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ABSTRACT This research introduces an innovative power management approach for wireless sensor networks in aquatic environmental monitoring. The study presents a dynamic algorithm that optimizes energy consumption in long range (LoRa) communication nodes by adaptively adjusting transmission power based on sensor–gateway distances. Leveraging GPS data and a log‐normal shadowing model, the method enables efficient power allocation. Field testing in an aquaculture setting demonstrates the energy‐aware power control (EAPC) algorithm's efficacy. Nodes at 500 m from gateways achieve up to 39% reduction in power consumption compared to fixed‐power systems. At 1250 m, savings decrease to 22%, and at 1500 m, to 11%, while maintaining reliable communication. This research advances sustainable aquatic resource management, offering applications in aquaculture, environmental conservation, and water resource management. By optimizing power usage, the approach contributes to more effective long‐term monitoring of aquatic environments. [ABSTRACT FROM AUTHOR]

Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions. [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]

Summary: Low‐Power Wide‐Area Network technologies, such as LoRa, are gaining popularity in the agricultural sector for field deployment. The crucial factors in these devices are their range and power efficiency. The energy consumption of a LoRa wireless sensor network is predominantly affected by transmission parameters like carrier frequency, bandwidth, transmit power, spreading factor, and coding rate. Incorrect chosen transmission parameters can lead to a reduction in the battery life of end nodes, requiring frequent battery replacements—a situation undesirable for field deployment. This study introduces a machine learning deployment in the form of a web application designed to monitor the energy consumption of end nodes in LoRa wireless sensor networks. The research initially employs 12 regression models, including Linear, Random Forest, K‐Nearest Neighbours, Decision Tree, Support Vector, Lasso, Ridge, AdaBoost, Gradient Boost, XGBoost, CatBoost, and LightGBM models. The findings of the study reveal that the LightGBM model surpasses other models in accurately predicting the energy consumption of Internet of Things (IoT) nodes, leading to its selection for the web application. This machine learning web application can be implemented in a programmable Long Range Wide Area Network (LoRaWAN) gateway to effectively monitor the energy consumption of IoT end nodes in the agricultural sector. [ABSTRACT FROM AUTHOR]

The advancement of smart metering technology is progressing steadily and inevitably across various key economic sectors. The utilizatio.n of remote sensors in agriculture presents unique characteristics and specific challenges. In this study, an on-site experiment was carried out on a Slovakian production farm to analyze the transition from traditional measurement methods to smart meters, focusing on timing decisions related to soil preparation and sowing and their relation to scientifically justified dates. Consequently, a clear distinction was observed in terms of the timing decisions made regarding agricultural activities during traditional, combined, and scientifically based approaches in meteorological data readings. This study contrasts these three scenarios and deliberates on the factors that need to be carefully evaluated before incorporating remote sensors into agricultural processes. This study serves as a valuable resource for individuals involved in the adoption of smart metering practices in the Eastern European agricultural sector and promotes an improved understanding of the interactions within smart-sensing, scientific developments, and land management that contribute to the goal of land-system sustainability. [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]

This paper presents a novel waste-heat-powered, wireless, and battery-less Industrial Internet of Things (IIoT) device designed for predictive maintenance in Industry 4.0 environments. With a focus on real-time quality data, this device addresses the limitations of current battery-operated IIoT devices, such as energy consumption, transmission range, data rate, and constant quality of service. It is specifically developed for heat-intensive industries (e.g., iron and steel, cement, petrochemical, etc.), where self-heating nodes, low-power processing platforms, and industrial sensors align with the stringent requirements of industrial monitoring. The presented IIoT device uses thermoelectric generators based on the Seebeck effect to harness waste heat from any hot surface, such as pipes or chimneys, ensuring continuous power without the need for batteries. The energy that is recovered can be used to power devices using mid-range wireless protocols like Bluetooth 5.0, minimizing the need for extensive in-house wireless infrastructure and incorporating light-edge computing. Consequently, up to 98% of cloud computation efforts and associated greenhouse gas emissions are reduced as data is processed within the IoT device. From the environmental perspective, the deployment of such self-powered IIoT devices contributes to reducing the carbon footprint in energy-demanding industries, aiding their digitalization transition towards the industry 5.0 paradigm. This paper presents the results of the most challenging energy harvesting technologies based on an all-silicon micro thermoelectric generator with planar architecture. The effectiveness and self-powering ability of the selected model, coupled with an ultra-low-power processing platform and Bluetooth 5 connectivity, are validated in an equivalent industrial environment to monitor vibrations in an electric machine. This approach aligns with the EU's strategic objective of achieving net zero manufacturing capacity for renewable energy technologies, enhancing its position as a global leader in renewable energy technology (RET). [ABSTRACT FROM AUTHOR]

iGA Istanbul Airport is the first and only airport established with a wireless Internet of Things (IoT). IoT systems offer an effective solution for receiving data from many points with advantages such as fast implementation, low installation costs and long lifetime. In addition to standard products, special wireless IoT sensors designed for specific needs are also used in iGA Istanbul Airport. This infrastructure provides data for the execution, improvement and development of airport processes. The big data and analysis application created with the data collected from IoT devices and system integrations manages efficiency and predictive maintenance activities. With the effective use of IoT systems, iGA Istanbul Airport has achieved significant gains in increasing customer satisfaction, managing predictive maintenance from a more proactive perspective, reducing environmental impacts and increasing operational efficiency. This paper discusses the use of the IoT infrastructure at iGA Istanbul Airport and the benefits it provides. [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]

Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living alone, the development of automatic fall alerting systems has garnered significant research attention over the past decade. A key element for deploying a fall detection system (FDS) based on wearables is the wireless transmission method employed to transmit the medical alarms. In this regard, the vast majority of prototypes in the related literature utilize short-range technologies, such as Bluetooth, which must be complemented by the existence of a gateway device (e.g., a smartphone). In other studies, standards like Wi-Fi or 3G communications are proposed, which offer greater range but come with high power consumption, which can be unsuitable for most wearables, and higher service fees. In addition, they require reliable radio coverage, which is not always guaranteed in all application scenarios. An interesting alternative to these standards is Low Power Wide Area Network (LPWAN) technologies, which minimize both energy consumption and hardware costs while maximizing transmission range. This article provides a comprehensive search and review of that works in the literature that have implemented and evaluated wearable FDSs utilizing LPWAN interfaces to transmit alarms. The review systematically examines these proposals, considering various operational aspects and identifying key areas that have not yet been adequately addressed for the viable implementation of such detectors. [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]

The enormous increase in the volume of waste caused by the population boom in cities is placing a considerable burden on waste processing in cities. The inefficiency and high costs of conventional approaches exacerbate the risks to the environment and human health. This article proposes a thorough approach that combines deep learning models, IoT technologies, and easily accessible resources to overcome these challenges. Our main goal is to advance a framework for intelligent waste processing that utilizes Internet of Things sensors and deep learning algorithms. The proposed framework is based on Raspberry Pi 4 with a camera module and TensorFlow Lite version 2.13. and enables the classification and categorization of trash in real time. When trash objects are identified, a servo motor mounted on a plastic plate ensures that the trash is sorted into appropriate compartments based on the model's classification. This strategy aims to reduce overall health risks in urban areas by improving waste sorting techniques, monitoring the condition of garbage cans, and promoting sanitation through efficient waste separation. By streamlining waste handling processes and enabling the creation of recyclable materials, this framework contributes to a more sustainable waste management system. [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.

While the spreading of the Internet of Things continues beyond expectations, the security of networking technologies used in this context remains an open issue. This paper provides a comprehensive overview of the state of the art on the security of Low Power Wide Area Networks (LPWANs), with a focus on Sigfox, LoRaWAN, and Narrowband Internet of Things. The paper covers five main areas: (1) security requirements and their implementation in these networks, such as authentication, encryption, access control, and key management; (2) categorization of attacks and threat modeling, with the identification of the attack vectors and the presentation of an attack categorization and analysis; (3) a detailed explanation of attacks documented on Sigfox, LoRaWAN, and Narrowband Internet of Things, examining the underlying vulnerabilities exploited, outlining potential consequences, and discussing countermeasures proposed to mitigate these attacks; (4) security enhancements proposed to address vulnerabilities in each network; (5) the integration of LPWANs with 5G and the consequent security challenges. This survey constitutes an important and missing resource for the study and the development of secure Internet of Things solutions based on Low Power Wide Area Networks, raising awareness of potential threats, and guiding future research efforts towards strengthening the security of these networks and of the broader IoT landscape.

This work aims to develop a prototype for autonomously monitoring highway weather conditions using thermoelectric energy. The prototype utilizes solar thermal energy, heat absorbed by the asphalt and soil heat to generate, convert, manage, and store energy. The study investigates the optimal functioning of the thermoelectric generator, including utilizing the soil as a heat sink and incorporating passive thermal control devices like heat pipes. A commercial ultra-low power converter model LTC3108 was experimentally tested for efficient energy conversion. The monitoring system is designed to acquire readings from sensors measuring relevant climate parameters such as temperature, atmospheric pressure, and humidity, focusing on ultra-low power characteristics. The acquired data is transmitted via LoRa technology to a server for processing and analysis. The prototype generated 286.24 J of energy with a maximum temperature gradient of 4.25 ºC in the thermoelectric generator. The energy cost of transmitting a 40-byte message through the LoRaWAN module was 0.625 J. The proposed climate data acquisition circuit had a theoretical consumption of 17.69 J. Experimental tests with the LTC3108 converter, and a 0.22 F supercapacitor demonstrated successful data transmission. This work provides a solid foundation for future studies utilizing thermoelectric energy in highway applications, resulting in a prototype suitable for real-world road scenarios. [ABSTRACT FROM AUTHOR]

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]

The power grid, manufacturing, and industrial automation are just a few Internet of Things (IoT) settings where publish/subscribe (p/s) systems are increasingly prevalent. These systems may handle a wide range of middleware and communication protocols, ensuring compatibility. The most well-liked publish/subscribe protocol is the Message Queue Telemetry Transport Protocol (MQTT), which uses an agent to transfer information between publishers and subscribers on certain subjects. MQTT can be quickly and simply deployed for IoT settings using a popular wireless MAC layer protocol like Long Range Wide Area Network (LoRaWAN), however, this has not been properly validated. MQTT can be readily set up in cloud environments to do research experiments. To provide an MQTT-based publication design that can handle the LoRaWAN proactive steps, the authors design and provide a simulation framework in NS-3 in this study. To do this, the authors make use of the LoRaWAN library from NS-3 and include connecting it with a middleman that links to numerous publications as well as clients. The authors support many topics at the broker while enabling unicast capabilities from the broker to LoRaWAN end devices. In other words, the proposed work activates the unicast capability from the middleman to LoRaWAN peripheral devices while handling multiple topics at the mediator. To illustrate the viability of our IoT architecture and evaluate its performance at scale, the authors performed several scenarios under it. [ABSTRACT FROM AUTHOR]

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance. [ABSTRACT FROM AUTHOR]

The high information rates and energy necessities for picture transmission overLow-Power Wide Area Network (LPWAN) protocols have for some time been an issue. One such protocol is vast Range (LoRa), which has created extreme worries over its appropriateness for picture transmission because of its low information rate, yet being powerful for sending information over tremendous distances. This study presents the application results of a coordinated LoRa and Deep Learning-based PC vision framework that can precisely recognize diseases of grape leaves from low-goal pictures. In particular, this work centers around coordinating the two advances -- LoRa and deep Learning -- to work with the transmission of pictures and the recognition of sicknesses. The framework utilizes a blend of recreation and on location tests, different LoRa settings, and tweaking the CNN model to accomplish this point. The assessment demonstrated the way that the proposed system could send pictures over LoRa while sticking to convention constraints (such low obligation cycle and data transmission). Sicknesses of grape leaves might be dependably distinguished by our superior model. The strategy requires no preparation information to change boundaries, and it is both successful and adaptable enough to oblige the unmistakable elements of each and every leaf sickness. It is vital to take note of that end-client trust in machine and deep learning models has fundamentally expanded because of novel arrangements in the Explainable Artificial Intelligence (XAI) space. In this work, we utilize the Graduate CAM technique to show the result layer choices made by the CNN. The perception discoveries show that there is a significant feeling of the sickness' spot area. The organization recognizes a few grape leaf illnesses along these lines. [ABSTRACT FROM AUTHOR]

LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns that requires attention. Existing device identification mechanisms use cryptography which has two major issues: (1) cryptography is hard on the device resources and (2) physical attacks might prevent them from being effective. Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. We discuss various categories of radio frequency fingerprinting techniques along with hardware imperfections that can be exploited to identify an emitter. Furthermore, we describe different deep learning algorithms implemented for the task of LoRa device classification and summarize the main approaches and results. We discuss several representations of the LoRa signal used as input to deep learning models. Additionally, we provide a thorough review of all the LoRa RF signal datasets used in the literature and summarize details about the hardware used, the type of signals collected, the features provided, availability, and size. Finally, we conclude this paper by discussing the existing challenges in deep learning-based LoRa device identification and also envisage future research directions and opportunities. [ABSTRACT FROM AUTHOR]

By 2030, it is expected that a trillion things will be connected. In such a scenario, the power required for the trillion nodes would necessitate using trillions of batteries, resulting in maintenance challenges and significant management costs. The objective of this research is to contribute to sustainable wireless sensor nodes through the introduction of an energy-autonomous wireless sensor node (EAWSN) designed to be an energy-autonomous, self-sufficient, and maintenance-free device, to be suitable for long-term mass-scale internet of things (IoT) applications in remote and inaccessible environments. The EAWSN utilizes Low-Power Wide Area Networks (LPWANs) via LoRaWAN connectivity, and it is powered by a commercial photovoltaic cell, which can also harvest ambient light in an indoor environment. Storage components include a capacitor of 2 mF, which allows EAWSN to successfully transmit 30-byte data packets up to 560 m, thanks to opportunistic LoRaWAN data rate selection that enables a significant trade-off between energy consumption and network coverage. The reliability of the designed platform is demonstrated through validation in an urban environment, showing exceptional performance over remarkable distances. [ABSTRACT FROM AUTHOR]

Clean, healthy ecosystems need waste management. Waste collection, disposal, and resource efficiency are issues in Bhagalpur, Bihar. IoT can improve Bhagalpur's waste management, save money, and ensure sustainability. IoT waste management might green Bhagalpur. Real-time monitoring, predictive analytics, public involvement, and data-driven decision making may improve Bhagalpur's environment, resource consumption, and quality of life. IoT trash management makes Bhagalpur greener and more efficient. Real-time IoT garbage bin fill sensors manage trash. Bin fill sensors save time, reduce trips, and enhance collection routes for trash management companies. IoT-enabled trash management helps Bhagalpur disperse resources. New vehicles, sensor data, and frequency changes may help authorities find high-waste areas. Optimizing fuel, carbon, and operations saves money. Manage waste IoT allows Bhagalpur predictive analytics. The system predicts waste generation and optimizes collection routes using historical and real-time data. This proactive approach reduces manual route planning, trip distance, and trash collection time. The idea monitors public and private garbage cans using PBLMU and HBLMU end sensors. Centrally stored and analyzed PBLMU and HBLMU waste bin empty levels and locations. Garbage collectors check cans for emptyness using a sophisticated GUI. These important tests validated system architecture: Eight trash cans with loRaWAN, eight HBLMUs and Wi-Fi. Intelligent interfaces monitored empty trashcans.PBLMUs' average current use was determined by monitoring sleep and active current contributions using an experimental device. PBLMU monitors public bin levels using sensor nodes. This architecture optimizes LoRaWAN wi-fi network and sensor node collection. Intelligence nodes monitor unit data. MSWN sensors were connected by another writer. [ABSTRACT FROM AUTHOR]

This article illustrates the development of SolarFertigation (SF), an IoT (Internet of Things) solution for precision agriculture. Contrary to similar systems on the market, SolarFertigation can monitor and optimize fertigation autonomously, based on the analysis of data collected through the cloud. The system is made up of two main components: the central unit, which enables the precise deployment and distribution of water and fertilizers in different areas of the agricultural field, and the sensor node, which oversees collecting environmental and soil data. This article delves into the evolution of the system, focusing on structural and architectural changes to develop an infrastructure suitable for implementing a predictive model based on artificial intelligence and big data. Aspects concerning both the sensor node, such as energy management, accuracy of solar radiation readings, and qualitative soil moisture measurements, as well as implementations to the hydraulic system and the control and monitoring system of the central unit, are explored. This article provides an overview of the results obtained from solar radiation and soil moisture measurements. In addition, the results of an experimental campaign, in which 300 salad plants were grown using the SolarFertigation system in a photovoltaic field, are presented. This study demonstrated the effectiveness and applicability of the system under real-world conditions and highlighted its potential in optimizing resources and increasing agricultural productivity, especially in agrivoltaic settings. [ABSTRACT FROM AUTHOR]

In recent years, long-range wide-area networks (LoRaWAN) have gained much attention as low-power wide-area networks. LoRaWAN uses ALOHA as the medium access control protocol, where the end devices transmit data randomly and retransmit it up to eight times if collisions occur. ALOHA is not energy efficient and works perfectly for a smaller network. Several techniques, including the use of synchronization and scheduling schemes, to deal with the limitations imposed by ALOHA in LoRaWAN have been reported in the literature. However, the existing synchronization and scheduling algorithms transmit synchronization messages randomly using one super frame with fixed time slots that accommodate devices using different spreading factors, which limit the LoRaWAN network's scalability. This work proposes a slotted synchronization mechanism for transmitting synchronization requests to the gateway. The performance of the slotted synchronization was evaluated through simulation using packet delivery ratio (PDR) and energy efficiency as the performance parameters. The results indicate that when the number of devices in the network increases, a time-slotted synchronization consumes less energy, on average, by about 0.2 mAh. The use of a slotted synchronization can improve the energy efficiency of the end devices as collisions are completely avoided, achieving a PDR of 100%. [ABSTRACT FROM AUTHOR]

Over the last years, a huge number of things has been connected to the Internet to support applications for smart cities, smart industries, smart agriculture and smart environment, etc. In order to fix bugs, update features or perform dynamic adaptations on these things, Efficient Firmware Update Over the Air (FUOTA) methods are required. In this paper, we propose a modular-based approach for the development of firmware of MCU-based IoT devices, and a new FUOTA method for devices communicating using LoRaWAN. This method enables partial and dynamic updates of modules forming the device firmware, and does not require a system reboot to apply these updates. This approach and this method have been implemented on STM32 MCUs. The experimental results show good performance in terms of update size and network traffic compared to the traditional FUOTA method and monolithic-based firmware development approach, which both impose full firmware updates. [ABSTRACT FROM AUTHOR]

Despite the ability of Low-Power Wide-Area Networks to offer extended range, they encounter challenges with coverage blind spots in the network. This article proposes an innovative energy-efficient and nature-inspired relay selection algorithm for LoRa-based LPWAN networks, serving as a solution for challenges related to poor signal range in areas with limited coverage. A swarm behavior-inspired approach is utilized to select the relays' localization in the network, providing network energy efficiency and radio signal extension. These relays help to bridge communication gaps, significantly reducing the impact of coverage blind spots by forwarding signals from devices with poor direct connectivity with the gateway. The proposed algorithm considers critical factors for the LoRa standard, such as the Spreading Factor and device energy budget analysis. Simulation experiments validate the proposed scheme's effectiveness in terms of energy efficiency under diverse multi-gateway (up to six gateways) network topology scenarios involving thousands of devices (1000–1500). Specifically, it is verified that the proposed approach outperforms a reference method in preventing battery depletion of the relays, which is vital for battery-powered IoT devices. Furthermore, the proposed heuristic method achieves over twice the speed of the exact method for some large-scale problems, with a negligible accuracy loss of less than 2%. [ABSTRACT FROM AUTHOR]

The research and management of Industry 4.0 increasingly relies on accurate real-time quality data to apply efficient algorithms for predictive maintenance. Currently, Low-Power Wide-Area Networks (LPWANs) offer potential advantages in monitoring tasks for predictive maintenance. However, their applicability requires improvements in aspects such as energy consumption, transmission range, data rate and constant quality of service. Commonly used battery-operated IIoT devices have several limitations in their adoption in large facilities or heat-intensive industries (iron and steel, cement, etc.). In these cases, the self-heating nodes together with the appropriate low-power processing platform and industrial sensors are aligned with the requirements and real-time criteria required for industrial monitoring. From an environmental point of view, the carbon footprint associated with human activity leads to a steady rise in global average temperature. Most of the gases emitted into the atmosphere are due to these heat-intensive industries. In fact, much of the energy consumed by industries is dissipated in the form of waste heat. With this scenario, it makes sense to build heat transformation collection systems as guarantors of battery-free self-powered IIoT devices. Thermal energy harvesters work on the physical basis of the Seebeck effect. In this way, this paper gathers the methodology that standardizes the modelling and simulation of waste heat recovery systems for IoT nodes, gathering energy from any hot surface, such as a pipe or chimney. The statistical analysis is carried out with the data obtained from two different IoT architectures showing a good correlation between model simulation and prototype behaviour. Additionally, the selected model will be coupled to a low-power processing platform with LoRaWAN connectivity to demonstrate its effectiveness and self-powering ability in a real industrial environment. [ABSTRACT FROM AUTHOR]

This paper elucidates the importance of creating backup communication channels in emergencies when access to cellular and satellite communication systems can be limited. The purpose of the study is to substantiate the use of LoRaWAN technology as a basis for building reliable backup communication facilities. A block diagram of the device has been proposed, which is used as an example for modeling the autonomy time of the device. It is shown that, depending on the mode of operation, this time ranges from 150 to 900 days. To estimate the radio accessibility zone of the device, the maximum transmission range was modeled using various models of radio wave propagation. The results of the investigations have shown that the developed emergency communication system based on the LoRaWAN protocol is effective and superior to other wireless communication technologies under certain conditions. [ABSTRACT FROM AUTHOR]

When building energy-efficient communication platforms for IoT, it is necessary to plan in advance a number of actions related to radio coverage and to anticipate possible problems to be solved before building the platform. Since all data traffic is transmitted wirelessly, it should provide reliable and quality radio coverage. Designing long-range LoRaWAN communications in outdoor environments with complex terrain is a challenging task that involves determining the correct location and height of the gateway to provide the necessary line-of-sight and minimize communication with reflected signals, as well as the appropriate equipment (e.g., transceiver antennas and radio modules). This article discusses the planning and research of such a type of radio coverage. For evaluation, the following were used: determining the coverage range and measuring the signal parameters, taking into account the receiver sensitivity; control of communication parameters; measurements and analysis in order to detect and eliminate existing disturbances and issues; and assessment of the quality of the service through survey and continuous monitoring in the coverage area. [ABSTRACT FROM AUTHOR]

In the context of an increasingly advanced era, Internet of Things (IoT) technology has emerged as a significant innovation across a range of fields. One of the most rapidly developing Internet of Things (IoT) technologies is the Long Range Wide Area Network (LoRaWAN). LoRaWAN is capable of long-distance communication while simultaneously consuming minimal power. In this study, we analyze the coverage of the LoRaWAN network in transmitting data with Cikumpa river water objects, with a 100–600 meters distance between the transmitter (TX) and receiver (RX). This study assesses the RSSI network quality, LoRaWAN SNR, and LoRaWAN network QoS quality concerning throughput, delay, jitter, and packet loss parameters. The testing results demonstrated that the LoRaWAN network coverage reaches a maximum distance of 600 meters. Researchers conducted the testing in the Cikumpa River area. They then analyzed the RSSI and SNR test results in the morning, afternoon, and evening. The results of the RSSI test and calculations demonstrate that as the distance between the transmitter and receiver increases, the RSSI value decreases. The RSSI testing conducted in the morning exhibited a range of -99 dBm to -121 dBm, with the SNR values spanning from -3.25 dB to 8.75 dB. The results of the daytime RSSI tests ranged from -104 dBm to -124 dBm, with the corresponding SNR values ranging from -8.50 dB to 9.00 dB. The RSSI test results for the afternoon period exhibited a range of -96 dBm to -120 dBm, while the SNR demonstrated a range of -7.25 dB to 9.00 dB. In addition, the quality of service (QoS) can be considered stable based on the results of the RSSI and SNR for each test. During the testing process, conducted at distances between 100 and 600 meters, there was no packet loss when data transmission occurred. This research demonstrates the potential for utilizing LoRaWAN technology to monitor a desired object remotely.