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This paper investigates and compares experimental techniques in irrigation systems, focusing on crucial factors such as soil fertility, moisture level, pH level, water availability, and temperature for efficient agriculture and increased food productivity. The proposed method involves transmitting data from sensors (e.g., soil-moisture, pH, temperature, and air moisture) to an IoT platform utilizing LoRa communication protocol, processing it, and utilizing it for developing a fuzzy-control action to determine the optimal irrigation valve opening while also notifying the irrigation water pH. LoRa is a low-power wide area network solution enabling longrange communication with minimal energy consumption. The objective is to create a smart irrigation system for efficient water management in agriculture. The system monitors parameters like soil moisture, ambient temperature, and water pH, utilizing a fuzzy logic controller to regulate irrigation and minimize water wastage. LabVIEW facilitates data processing, valve control, and sensor data acquisition. LoRa is used for data transmission to a data concentrator and then broadcasted to the TTN platform for visualization and analysis. This setup demonstrates the effectiveness of LoRa and the fuzzy logic controller in achieving resourceefficient irrigation practices for sustainable agriculture. [ABSTRACT FROM AUTHOR]

Internet of things phenomenon has brought up distinctive technologies that are using wireless communication and appearing in smart city applications. Long range (LoRa) modulation technique has pulled up the market and forced the announcement of LoRa wide area network (LoRaWAN) standard in 2021 by ITU-T with Y.4480 standard code. LoRaWAN is a medium access control protocol using low power wide area network approaches with the aim of long-range coverage and management of many end devices. LoRaWAN networks are emerging all over the world with some existing optimization, planning and network allocation problems that need to be overcome. This paper focuses on comparative analysis and interpretation of measurements performed in a LoRaWAN network deployed in an 18-floor building with a LoRaWAN gateway on the roof. The research covers results of comparative measurements between end device and Adeunis field test device (AFTD) for received signal strength indicator (RSSI), signal-to-noise ratio (SNR) and spreading factor (SF). End devices have been randomly selected from 18th, 12th, 6th and 1st floors and their daily performance data have been gathered through the network server. AFTD has been used to get 100 sample measurements for each floor. Maximum and average RSSI values obtained from end device measurements are higher than ones measured with AFTD except the case in the 18th floor. Excluding the maximum SNR values at the 1st and the 18th floors, all SNR values measured with AFTD are higher than ones obtained from end device measurements. SF measurements show that higher SF values are more likely to be used with increasing distances to the gateway as expected from the theoretical background. [ABSTRACT FROM AUTHOR]

As a promising architecture of next‐generation network, software defined‐information centric network (SD‐ICN) inherits the advantages of software defined network (SDN) and information‐centric network (ICN) to enable flexible and fast content retrieval, especially in the current era of artificial intelligence. However, the existing researches mainly focus on a single respective in this field, which motivates in comprehensively providing a forward‐looking guidance and development direction for scholars and engineers. To this end, the latest developments of SD‐ICN is presented. First, the widely‐accepted concepts and impacts on traditional networks are introduced. Second, the shortcomings of SDN and ICN over conventional networks are respectively analyzed to illustrate the necessity of SD‐ICN. Third, based on extensive analysis and deep deliberation, a methodical taxonomy for existing combination studies is proposed. They are divided into SDN over ICN, ICN over SDN, and mutual immersive pattern. Fourth, the performances of three integration categories are compared and the limitations of related works are highlighted. Fifth, the maturity index from six development indicators are evaluated. Further, the maturity and practicality of these schemes are generalized. Based on the above studies and comparisons, the lessons learned by SDN and ICN developments are concluded. Finally, future research directions and opportunities are discussed for the readers. [ABSTRACT FROM AUTHOR]

The majority of IoT implementations demand sensor nodes to run reliably for an extended time. Furthermore, the radio settings can endure a high data rate transmission while optimizing the energy-efficiency. The LoRa/LoRaWAN is one of the primary low-power wide area network (LPWAN) technologies that has highly enticed much concentration. The energy limits is a significant issue in wireless sensor networks since battery lifetime that supplies sensor nodes have a restricted amount of energy and neither expendable nor rechargeable in most cases. A common hypothesis is that the energy consumed by sensors in sleep mode is negligible. With this hypothesis, the usual approach is to consider subsets of nodes that reach all the iterative targets. These subsets also called coverage sets, are then put in the active mode, considering the others are in the low-power or sleep mode. In this paper, we address this question by proposing an energy consumption model based on LoRa and LoRaWAN, which optimizes the energy consumption of the sensor node for different tasks for a period of time. Our energy consumption model assumes the following, the processing unit is in on-state along the working sequence which enhances the MCU unit by constructing it in low-power modes through most of the activity cycle, a constant time duration, and the radio module sends a packet of data at a specified transmission power level. The proposed analytical approach permits considering the consumed power of every sensor node element where the numerical results show that the scenario in which the sensor node transfers data to the gateway then receives an acknowledgment RX2 without receiving RX1 consumes the most energy; furthermore, it can be used to analyze different LoRaWAN modes to determine the most desirable sensor node design to reach its energy autonomy where the numerical results detail the impact of scenario, spreading factor, and bandwidth on power consumption. [ABSTRACT FROM AUTHOR]

Remote patient monitoring is a critical component of digital medicine, and the COVID-19 pandemic has further highlighted its importance. Wearable sensors aimed at noninvasive extraction and transmission of high-fidelity physiological data provide an avenue toward at-home diagnostics and therapeutics; however, the infrastructure requirements for such devices limit their use to areas with well-established connectivity. This accentuates the socioeconomic and geopolitical gap in digital health technology and points toward a need to provide access in areas that have limited resources. Low-power wide area network (LPWAN) protocols, such as LoRa, may provide an avenue toward connectivity in these settings; however, there has been limited work on realizing wearable devices with this functionality because of power and electromagnetic constraints. In this work, we introduce wearables with electromagnetic, electronic, and mechanical features provided by a biosymbiotic platform to realize high-fidelity biosignals transmission of 15 miles without the need for satellite infrastructure. The platform implements wireless power transfer for interaction-free recharging, enabling long-term and uninterrupted use over weeks without the need for the user to interact with the devices. This work presents demonstration of a continuously wearable device with this long-range capability that has the potential to serve resource-constrained and remote areas, providing equitable access to digital health. [ABSTRACT FROM AUTHOR]

Seizures beget seizures is a longstanding theory that proposed that seizure activity can impact the structural and functional properties of the brain circuits in ways that contribute to epilepsy progression and the future occurrence of seizures. Originally proposed by Gowers, this theory continues to be quoted in the pathophysiology of epilepsy. We critically review the existing data and observations on the consequences of recurrent seizures on brain networks and highlight a range of factors that speak for and against the theory. The existing literature demonstrates clearly that ictal activity, especially if recurrent, induces molecular, structural, and functional changes including cell loss, connectivity reorganization, changes in neuronal behavior, and metabolic alterations. These changes have the potential to modify the seizure threshold, contribute to disease progression, and recruit wider areas of the epileptic network into epileptic activity. Repeated seizure activity may, thus, act as a pathological positive‐feedback mechanism that increases seizure likelihood. On the other hand, the time course of self‐limited epilepsies and the presence of seizure remission in two thirds of epilepsy cases and various chronic epilepsy models oppose the theory. Experimental work showed that seizures could induce neural changes that increase the seizure threshold and decrease the risk of a subsequent seizure. Due to the complex nature of epilepsies, it is wrong to consider only seizures as the key factor responsible for disease progression. Epilepsy worsening can be attributed to the various forms of interictal epileptiform activity or underlying disease mechanisms. Although seizure activity can negatively impact brain structure and function, the "seizures beget seizures" theory should not be used dogmatically but with extreme caution. [ABSTRACT FROM AUTHOR]

Multiple Gateways (GWs) provide network connectivity to Internet of Things (IoT) sensors in a Wide Area Network (WAN). The End Nodes (ENs) can connect to any GW by discovering and acquiring its periodic beacons. This provides GW diversity, improving coverage area. However, simultaneous periodic beacon transmissions among nearby GWs lead to interference and collisions. In this study, the impact of such intra-network interference is analyzed to determine the maximum number of GWs that can coexist. The paper presents a new collision model that considers the combined effects of the Medium Access Control (MAC) and Physical (PHY) layers. The model takes into account the partial overlap durations and relative power of all colliding events. It also illustrates the relationship between the collisions and the resulting packet loss rates. A performance evaluation is presented using a combination of analytical and simulation methods, with the former validating the simulation results. The system models are developed from experimental data obtained from field measurements. Numerical results are provided with Gaussian Frequency Shift Keying (GFSK) modulation. This paper provides guidance on selecting GFSK modulation parameters for low bit-rate and narrow-bandwidth IoT applications. The analysis and simulation results show that larger beacon intervals and frequency hopping help in reducing beacon loss rates, at the cost of larger beacon acquisition latency. On the flip side, the gateway discovery latency reduces with increasing GW density, thanks to an abundance of beacons. [ABSTRACT FROM AUTHOR]

New networking issues are presented by the increasing need for a wide variety of applications, which has spurred the creation of a new internet of things (IoT) paradigm, such as long range (LoRa). The LoRa protocol uses a patented kind of spread spectrum modulation to provide low-power, long-range communication. In this paper, we provide a comprehensive review of LoRa-IoT in terms of IoT applications, LoRa class, security and privacy requirements, and the evolution of LoRa technology. This review analysis and compares long range wide area network (LoRaWAN) to wireless technology (e.g., Bluetooth, LoRa, 5G, Sigfox, long term evolution-M (LTE-M), Wi-Fi, Z-wave, Zigbee) and provides a list of environment simulators (e.g., OMNeT++, MATLAB, ns-3, SimPy) to carry out experiment for LoRa-IoT. Finally, this review does not only review literature recently studied for LoRa-IoT but also discusses challenges and future directions. [ABSTRACT FROM AUTHOR]

Private set intersection (PSI) is a pivotal subject in the realm of privacy computation. Numerous research endeavors have concentrated on situations involving vast and imbalanced sets. Nevertheless, there is a scarcity of existing PSI protocols tailored for small sets. Those that exist are either restricted to interactions between two parties or necessitate resource-intensive homomorphic operations. To bring forth practical multiparty private set intersection solutions for small sets, we present two multiparty PSI protocols founded on the principles of Oblivious Key–Value Stores (OKVSs), polynomials, and gabled cuckoo tables. Our security analysis underscores the resilience of these protocols against malicious models and collision attacks. Through experimental evaluations, we establish that, in comparison to related endeavors, our protocols excel in small-set contexts, particularly in low-bandwidth wide area network (WAN) settings. [ABSTRACT FROM AUTHOR]

The existing highway construction monitoring systems are independently deployed. This paper proposes an intelligent monitoring system for highway construction based on remote wide area networks. By employing the tracking devices equipped with inertial measurement units and global positioning systems, the workers' activity space is monitored. People and equipment entering the site are registered by radio frequency identification tags with protective measures. Motion sensors are employed to monitor the changes in scaffolding, building structures, and highway low-grade inclinations. In addition, a light-weight object detection (LOD) algorithm is proposed to detect construction workers who fail to wear safety helmets. The LOD algorithm replaces the standard convolutional networks with depthwise separable convolution networks. It introduces the receptor field module, employs the LOD-NMS algorithm and Mish activation function, and sets an appropriate prior box according to the characteristics and ratios of people's heads in Asia, thus well balancing the precision and prediction efficiency. Experiments are conducted on the WIDER FACE open source data set and the real data sets. The results show LOD algorithm not only detects workers' safety helmets in real time with high precision, but also achieves higher recognition accuracy than traditional algorithms despite the low-resolution input images. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a networking architecture with improved efficiency achieved by moving networking decisions from the data plane to provide them critically at the control plane. In a traditional SDN, typically, a single controller is used. However, the complexity of modern networks due to their size and high traffic volume with varied quality of service requirements have introduced high control message communications overhead on the controller. Similarly, the solution found using multiple distributed controllers brings forth the 'controller placement problem' (CPP). Incorporating switch roles in the CPP modelling during network partitioning for controller placement has not been adequately considered by any existing CPP techniques. This article proposes the controller placement algorithm with network partition based on critical switch awareness (CPCSA). CPCSA identifies critical switch in the software defined wide area network (SDWAN) and then partition the network based on the criticality. Subsequently, a controller is assigned to each partition to improve control messages communication overhead, loss, throughput, and flow setup delay. The CPSCSA experimented with real network topologies obtained from the Internet Topology Zoo. Results show that CPCSA has achieved an aggregate reduction in the controller's overhead by 73%, loss by 51%, and latency by 16% while improving throughput by 16% compared to the benchmark algorithms. [ABSTRACT FROM AUTHOR]

Communication distances of wireless sensor networks (WSNs) are greatly limited in settings where vegetation coverage is moderate or dense, and power consumption can be an issue in remote environmental settings. A newer innovative technology called "Low Power Wide Area Sensor Networks" (LPWAN) is capable of greater communication distances while consuming less power than traditional WSNs. This research evaluates the design and in-field performance of a LPWAN configuration in headwater catchments to measure environmental variables. The performance of the Beta LPWAN deployment indicate reduced signal strength in topographic valleys, but better actual than modelled data transmission performance. System performance during extreme cold temperatures (below – 15 ºC) resulted in increased sensor down time. The configuration of antennae combinations provides the greatest improvement in signal strength and system performance. This technology facilitates remote collection of physically-based, spatially-distributed information within regions with limited accessibility, ultimately advancing data collection capabilities into areas that are not feasible to visit regularly. [ABSTRACT FROM AUTHOR]

The Internet of Remote Things (IoRT) offers an exciting landscape for the development and deployment of remote wireless sensing nodes (WSNs) which can gather useful environmental data. Low Power Wide Area Networks (LPWANs) provide an ideal network topology for enabling the IoRT, but due to the remote location of these WSNs, the power and energy requirements for such systems must be accurately determined before deployment, as devices will be running on limited energy resources, such as long-life batteries or energy harvesting. Various sensor modules that are available on the consumer market are suitable for these applications; however, the exact power requirements and characteristics of the sensor are often not stated in datasheets, nor verified experimentally. This study details an experimental procedure where the energy requirements are measured for various sensor modules that are available for Arduino and other microcontroller units (MCUs). First, the static power consumption of continually powered sensors was measured. The impact of sensor warm-up time, associated with powering on the sensor and waiting for reliable measurements, is also explored. Finally, the opportunity to reduce power for sensors which have multiple outputs was investigated to see if there is any significant reduction in power consumption when obtaining readings from fewer outputs than all that are available. It was found that, generally, CO2 and soil moisture sensors have a large power requirement when compared with temperature, humidity and pressure sensors. Limiting multiple sensor outputs was shown not to reduce power consumption. The warm-up time for analog sensors and digital sensors was generally negligible and in the order of 10–50 ms. However, one CO2 sensor had a large overhead warm-up time of several seconds which added a significant energy burden. It was found that more, or as much, power could be consumed during warm-up as during the actual measurement phase. Finally, this study found disparity between power consumption values in datasheets and experimental measurements, which could have significant consequences in terms of battery life in the field. [ABSTRACT FROM AUTHOR]

This study focuses on the recently emerged Internet of Vehicles (IoV) concept to provide an integrated agricultural vehicle/machinery tracking system through two leading low power wide area network (LPWAN) technologies, namely LoRa and NB-IoT. The main aim is to investigate the theoretical coverage limits by considering the urban, suburban, and rural environments. Two vehicle tracking units (VTUs) have been designed for LoRa and NB-IoT connectivity technologies that can be used as reference hardware in coverage analysis. On this basis, the closed-form explicit analytical expressions of the maximum transmission range have been derived using the Hata path loss model. Besides, the computer simulation results have been validated via the maps from XIRIO online radio planning tool. In light of the obtained findings, several evaluations have been made to enhance the LPWAN-based agricultural vehicle tracking feasibility in smart farms. [ABSTRACT FROM AUTHOR]

Coverage limitation due to signal attenuation is one of the main challenges facing single-hop LoRaWAN when deployed in IoT applications that require low power consumption and operate over a wide area network. The challenge can be figured out using the dense deployment of typical LoRaWAN gateways to ensure devices can transfer their data to the network server successfully. However, this is considered a costly path because of the requirement of deploying a larger number of gateways. To cope with this, a multi-hop communication strategy can be utilized to extend the coverage of employed LoRaWAN networks, eliminating the need for dense gateway deployment. Thus, this paper proposes a Listen-To-Talk-based Multi-hop LoRaWAN LTM-LoRaWAN, a multi-hop relaying system for a wide area LoRaWAN network. It supports out-of-range devices to deliver their traffic to the gateway without using complex routing mechanisms or routing topology construction. Moreover, the proposed system is dynamic and does not require any special entities or assign specific tasks to specific devices. The paper provides a detailed description of the design and configuration of the proposed LTM-LoRaWAN. Using simulation, the feasibility of the proposed system was demonstrated. The evaluation result showed that the proposed system outperformed one-hop LoRaWAN and achieved better reliability in traffic delivery. [ABSTRACT FROM AUTHOR]

Low Power Wide Area Networks (LPWANs) have become a popular option for modern wireless communication technologies. Long Range (LoRa) protocol was designed for LPWAN, which offers long-distance communication, low-power consumption, and simultaneous transmissions. Long-distance communication necessitates an extremely low signal-to-noise ratio at the receiver. Additionally, low power consumption necessitates less signaling, which leads to the usage of simpler protocols like ALOHA and less coordinated communication. Therefore, as the number of devices equipped with this technology grows, its performance will degrade naturally as a result of scalability and interference difficulties. Deep learning holds great promise for resolving these problems through data-driven approaches and enhances the efficiency of LoRaWAN in usage of limited spectrum resources. In this work, we present the design of an end-to-end communication system as an autoencoder architecture utilizing deep learning. This adaptable architecture is able to efficiently capture channel impairments while simultaneously optimizing the operations of the transmitter and receiver together. The autoencoder is designed with multiple system model elements, including a sender net that simulates a LoRa transmitter and modulates data, a channel net that models channel impairments, and a receiver net that acts as a LoRa receiver to demodulate and retrieve the original data. The proposed autoencoder model is trained and evaluated with LoRa samples generated by simulation, and it is shown to be a high performer by comparing with the traditional LoRaWAN in terms of Bit Error Rate (BER) and Packet Success Rate (PSR) measurements. [ABSTRACT FROM AUTHOR]

Long Range Wide Area Network (LoRaWAN) technology has established new concepts for long-range wireless communication, being widely used in the implementation of IoT solutions. Therefore, it is crucial to validate the coverage of the signal and to know the distance at which a LoRAWAN communication system can be connected. This document investigates LoRaWAN technology for cases based on urban environments, so it can be used as a guide for those projects that require predicting the connection distance range of a LoRaWAN link. In addition, it serves as a tool for the reader when it comes to predicting the coverage of Long-Range Wide Area Network (LoRaWAN) technology. Measurements were made in a LoRaWAN network deployed in urban environments, where RSSI measurements were made in the city of Bogotá D.C., Colombia. Experimentally RSSI values were compared with four different propagation models at a frequency of 915 MHz in urban environments. The contribution of this work is an adjustment to widely used prediction models, according to the recommendation of the International Telecommunications Union (ITU) ITU-R P.1546, which allows estimating coverage in scenarios with characteristics similar to Bogotá D.C. This allows to know with precision the coverage before implementing the LoRaWAN communications system at 915 MHz. The results of comparing field measurements with fitted propagation models show that the Okumura model is the best predictor of coverage with a minimum error rate. [ABSTRACT FROM AUTHOR]

This paper presents a scheme for power swing detection and stability assessment for multi-machine power systems in addition to a brief survey on that topic. The scheme adopts smart grid perception. It is an asynchronous scheme that uses the rate of change of the angle of the generator bus voltage instead of synchronously estimating the absolute rotor angle value for stability assessment. Therefore, it does not require phasor measurement units (PMUs). Instead, it uses commercially available intelligent electronic devices (IEDs). It introduces a new application of the multicast Generic Object Oriented Substation Event (GOOSE) protocol or its routable version (R-GOOSE) to reflect the disturbance in the power grid on the transmission pattern of data packets over the grid data network. In this scheme, the angle change information is transmitted in the form of GOOSE packets simultaneously from the IEDs that readily measure the angles of the affected generators. On the other hand, the detecting device identifies the stability status of each affected generator. The proposed scheme is validated by simulating disturbances on the IEEE 39-bus system. The results show that the scheme can discriminate between stable and unstable swings. In addition, the loss of synchronism of the unstable generator is predicted ahead of its occurrence by enough time to take an appropriate action. [ABSTRACT FROM AUTHOR]

Synchrophasor-driven smart grid applications aiming to orchestrate a diverse set of Distributed Energy Resources (DERs) require extensive infrastructure including substantial instrumentation hardware, communication network extensions and controller installations for coordinated operation. This can make the overall installation expensive. Additionally, due to the computational complexity and data-intensive nature of the PDC functionality, most of the existing PDC implementations are on a purely software level, making them unsuitable for the real-time applications. To address this, the current paper proposes an alternate architecture for the real-time synchrophasor-based control of DER applications (e.g., microgrids) incorporating a centralized synchronization hardware designed to replace aggregation Phasor Data Concentrators (PDCs) and supplementary control algorithms into a singular reconfigurable hardware. This particular hardware is termed a Synchrophasor Synchronization Gateway and Controller (SSGC). The robustness of the proposed architecture is tested by using real-time (RT) Controller Hardware-In-the-Loop (CHIL) simulation-based experiments by manipulating the communication network that connects the SSGC with multiple Phasor Measurement Unit (PMU) streams broadcasting data through the IEEE C37.118.2 protocol in real time. These PMU streams were generated by using a real-time microgrid model running on a Typhoon HIL 604 simulator. To manipulate the communication interface between the proposed SSGC hardware and the PMU streams, a configurable Wide Area Network (WAN) emulator and communication network impairment appliance deployed in the Candela Technologies CT910 external hardware was utilized. The real-time control system was expanded by incorporating a low-pass filter to eliminate the potential overswitching of a Battery Energy Storage System (BESS). The proposed architecture demonstrated a reliable performance under ideal to moderately tampered communication networks. However, under a significantly corrupted network, the performance of this architecture is acutely affected. [ABSTRACT FROM AUTHOR]

6G networks can be truly intelligent wireless systems that have the flexibility for continuous technological improvements. However, security events affecting information systems have grown increasingly complicated and sophisticated. Intruders may be able to operate due to a lack of evidence authenticity. Using network forensics methods can solve many cybercrime instances. These methods can be used to locate the intruder and to determine the intrusion's origin and how it can be avoided in the future. Existing network forensics techniques confront security threats and vulnerabilities in several networks, such as personal area networks, local area networks, wide area networks, and wireless wide area networks under telecom networks, in practice, but cannot operate in 6G and beyond telecom networks, which are critical at this moment. Thus, a comprehensive literature analysis of telecom networks is conducted to identify existing concerns and challenges in conducting digital communication forensics. Hence, a digital forensics system for 6G and beyond networks is proposed as an initial idea on the basis of the highlighted issues, thereby assessing security incidents and providing justified evidence that explains the footprints of the attackers. [ABSTRACT FROM AUTHOR]

With the access of massive terminals of the Internet of Things (IoT), the low-power wide-area networks (LPWAN) applications represented by Long Range Radio (LoRa) will grow extensively in the future. The specific Long Range Wide Area Network (LoRaWAN) protocol within the LoRa network considers both low power consumption and long-range communication. It can optimize data transmission to achieve low communication latency, ensuring a responsive system and a favorable user experience. However, due to the limited resources in LoRa networks, if certain terminals have heavy traffic loads, it may result in unfair impacts on other terminals, leading to increased data transmission latency and disrupted operations for other terminals. Therefore, effectively optimizing resource allocation in LoRa networks has become a key issue in enhancing LoRa transmission performance. In this paper, a Mixed Integer Linear Programming (MILP) model is proposed to minimize network energy consumption under the maximization of user fairness as the optimization goal, which considers the constraints in the system to achieve adaptive resource allocation for spreading factor and transmission power. In addition, an efficient algorithm is proposed to solve this optimization problem by combining the Gurobi mathematical solver and heuristic genetic algorithm. The numerical results show that the proposed algorithm can significantly reduce the number of packet collisions, effectively minimize network energy consumption, as well as offering favorable fairness among terminals. [ABSTRACT FROM AUTHOR]

The Polar Team Pro computer software program simultaneously analyzes heart rate variability (HRV) for multiple athletes and requires little to no expertise from the user. Still, there remains the need to examine the accuracy and reliability of this candidate software when compared to the recognized gold standard HRV computer software program (Kubios HRV). Twenty-one (n = 21) healthy female soccer players volunteered to be participants. An ultra-short recording period (60-s) of R–R intervals (ms) was collected in the supine position in a controlled laboratory setting over two data collection periods spaced one month apart. R–R intervals were exported into both the candidate and reference computer software for analysis. The root mean square of the successive differences (rMSSD) were calculated along with mean R–R interval length. rMSSD values derived from the candidate software were compared to both the raw and artefact corrected values from the reference software. After performing artefact correction, mean (± SD) rMSSD values were not statistically different between software (candidate = 61.2 ± 31.0 ms 'vs' reference = 63.1 ± 31.1 ms, p = 0.214). Mean (± SD) R–R intervals were significantly different (candidate = 893.4 ± 119.8 ms 'vs' reference = 882.3 ± 111.3 ms, p = 0.003). Excellent reliability in artefact corrected rMSSD (r = 0.95, p < 0.001) and mean R–R interval length (r = 0.99, p < 0.001) was observed. The candidate software showed strong agreement and excellent reliability in calculating rMSSD when compared to the gold standard after artefact correction was applied. [ABSTRACT FROM AUTHOR]

The article describes a modern system of control and communication integrated in an in-situ soil sensor to make it autonomous in operation, energy and data transfer at low cost. Network of such sensors can monitor continuously soil moisture over a catchment with low maintenance. A recent industrial standard of radio communication, Long Range Wide Area Network (LoRaWAN) is presented. It permits rapid deployment of a sensor in a remote observatory, and the transfer in real time of sensor data to a central web-accessed database, via a LoRaWAN receiver, or gateway, 12 km away. In this situation transmission loss can reach a rate of 30%. Good quality antenna can lower it below 5% without extensive cost. Double messaging and other transmission algorithms are a possibility, taking into account consumption. Currently, sensors measuring one point every ten minutes can last seven months with four cheap AA alkaline batteries. Further gains would increase lifetime in same conditions by 30%. [ABSTRACT FROM AUTHOR]

In maritime settings, effective communication between vessels and land infrastructure is crucial, but existing technologies often prove impractical for energy-sensitive IoT applications, like deploying sensors at sea. In this study, we explore the viability of a low-power, cost-effective wireless communication solution for maritime sensing data. Specifically, we conduct an experimental assessment of the Azorean Long Range Wide Area Network (LoRaWAN) coverage. Our tests involve positioning the gateway at the island's highest point and installing end nodes on medium-sized fishing vessels. Through measurements of received signal strength indicator (RSSI), signal-to-noise ratio (SNR), and lines of sight (LOS), we showcase the potential of LoRaWAN transmissions to achieve communication distances exceeding 130 km in a LOS-free scenario over the ocean. These findings highlight the promising capabilities of LoRaWAN for reliable and long-range maritime communication of sensing data. [ABSTRACT FROM AUTHOR]

In the past decade, Long-Range Wire-Area Network (LoRaWAN) has emerged as one of the most widely adopted Low Power Wide Area Network (LPWAN) standards. Significant efforts have been devoted to optimizing the operation of this network. However, research in this domain heavily relies on simulations and demands high-quality real-world traffic data. To address this need, we monitored and analyzed LoRaWAN traffic in four European cities, making the obtained data and post-processing scripts publicly available. For monitoring purposes, we developed an open-source sniffer capable of capturing all LoRaWAN communication within the EU868 band. Our analysis discovered significant issues in current LoRaWAN deployments, including violations of fundamental security principles, such as the use of default and exposed encryption keys, potential breaches of spectrum regulations including duty cycle violations, SyncWord issues, and misaligned Class-B beacons. This misalignment can render Class-B unusable, as the beacons cannot be validated. Furthermore, we enhanced Wireshark's LoRaWAN protocol dissector to accurately decode recorded traffic. Additionally, we proposed the passive reception of Class-B beacons as an alternative timebase source for devices operating within LoRaWAN coverage under the assumption that the issue of misaligned beacons can be addressed or mitigated in the future. The identified issues and the published dataset can serve as valuable resources for researchers simulating real-world traffic and for the LoRaWAN Alliance to enhance the standard to facilitate more reliable Class-B communication. [ABSTRACT FROM AUTHOR]

Recently, research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase. Physical expansion limits exist for traditional standalone storage systems, such as I/O and file system capacity. However, the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location. And this leads to system performance degradation due to low locality occurring in a Wide Area Network (WAN) environment with high network latency. This problem hinders deploying distributed storage systems to multiple data centers over WAN. It lowers the scalability of distributed storage systems to accommodate data storage needs. This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance. The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot, warm, and cold. With assigned data temperature, the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment, leading to overall system performance degradation. This paper also conducts simulations to evaluate the proposed and existing distributed storage methods. The result shows that our proposed method reduced latency by 38% compared to the existing method. Therefore, the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods, such as consistent hashing. [ABSTRACT FROM AUTHOR]

The transit service in a city should be the most efficient, least polluting, most accessible, and sustainable means of transportation for its citizens. However, serious shortcomings have been detected, mainly in medium-sized cities in developing countries. These shortcomings are related to a lack of user information, insecurity, low service availability, and repeated stops in inappropriate and/or unauthorized places. Some of these shortcomings contribute to high accident rates and traffic congestion. The development of tools to improve the characteristics and conditions of transit service in cities has become an imperative need to improve the quality of life of citizens and city sustainability. Transit service tracking is relevant in aspects such as online location information to travelers and control by transport companies for compliance with speed limits, schedules, routes, and stops. This research proposes a transit vehicle tracking system based on the Internet of Vehicles (IoV) in Vehicle-to-Roadside (V2R) classification. The proposed system is ideal for the use of electric vehicles due to the low power consumption of the tracking device. This system uses Intelligent Transportation Systems (ITS) tracking service architecture, Long Range (LoRa) communication technology, and its LoRa Wide Area Network (LoRaWAN) protocol. Additionally, the system offers real-time location prediction in the absence of position data. The IoV tracking device integrates a GPS-LoRa module card with an Inertial Measurement Unit (IMU). A location prediction algorithm was implemented to train and store a prediction model with previously collected data from tracking devices. To evaluate the developed model, a case study in the city of Popayán (Colombia) was implemented, using three routes for testing. The results of the system implementation were satisfactory, obtaining an average coverage of 60.4% of the routes in the final field tests through LoRa communication. For the remaining 39.6% of the routes, location data prediction was used, with an average accuracy of 177 m with respect to the real location. Considering the obtained results, a tracking system such as the one proposed in this article can be used in the transit systems of medium-sized cities in developing countries to improve service quality and fleet control. [ABSTRACT FROM AUTHOR]

With environmental issues arising from the excessive use of fossil fuels, clean energy has gained widespread attention, particularly the application of lithium-ion batteries. Lithium-ion batteries are integrated into various industrial products, which necessitates higher safety requirements. Narrowband Internet of Things (NB-IoT) is an LPWA (Low Power Wide Area Network) technology that provides IoT devices with low-power, low-cost, long-endurance, and wide-coverage wireless connectivity. This study addresses the shortcomings of existing lithium-ion battery pack detection systems and proposes a lithium-ion battery monitoring system based on NB-IoT-ZigBee technology. The system operates in a master-slave mode, with the subordinate module collecting and fusing multi-source sensor data, while the master control module uploads the data to local monitoring centers and cloud platforms via TCP and NB-IoT. Experimental validation demonstrates that the design functions effectively, accomplishing the monitoring and protection of lithium-ion battery packs in energy storage power stations. [ABSTRACT FROM AUTHOR]

This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of this study is to propose a "pragmatic" artifact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of this study is that Narrow Band-Internet of Things (NB-IoT) and Long Range Wide Area Network (LoRaWAN) technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability. [ABSTRACT FROM AUTHOR]

Virtual Private LAN Services (VPLS) is an ethernet-based Virtual Private Network (VPN) service that provides multipoint-to-multipoint Layer 2 VPN service, where each site is geographically dispersed across a Wide Area Network (WAN). The adaptability and scalability of VPLS are limited despite the fact that they provide a flexible solution for connecting geographically dispersed sites. Furthermore, the construction of tunnels connecting customer locations that are separated by great distances adds a substantial amount of latency to the user traffic transportation. To address these issues, a novel Hierarchical VPLS (H-VPLS) architecture has been developed using 802.1Q tunneling (also known as Q-in-Q) on high-speed and commodity routers to satisfy the additional requirements of new VPLS applications. The Vector Packet Processing (VPP) performs as the router's data plane, and FRRouting (FRR), an open-source network routing software suite, acts as the router's control plane. The router is designed to seamlessly forward VPLS packets using the Request For Comments (RFCs) 4762, 4446, 4447, 4448, and 4385 from The Internet Engineering Task Force (IETF) integrated with VPP. In addition, the Label Distribution Protocol (LDP) is used for Multi-Protocol Label Switching (MPLS) Pseudo-Wire (PW) signaling in FRR. The proposed mechanism has been implemented on a software-based router in the Linux environment and tested for its functionality, signaling, and control plane processes. The router is also implemented on commodity hardware for testing the functionality of VPLS in the real world. Finally, the analysis of the results verifies the efficiency of the proposed mechanism in terms of throughput, latency, and packet loss ratio. [ABSTRACT FROM AUTHOR]

Wireless mesh networks (WMNs) offer a pragmatic solution with a cost-effective ratio when provisioning ubiquitous broadband internet access and diverse telecommunication systems. The conceptual underpinning of mesh networks finds application not only in IEEE networks, but also in 3GPP networks like LTE and the low-power wide area network (LPWAN) tailored for the burgeoning Internet of Things (IoT) landscape. IEEE 802.11s is well known for its facto standard for WMN, which defines the hybrid wireless mesh protocol (HWMP) as a layer-2 routing protocol and airtime link (ALM) as a metric. In this intricate landscape, artificial intelligence (AI) plays a prominent role in the industry, particularly within the technology and telecommunication realms. This study presents a novel methodology for the computation of routing metrics, specifically the ALM. This methodology implements the network simulator ns-3 and the RStudio as a statistical computing environment for data analysis. The former has enabled for the creation of scripts that elicit a variety of scenarios for WMN where information is gathered and stored in databases. The latter (RStudio) takes this information, and at this point, two linear predictions are supported. The first uses linear models (lm) and the second employs general linear models (glm). To conclude this process, statistical tests are applied to the original model, as well as to the new suggested ones. This work substantially contributes in two ways: first, through the methodological tool for the metric calculation of the HWMP protocol that belongs to the IEEE 802.11s standard, using lm and glm for the selection and validation of the model regressors. At this stage the ANOVA and STEPWIZE tools of RStudio are used. The second contribution is a linear predictor that improves the WMN's performance as a priori mechanism before the use of the ns-3 simulator. The ANCOVA tool of RStudio is employed in the latter. [ABSTRACT FROM AUTHOR]

The virtualized framework was generated among the terminal user and the computer platform by the software called virtual machine. For the fundamental bare hardware an identical interface was obtained and the end-users are managed by the virtual machine software. For the cloud infrastructure and cloud computing services, simulation and modeling are given by an extensible simulation tool called CloudSim. During virtual machine migration the following challenges occur; Transfer rate, Page resend, Missing pages, migration over WAN network, large applications, Resource availability, Address wrapping, and migration in high-speed LAN. In cloud computing, scheduling is required to reduce task completion time and to boost the effective use of resources. It allocates the particular work to a particular resource at a particular time. Resource allocation is also required for assigning the available resources to the required cloud application over the internet. It allows the service provider to control every individual module resource. To get quasi-optimal solutions several meta-heuristic procedures are implemented with a Cloud simulator. [ABSTRACT FROM AUTHOR]

In this research, the main objective is to design a smart meter to measure electricity consumption in households with communications based on long-range wide area network (LoRaWAN) wireless technology. Two devices have been designed, a smart meter for electrical energy in households (SMEEH) and a LoRa network supervisor (LNS), which optimises the LoRAWAN parameters using the teaching–learning-based optimisation (TLBO) algorithm. This algorithm allows obtaining the parameters' spreading factor, bandwidth, and code rate so that the minimum value of the packet loss rate (PLR) is reached, and the load profiles of the households are modelled in real time using cloud data storage. The algorithm implemented in the LNS determines the most appropriate parameters of the LoRaWAN by checking data traffic in real time. The household's electrical energy measuring system obtains data through sensors. Load profiles of households obtained by measuring the voltage, current, and active power with the SMEEH using the TLBO algorithm are more accurate. They allow the consumer and/or company to adjust the time and perform actions such as energy management and tariff adjustments. In addition, LoRaWAN optimisation means less data loss, more reliable load profiles, mean relative errors lower than 7.7%, and improved network performance. The TBLO algorithm uses short computation times to get the best solution for the LoRaWAN in real time and achieves a PLR of around 20% using the LNS. [ABSTRACT FROM AUTHOR]

The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network. [ABSTRACT FROM AUTHOR]

In the current scenarios most of the business growth comes from the user experience, while using the business applications. Any enterprise network performance depends on the fundamental process of securely serving the user with applications. The network usage patterns have completely changed from just data transfer, voice, and video transmissions to serving entire applications over the networks. With all the modern applications served over the private and public clouds the network resources utilization must be optimized and take full advantage of them which is not possible with current network architectures. The enterprises are currently shifting towards software defined implementation due to many reasons like consistent policy application across the network, run time analytics for troubleshooting the problems and making the network cloud computing ready. Software defined approaches provide centralized security monitoring and control, user data and identity-based segmentation over the network, dynamic recovery from network infrastructure failure and finally virtualization of almost everything. This paper is an overview of the functioning of enterprises in the networking perspective along with current software defined approaches being used in enterprise networking which will soon take over the traditional networks by solving the challenges present in the software defined networking. [ABSTRACT FROM AUTHOR]

Low power wide area network (LPWAN) is a wireless communication technology that offers large coverage, low data rates, and low power consumption, making it a suitable choice for the growing Internet of Things and machine-to-machine communication applications. Long range (LoRa), an LPWAN technology, has recently been used in the industrial, scientific and medical (ISM) band for various low-power wireless applications. The coverage and data rate supported by these devices in the ISM band is well-studied in the literature. In this paper, we study the usage of TV white spaces (TVWS) for LoRa transmissions to address the growing spectrum demand. Additionally, the range and data rate of TVWS-based LoRa, for different transmission parameter values using different path-loss models and for various scenarios such as free space, outdoor and indoor are investigated. A path-loss model for TVWS-based LoRa is also proposed and explored, and the evaluations show that TVWS offers a longer range. This range and data rate study would be useful for efficient network planning and system design for TVWS-based LoRa LPWANs. [ABSTRACT FROM AUTHOR]

Enabling technologies of Agriculture 4.0 such as IoT-driven Precision Agriculture (PA), Unmanned Aerial Vehicles (UAVs), and big data analytics are collaborating for the transformation of global agribusiness. In PA applications, IoT devices sense, collect, and transmit data to the cloud or edge for processing and processed data are stored in data centers. This exchange of a very large amount of information amongst billions of interconnected devices demands massive energy in PA applications. The growth of IoT devices is exponentially increasing directly or indirectly generating Green House Gases and causing energy deficiency in power-hungry IoT components like sensors. Thus, adopting green solutions is inevitable to promote energy-conserving, environment-friendly, and cost-effective IoT component designs. Inspired by achieving a green environment for IoT, we first give an overview of different data processing and energy-conserving strategies using machine learning, cloud computing, and edge computing. We then discuss and evaluate different Green IoT (GIoT) solutions that can be implemented for GIoT-based PA leveraging UAVs, Low Power Wide Area Networks (LPWANs), and 5G networks along with several implementation concerns. In addition, the sustainable progress towards Agriculture of 5.0 by integrating GIoT components is discussed to make the IoT greener using 5G networks and beyond. Based on the current survey, we have conceptualized a GIoT framework for designing energy-conserving, cost-effective, and environment-friendly PA applications while enabling ubiquitous connectivity. Finally, this paper systematically summarizes different security threats in GIoT layers and analyzes the measures that can be adopted to mitigate those threats in detail. [ABSTRACT FROM AUTHOR]

Low Power Wide Area Networks (LPWANs) are a class of wireless technologies with characteristics such as large coverage areas, low bandwidth, very small payload size, and long battery life operation. LPWANs applications are growing rapidly in the industry, although their restricted resources have created some vulnerabilities and attacks. Bit flipping attack is such a potential attack in encrypted LPWANs data in which an attacker, with the knowledge of the bit location of each data field, can alter data without needing decryption of the message. Current methods of bit flip detection are based on the use of traditional integrity checking methods such as digital signature, message integrity code, or some sender-side methods that either increase the sender's power consumption or the size of the message payload, hence are not practical in LPWANs. This paper proposed a novel bit flipping detection method through machine learning which eliminates the need to increase neither the payload size nor the sender's power consumption. The proposed method is based on pattern recognition of true messages sequence using a deep neural network which is then capable to detect tampered messages accordingly. The performance of the method has been evaluated in some case studies and experiments including different modes of attacks, applications, and environment and the results show up to 99.84% accuracy in bit flipping attack detection. [ABSTRACT FROM AUTHOR]

At present, the influence of the surrounding environment on the health of humans is attracting increasing attention, and the indoor environment plays a significant role in people's health. Therefore, it is necessary to effectively monitor indoor environments in real time. With the increase of end‐devices, the rapid growth of data is exerting a pressure on the network bandwidth. To solve this problem, a LoRaWAN monitoring system for indoor environments based on embedded edge computing is proposed here. A hardware architecture of end‐devices and the workflow of the LoRaWAN gateway are designed. In the gateway, edge computing is applied to manage the integrated access device and clean data. The experimental results show that the data reduction rate increased obviously after using edge computing. The environmental data can be transmitted in real time, and the communication efficiency of the system can be improved. [ABSTRACT FROM AUTHOR]

Among other technologies, the Internet of Things (IoT) is one of the promising technologies that will help us to make our environment smarter and gather, analyze and process sensor data without any human intervention. Low Power Wide Area Network (LPWAN) is a technology that is part of the IoT, in which is the long-range (LoRa) technology that can provide wide coverage at a cost of very low power and that can boost data communication in our smart environment. The ease of their integration with the electronic system has opened a lot of new scope in the field of low-power wide-area networks in IoT applications. This paper provides a well-rounded review of LPWAN technology and in-depth details of LoRa technology. A good insight is given into the implementation of LoRa technology to design smart cities and the types of antenna technology used in LoRa data communications. Finally, a few open issues, possible solutions and future development are pointed out. [ABSTRACT FROM AUTHOR]

Low power wide area networks (LPWANs) are made to survive conditions of extensive installation. Technological innovations, including Global Network Operator, Long Range Wide Area Network (LoRaWAN), Narrowband Internet of Things (NB-IoT), Weightless, Sigfox, etc., have adopted LPWANs. LoRaWAN is currently regarded to be one of the most cutting-edge and intriguing technology for the widespread implementation of the IoT. Although LoRaWAN offers the best features that make it fit with Internet - of - things specifications, there are still certain technical issues to overcome, such as link coordination, resource allocation and reliable transmission. In LoRaWAN, End-devices transmit randomized uplink frames to the gateways using un-slotted random-access protocol. This randomness with the restrictions placed on the gateways is a reason that leads to a considerable decline in network performance, in particular downlink frames. In this paper, we propose a new approach to increase Acknowledgement (ACK) messages throughput. The suggested method takes advantage of both class A and class B features to enhance and assist LoRaWAN's reliability by ensuring that an ACK message is sent for every confirmed uplink while retaining the minimum energy level that is utilized by nodes. [ABSTRACT FROM AUTHOR]

The existing synchronization algorithms based on FFT have large synchronization error. To solve this problem, this paper proposed an optimized synchronization method for LoRa preamble signal. Firstly, according to the time-frequency conversion characteristics of Chirp signal, it estimated the fractional frequency offset and fractional time delay of the preamble signal respectively by using differential algorithm and interpolation algorithm. Based on this, it derived the integer frequency offset and integer time delay by using a joint estimation algorithm based on FFT. Then, it theoretically analyzed the effect of the residual synchronization error on the demodulation performance of LoRa signal. The simulation results show that the proposed synchronization method can meet the demodulation performance requirement at the corresponding required signal-to-noise ratio（SNR). [ABSTRACT FROM AUTHOR]

Long Range Wide Area Network (LoRaWAN) has been developed to meet the requirements for the enormous device-to-device communication of Internet of Things (IoT) networks, which consist of a large number of participating devices spread over large coverage areas with low data rates and low power consumption. It supports communications in both directions, uplink, and downlink directions. However, the downlink communication in the current LoRaWAN raises the bottleneck issue at gateways due to the used gateway selection algorithm. This paper proposes a novel gateway selection algorithm based on the duty cycle time-off values for the existing gateways, Duty Cycle Gateway Selection (DCGS), to direct acknowledgment packets as downlink traffic towards the most suitable gateway. Thus, the proposed system avoids subsequent retransmission of previously sent traffic that leads to excessive traffic overloading the network. The proposed system avoids exhausting a gateway duty cycle with downlink traffic by distributing the downlink traffic among available gateways based on the duty cycle time off. DCGS is evaluated using FloRa and INET frameworks in the well-known network simulator OMNeT++. The result shows the superior performance of the proposed approach over the existing Signal-to-Noise ratio (SNR) based selection mechanism. It clearly indicates that the DCGS maintains a better confirmed packet delivery rate while reducing number of retransmissions, collisions, and power consumption. [ABSTRACT FROM AUTHOR]

Context. The emergence of the Internet of Things (IoT) has led to the development of various low-power wide area network (LPWAN) technologies that are designed to provide transmission of small data packets over long distances with minimal energy consumption. The two most well-known LPWAN technologies are LoRaWAN and Sigfox. This study aims to compare the energy efficiency of these two technologies to determine their suitability for use in autonomous solutions. Objective. The objective of this study is to compare the energy efficiency of LoRaWAN and Sigfox technologies for IoT devices. The comparison will help determine which technology is better for autonomous solutions when devices need to operate for extended periods of time without frequent battery replacements. Method. In this work, taking into account the specifications of the investigated radio technologies, mathematical modeling of the time of data transmission or reception is used depending on the payload, and information on the power supply current is taken from official datasheets for the components of the investigated devices. Results. The results of the study show that both LoRaWAN and Sigfox are energy-saving technologies, but LoRaWAN is generally more energy-efficient than Sigfox. In addition, LoRaWAN has adaptive modes and significantly more manual settings, which in some cases further reduces the energy per bit of data compared to Sigfox. Conclusions. LoRaWAN is the best choice for autonomous solutions where energy efficiency is crucial. This study provides valuable information for designers and developers of IoT devices, allowing them to make informed decisions when choosing LPWAN technologies for their autonomous solutions. [ABSTRACT FROM AUTHOR]