IoT devices, communication technologies, and processing), their applications, and research timelines. The communication protocols that have established uses in agriculture are reviewed first in this article. Various wireless communication protocols such as WiFi, ZigBee, SigFox, LoRa, RFID, NFMI, Terahertz, and NB-IoT were summarized, and their applications in various fields were also studied. These protocols in smart agriculture can effectively and efficiently address environmental data, water saving, monitoring of animal behavior, accuracy, power efficiency, cost reduction due to low power consumption, accuracy, wide transmission, simple in operation and cost effective. The most commonly used microcontrollers are Arduino (to develop autonomous machines), Raspberry Pi (to store data), and 8-bit microcontroller (to process data). In addition, it is important to take advantage of modern communication technology to enhance crop production. This study also examines the future opportunities and trends for IoT applications in smart agriculture, along with the ongoing challenges and issues that need addressing. Furthermore, it provides crucial insights and guidance for future research and the development of IoT solutions. These advancements aim to improve agricultural productivity and quality while facilitating the transition to a more sustainable agroecological future. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Intelligent Transportation Systems & Applications is the property of Journal of Intelligent Transportation Systems & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency. [ABSTRACT FROM AUTHOR]

In this letter, we consider a system based on LoRa (Long Range), which is one of the widely used IoT (Internet of Things) communication technologies. Recently, as the application areas of LoRa have expanded from low-speed to high-speed moving objects, an increase in error rates due to Doppler shift in high-speed environments has been reported. To address this issue, this letter proposes a new LoRa modulation scheme where information is conveyed by varying the frequency difference between two chirp signals. Additionally, an efficient demodulation algorithm is proposed to successfully demodulate information at the receiver even in the presence of residual carrier frequency offset. Numerical results demonstrate that the proposed LoRa modulation scheme can guarantee low error rates in mobile environments. [ABSTRACT FROM AUTHOR]

Long-Range Wide-Area Network (LoRaWAN) technology offers efficient connectivity for numerous end devices over a wide coverage area in the Internet of Things (IoT) network, enabling the exchange of data over the Internet between even the most minor Internet-connected devices and systems. One of LoRaWAN's hallmark features is the Adaptive Data Rate (ADR) algorithm. ADR is a resource allocation function which dynamically adjusts the network's data rate, airtime, and energy dissipation to optimise its performance. The allocation of spreading factors plays a critical function in defining the throughput of the end device and its robustness to interference. However, in practical deployments, LoRaWAN networks experience considerable interference, severely affecting the packet delivery ratio, energy utilisation, and general network performance. To address this, we present a novel ADR framework, SSFIR-ADR, which utilises randomised spreading factor allocation to minimise energy consumption and packet collisions while maintaining optimal network performance. We implement a LoRa network composed of a single gateway that connects loads of end nodes to a network server. In terms of energy use, packet delivery rate, and interference rate (IR), our simulation implementation does better than LoRaWAN's legacy ADR scheme for a range of application data intervals. [ABSTRACT FROM AUTHOR]

Low Power Wide Area Networks (LPWAN) are prominent option of wireless communication technology for dense Internet of Things (IoT) applications. With a growing population of resource-constrained IoT devices, meeting various communication requirements in dynamic and dense wireless networks has become a significant problem. Long Range (LoRa) was designed for LPWAN, which features long-distance communication, low-power consumption, and simultaneous transmission of multiple end devices. However, LoRa deployment in dense IoT networks facing several challenges like interference, scalability, security, and reliability. In recent times numerous techniques have been developed for interference mitigation. As these techniques used a range of methodologies to address the interference challenge, it is necessary to thoroughly analyze current solutions. This paper presents a comprehensive overview of the existing literature on interference issues and the solution approaches in LoRa. Initially, the challenges in dense IoT networks are discussed. We next present the fundamentals of LoRa and the classification of interference in the different categories. In each type of interference, the available methodologies are categorized based on their solution approaches. The analysis of different solution approaches is summarized by examining various issues of the LoRa network. Finally, the open issues and future directions related to the interference in the LoRa network are discussed. [ABSTRACT FROM AUTHOR]

Summary A road‐weather information system (RWIS) is an attractive application in the context of intelligent transportation systems (ITSs), which is used where climatic conditions and meteorological events worsen traffic safety. The primary goal is to significantly reduce potential accidents by sending early warning messages to prompt immediate action. For this purpose, road weather information stations are placed at critical points on the highways. In addition to responsible transportation agencies, enabling direct warning message transfer from roadside infrastructure to vehicles can significantly improve the effectiveness of a typical RWIS application. Dedicated short range communication (DSRC) and cellular vehicle to everything (C‐V2X) technologies are extensively used for data exchange between vehicles (V2V) and between vehicles and road infrastructure (V2I or I2V) under the umbrella term of vehicle‐to‐everything (V2X) communications. Latency and coverage are key performance indicators of the vehicular network that determine how drivers react to risks in adverse road and weather conditions. V2X‐based connectivity mainly aims to offer low latency in message delivery, but it is not possible to obtain a wide‐range coverage due to significant propagation loss in the dedicated 5.9 GHz ITS frequency band. In this study, we presented a long range (LoRa)‐based RWIS application that uses I2 V communication at sub‐GHz bands. Our main contribution is proposing a novel method for RWIS implementations, where the LoRa gateways run in a half‐duplex way to collect information from road weather information stations in the uplink channel and then broadcast warning messages to vehicles in the downlink channel. To validate the applicability of the proposed method, we presented a feasibility study that includes transmission range and latency analysis. Our theoretical calculations' results validate the more extended transmission range and applicability of LoRa connectivity on RWIS applications, especially for common scenarios like black ice where the climate risks do not occur suddenly. We also showed that our methodology would be beneficial due to the ability to offer wide coverage, especially in the areas where cellular base stations have not yet existed or V2X road infrastructure has not yet been established. [ABSTRACT FROM AUTHOR]

We introduce a novel LoRa-based multi-hop communication architecture as an alternative to the public internet for earthquake early warning (EEW). We examine its effectiveness in generating a meaningful warning window for the New Zealand-based decentralised EEW sensor network implemented by the CRISiSLab operating with the adapted Propagation of Local Undamped Motion (PLUM)-based earthquake detection and node-level data processing. LoRa, popular for low-power, long-range applications, has the disadvantage of long transmission time for time-critical tasks like EEW. Our network overcomes this limitation by broadcasting EEWs via multiple short hops with a low spreading factor (SF). The network includes end nodes that generate warnings and relay nodes that broadcast them. Benchmarking with simulations against CRISiSLab's EEW system performance with internet connectivity shows that an SF of 8 can disseminate warnings across all the sensors in a 30 km urban area within 2.4 s. This approach is also resilient, with the availability of multiple routes for a message to travel. Our LoRa-based system achieves a 1–6 s warning window, slightly behind the 1.5–6.75 s of the internet-based performance of CRISiSLab's system. Nevertheless, our novel network is effective for timely mental preparation, simple protective actions, and automation. Experiments with Lilygo LoRa32 prototype devices are presented as a practical demonstration. [ABSTRACT FROM AUTHOR]

This review offers an extensive examination of the evolving landscape of Long Range (LoRa) Microstrip Patch Antennas (MPAs), highlighting their crucial role in optimizing LoRa systems within the Internet of Things (IoT). As the demand for energy-efficient standards like LoRa grows with the expanding IoT market, this research becomes increasingly relevant. This comprehensive review, the first of its kind, serves as a foundational resource for researchers seeking to optimize LoRa systems within the IoT. The study has categorized these LoRa MPAs – including monopole, Planar Inverted F Antenna (PIFA), dipole, yagi-uda, and array – into single band, dual-band, multiband, and wearable antennas, thus providing substantial viewpoints on their diverse design architectures and performance characteristics. Through systematic tabulation, the review facilitates a thorough comparison of antenna advancements. Notably, the review addresses inherent challenges in LoRa MPAs, emphasizing critical aspects that necessitate attention, including the need for miniaturization and integration, advancements in substrate materials and fabrication techniques, and the imperative for reconfigurable and adaptive antennas. Various approaches to enhance antenna performance are explored, including the metamaterial incorporation, slot-based enhancements, Electromagnetic Band Gap (EBG), dielectric resonator antennas (DRAs), substrate material considerations, and corrugation techniques. Looking ahead, the paper explores the future trends and subtle considerations that are poised to shape the trajectory of LoRa MPAs. To the best of our knowledge, this paper represents the first comprehensive review on the multifaceted topic of LoRa MPAs, serving as a foundational resource for researchers in the field. [ABSTRACT FROM AUTHOR]

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as 7.3 dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 M Hz , the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)—LoRa, etc. [ABSTRACT FROM AUTHOR]

Long-range frequency hopping spread spectrum (LR-FHSS) is a pivotal advancement in the LoRaWAN protocol that is designed to enhance the network's capacity and robustness, particularly in densely populated environments. Although energy consumption is paramount in LoRaWAN-based end devices, this is the first study in the literature, to our knowledge, that models the impact of this novel mechanism on energy consumption. In this article, we provide a comprehensive energy consumption analytical model of LR-FHSS, focusing on three critical metrics: average current consumption, battery lifetime, and energy efficiency of data transmission. The model is based on measurements performed on real hardware in a fully operational LR-FHSS network. While in our evaluation, LR-FHSS can show worse consumption figures than LoRa, we find that with optimal configuration, the battery lifetime of LR-FHSS end devices can reach 2.5 years for a 50 min notification period. For the most energy-efficient payload size, this lifespan can be extended to a theoretical maximum of up to 16 years with a one-day notification interval using a cell-coin battery. [ABSTRACT FROM AUTHOR]

To evaluate the ecosystem services of silvopastoral systems through grazing activities, an advanced Internet of Things (IoT) framework is introduced for capturing extensive data on the spatial dynamics of sheep and goat grazing. The methodology employed an innovative IoT system, integrating a Global Navigation Satellite System (GNSS) tracker and environmental sensors mounted on the animals to accurately monitor the extent, intensity, and frequency of grazing. The experimental results demonstrated the high performance and robustness of the IoT system, with minimal data loss and significant battery efficiency, validating its suitability for long-term field evaluations. Long Range (LoRa) technology ensured consistent communication over long distances, covering the entire grazing zone and a range of 6 km in open areas. The superior battery performance, enhanced by a solar panel, allowed uninterrupted operation for up to 37 days with 5-min interval acquisitions. The GNSS module provided high-resolution data on movement patterns, with an accuracy of up to 10 m after firmware adjustments. The two-part division of the device ensured it did not rotate on the animals' necks. The system demonstrated adaptability and resilience in various terrains and animal conditions, confirming the viability of IoT-based systems for pasture monitoring and highlighting their potential to improve silvopastoral management, promoting sustainable practices and conservation strategies. This work uniquely focuses on documenting the shepherd's role in the ecosystem, providing a low-cost solution that distinguishes itself from commercial alternatives aimed primarily at real-time flock tracking. [ABSTRACT FROM AUTHOR]

This study investigates communication between UAVs using long range (LoRa) devices, focusing on the interaction between a LoRa gateway UAV and other UAVs equipped with LoRa transmitters. By conducting experiments across various geographical regions, this study aims to delineate the fundamental boundary conditions for the efficient control of a UAV fleet. The parameters under analysis encompass inter-device spacing, radio interference effects, and terrain topography. This research yields pivotal insights into communication network design and optimization, thereby enhancing operational efficiency and safety within diverse geographical contexts for UAV operations. Further research insights could involve a weather analysis and implementation of improved solutions in terms of communication systems. [ABSTRACT FROM AUTHOR]

Networks for the Internet of Things typically use a gateway to provide connectivity between a low bit rate, low capability sensor network and the broader Internet. The gateway can be subject to very high traffic loads, many concurrent processes and needs to be highly reliable. Functional programming languages such as Erlang and Elixir have proven to be an effective programming paradigm for such scenarios, notably in large-scale telecommunications switches. In this paper, we report on our experience of developing a gateway between a LoRa network and an MQTT broker using the functional programming language Elixir and the more conventional language C++. To obtain an understanding of this approach to development, we first developed an initial prototype on a single-board computer using Elixir. We then developed the same system in C++ and ran experiments to compare the two systems' performance. In order to understand the performance of such systems on low-end IoT devices, we then developed the same system on a low-cost ESP32 micro-controller in both C++ and Elixir. We were able to run the Elixir-based system on an ESP32 micro-controller but found that its performance was significantly poorer than the same system written in C++. We conclude that functional programming has great potential for the development of IoT systems, but work needs to be carried out to improve the supporting libraries and underlying virtual machines. We also note that learning to program in a functional programming language has quite a steep learning curve. [ABSTRACT FROM AUTHOR]

LoRa (long-range) is an emerging low-power wide-area network technology. Facing challenges such as frequency selective fading multipath channels, existing LoRa schemes are subjected to issues like low data rates and high system complexity. To address these issues, a multi-mode LoRa (MM-LoRa-Mod) based modulation scheme was proposed. The frequency shifts of the multiple multiplexed chirp signals for information transmission were utilized in this scheme, thereby significantly enhancing data rates. Additionally, for underwater acoustic communication scenarios, a low-complexity non-coherent detection algorithm for MM-LoRa-Mod was introduced. Through the evaluation of MM-LoRa-Mod's bit error rate performance in multipath channels, Watermark underwater acoustic channels, and shallow water acoustic channels, excellent anti-multipath fading and anti-Doppler frequency shift capabilities were demonstrated, indicating the high reliability of MM-LoRa-Mod in underwater acoustic communication scenarios. [ABSTRACT FROM AUTHOR]

The emergence of long-range (LoRa) technology, together with the expansion of uncrewed aerial vehicles (UAVs) use in civil applications have brought significant advances to the Internet of Things (IoT) field. In this way, these technologies are used together in different scenarios, especially when it is necessary to have connectivity in remote and difficult-to-access locations, providing coverage and monitoring of greater areas. In this sense, this article seeks to determine the best positioning for the LoRa gateway coupled to the drone and the optimal spreading factor (SF) for signal transmission in a LoRa network, aiming to improve the connected devices signal-to-noise ratio (SNR), considering a suburban and densely wooded environment. Then, multi-layer perceptron (MLP) networks and generalized regression neural networks (GRNN) were trained to predict the signal behavior and determine the best network to represent this behavior. The MLP network presented the lowest RMSE, 2.41 dB, and was selected for use jointly with the bioinspired Grey-Wolf optimizer (GWO). The optimizer proved its effectiviness being able to adjust the number of UAVs used to obtain 100% coverage and determine the best SF used by the endnodes, guaranteeing a higher transmission rate and lower energy consumption. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

LoRa technology is a recent technology belonging to the Low Power and Wide Area Networks (LPWANs), which offers distinct advantages for wireless communications and possesses unique features. Among others, it can be used for localization procedures offering minimal energy consumption and quite long-range transmissions. However, the exact capabilities of LoRa localization performance are yet to be employed thoroughly. This article examines the efficiency of the LoRa technology in localization tasks using Time Difference of Arrival (TDoA) measurements. An extensive and concrete experimental study was conducted in a real-world setup on the University of Patras campus, employing both real-world data and simulations to assess the precision of geodetic coordinate determination. Through our experiments, we implemented advanced localization algorithms, including Social Learning Particle Swarm Optimization (PSO), Least Squares, and Chan techniques. The results are quite interesting and highlight the conditions and parameters that result in accurate LoRa-based localization in real-world scenarios in smart cities. In our context, we were able to achieve state-of-the-art localization results reporting localization errors as low as 300 m in a quite complex 8 km × 6 km real-world environment.

This review offers an extensive examination of the evolving landscape of Long Range (LoRa) Microstrip Patch Antennas (MPAs), highlighting their crucial role in optimizing LoRa systems within the Internet of Things (IoT). As the demand for energy-efficient standards like LoRa grows with the expanding IoT market, this research becomes increasingly relevant. This comprehensive review, the first of its kind, serves as a foundational resource for researchers seeking to optimize LoRa systems within the IoT. The study has categorized these LoRa MPAs – including monopole, Planar Inverted F Antenna (PIFA), dipole, yagi-uda, and array – into single band, dual-band, multiband, and wearable antennas, thus providing substantial viewpoints on their diverse design architectures and performance characteristics. Through systematic tabulation, the review facilitates a thorough comparison of antenna advancements. Notably, the review addresses inherent challenges in LoRa MPAs, emphasizing critical aspects that necessitate attention, including the need for miniaturization and integration, advancements in substrate materials and fabrication techniques, and the imperative for reconfigurable and adaptive antennas. Various approaches to enhance antenna performance are explored, including the metamaterial incorporation, slot-based enhancements, Electromagnetic Band Gap (EBG), dielectric resonator antennas (DRAs), substrate material considerations, and corrugation techniques. Looking ahead, the paper explores the future trends and subtle considerations that are poised to shape the trajectory of LoRa MPAs. To the best of our knowledge, this paper represents the first comprehensive review on the multifaceted topic of LoRa MPAs, serving as a foundational resource for researchers in the field.

In power defect detection, the complexity of scenes and the diversity of defects pose challenges for manual defect identification. Considering these issues, this paper proposes utilizing a multimodal large model to assist power professionals in identifying power scenes and defects through image-text interactions, thereby enhancing work efficiency. This paper presents a fine-tuned multimodal large model for power defect image-text question-answering, addressing challenges such as training difficulties and the lack of image-text knowledge specific to power defects. This paper utilizes the YOLOv8 to create a dataset for multimodal power defect detection, enriching the image-text information in the power defect domain. By integrating the LoRA and Q-Former methods for model fine-tuning, the algorithm enhances the extraction of visual and semantic features and aligns visual and semantic information. The experimental results demonstrate that the proposed multimodal large model significantly outperforms other popular multimodal models in the domain of power defect question-answering. [ABSTRACT FROM AUTHOR]

This study investigates communication between UAVs using long range (LoRa) devices, focusing on the interaction between a LoRa gateway UAV and other UAVs equipped with LoRa transmitters. By conducting experiments across various geographical regions, this study aims to delineate the fundamental boundary conditions for the efficient control of a UAV fleet. The parameters under analysis encompass inter-device spacing, radio interference effects, and terrain topography. This research yields pivotal insights into communication network design and optimization, thereby enhancing operational efficiency and safety within diverse geographical contexts for UAV operations. Further research insights could involve a weather analysis and implementation of improved solutions in terms of communication systems.

This study introduces an artificial intelligence system implemented on the ESP32-CAM platform, aimed at conducting optical character recognition (OCR) on water meters. Leveraging LoRa technology for data transmission ensures efficient energy utilization and convenient long-range communication capabilities. The system achieves an impressive accuracy rate of 98.2% in identifying water meter readings, showcasing its reliability. Proposed as a feasible solution, it offers the advantages of low energy consumption, cost-effectiveness, and flexibility in widespread deployment, particularly leveraging existing water meter infrastructure. Thus, this research presents a promising choice for various applications beyond merely reading water meter readings. Its efficient and accurate OCR functionality makes it suitable for diverse scenarios, ranging from smart city initiatives to industrial automation processes. With its ability to integrate seamlessly into existing infrastructure and deliver reliable results, this system stands poised to revolutionize OCR applications in various domains, contributing to enhanced efficiency and productivity.

Cardiovascular disease is the leading cause of death globally. This research implements a system for monitoring heart rate, electrocardiogram, and providing alerts for potential risks to patients based on data collected using the LSTM machine learning model. The wearable device is compact in size with a long battery life. The information collected from the device can be remotely monitored by doctors through a visual interface on a web server model, and patients can self-monitor their electrocardiogram status through an application on a mobile device. By integrating the LSTM model into the design, this study has addressed two issues: predicting the trend of the electrocardiogram signal and detecting abnormalities in the predicted signal. This allows users to self-monitor their personal status and doctors to better adjust the treatment method for the patient’s health.

With the advancement of communication technology, many innovative applications have developed in agriculture as a result of the integration of the Internet of Things (IoT) with unmanned aerial vehicles (UAVs), leading to the modernization of agriculture. This study seeks to propose an effective and low-cost platform for comprehensive monitoring of environmental parameters using IoT and drones.The preparation of this paper was based on a platform that was tested in a realistic environment on a farm near the city of Al-Median in Tunisia, where the platform was built to suit the realistic environment of a farm in Baghdad, through the use of sensors above and below the ground, which meets the experimental work and standards for automated and real-time monitoring. For environmental standards, the unified theory of acceptance and use of technology model was used, which is a model based on four basic elements: 1) expected performance, 2) expected effort, 3) social impact, and 4) facilitating conditions for obtaining results. The unique integration of IoT sensors with drones has shown impressive experimental results, indicating the possibility of performing both automatic and manual actions by humans. These smart moves contribute significantly to promoting precision agriculture, leading to a significant increase in agricultural production and conservation of natural resources.

Summary The long‐range wide area network (LoRaWAN) is a standard for the Internet of Things (IoT) because it has low cost, long range, not energy‐intensive, and capable of supporting massive end devices (EDs). The adaptive data rate (ADR) adjusts parameters at both EDs and the network server (NS). This includes modifying the transmission spreading factor (SF) and transmit power (TP) to minimize packet errors and optimize transmission performance at the NS. The ADR managed by NS aims to provide reliable and energy‐efficient resources (e.g., SF and TP) to EDs by monitoring the packets received from the EDs. However, since the channel condition changes rapidly in LoRaWAN due to mobility, the existing ADR algorithm is unsuitable and results in a significant amount of packet loss and retransmissions causing an increase in energy consumption. In this paper, we enhance the ADR by introducing Kalman filter‐based ADR (KF‐ADR) and moving median‐based ADR (Median‐ADR), which estimate the optimal SNR by considering the mobility later used to assign the SF and TP to EDs. The simulation results showed that the proposed techniques outperform the legacy ADRs in terms of convergence period, energy consumption, and packet success ratio. [ABSTRACT FROM AUTHOR]

Low-Earth-orbit (LEO) satellites are widely acknowledged as a promising infrastructure solution for global Internet of Things (IoT) services. However, the Doppler effect presents a significant challenge in the context of long-range (LoRa) modulation uplink connectivity. This study comprehensively examines the operational efficiency of LEO satellites concerning the Doppler weather effect, with state-of-the-art artificial intelligence techniques. Two LEO satellite constellations—Globalstar and the International Space Station (ISS)—were detected and tracked using ground radars in Perth and Brisbane, Australia, for 24 h starting 1 January 2024. The study involves modelling the constellation, calculating latency, and frequency offset and designing a hybrid Iterative Input Selection–Long Short-Term Memory Network (IIS-LSTM) integrated model to predict the Doppler weather profile for LEO satellites. The IIS algorithm selects relevant input variables for the model, while the LSTM algorithm learns and predicts patterns. This model is compared with Convolutional Neural Network and Extreme Gradient Boosting (XGBoost) models. The results show that the packet delivery rate is above 91% for the sensitive spread factor 12 with a bandwidth of 11.5 MHz for Globalstar and 145.8 MHz for ISS NAUKA. The carrier frequency for ISS orbiting at 402.3 km is 631 MHz and 500 MHz for Globalstar at 1414 km altitude, aiding in combating packet losses. The ISS-LSTM model achieved an accuracy of 97.51% and a loss of 1.17% with signal-to-noise ratios (SNRs) ranging from 0–30 dB. The XGB model has the fastest testing time, attaining ≈0.0997 s for higher SNRs and an accuracy of 87%. However, in lower SNR, it proves to be computationally expensive. IIS-LSTM attains a better computation time for lower SNRs at ≈0.4651 s, followed by XGB at ≈0.5990 and CNN at ≈0.6120 s. The study calls for further research on LoRa Doppler analysis, considering atmospheric attenuation, and relevant space parameters for future work. [ABSTRACT FROM AUTHOR]

The number of applications of low-power wide-area networks (LPWANs) has been growing quite considerably in the past few years and so has the number of protocol stacks. Despite this fact, there is still no fully open LPWAN protocol stack available to the public, which limits the flexibility and ease of integration of the existing ones. The closest to being fully open is LoRa; however, only its medium access control (MAC) layer, known as LoRaWAN, is open and its physical and logical link control layers, also known as LoRa PHY, are still only partially understood. In this paper, the essential missing aspects of LoRa PHY are not only reverse engineered, but also, a new design of the transceiver and its sub-components are proposed and implemented in a modular and flexible way using GNU Radio. Finally, some examples of applications of both the transceiver and its components, which are made to be run in a simple setup by using cheap and widely available off-the-shelf hardware, are given to show how the library can be used and extended. [ABSTRACT FROM AUTHOR]

In situations when the precise position of a machine is unknown, localization becomes crucial. This research focuses on improving the position prediction accuracy over long-range (LoRa) network using an optimized machine learning-based technique. In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology, this study proposed an optimized machine learning (ML) based algorithm. Received signal strength indicator (RSSI) data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building. The noise factor is also taken into account, and the signal-to-noise ratio (SNR) value is recorded for every RSSI measurement. This study concludes the examination of reference point accuracy with the modified KNN method (MKNN). MKNN was created to more precisely anticipate the position of the reference point. The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity. [ABSTRACT FROM AUTHOR]

Smart agriculture stands as a promising domain for IoT-enabled technologies, with the potential to elevate crop quality, quantity, and operational efficiency. However, implementing a smart agriculture system encounters challenges such as the high latency and bandwidth consumption linked to cloud computing, Internet disconnections in rural locales, and the imperative of cost efficiency for farmers. Addressing these hurdles, this paper advocates a fog-based smart agriculture infrastructure integrating edge computing and LoRa communication. We tackle farmers' prime concern of animal intrusion by presenting a solution leveraging low-cost PIR sensors, cameras, and computer vision to detect intrusions and predict animal locations using an innovative algorithm. Our system detects intrusions pre-emptively, identifies intruders, forecasts their movements, and promptly alerts farmers. Additionally, we compare our proposed strategy with other approaches and measure their power consumptions, demonstrating significant energy savings afforded by our strategy. Experimental results highlight the effectiveness, energy efficiency, and cost-effectiveness of our system compared to state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Safe drinking water is essential to a healthy lifestyle and has been recognised as a human right by numerous countries. However, the realisation of this right remains largely aspirational, particularly in impoverished nations that lack adequate resources for water quality testing. Kenya, a Sub-Saharan country, bears the brunt of this challenge. Pesticide imports in Kenya increased by 144% from 2015 to 2018, with sales data indicating that 76% of these pesticides are classified as highly hazardous. This trend continues to rise. Over 70% of Kenya's population resides in rural areas, with 75% of the rural population engaged in agriculture and using pesticides. Agriculture is the country's main economic activity, contributing over 30% of its gross domestic product (GDP). The situation is further exacerbated by the lack of monitoring for pesticide residues in surface water and groundwater, coupled with the absence of piped water infrastructure in rural areas. Consequently, contamination levels are high, as agricultural runoff is a major contaminant of surface water and groundwater. The increased use of pesticides to enhance agricultural productivity exacerbates environmental degradation and harms water ecosystems, adversely affecting public health. This study proposes the development of a wireless sensor system that utilizes radio-frequency identification (RFID), Long-range (LoRa) protocol and a global system for mobile communications (GSM) for monitoring pesticide prevalence in groundwater sources. From the system design, individuals with limited literacy skills, advanced age, or non-expert users can utilize it with ease. The reliability of the LoRa protocol in transmitting data packets is thoroughly investigated to ensure effective communication. The system features a user-friendly interface for straightforward data input and facilitates broader access to information by employing various remote wireless sensing methods. [ABSTRACT FROM AUTHOR]

In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%. [ABSTRACT FROM AUTHOR]

In recent years, with the development of Internet of Things technology, the demand for low-power wireless communication technology has been growing, giving rise to LoRa technology. A LoRa network mainly consists of terminal nodes, gateways, and LoRa network servers. As LoRa networks often deploy many terminal node devices for environmental sensing, the limited resources of LoRa technology, the explosive growth in the number of nodes, and the ever-changing complex environment pose unprecedented challenges for the performance of the LoRa network. Although some research has already addressed the challenges by allocating channels to the LoRa network, the impact of complex and changing environmental factors on the LoRa network has yet to be considered. Reasonable channel allocation should be tailored to the situation and should face different environments and network distribution conditions through continuous adaptive learning to obtain the corresponding allocation strategy. Secondly, most of the current research only focuses on the channel adjustment of the LoRa node itself. Still, it does not consider the indirect impact of the node's allocation on the entire network. The Inner External DQN SF allocation method (IEDQN) proposed in this paper improves the packet reception rate of the whole system by using reinforcement learning methods for adaptive learning of the environment. It considers the impact on the entire network of the current node parameter configuration through nested reinforcement learning for further optimization to optimize the whole network's performance. Finally, this paper evaluates the performance of IEDQN through simulation. The experimental results show that the IEDQN method optimizes network performance. [ABSTRACT FROM AUTHOR]

The paper discusses the wireless communication modules used for monitoring photovoltaic systems-ZigBee, Wi-Fi, Bluetooth, GSM and LoRa. The aim is to compare the main features, advantages and disadvantages of each of these technologies. The analysis identifies the need for reliability, low power consumption and secure data transmission in the design of the systems. [ABSTRACT FROM AUTHOR]

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This research presents the design and development of a near-space picosatellite platform intended to operate at high altitudes between 20–40 km above sea level. These picosatellites function similarly to orbital satellites but float and travel with the wind in the atmosphere instead of orbiting the Earth. The platform utilizes a super-pressure balloon to provide buoyancy, allowing the picosatellite to remain airborne and operational for several days to months, capable of flying around the world multiple times. This study focuses on the cost-effective design of high-altitude platforms, telecommunication systems, and energy consumption for the picosatellite, enabling it to communicate with ground stations from anywhere in the world while consuming low power. The use of common COTS (Commercial Off-The-Shelf) equipment aims to enhance accessibility for educational applications. This research gathered data from our previous studies for over 20 high-altitude platform flights, analyzed the necessary factors for whole system design, and developed a new prototype that has been successfully built and tested. [ABSTRACT FROM AUTHOR]

This article presents a novel system, LLDPC,1 which brings Low-Density Parity-Check (LDPC) codes into Long Range (LoRa) networks to improve Forward Error Correction, a task currently managed by less efficient Hamming codes. Three challenges in achieving this are addressed: First, Chirp Spread Spectrum (CSS) modulation used by LoRa produces only hard demodulation outcomes, whereas LDPC decoding requires Log-Likelihood Ratios (LLR) for each bit. We solve this by developing a CSS-specific LLR extractor. Second, we improve LDPC decoding efficiency by using symbol-level information to fine-tune LLRs of error-prone bits. Finally, to minimize the decoding latency caused by the computationally heavy Soft Belief Propagation (SBP) algorithm typically used in LDPC decoding, we apply graph neural networks to accelerate the process. Our results show that LLDPC extends default LoRa's lifetime by 86.7% and reduces SBP algorithm decoding latency by 58.09×. [ABSTRACT FROM AUTHOR]

Accurately predicting homeowners' aesthetic preferences is crucial in interior design. This study develops a fine-tuning model (LORA) for interior design styles corresponding to different MBTI personality types, leveraging the Stable Diffusion Web UI platform and integrating it into a generative artificial intelligence framework. Subsequently, personalized aesthetic preference architectural interior renderings are recommended based on homeowners' personality traits, aiming to achieve an adaptive interior design approach. To achieve more precise adaptive solutions, this research surveys the style and color tendencies of respondents with different MBTI personality types and adds style description prompts to assist in image generation. The study finds that this method can better predict the interior design styles favored by certain MBTI personality types. This research contributes to addressing aesthetic biases between designers and homeowners, bringing innovative ideas and methods to interior design, and is expected to enhance homeowners' satisfaction. [ABSTRACT FROM AUTHOR]

LoRa systems are emerging as a promising technology for wireless sensor networks due to their exceptional range and low power consumption. The successful deployment of LoRa networks relies on accurate propagation models to facilitate effective network planning. Therefore, this review explores the landscape of propagation models supporting LoRa networks. Specifically, we examine empirical propagation models commonly employed in communication systems, assessing their applicability across various environments such as outdoor, indoor, and within vegetation. Our investigation underscores the prevalence of logarithmic decay in most empirical models. In addition, we survey the relationship between model parameters and environmental factors, clearing their nuanced interplay. Analyzing published measurement results, we extract the log-distance model parameters to decipher environmental influences comprehensively. Drawing insights from published measurement results for LoRa, we compare them with the model's outcomes, highlighting successes and limitations. We additionally explore the application of multi-slope models to LoRa measurements to evaluate its effectiveness in enhancing the accuracy of path loss prediction. Finally, we propose new lines for future research in propagation modelling to improve empirical models. [ABSTRACT FROM AUTHOR]

Marine diesel engines are the preferred power equipment for ships and are the most important component among the numerous electromechanical devices on board. Accidents involving these engines can potentially cause immeasurable damage to the vessel, making fault detection in marine diesel engines crucial. This design enables the detection and reporting of faults in marine diesel engines at the earliest possible time through the computation of convolutional neural networks, which is of great significance for ensuring the safe navigation of ships. For this functionality, the Xilinx ZYNQ-7000 XC7Z010 is selected as the main control chip, and the LoRa wireless network is used as the transmission module. The FreeRTOS embedded operating system is ported, with sensor data collection completed on the PS side of the ZYNQ chip and algorithm acceleration calculations on the PL side. Data are then transmitted to the host computer via the LoRa module paired with a custom protocol. Experimental test results show that the program provides stable data transmission, with each module of the algorithm generally accelerating by more than 95% and an accuracy rate of 92.86%. Additionally, the host computer can display the received data in real time. The custom protocol's header also allows for precise judgments about the completeness and origin of messages, facilitating the expansion of other SOC's message uplink and the host computer's message downlink. [ABSTRACT FROM AUTHOR]

Dynamic liquid level monitoring and measurement in oil wells is essential in ensuring the safe and efficient operation of oil extraction machinery and formulating rational extraction policies that enhance the productivity of oilfields. This paper presents an intelligent infrasound-based measurement method for oil wells' dynamic liquid levels; it is designed to address the challenges of conventional measurement methods, including high costs, low precision, low robustness and inadequate real-time performance. Firstly, a novel noise reduction algorithm is introduced to effectively mitigate both periodic and stochastic noise, thereby significantly improving the accuracy of dynamic liquid level detection. Additionally, leveraging the PyQT framework, a software platform for real-time dynamic liquid level monitoring is engineered, capable of generating liquid level profiles, computing the sound velocity and liquid depth and visualizing the monitoring data. To bolster the data storage and analytical capabilities, the system incorporates an around-the-clock unattended monitoring approach, utilizing Internet of Things (IoT) technology to facilitate the transmission of the collected dynamic liquid level data and computed results to the oilfield's central data repository via LoRa and 4G communication modules. Field trials on dynamic liquid level monitoring and measurement in oil wells demonstrate a measurement range of 600 m to 3000 m, with consistent and reliable results, fulfilling the requirements for oil well dynamic liquid level monitoring and measurement. This innovative system offers a new perspective and methodology for the computation and surveillance of dynamic liquid level depths. [ABSTRACT FROM AUTHOR]