Wide Area Network Research Articles

Lora Based Coal Mine Safety Monitoring and Security System Using Smart Helmet

The process of removing coal from the earth is called coal mining. Coal is used as fuel by the steel and cement industries to extract iron from iron ore and to make cement. The category that includes the underground mining business includes all factors that require routine monitoring, including methane gas, high temperatures, fire accidents, and so on. The safe production level of coal mines is still low, and frequent tragedies there result in significant losses of life and property. It is crucial to keep an eye on the working conditions in coal mines since the variety of work done there and the complexity of the mine environment are the main causes of disasters. The coal mine based on LoRa Long-Range Wide Area Network (LoRaWAN) technology is used by the Safety Monitoring and Security System to improve security and safety in coal mines. This system incorporates cutting-edge sensors to continuously monitor the surrounding environment, gas concentrations, and worker welfare. LoRa enables seamless communication and quick identification of potential risks because to its long range capabilities.

An Integrated Smart Metering System Using LORA Technology

Precise meter readings are essential for effectively regulating residential energy systems in India's smart city context. However, due to the high number of users and the absence of daily consumption monitoring, standard meter reading methods are expensive and time-consuming, which causes consumer unhappiness. The suggested approach to address this issue is to put in place an integrated wireless smart energy system that makes use of smart metering technologies. The way utilities manage energy could be completely changed by this method. The integrated system is intended to take the place of the traditional digital energy meters, which are primarily used to record meter readings properly for payment purposes. This allows for automatic meter readings without the need for frequent travels to the location where the meters are located. Low Power Wide Area Networks (LPWAN) technology, which is renowned for its low power consumption, cost-effectiveness, long-range coverage, and efficient penetration, are used in this technology to enable the transfer of accurate and secure data on energy usage in real-time. The experiments that resulted from the trials allowed the energy to successfully communicate and documented reliable readings. By providing users with real-time usage data and guarding against overloaded scenarios, the system also benefits consumers. Additionally, the device reports electricity theft to the utility company. The shortcomings of the conventional systems and design difficulties are addressed by the suggested system.

Automated Lane Centering: An Off-the-Shelf Computer Vision Product vs. Infrastructure-Based Chip-Enabled Raised Pavement Markers.

Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ±0.016 m-1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle's position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs.

Asynchronous Multi-Class Traffic Management in Wide Area Networks

Asynchronous Multi-Class Traffic Management in Wide Area Networks

Critical Review on Water Quality Analysis using IoT and Machine Learning Models

Water quality and its management are the most precise concerns confronting humanity globally. This article evaluates the various sensors used for water quality monitoring and focuses on the water quality index considering the multiple physical, chemical, and biological parameters. A Review of Internet of Things (IoT) research for water quality monitoring and analysis, sensors used for water quality can help remote monitoring of the water quality parameters using various IoT-based sensors that convey the assembled estimations utilizing Low-Power Wide Area Network innovations. Overall, the IoT system was 95% accurate in measuring pH, Turbidity, TDS, and Temperature, while the traditional method was only 85% accurate. Also, this study reviewed the different A.I. techniques used to assess water quality, including conventional machine learning techniques, Support Vector Machines, Deep Neural Networks, and K-nearest neighbors. Compared to traditional methods, machine learning and deep learning can significantly increase the accuracy of measurements of groundwater quality. However, various variables, such as the caliber of the training data, the water quality metrics' complexity, and the monitoring frequency, will affect the accuracy. The geographical information system (GIS.) is used for spatial data analysis and managing water resources. The quality of its data is also reviewed in the paper. Based on these analyses, the study has forecasted the future sensors, Geospatial Technology, and machine learning techniques for water quality monitoring and analysis.

Review of LoRaWAN: Performance, Key Issues and Future Perspectives

In the last few years, the Low-powered wide area networks (LPWAN) have gained popularity and massively deployed, especially in smart cities and agriculture, due to their advantages, such as energy efficiency, extensive coverage, and low cost. The long-range wide area networks (LoRaWAN) protocol is a new technology. It attracts the attention of many research centers worldwide as it allows data transmission at a low cost across long distances. This article reviews the performance of LoRa and LoRaWAN for both investigations in indoor and outdoor environments. Moreover, a performance analysis of this technology is made by focusing on five main indicators, which are coverage, time on air (TOA), packet error rate (PER), received signal strength indication (RSSI), and signal-to-noise ratio (SNR) while considering technical characteristics is included. The investigation settings were divided into two categories: simulation and testbed in a real-world application. Consequently, a table of summary of indicators used by the researcher was made to make it easier for other researchers to find references for their topics relevant to this review. Next, the identified key issues and solutions are discussed in this review. The issues discussed significantly affect the performance of various monitoring technologies regarding energy management, quality of service, coverage, signal interference, and data handling. Finally, this review will discuss the future perspective, provide valuable suggestions for future research works, and lead toward improving current LoRaWAN technology.

Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks.

Low-Power Wide-Area Networks constitute a leading, emerging Internet-of-Things technology, with important applications in environmental and industrial monitoring and disaster prevention and management. In such sensor networks, external detectable events can trigger synchronized alarm report transmissions. In LoRaWANs, and more generally in networks with a random access-based medium access algorithm, this can lead to a cascade of frame collisions, temporarily resulting in degraded performance and diminished system operational capacity, despite LoRaWANs' physical layer interference and collision reduction techniques. In this paper, a novel scheduling algorithm is proposed that can increase system reliability in the case of such events. The new adaptive spatial scheduling algorithm is based on learning automata, as well as previous developments in scheduling over LoRaWANs, and it leverages network feedback information and traffic spatial correlation to increase network performance while maintaining high reliability. The proposed algorithm is investigated via an extensive simulation under a variety of network conditions and compared with a previously proposed scheduler for event-triggered traffic. The results show a decrease of up to 30% in average frame delay compared to the previous approach and an order of magnitude lower delay compared to the baseline algorithm. These findings highlight the importance of using spatial information in adaptive schemes for improving network performance, especially in location-sensitive applications.

Optimizing the Charging Mobility of WPT-Enabled UAV to Enhance the Stability of Solar-Powered LoRaWAN IoT

In the majority of Internet of Things (IoT) applications, persistent and stable operation is a crucial requirement. While environmental energy-harvesting technologies can enhance IoT’s persistence, they do not guarantee stability. Therefore, we aim to address the stability challenges in solar-powered IoT (SP-IoT) by employing wireless power transmission (WPT) through unmanned aerial vehicles (UAVs). This study focuses on determining the optimal charging mobility of drones for WPT to enhance the stability of nodes operating in a wide area network (WAN)-based SP-IoT environment. The proposed scheme identifies nodes with insufficient solar energy harvesting and defines the optimal charging mobility parameters (hovering position, hovering time, and moving path) to efficiently transmit the drone’s energy to these nodes in a balanced manner. The experimental results confirm that the proposed scheme significantly improves the stability of solar-powered IoT nodes by optimally utilizing the limited energy of the drone.

Enabling The E-NOSE: Evaluating Network Technologies for Optimized Odor Data Transmission in IoT and Edge Computing Applications

Electronic Noses (e-noses) are advanced sensors that are being increasingly used in the Internet of Things (IoT) field. They have various applications in environmental monitoring and medical diagnostics. Optimal transmission of odor-related data is essential for fully harnessing the capabilities of these electronic noses in various network environments. This research paper examines the issue of transmitting odor data in Internet of Things (IoT) applications by assessing recent progress in communication and network technologies. A thorough analysis of various network alternatives, such as Ethernet, cellular (4G/5G), Wi-Fi, Bluetooth, Zigbee, and LoRa, is provided, accompanied by an elaborate table displaying precise technical specifications. The analysis of LoRa, a Low-Power Wide-Area Network (LPWAN) technology, encompasses an investigation into its architecture, modulation method, device classifications, and protocols such as LoRaWAN. The analysis of Edge Computing and cloud-based approaches for managing odor data in IoT culminates in a tabular presentation that succinctly outlines their advantages and disadvantages. This study provides valuable information to enhance the efficiency of transmitting odor data and effectively utilize electronic nose technology in different emerging applications in the Internet of Things (IoT) field.

Implementation of auto failover on SD-WAN technology with BGP routing method on Fortigate routers at XYZ company

The current technological development is SD-WAN (Software-Defined Wide Area Network) which provides highperformance access for users located far from the head office so as to allow faster network connections and has been facilitated automation techniques for branch offices. This research solves the problem of XYZ company because it is known that the company requires network connectivity with a high SLA (Service Level Agreement) and no downtime in the information exchange process. This research hypothesis assumes that using SD-WAN would be ideal and the problems with XYZ company were resolved. The purpose of this research is the implementation of a WAN network using SD-WAN technology against two ISPs on the FortiGate router, as well as testing QoS (Quality of Service) that has been configured using the BGP (Border Gateway Protocol) routing method. This research plan consists of ISP-A using IP-VPN (Internet Protocol-Virtual Private Network) and ISP-B using broadband Internet. The test scenario was carried out using 3 methods, namely Full Service Scenario, Fail Over Scenario-1 when the IP VPN service is down and Fail Over Scenario-2 when the broadband Internet service is down. The final results of the research have obtained "Satisfactory" results for both services, including the average index on ISP-A and ISP-B of 3.7.

A Quantum-Safe Software-Defined Deterministic Internet of Things (IoT) with Hardware-Enforced Cyber-Security for Critical Infrastructures

The next-generation “Industrial Internet of Things” (IIoT) will support “Machine-to-Machine” (M2M) communications for smart Cyber-Physical-Systems and Industry 4.0, and require guaranteed cyber-security. This paper explores hardware-enforced cyber-security for critical infrastructures. It examines a quantum-safe “Software-Defined-Deterministic IIoT” (SDD-IIoT), with a new forwarding-plane (sub-layer-3a) for deterministic M2M traffic flows. A “Software-Defined Networking” (SDN) control plane controls many “Software-Defined-Deterministic Wide-Area Networks” (SDD-WANs), realized with FPGAs. The SDN control plane provides an “Admission-Control/Access-Control” system for network-bandwidth, using collaborating Artificial Intelligence (AI)-based “Zero Trust Architectures” (ZTAs). Hardware-enforced access-control eliminates all congestion, BufferBloat, and DoS/DDoS attacks, significantly reduces buffer-sizes, and supports ultra-reliable-low-latency communications in the forwarding-plane. The forwarding-plane can: (i) Encrypt/Authenticate M2M flows using quantum-safe ciphers, to withstand attacks by Quantum Computers; (ii) Implement “guaranteed intrusion detection systems” in FPGAs, to detect cyber-attacks embedded within billions of IIoT packets; (iii) Provide guaranteed immunity to external cyber-attacks, and exceptionally strong immunity to internal cyber-attacks; (iv) Save USD 100s of billions annually by exploiting FPGAs; and (v) Enable hybrid Classical-Quantum networks, by integrating a “quantum key distribution” (QKD) network with a classical forwarding plane with exceptionally strong cyber-security, determined by the computational hardness of cracking Symmetric Key Cryptography. Extensive experimental results for an SDD-WAN over the European Union are reported.

A Stochastic Computational Graph with Ensemble Learning Model for solving Controller Placement Problem in Software-Defined Wide Area Networks

The Preponderance of literature has established that most of the metaheuristic algorithms were associated with identified challenges in solving the Controller Placement Problem in SD-WAN. This study proposed a Stochastic Computational Graph Model with an Ensemble Learning (SCGMEL) approach to address the scalability, intelligence, and high computational complexity challenges experienced by the existing metaheuristic algorithms. The proposed SCGMEL used stochastic gradient descent with momentum and learning rate decay, a computational graph model, and the eXtreme Gradient Boosted Trees (XGBoost) algorithm as the optimization and machine learning approaches. The proposed solution was tested using datasets from Internet Zoo topology with six objective functions: load balancing, maximum controller failure, average controller-to-controller latency, average switch-to-controller latency, and maximum controller-to-controller latency. The XGBoost outperformed other regression models, in predicting the number of controllers, with mean absolute error of 1.855751 versus 1.883536, 3.729863, and 3.829268 for the random forest, logistic regression, and K-nearest neighbor, respectively. Furthermore, the execution time, average and total CPU usages of the algorithms demonstrated the computational efficiency of the proposed SCGMEL over ANSGA-III, NSGA-II, and MOPSO with percentage decreases of 99.983%, 99.985%, and 99.446%, respectively. Consequently, the proposed SCGMEL was recommended for controller placement in SD-WAN, subject to the usage conditions.

LoRa localisation using single mobile gateway

Effective use of GPS and mobile networks for localisation in rangeland areas is constrained by their high power consumption and high deployment costs. Long-range (LoRa), a low-power wide area network (LPWAN) technology, can be employed to mitigate these challenges. In contrast to prior research where the prevalent approaches entail multiple gateways. This work proposes a valuable methodology focused on a single mobile LoRa gateway for localisation. A particle filtering and machine learning-based pipeline is employed to map the distance between a target node and the gateway from the received signal strength indicator (RSSI). Particle filtering is used to reduce the impact of noise on the RSSI values. Then, several machine learning techniques, such as support vector machines, random forest, and k-nearest neighbour, are used on the RSSI values to estimate the distance. The estimated distance is then used for tracking using a centroid pseudo-trilateration method. The proposed method was tested in a real-world semi-line-of-sight setting, using three datasets generated by LoRaWAN-specified hardware components and a server. Two forms of experiments were performed: active searching and passive monitoring. We propose an iterative estimation process to address the dilution of precision caused by the initial positions of the gateway required for active searching applications. The results show that active searching typically requires 2 to 3 hops to reach a target node. The accuracy of passive monitoring depends on the proximity of the gateway, which varies from 20 m to 170 m. This proposed approach has the potential to open the way for localising, tracking, or monitoring target objects within sparsely populated rangeland areas, even when resources are severely constrained.

Enhancing Security and Trust in Internet of Things through Meshtastic Protocol Utilising Low-Range Technology

The rapid proliferation of Internet of Things (IoT) devices has raised significant concerns regarding the trustworthiness of IoT devices, which is becoming a crucial aspect of our daily lives. In this paper, we deal with this important aspect by taking into account Meshtastic, a dynamic mesh networking protocol that offers robustness and adaptability, important characteristics for the dynamic and heterogeneous IoT environment. LoRaWAN (Low-Range Wide Area Network), a low-power, long-range wireless communication standard, introduces energy efficiency and extends the reach of IoT networks, enabling secure communication over extended distances. To improve the trustworthiness of IoT devices, we present an integrated approach that leverages the strengths of Meshstastic’s dynamic mesh networking capabilities and LoRa’s low-power, long-range communication, along with the integration of a reputation model specifically designed for IoT. We evaluated the performance of the proposed solution through several simulations and real-world experiments. The results show that the devices’ measured values of trust reflect the real behaviour of the devices. These findings underscore the viability and applicability of the Meshtastic protocol utilising LoRa technology as a pivotal step towards establishing resilient and trustworthy IoT infrastructures in the face of evolving security challenges.

LoRaWAN for Vehicular Networking: Field Tests for Vehicle-to-Roadside Communication.

Vehicular wireless networks are one of the most valuable tools for monitoring platforms in the automotive domain. At the same time, Internet of Things (IoT) solutions are playing a crucial role in the same framework, allowing users to connect to vehicles in order to gather data related to their working cycle. Such tasks can be accomplished by resorting to either cellular or non-cellular wireless technologies. While the former can ensure low latency but require high running costs, the latter can be employed in quasi-real-time applications but definitely reduce costs. To this end, this paper proposes the results of two measurement campaigns aimed at assessing the performance of the long-range wide-area network (LoRaWAN) protocol when it is exploited as an enabling technology to provide vehicles with connectivity. Performances are evaluated in terms of packet loss (PL) and received signal strength indicator (RSSI) in wireless links. The two testing scenarios consisted of a transmitter installed on a motorbike running on an elliptical track and a receiver placed in the centre of the track, and a transmitter installed on the roof of a car and a receiver placed next to a straight road. Several speeds were tested, and all the spreading factors (SFs) foreseen by the protocol were examined, showing that the Doppler effect has a marginal influence on the receiving performance of the technology, and that, on the whole, performance is not significantly affected by the speed. Such results prove the feasibility of LoRaWAN links for vehicular network purposes.

Adaptive overlay selection at the SD-WAN edges: A reinforcement learning approach with networked agents

Software-Defined Wide Area Networks (SD-WANs) have emerged as a promising solution to address the connectivity demands of modern distributed enterprises. However, the effective application of the Software Defined Networking (SDN) paradigm in such broad and dynamic environments remains a significant challenge. In this paper, we present two novel contributions. First, we design a decentralized control plane for SD-WANs that leverages edge-based network monitoring and overlays’ configuration. Then we present a Reinforcement Learning-based orchestration plane that leverages local information for the enforcement of SD-WAN policies. Since traditional approaches suffer either a lack of scalability due to the problem’s complexity or suboptimal performance due to isolated decision-making, the proposed approach leverages a cooperative Multi-Agent Reinforcement Learning framework. Our novel cooperative approach is based on per-site agents that exchange a small amount of information to enhance performance while preserving scalability. To validate the efficacy of our proposed approach, we conducted an extensive experimental evaluation considering diverse SD-WAN scenarios. Results show that our framework is able to satisfy global network policies for a multi-site SD-WAN with different QoS requirements and cost constraints.

Machine learning classifiers for fall detection leveraging LoRa communication network

Today, health monitoring relies heavily on technological advancements. This study proposes a low-power wide-area network (LPWAN) based, multinodal health monitoring system to monitor vital physiological data. The suggested system consists of two nodes, an indoor node, and an outdoor node, and the nodes communicate via long range (LoRa) transceivers. Outdoor nodes use an MPU6050 module, heart rate, oxygen pulse, temperature, and skin resistance sensors and transmit sensed values to the indoor node. We transferred the data received by the master node to the cloud using the Adafruit cloud service. The system can operate with a coverage of 4.5 km, where the optimal distance between outdoor sensor nodes and the indoor master node is 4 km. To further predict fall detection, various machine learning classification techniques have been applied. Upon comparing various classifier techniques, the decision tree method achieved an accuracy of 0.99864 with a training and testing ratio of 70:30. By developing accurate prediction models, we can identify high-risk individuals and implement preventative measures to reduce the likelihood of a fall occurring. Remote monitoring of the health and physical status of elderly people has proven to be the most beneficial application of this technology.

LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions.

Designing and deploying telecommunications and broadcasting networks in the challenging terrain of the Amazon region pose significant obstacles due to its unique morphological characteristics. Within low-power wide-area networks (LPWANs), this research study introduces a comprehensive approach to modeling large-scale propagation loss channels specific to the LoRaWAN protocol operating at 915 MHz. The objective of this study is to facilitate the planning of Internet of Things (IoT) networks in riverside communities while accounting for the mobility of end nodes. We conducted extensive measurement campaigns along the banks of Universidade Federal do Pará, capturing received signal strength indication (RSSI), signal-to-noise ratio (SNR), and geolocated point data across various spreading factors. We fitted the empirical close-in (CI) and floating intercept (FI) propagation models for uplink path loss prediction and compared them with the Okumura-Hata model. We also present a new model for path loss with dense vegetation. Furthermore, we calculated received packet rate statistics between communication links to assess channel quality for the LoRa physical layer (PHY). Remarkably, both CI and FI models exhibited similar behaviors, with the newly proposed model demonstrating enhanced accuracy in estimating radio loss within densely vegetated scenarios, boasting lower root mean square error (RMSE) values than the Okumura-Hata model, particularly for spreading factor 9 (SF9). The radius coverage threshold, accounting for node mobility, was 945 m. This comprehensive analysis contributes valuable insights for the effective deployment and optimization of LoRa-based IoT networks in the intricate environmental conditions of the Amazon region.

Reliable edge-to-core optical networks: An optimal algorithm for maximal path diversity

With the emergence of IoT applications, 5G, and edge computing, network resource allocation has shifted toward the edge, bringing services closer to the end users. These applications often require communication with the core network for purposes that include cloud storage, compute offloading, 5G-and-Beyond transport communication between centralized unit (CU), distributed unit (DU) and core network, centralized network monitoring and management, etc. As the number of these services increases, efficient and reliable connectivity between the edge and core networks is of the essence. Wavelength Division Multiplexing (WDM) is a well-suited technology for transferring large amounts of data by simultaneously transmitting several wavelength-multiplexed data streams over each single fiber optics link. WDM is the technology of choice in mid-haul and long-haul transmission networks, including edge-to-core networks, to offer increased transport capacity.Optical networks are prone to failures of components such as network fiber links, sites, and transmission ports. A single network element failure alone can cause significant traffic loss due to the disruption of many active data flows. Thus, fault-tolerant and reliable network designs remain a priority. The architecture called “dual-hub and dual-spoke” is often used in metro area networks (MANs). A dual-hub, or in general a multi-hub network, consists of a set of designated destination nodes (hubs) in which the data traffic from all other nodes (the peripherals) should be directed to the hubs. Multiple hubs offer redundant connectivity to and from the core or wide area network (WAN) through geographical diversity. The routing of the connections (also known as lightpaths) between the peripheral node and the hubs has to be carefully computed to maximize path diversity across the edge-to-core network. This means that whenever possible the established redundant lightpaths must not contain a common Shared Risk Link Group (SRLG).An algorithm is proposed to compute the most reliable set of SRLG disjoint shortest paths from any peripheral to all hubs. The proposed algorithm can also be used to evaluate the overall edge-to-core network reliability quantified through a newly introduced figure of merit.

Energy-focused simulation of edge computing architectures in 5G networks

While cloud computing is crucial in processing data from devices with low computational power, the latency introduced by the Internet backhaul limits real-time applications. By situating computing resources at the network’s edge, edge computing offers low-latency services by offloading computations from high-performance computing (HPC) data centers to the edge servers, reducing wide Area network (WAN) strain. As a result, edge computing has unlocked opportunities for innovative applications that were previously unfeasible, such as connected vehicles or medical robotics. Nonetheless, deploying the infrastructure required to support edge computing services raises sustainability and energy consumption concerns. Consequently, the development of tools enabling researchers to explore innovative approaches to reducing the energy impact of edge computing is crucial. In this work, we present MintEDGE, a network simulator focused on the energy consumption of edge computing. Our simulator allows testing energy-saving approaches and task placement algorithms in realistic large-scale scenarios encompassing entire regions.