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The advancement of IT systems necessitates efficient communication methods essential across various sectors, from streaming platforms to cloud-based solutions and Industry 4.0 applications. Enhancing Quality of Service (QoS) in computer networks by focusing on bandwidth and communication delay is critical. Mechanisms like Active Queue Management (AQM) techniques are used, but more advanced solutions are needed to utilize the technological advances in communication technologies. Such advancements are Software-Defined Networks (SDN). Introduced by the SDN decoupling between the control plane and the data plane, it enables advanced real-time traffic shaping and centralized traffic management. This shift allows dynamic routing and improved QoS mechanisms, with research exploring multi-path routing. This paper proposes an extension to the Fluid Flow analysis model for complex networks. This modification allows for the simulation of various networking topologies and can be used to test novel routing and active queue management algorithms in more detail. The obtained numerical analysis demonstrates the model's advantages over traditional methods, enabling the exploration of new scenarios. [ABSTRACT FROM AUTHOR]

Summary: It is estimated that there will be over two dozen billion Internet of Things (IoT) connections in the future as the number of connected IoT devices grows rapidly. Due to characteristics like low power consumption and extensive coverage, low‐power wide area networks (LPWANs) have become particularly relevant for the new paradigm. Long range wide area network (LoRaWAN) is one of the most alluring technological advances in these networks. Although it is one of the most developed LPWAN platforms, there are still unresolved issues, such as capacity limitations. Hence, this research introduces a novel resource scheduling technique for the LoRAWAN network using deep reinforcement learning. Here, the information on the LoRaWAN nodes is learned by the reinforcement technique, and the knowledge is utilized to allocate resources to improve the packet delivery ratio (PDR) performance through a proposed coati optimal Q‐reinforcement learning (CO_QRL) model. Here, Q‐reinforcement learning is utilized to learn the information about nodes, and the coati optimization algorithm (COA) helps to choose the optimal action for enhancing the reward. In the proposed scheduling algorithm, the weighted sum of successfully received packets is treated as a reward, and the server allocates resources to maximize this Q‐reward. The evaluation of the proposed method based on PDR, packet success ratio (PSR), packet collision rate (PCR), time, delay, and energy accomplished the values of 0.917, 0.759, 0.253, 85, 0.029, 7.89, and 10.08, respectively. [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]

This study investigates the impact of environmental conditions on the structural integrity and energy dynamics of historical masonry buildings using an IoT (Internet of Things) LoRaWAN-based (Long Range Wide Area Network) wireless sensor system. Over a six-month period, sensors were used to monitor wall temperature, wall humidity, air temperature, air humidity, crack width, and crack displacement. The data revealed significant correlations between environmental parameters and structural changes. Higher temperatures were associated with increased crack width, while elevated humidity levels correlated with greater crack displacement, showing the potential weakening of the masonry structure. Seasonal variations highlighted the cyclical nature of these changes, emphasizing the need for seasonal maintenance. Additionally, the findings suggest that managing temperature and humidity levels can optimize the building's energy efficiency by reducing the need for additional heating or cooling. The use of LoRaWAN sensors provided real-time, remote monitoring capabilities, offering a cost-effective and scalable solution for preserving historical buildings. This study underscores the importance of continuous environmental and structural monitoring for the preservation of heritage sites. It also highlights the potential for integrating proactive maintenance strategies and energy optimization, ensuring long-term sustainability. By leveraging this IoT-based approach, this research contributes to the broader field of heritage conservation, offering a universal framework that can be applied to historical buildings worldwide, enhancing both their structural integrity and energy performance. [ABSTRACT FROM AUTHOR]

Summary: Vehicle utilization has increased on a broader scale in today's reality. Because of insufficient emergency services, increased motor traffic has resulted in the rise of traffic collisions, and widespread accidents lead to the demise of lives and property. Generally, the consequence death rate happens each year, originating from delays in rescue activities. However, vehicles are embedded with trend technology. Still, the accident count will rise day by day due to delays in transmitting the information to the concerned person or delays in rescue activities. The proposed work offers an effective solution to the aforementioned issue by assisting low‐power and long‐range (LoRa) architecture with edge node ultrasonic sensors for identifying the abnormalities and to provide an efficient transport ambulance from the unexpected mishap spot to the closest hospital where emergency well‐being can be provided with the help of cloud technology. Also, emergency information can be sent to the cloud immediately, and its response is the way of alerting the surroundings and notifying the appropriate hospital with the aid of a GPS‐enabled LoRa‐shield. Further, comparing the proposed technology with other LPWAN (low‐power wide area networks) in terms of coverage, cost, capacity, and energy consumption proves efficient communication in the development of intelligent vehicular networks towards a smart city. [ABSTRACT FROM AUTHOR]

This review article provides a comprehensive analysis of the latest advancements and persistent challenges in Software-Defined Wide Area Networks (SD-WANs), with a particular emphasis on the multi-objective Controller Placement Problem (CPP). As SD-WAN technology continues to gain prominence for its capacity to offer flexible and efficient network management, the task of 36optimally placing controllers—responsible for orchestrating and managing network traffic—remains a critical yet complex challenge. This review delves into recent innovations in multi-objective controller placement strategies, including clustering techniques, heuristic-based approaches, and the integration of machine learning and deep learning models. Each methodology is critically evaluated in terms of its ability to minimize network latency, enhance fault tolerance, and improve overall network performance. Furthermore, this paper discusses the inherent limitations and challenges associated with these techniques, providing a critical evaluation of their current utility and outlining potential avenues for future research. By offering a thorough overview of state-of-the-art approaches to multi-objective controller placement in SD-WANs, this review aims to inform ongoing advancements and highlight emerging research opportunities in this evolving field. [ABSTRACT FROM AUTHOR]

The advanced study will focus on developing a secure and cost-effective photovoltaic monitoring system using Long Range Wide Area Network (LoRaWAN) and Artificial Intelligence technologies for highly risky missions. With the world shifting to using renewable energy, the development of secure and efficient cost-effective monitoring systems for photovoltaic installations becomes very important. However, most of the solutions in use for PV installation monitoring are characterized by high costs, hence their limited use, especially in developing regions. In this regard, the present work presents country-specific protocols designed for the country of Turkey. The proposed system in this study is expected to offer real-time monitoring and data collection at a very meager cost by utilizing inexpensive microcontrollers and sensors. The accuracy rate demonstrated by the research is highly above board: 97.65%. It costs $210, with a very low power consumption of 0.5W and a range of 10 km. The major features underpin real-time monitoring with wide area coverage for effectiveness and efficiency in PVs monitoring. Test results in many places around Turkey have turned out reliable and accurate to justify a possible reduction in operational costs without compromising the quality of data received. This design is reinforced by a detailed mathematical model that assures one of its thorough analysis in performance. There is custom-built firmware that enhances communication efficiency and ensures data integrity in implementation. Comparative studies carried out with existing technologies indicate that the proposed system is cost-effective and operationally sound. This work gives a scalable, low-cost solution that probably might raise the uptake of PV systems in economically constrained areas. [ABSTRACT FROM AUTHOR]

This research paper explores the application of extended-gate field-effect transistors (EGFET) as a potentiometric sensing method for pH detection within an internet of things (IoT) system. The pH EGFET sensor is integrated with a long-range (LoRa) microcontroller, enabling data transmission via a low-power, long-range wide-area network (LoRaWAN) IoT framework to a dedicated IoT application server. The framework utilizes a message queuing telemetry transport (MQTT) broker, employing a publish/subscribe message architecture for efficient data transmission. The study focuses on addressing the problem of determining whether EGFET technology can provide precise and dependable measurements in various settings. To achieve this, the data from the IoT framework is compared with data signals from a semiconductor parametric analyzer and a readout interfacing circuit serial data acquisition (DAQ). From the study, EGFET sensors provide a sensitivity of 61.1 mV/pH with a linearity of 0.9968 through the IoT method. Meanwhile, non-IoT methods yield slightly different sensitivities of 53.1 and 50.5 mV/pH with comparable linearity of 0.9984 and 0.9979. Overall, the research demonstrates the versatility of EGFET technology, highlighting its effective use in various sensing instruments, while ensuring reliable data transfer through the LoRaWAN framework. [ABSTRACT FROM AUTHOR]

This paper presents an energy detection-based spectrum sensing approach implemented on the licensed Low Power Wide Area Network (LPWAN) i.e. Narrow Band-IoT communication model. Energy detection-based spectrum sensing depends upon the perceived signal strength of a primary user. Current work utilizes the computed Primary User's SNR present in NB-IoT network to evaluate the probability of detection at a given sampling time (NS), distance and defined probability of false alarm (PFA). Effect of multiple relays in spectrum detection performance is also investigated. Furthermore, for optimal energy detection relay and non-relay-based NB-IoT models are compared and analyzed using MATLAB. Performance evaluation of spectrum detection in presence of Noise uncertainty and Dynamic threshold for differing values of distance, sensing time (NS) and SNR in terms of Probability of False Alarm (PFA) and Probability of Detection (PD) has also been investigated. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices are ubiquitous in various applications, such as smart homes, asset and people tracking, and city management systems. However, their deployment in adverse conditions, including unstable internet connectivity and power sources, present new cybersecurity challenges through new attack vectors. The LoRaWAN protocol, with its open and distributed network architecture, has gained prominence as a leading LPWAN solution, presenting novel security challenges. This paper proposes the implementation of machine learning algorithms, specifically the K-Nearest Neighbours (KNN) algorithm, within an Intrusion Detection System (IDS) for LoRaWAN networks. Through behavioural analysis based on previously observed packet patterns, the system can detect potential intrusions that may disrupt critical tracking services. Initial simulated packet classification attained over 90% accuracy. By integrating the Suricata IDS and extending it through a custom toolset, sophisticated rule sets are incorporated to generate confidence metrics to classify packets as either presenting an abnormal or normal behaviour. The current work uses third-party multi-vendor sensor data obtained in the city of Lisbon for training and validating the models. The results show the efficacy of the proposed technique in evaluating received packets, logging relevant parameters in the database, and accurately identifying intrusions or expected device behaviours. We considered two use cases for evaluating our work: one with a more traditional approach where the devices and network are static, and another where we assume that both the devices and the network are mobile; for example, when we need to report data back from sensors on a rail infrastructure to a mobile LoRaWAN gateway onboard a train. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive risk assessment of various wide area network (WAN) technologies as applied to Operational Technology (OT) infrastructures, thus uncovering which WAN technology is best suited for OT to mitigate the risks of Denial of View (DoV), Denial of Control (DoC), and Denial of Service (DoS). A new risk weight-based evaluation approach is proposed following NIST CSF and ISA/IEC 62443 standard risk scoring (RS). In this approach, RS was modified by introducing new risk metrics, namely, risk (Rn), mitigation (Mm), risk prioritization (WRn), and mitigation prioritization (WMm) to create a specialized probability formula to assess risks on OT WAN infrastructure. The proposed formula has been implemented to automate data analysis and risk scoring across nine WAN technologies. The obtained results demonstrated that software-defined wide area network (SD-WAN) has the best security features that even overshadow its vulnerabilities to perform not just as a WAN solution but as a security solution against DoV, DoC, and DoS. Furthermore, this paper identifies and highlights what to prioritize when designing and assessing an SD-WAN setup. In addition, this paper proposes an SD-WAN-based architecture to reduce DoV, DoC, and DoS risks. [ABSTRACT FROM AUTHOR]

This paper proposes the GD (Geometric Distribution) algorithm, a novel approach to enhance the default Adaptive Data Rate (ADR) mechanism in the Long-Range Wide Area Network (LoRaWAN). By leveraging the Probability Mass Function (PMF) of the GD model, the algorithm effectively addresses biased node distributions encountered in real-world scenarios. Its ability to finely adjust the weight factor (w) or the probability of success in allocating SFs enables the optimization of spreading factor (SF) allocation, thereby achieving the optimal Data Extraction Rate (DER). To evaluate the algorithm's performance, simulations were conducted using the fixed node pattern derived from actual dairy farm locations in Ratchaburi province, Thailand. Comparative analyses were performed against the uniform random node pattern and existing algorithms, including the ADR, EXPLoRa, QCVM, and SD. The GD algorithm significantly outperformed existing methodologies for both fixed and uniform random node patterns, achieving a 14.3% and 4.8% improvement in DER over the ADR, respectively. While the GD algorithm consistently demonstrated superior DER values across varying coverage areas and payload sizes, it incurred a slight increase in energy consumption due to node allocations to higher SFs. Therefore, the trade-off between DER and energy consumption must be carefully weighed against the specific application. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a prediction‐based algorithm designed to address out‐of‐step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi‐machine power system is evaluated using the equal‐area criterion (EAC). The proposed algorithm calculates the characteristics of the P‐δ curves within the EAC framework after a large disturbance. The critical P‐δ trace is determined by analysing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. Furthermore, a complementary strategy is proposed to predict the OOS condition in integrated inverter‐based power systems. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73‐bus IEEE test power system.

Optimizing urban water distribution systems is essential for reducing economic losses, minimizing water wastage, and addressing resource access gaps, particularly in drought-prone regions impacted by climate change. We apply advanced artificial intelligence (AI) techniques and the Internet of Things (IoT) to optimize water networks in Spain using simulation. By employing EPANET for hydraulic modeling and a linear regression-based algorithm for optimization, we achieved up to 96.62% system efficiency with a mean absolute error of 0.049. Our approach demonstrates the potential to conserve up to 648,000 L of water daily at high-demand nodes, contributing to substantial resource savings across urban water networks. We propose a global architecture utilizing Low Power Wide Area Network and Low Earth Orbit solutions for widespread deployment. This study underscores the potential of AI in water network optimization and suggests future research avenues for implementing the proposed architecture in real urban water systems. [ABSTRACT FROM AUTHOR]

A cloud computing environment is a distributed system where idle resources are accessible across a wide area network, such as the Internet. Due to the diverse specifications of these resources, computational clouds exhibit high heterogeneity. Task scheduling, the process of dispatching cloud applications onto processing nodes, becomes a critical challenge in such environments. Ensuring high utilization in this heterogeneous environment entails identifying suitable machines or virtual machines capable of efficiently executing jobs, constituting a multi-objective optimization problem. This paper proposes a dynamic Learning Automata-based Task Assignment algorithm, named LATA, to address this challenge. In the algorithm, each application is represented as a Directed Acyclic Graph, with tasks as nodes and data dependencies as edges. Initially, tasks are grouped based on their data dependencies to consolidate independent tasks into one group. Subsequently, a variable-structure learning automaton is assigned to each group of tasks to identify appropriate task-machine combinations. The primary objectives of LATA include minimizing makespan and energy consumption by facilitating efficient task placement to achieve load balance and maximize resource utilization. Additionally, an enhancement is proposed, involving the use of a different grouping policy prior to task assignment to further improve performance. Computer simulation results demonstrate the superior performance of the proposed algorithms in highly heterogeneous environments compared to state-of-the-art algorithms. Notably, total execution time and energy consumption decrease by up to 50% and 37%, respectively. [ABSTRACT FROM AUTHOR]

Modern organizations have migrated from localized physical offices to work-from-home environments. This surge in remote work culture has exponentially increased the demand for and usage of Virtual Private Networks (VPNs), which permit remote employees to access corporate offices effectively. However, the technology raises concerns, including security threats, latency, throughput, and scalability, among others. These newer-generation threats are more complex and frequent, which makes the legacy approach to security ineffective. This research paper gives an overview of contemporary technologies used across enterprises, including the VPNs, Zero Trust Network Access (ZTNA), proxy servers, Secure Shell (SSH) tunnels, the software-defined wide area network (SD-WAN), and Secure Access Service Edge (SASE). This paper also presents a comprehensive cybersecurity framework named Zero Trust VPN (ZT-VPN), which is a VPN solution based on Zero Trust principles. The proposed framework aims to enhance IT security and privacy for modern enterprises in remote work environments and address concerns of latency, throughput, scalability, and security. Finally, this paper demonstrates the effectiveness of the proposed framework in various enterprise scenarios, highlighting its ability to prevent data leaks, manage access permissions, and provide seamless security transitions. The findings underscore the importance of adopting ZT-VPN to fortify cybersecurity frameworks, offering an effective protection tool against contemporary cyber threats. This research serves as a valuable reference for organizations aiming to enhance their security posture in an increasingly hostile threat landscape. [ABSTRACT FROM AUTHOR]

Deploying the Internet of Things (IoT) in integrated edge‐cloud environments exposes the IoT traffic data to performance issues such as delay, bandwidth limitation etc. Recently, Software‐Defined Wide Area Network (SD‐WAN) has emerged as an architecture that originates from the Software‐Defined Network (SDN) paradigm and provides solutions for networking multiple data centers by allowing network administrators to manage and control network layers. In this article, an SDWAN‐based policy for traffic management in IoT is introduced in which the Quality of Service (QoS) metrics such as end‐to‐end delay and bandwidth utilization are optimized. The proposed method implements the traffic management policy in the SDWAN controller. When the IoT traffic flows reach the SDWAN infrastructure network, graph search algorithms are performed to find the near‐optimal paths that affect the end‐to‐end delay of traffic flows. Because of the ability of deep learning to process complex data, a deep RNN model is used to predict the network state information, such as link latency and available bandwidth, before the traffic flows reach the infrastructure network. The proposed method consists of four key modules to predict the routes for future time intervals: (a) an SD‐WAN topology updater unit that checks the link changes and availability, (b) the network state information collector, which collects the network state information to create a dataset, (c) the learning unit, which trains a deep RNN model using the created dataset, and (d) the route predictor unit, which uses the trained model to predict the network state information using a heuristic algorithm to determine the routes. The simulation results showed that the deep RNN model can achieve high accuracy and low Mean Absolute Error (MAE), and the proposed method outperforms shortest‐path algorithms in terms of latency. At the same time, the available bandwidth is almost fairly distributed among all network links. [ABSTRACT FROM AUTHOR]

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]

Due to the advantages of LoRa technology such as long communication distance, low power consumption, and strong scalability, LoRa network has become one of the most widely used technologies in the field of low power wide area network (LPWAN). However, its increasing and rich application scenarios also pose new challenges to the security of LoRa network. It conducted a detailed survey to address the lack of comprehensive discussion on current LoRa network attack and defense methods. Firstly, it analyzed the LoRa network architecture, it summarized and the security differences between multiple versions of the LoRaWAN protocol. Secondly, through the study of a large number of documents, it analyzed the related technologies for LoRa network attack and defense. On this basis, it proposed a generative Al-based anti-RF fingerprinting mechanism-CAI-Anti-RFFI. Finally, it analyzed and prospected the possible development directions of LoRa network attack and de fense technology in the future. [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]

The Internet of Things (IoT) connects countless devices, such as sensors and actuators, necessitating an efficient long-range communication technology. Low Power Wide Area Network (LPWAN) solutions, such as LoRAWAN, SIGFOX, and NB-IoT, address this demand. LoRAWAN, known for its low power consumption, excels in Line of Sight (LOS) conditions, offering an effective long-range wireless communication. It's ideal for monitoring open areas. In Non-Line of Sight (NLOS) scenarios, LoRAWAN provides wide coverage and energy efficiency, though the signal quality may slightly decline. This research tests LoRAWAN's performance for sensor data communication both inside multi-story buildings (up to 8 storeys) and outside. The results show successful data transmission in both scenarios, including up to 2.60km with a 35 dBi outdoor antenna. [ABSTRACT FROM AUTHOR]

The millimeter wave communication technology used for drones could combine the advantages of drones and millimeter waves, providing high-speed data transmission and wide area network coverage capabilities, and has broad application prospects in military and civilian communication systems. Millimeter wave active antennas have the advantages of miniaturization, high frequency band, and flexible shaping, which is of great significance for ensuring the high-speed dynamic communication ability of drone platforms. In this paper, a millimeter wave active antenna suitable for unmanned aerial vehicles (UAVs) is designed and verified, operating in 24.75-27.5 GHz and adopting Antenna in Package (AiP) design. Frequency band test and communication performance test is conducted. To open and close the RF channels, the antenna's operating frequency range can be shown in the vector network analyzer which meets the design frequency band 24.75-27.5 GHz requirements. By loading 5G millimeter wave standard signals, the antenna can achieve real-time demodulation of 100 MHz, 256QAM signals. The test shows that the system can meet the requirements of beam tracking and real-time information transmission during high-speed dynamic flight of UAVs. It has broad application prospects in UAV communication systems. [ABSTRACT FROM AUTHOR]

This paper presents a prediction‐based algorithm designed to address out‐of‐step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi‐machine power system is evaluated using the equal‐area criterion (EAC). The proposed algorithm calculates the characteristics of the P‐δ curves within the EAC framework after a large disturbance. The critical P‐δ trace is determined by analysing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. Furthermore, a complementary strategy is proposed to predict the OOS condition in integrated inverter‐based power systems. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73‐bus IEEE test power system. [ABSTRACT FROM AUTHOR]

Water scarcity is nowadays a critical global concern and an efficient management of water resources is paramount. This paper presents an original approach for monitoring Water Distribution Systems (WDSs) through Internet of Things (IoT) that involves the integration of multiple sensors placed across the distribution network to accurately measure water flow. To enhance energy efficiency for green monitoring and communication process, we harness the power of graph theory and graph signal processing to represent in a tunable and accurate way the water flow and simultaneously minimize the number of IoT sensors communicating those measurements. We propose a graph model where water flow is represented as signal on graph and we introduce an algorithm, named GraphSmart, designed to reconstruct the graph signal when certain measurements are unknown or missing. Our framework is applied on a synthetic realistic environment within the context of LoRaWAN (Long Range Wide Area Network), an infrastructure and protocol designed for ultra-low-power IoT devices. Our findings show that GraphSmart significantly reduces energy consumption while ensuring precise flow estimation. Our research demonstrates high potential for energy-efficient and accurate water flow monitoring, paving the way to improve the management of WDSs and enabling water operators to address water scarcity challenges. [ABSTRACT FROM AUTHOR]

Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) is a new physical layer option that has been recently added to the LoRa family with the promise of achieving much higher network capacity than the previous versions of LoRa. In this article, we present our evaluation of LR-FHSS based on real-world packet traces collected with an LR-FHSS device and a receiver we designed and implemented in software. We overcame challenges due to the lack of documentation of LR-FHSS, and our study is the first of its kind that processes signals transmitted by an actual LR-FHSS device with practical issues such as frequency error. Our results show that LR-FHSS meets its expectations in communication range and network capacity. We also propose customized methods for LR-FHSS that improve its performance significantly, allowing our receiver to achieve higher network capacity than those reported earlier. [ABSTRACT FROM AUTHOR]

This research examines the enhancement of low power electronic systems, such as low power wide area networks (LPWANs), by incorporating a mix of renewable energy sources like solar and wind in China. It addresses the challenges posed by the variability of these sources due to climatic and temporal factors by merging diverse energy harvesting (EH) methods to bolster power stability and availability. The study assesses the most effective combination of solar, wind, and rain EH for steady power supply. A novel algorithm is introduced for energy input management based on real‐time resource availability, with the goal of prolonging battery life by avoiding complete charge or discharge cycles. The feasibility of this multisource approach in possibly reducing the need for extra energy storage is evaluated. The investigation includes simulations using Chinese weather data to explore different combinations of energy sources. A real‐life system utilizing solar and wind energy, guided by the developed algorithm, was also implemented for empirical comparison. Findings suggest that solar and wind energies have a higher power yield than rain harvesting, with actual wind energy collection often falling short of simulated forecasts. The solar–wind energy mix achieves a 99% system availability and facilitates a more compact design owing to their high power density. Although the input‐switching algorithm lessens the frequency of complete battery drainage, a small degree of energy storage is still essential for maintaining system reliability. This integrated EH method, compared to single‐source options, allows for a smaller energy storage requirement. Moreover, the algorithm limits battery charging to 80%, significantly enhancing battery lifespan. [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]

In today's modern and globalized world, connectivity is a key factor for businesses, production facilities, sensor networks, and ordinary people. However, there are still populated areas which are not covered by ground-based telecommunications infrastructure. This is where telecommunication satellite constellations come in, as they can provide coverage to remote and uninhabited regions and fill existing connectivity gaps to ensure data transfer. LoRa is one of the technologies designed for data transmissions over long distances with low power consumption. Alongside with other technologies of the Low-Power Wide Area Network family, it is widely used for Internet of Things applications. LoRa chirp spread spectrum modulation is robust against the Doppler frequency shifts encountered in low earth orbits, and it has already been used in IoT satellite communications. Due to the low transmitted signal power, the achieved data rate is not high, making it a suitable technology for telecommunications payloads on CubeSat platforms for messaging services. As compared to existing traditional communication satellite systems, CubeSat constellations are low-cost and may offer an affordable connectivity service to developing regions. This study is divided in two parts. In Part I the demand model is built based on the population distribution not covered by cell towers. The LoRa link performance is analyzed, considering the impact of LoRa channel parameters variation, such as spreading factor and channel bandwidth, while satellite orbital height, transmission antenna beamwidth, and transmitter peak power have a direct impact on the payload mass. Among thousands of possible configurations, 73 feasible payload designs have been downselected. In Part II of the study, the satellite mass and the total system cost are estimated based on the payload parameters obtained. Messages transmission simulation via a constellation is conducted in order to identify optimal constellation architectures for messaging service, as well as the main drivers of the system economic profitability. The presented analysis results provide a deeper understanding of LoRa connectivity advantages and limitations together with the performance drivers, which will support the optimization of future LoRa-based satellite communication systems and other IoT satellite constellations. • A general framework for communications satellite constellation is suggested. • LoRa technology feasibility for messaging service via satellites has been proved. • LoRa channel capacity is sufficient for 1 message per day per uncovered user. • SF drives achievable channel capacity, orbit height and antenna beamwidth. • 73 payloads were downselected, with 2x2-8x8 patch antennas and transceivers of 1-2W. [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]

This study addresses the growing threat of network attacks by exploring their types and analyzing the challenges associated with their precise detection. To mitigate these threats, we propose a novel cyber security approach that integrates Genetic Algorithm (GA) and neural network architecture. The GA is employed for the selection and optimization of attributes that represent DDoS and malware attack features. These optimized features are then fed into a neural network for training and classification. The effectiveness of the proposed approach was evaluated through precision, recall, and F-measure analyses, demonstrating superior detection capabilities for DDoS and malware attacks compared to existing methods. Furthermore, we introduce a hybrid approach that combines Swarm Intelligence (SI) and nature-inspired techniques. The GA is utilized to select features and reduce the dataset size, followed by the application of Discrete Wavelet Transform (DWT) with Artificial Bee Colony (ABC) to further filter irrelevant features. The results show that this hybrid approach significantly enhances the accuracy and efficiency of network attack detection in wide area networks. [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]

IoT is critical in many application areas, such as smart cities, health care, and surveillance systems. Each application has its own QoS requirements. Dynamic traffic management in an IoT network is essential for optimal load balancing and routing. It also allows applications to achieve their desired level of QoS. Osmotic computing is a paradigm for edge/cloud integration. In this paradigm, to balance the load of the network hosts, the services must migrate from a higher resource-utilized data center to a smaller one. According to the osmotic computing approach, each IoT application could be broken into some Micro-Elements (MELs), and each MEL resides on a resource on the edge or cloud data center. Usually, in an IoT osmotic environment, services must be executed by the edge hosts. Some remaining services must migrate to the cloud data centers if the edge hosts lack computational resources. Therefore, such data migration may produce massive traffic across the network. Moreover, the traffic sometimes must pass through a particular route, which includes some pre-specified nodes, for security or monitoring reasons. The routes must be optimized regarding QoS metrics such as delay, jitter, and packet loss ratio. Therefore, finding an optimal path between the source and the destination MEL is essential. Deep learning can facilitate this process by exploiting the massive routing data to find the optimal routes with pre-specified node(s). For this purpose, this paper proposes a new traffic management algorithm based on a deep RNN model. The algorithm predicts the alternative optimal routes, including the desired node (s), in an IoT osmotic environment. A collection of paths is generated using the minimum-distance maximum-bandwidth routing algorithm to create the dataset. The IoT osmotic environment consists of three main layers: the edge data center, Software-Defined Wide Area Network (SDWAN) infrastructure, and cloud data centers. The proposed traffic management algorithm is implemented in the controller of each layer. The simulation results showed that the osmotic approach increased the energy consumption of the edge devices and reduced the transaction time. Because the data is processed near the user, the flow size of the traffic, which is sent across the network, is reduced. The experimental results also showed that the model could achieve up to 94% accuracy. The model training and prediction time do not affect the application's total running time. [ABSTRACT FROM AUTHOR]

Controller placement issue (CPI) in software-defined networks (SDNs) describes the controllers' number, location, and assigning of forwarding devices to controllers. Recently, mathematical formulations and algorithms have been proposed to solve various problems in SDNs and software-defined wide area networks. The comprehensive literature review can be divided into four groups according to objectives: (i) minimizing latency between forwarding devices or switches and their corresponding controllers, and minimizing latency between controllers, (ii) improving network resilience and stability, (iii) minimizing energy consumption and installation costs and (iv) using multi-objective approaches. In addition to the objectives of each research, the importance of this paper is to examine the CPI in terms of reducing the network search space in order to optimally place the controller and how to assign switches to the controllers. In this paper, first the mathematical formulations in previous studies will be examined and then, for solving CPI, the existing algorithms will be discussed. Different classifications of CPIs and related formulas/algorithms, descriptions, advantages and disadvantages will be separately provided. A comprehensive comparison of proposed approaches with their advantages and disadvantages in the summarized tables will be provided. Also, a comparative discussion of different statistics of CPIs will be presented in terms of some technical features such as objective-oriented problems and parameters in four categories, estimated environments, and efficient estimating factors in CPIs. Finally, we explained the future studies' challenges, problems related to CPIs, ideas and following orientations in this field. [ABSTRACT FROM AUTHOR]

In wide area SDP networks, the failure of SDP components caused by malicious attacks will be accompanied by different deployment locations, profoundly affecting network service latency. However, traditional deployment methods based on prior knowledge are no longer applicable to dynamic SDP networks. This article proposes a dynamic and dimensionally variable deployment mechanism DDPG-SDPCR for SDP components based on DDPG, which enhances the network's endogenous security capability and improves attack tolerance. Based on this, we constructed corresponding mathematical models for latency, load balancing, and attack tolerance. The DDPG-SDPCR mechanism dynamically deploys new SDP nodes to replace faulty nodes based on the real-time status of the network, thereby achieving imperceptible attack tolerance for users. We have implemented a wide area SDP prototype with endogenous security capabilities and evaluated it under different network topologies, traffic sizes, and network attacks. The evaluation results indicate that under high traffic conditions, our proposed redeployment mechanism outperforms the baseline by 36.42% in latency, and only increases by 19.24% compared to the non attacked situation. [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 aims to address the challenges of climate change, which has led to extreme temperature events and reduced rainfall, using Internet of Things (IoT) technologies. Specifically, we monitored the effects of drought on maize crops in the Republic of Croatia. Our research involved analyzing an extensive dataset of 139,965 points of weather data collected during the summer of 2022 in different areas with 18 commercial sensor nodes using the Long-Range Wide Area Network (LoRaWAN) protocol. The measured parameters include temperature, humidity, solar irradiation, and air pressure. Newly developed maize-specific predictive models were created, taking into account the impact of urbanization on the agrometeorological parameters. We also categorized the data into urban, suburban, and rural segments to fill gaps in the existing literature. Our approach involved using 19 different regression models to analyze the data, resulting in four regional models per parameter and four general models that apply to all areas. This comprehensive analysis allowed us to select the most effective models for each area, improving the accuracy of our predictions of agrometeorological parameters and helping to optimize maize yields as weather patterns change. Our research contributes to the integration of machine learning and AI into the Internet of Things for agriculture and provides innovative solutions for predictive analytics in crop production. By focusing on solar irradiation in addition to traditional weather parameters and accounting for geographical differences, our models provide a tool to address the pressing issue of agricultural sustainability in the face of impending climate change. In addition, our results have practical implications for resource management and efficiency improvement in the agricultural sector. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) based smart strategies are often resource constrained with respect to energy, computation and memory. Outdated communication protocols are inappropriate for IoT ecosystem because of large overhead, lack of Quality of Service (QoS) and increased complexity. As billions of devices are required to be deployed over diverse applications, the IoT communication system acts as a prominent aspect and so the selection of suitable IoT communication protocol is highly essential. Also, there is a greater need of analysing the protocol behaviour under diverse network conditions. Hence to select a suitable protocol by addressing the limitations, this research paper mainly focuses on comparing lightweight application layer protocols, including Message Queuing Telemetry Transport (MQTT), Constrained Application Protocol (CoAP) and MQTT for sensor Network (MQTTSN). Evaluating the performance of protocol libraries in real environment is highly significant because it helps to discover potential interoperability and compatibility challenges. Also, it can reveal the protocol's ability in handling scalability and its support in dealing a number of devices efficiently. A testbed named "ProtoLab" has been created for evaluating the performances of CoAP, MQTT and MQTTSN protocols under variable network condition. Using the testbed, the client and server can exchange the data packets under the variable network condition created with the help of network emulator. The data packets can be received and exported using the wireshark application to create a dataset for analysis. Different parameters like round trip time, duplication, round trip reliability, server response time, reliability towards the client to server and client overhead are analysed by configuring loss, corruption, reordering and network delay in the network emulator using wide area network emulator (WANEM) to evaluate the performance of IoT communication protocols. Variable network conditions are considered and analysed using real-time ProtoLab testbed by varying the parameters. The results and observations analyzed through this research can support IoT application developers in making informed decisions while selecting communication protocols for different applications. On analysing the parameters under diverse network conditions, the MQTTSN protocol performs comparatively better in terms of resource efficient delivery in constrained environment. Meanwhile, the MQTT protocol is analysed to be better when concerned with reliability. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) is the third evolution of the traditional Internet, enabling interaction and communication among machines. IoT platforms and networks have been developed, and market sectors have recently started developing specific IoT applications and services. Integrating heterogeneous IoT networks with the existing ones, mainly cellular networks, is in great demand. IoT represents one of the prominent use cases of the sixth-generation (6G) cellular system, as announced by the Third Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU). This study focuses on Heterogeneous IoT networks over 6G networks with ultra-dense deployment using Mobile edge computing and software-defined wide-area network (MEC/SDWAN). This software-defined wide-area network is used for designing systematic Wireless Inventory Trackers (WIT). Wireless inventory trackers identify the exact location. However, the heterogeneous network consists of interconnected nodes and links of different types with a software-defined Wide Area Network(SD-WAN). To address the heterogeneous network system capacity issue, the HIoT and Ultra-Dense deployment are employed for making the promising technologies in the 6G to manage them, enhancing the spatial reuse through flexible, intensive deployment. This procedure helps to improve the prolonged lifetime of up to 2.5 h with low energy consumption of up to (3%) on efficient communications in the 6G cellular network. [ABSTRACT FROM AUTHOR]

The proliferation of solar panel installations presents significant societal and environmental advantages. However, many panels are situated in remote or inaccessible locations, like rooftops or vast desert expanses. Moreover, monitoring individual panel performance in large-scale systems poses a logistical challenge. Addressing this issue necessitates an efficient surveillance system leveraging wide area networks. This paper introduces an Internet of Sensing Things (IoST)-based monitoring system integrated with sun-tracking capabilities for solar panels. Cutting-edge sensors and microcontrollers collect real-time data and securely store it in a cloud-based server infrastructure, enabling global accessibility and comprehensive analysis for future optimization. Innovative techniques are proposed to maximize power generation from sunlight radiation, achieved through continuous panel alignment with the sun's position throughout the day. A solar tracking mechanism, utilizing light-dependent sensors and servo motors, dynamically adjusts panel orientation based on the sun's angle of elevation and direction. This research contributes to the advancement of efficient and sustainable solar energy systems. Integrating state-of-the-art technologies ensures reliability and effectiveness, paving the way for enhanced performance and the widespread adoption of solar energy. Additionally, the paper explores anomaly prediction using Rayleigh distribution, offering insights into potential irregularities in solar panel performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has expanded rapidly, allowing for a network of sensors and gadgets to collect and share information to make people's lives easier and more convenient. As the Internet of Things (IoT) grows, however, energy efficiency becomes a major issue, especially for portable and wireless gadgets. Low-power, long-range communication capabilities are needed, and Long-Range Wide Area Network (LoRaWAN) has emerged as a viable solution to meet this need. This study provides an in-depth analysis of the LoRaWAN-based, low-power Internet of Things. The suggested network architecture is optimized for low power consumption and high connectivity for numerous Internet of Things (IoT) use cases. This low-power Internet of Things network relies on LoRaWAN gateways, end devices, and a server to function. LoRaWAN is a technology that enables the low-power, long-range transmission of data packets. The results show that the optimized case and nonoptimized case have a delivery ratio of 0.85 to 0.73 from node 100 to 500. LoRaWAN significantly reduces energy usage compared to conventional IoT connectivity alternatives, making it a fantastic option for battery-powered devices in far-flung or limited-resource locations. Finally, the adoption of LoRaWAN provides a viable solution to address the energy efficiency concerns in IoT networks, hence allowing for sustainable, long-lasting IoT installations and enabling a wide variety of new applications within the IoT ecosystem. Furthermore, addresses the potential applications of this technology in the future, including upgrades and integration with other technologies like edge computing and 5G networks. [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]

SD-WANs are an innovative software-defined network (SDN) technology used to reinvent networks, services, and applications in wide area network (WANs). The development of SD-WANs ranges from network optimization in the past to service provision platforms at present and distributed computing systems in the future. The existing surveys on SD-WANs are fragmented, covering specific problems only, and are not comprehensive with detailed research directions. This paper seeks to provide a systematic survey on SD-WANs by introducing major research directions and stating specific problems. Therefore, four major research directions related to traffic engineering, network optimization and systems, service orchestration, and the security issues of SD-WANs are sequentially introduced, along with detailed statements relating to specific problems and the classification of state-of-the-art research. Finally, the trends and challenges regarding SD-WANs are summarized and our future work is described. [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]

"NetNotes" is a document containing a series of questions and responses from a Confocal Listserver. The topics discussed include the temperature rating of Olympus immersion oil, removing hardened immersion oil from Nikon objectives, and light-sheet systems for imaging 96-well plates. Participants share their experiences and recommendations for addressing these issues. It is important to note that the perspectives provided are from individuals with diverse backgrounds and may not reflect official manufacturer recommendations. The discussion covers topics such as imaging BSL-2 samples safely, recommendations for stage-top incubators for live organoid imaging, building a microscope objective heater, and configuring an IR fluorescence microscope for imaging carbon nanotubes in plant samples. The text also includes inquiries and responses related to finding SEM software libraries, a common repository for microscopy scripts, pre-written code for SEM functions, the use of methanol in cryo-EM samples, and astigmatism in low magnification mode on a TFS microscope. [Extracted from the article]

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]