LoRaWAN Network Research Articles

Water Quality Monitoring and Assessment System Along Tigris River in Mosul City

This paper aims to address the issue of water pollution in the Tigris River in Mosul city through continuous water quality monitoring. The proposed solution involves the use of LoRa-WAN wireless technology. Wireless sensor nodes would be placed at various stations along the river in Mosul, transmitting data to a LoRa gateway at the Nineveh Water Directorate. This gateway would then communicate with a server via a Cloud platform on the internet. The study utilized the OMNET++ program to simulate data transfer from sensor nodes to the LoRa gateway. Two main contributions are highlighted: an environmental contribution in providing data on water pollution levels and a technological contribution in utilizing the LoRa network for water quality monitoring. Thirteen parameters were measured at nine river stations. The sensor nodes will send notifications to the LoRa-WAN network if any parameter exceeds a predetermined value. The simulations analyzed the received signal strength indicator and packet delivery ratio with 117 sensor nodes managed by one LoRa gateway, covering a 20km area. The LoRa-WAN technology demonstrated satisfactory results in the simulated scenarios, improving network performance by studying the impact of increasing distance between sensor nodes and the gateway on RSSI values for different transmitting power levels (2-14 dBm).

RTPL: A Real-Time Communication Protocol for LoRa Network

The industrial Internet of Things (IIoT) is prominently emerging in applications of large-scale and wide-area applications, such as oilfield management, smart grid management, real-time equipment monitoring, and integration of traffic management systems for smart cities. Relying on short-range wireless technologies (e.g., WirelessHART and ISA100.11a), traditional wireless solutions for industrial automation find it challenging to support the expansive scale of today’s IIoT. To address this limitation, we propose to adopt LoRaWAN, a prominent low-power wide-area network technology, for industrial automation. LoRaWAN for industrial automation poses some unique challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. LoRaWAN traditionally adopts a simple protocol based on ALOHA with no collision avoidance or Listen Before Talk with Clear Channel Assessment and Random Backoff mechanisms to minimize energy consumption, which are less suitable for real-time communication. Existing real-time protocols for short-range technologies cannot be applied to a LoRaWAN network due to its unique characteristics such as asymmetry between downlink and the uplink spectrum, predefined modes (or classes) of operation, and concurrent reception through orthogonal spreading factors. In this paper, we address these challenges and propose RTPL- a Real-Time communication Protocol for LoRaWAN networks. RTPL is a low-overhead and conflict-free communication protocol allowing autonomous real-time communication of low-energy devices and exploits LoRa’s capability of parallel communication. We implement our approach on LoRa devices and evaluate through both physical experiments and extensive simulations. All results show that RTPL achieves on average 75% improvement in real-time performance without sacrificing throughput or energy compared to traditional LoRaWAN.

Intelligent IoT Platform for Agroecology: Testbed

Smart farming is set to play a crucial role in the sustainable transformation of agriculture. The emergence of precision agriculture, facilitated by Internet of Things (IoT) platforms, makes effective communication among the various sensors and devices on farms essential. The development of smart sensors that utilize artificial intelligence (AI) algorithms for advanced edge computations only intensifies this need. Moreover, once data are collected, farmers frequently find it challenging to apply them effectively, especially in alignment with agroecological principles. In this context, this paper introduces an energy-efficient platform for embedded AI sensors that leverages the LoRaWAN network, along with a knowledge-based system to aid farmers in decision-making rooted in sensor data and agroecological practices. This paper focuses on the deployment of an end-to-end IoT platform that integrates a wireless sensor network (WSN), embedded AI, and a knowledge base.

Supporting critical downlink traffic in LoRaWAN

LoRaWAN, a low-power wide-area network (LPWAN) technology, has been successfully used in the Internet of Things (IoT) industry over the last decade. It is an easy-to-use, long-distance communication protocol combined with minimal power consumption. Supporting critical downlink traffic in LoRaWAN networks is crucial for ensuring the reliable and efficient delivery of essential data in certain actuating applications. However, challenges arise when prioritizing critical downlink traffic, including commands, alerts, and emergency notifications that demand immediate attention from actuating devices. This paper explores strategies to improve downlink traffic delivery in LoRaWAN networks, focusing on enhancing reliability, fairness, and energy efficiency through prioritization techniques and network parameter configurations in the EU868 spectrum. Theoretical as well as simulation results provide insights into the effectiveness of the available solutions for supporting critical downlink traffic in LoRaWAN networks.

A Comprehensive Strategy for Implementing Adaptive Data Rate (ADR) in LoRaWAN Technology: Optimizing IoT Networks

The Internet of Things (IoT) is increasingly essential for creating smart environments by establishing long range connectivity among devices. LoRaWAN is a prominent technology choice for these applications due to its energy efficiency and extensive coverage. Central to LoRaWAN’s functionality is the Adaptive Data Rate (ADR) mechanism, which optimizes network performance by adjusting each device’s data rate and transmission power dynamically. This study delves into ADR’s operational principles, benefits, and implementation challenges in LoRaWAN networks. The proposed simulation model, featuring configurable parameters like the spreading distribution factor (SF), the adjustable number of devices, and the SNR threshold, denotes enhanced network performance after implementing the ADR algorithm. With an SNR threshold of 15 dB, ADR reduced the average transmission power of 200 devices from 7 dBm to 3 dBm after twenty iterations, underscoring ADR’s role in minimizing energy consumption while improving network efficiency.

INTELLIGENT MONITORING FOR DETECTIONS AND ESTIMATING RADIOLOGICAL PLUME DISPERSION IN AQUATIC ENVIRONMENTS: RIVERS AND SEAS

This work continues the presentation INTELLIGENT MONITORING FOR RADIOLOGICAL PLUME DISPERSION ESTIMATION - INTELLIGENT MONITORING FOR RADIOLOGICAL PLUM DISPERSION ESTIMATION, developed as part of the event hosted by ABDAN at the third edition of the Nuclear Trade & Technology Exchange – NT2E, Brazilian Nuclear Olympics (ONB), Hackapower. The primary objective was to develop a system for monitoring radiological plumes in aquatic environments, using a model based on Gaussian Process Regression (GPR) and Particle Swarm Optimization (PSO). The proposed system aims to estimate radiation dispersion in scenarios where plant systems are unavailable, similar to the Fukushima accident. Given this context, it was necessary to create mobile devices with radiation detection capabilities to provide accurate data on dose rate distribution. The model was developed to predict dose distribution using simulated data from a hypothetical accident and involved the use of LoRaWAN networks for drone communication and a firefly algorithm for signaling areas with different radiation levels. The implementation of GPR utilized the ScikitLearn library, while PSO was applied through the pyswarm library, focusing on optimization based on information entropy. Results indicated that the model successfully reconstructed the dose rate profile with an estimate close to the actual values, although data non-uniformity may have impacted accuracy. The use of drones for data collection proved innovative and effective, enabling real-time analysis and offering a robust solution for radiological monitoring in emergency scenarios. Analysis of radiation permeability variation in different aquatic environments highlighted the importance of adjusting measurements according to water density and composition. In conclusion, the work achieved its goal by developing an intelligent system for radiation dispersion estimation, and future work should explore new scenarios and dynamics to enhance model accuracy in real radiological emergency situations.

EVALUASI KINERJA DAN EFISIENSI JARINGAN LORAWAN UNTUK APLIKASI IOT DI LINGKUNGAN SUBURBAN

The current era of digitalization is rapidly advancing, especially in the utilization of Internet of Things (IoT) technology for various purposes, including in the transportation and vehicle rental sectors. This research focuses on the application of the LoRaWAN network in supporting the electric scooter rental system in suburban environments, particularly in the Kintamani area. The designed system allows real-time monitoring of electric scooter locations through the integration of GPS modules, and also provides additional security features with an emergency button for urgent situations. The system is also equipped with rental time settings to maximize efficiency in managing electric scooter rentals. LoRaWAN was chosen for its ability to transmit data over long distances with low power consumption, which is highly suitable for suburban areas with wide coverage and limited infrastructure. This research not only evaluates the network's performance in terms of communication but also reviews the system's efficiency in terms of the number of devices that can be connected simultaneously. The evaluation focuses on how optimally this system can be used on a larger scale, especially in supporting the growing needs of IoT. This study provides insights into the potential development of a safer and more efficient IoT-based bike rental system in suburban environments.

Probabilistic performance evaluation of the class-A device in LoRaWAN protocol on the MAC layer

LoRaWAN is a network technology that provides a long-range wireless network while maintaining low energy consumption. It adopts the pure Aloha MAC protocol and the duty-cycle limitation at both uplink and downlink on the MAC layer to conserve energy. Additionally, LoRaWAN employs orthogonal parameters to mitigate collisions. However, synchronization in star-of-star topology networks and the complicated collision mechanism make it challenging to conduct a quantitative performance evaluation in LoRaWAN. Our previous work proposes a Probabilistic Timed Automata (PTA) model to represent the uplink transmission in LoRaWAN. It is a mathematical model that presents the nondeterministic and probabilistic choice with time passing. However, this model remains a work in progress. This study extends the PTA model to depict Class-A devices in the LoRaWAN protocol. The complete characteristics of LoRaWAN’s MAC layer, such as duty-cycle limits, bidirectional communication, and confirmed message transmission, are accurately modeled. Furthermore, a comprehensive collision model is integrated into the PTA. Various properties are verified using the probabilistic model checker PRISM, and quantitative properties are calculated under diverse scenarios. This quantitative analysis provides valuable insights into the performance and behavior of LoRaWAN networks under varying conditions.

Modeling a LoRAWAN Network for Vehicle Wildlife Collision Avoidance System on Rural Roads

Modeling a LoRAWAN Network for Vehicle Wildlife Collision Avoidance System on Rural Roads

Waste management 2.0 leveraging internet of things for an efficient and eco-friendly smart city solution.

Waste management poses a major challenge for cities worldwide, with significant environmental, economic, and social impacts. This paper proposes a novel waste management system leveraging recent advances in the Internet of Things (IoT), algorithms, and cloud analytics to enable more efficient, sustainable, and eco-friendly waste collection and processing in smart cities. An ultrasonic sensor prototype is tailored for reliable fill-level monitoring. A LoRaWAN and cellular network architecture provides city-wide connectivity. A cloud platform handles sensor data storage, processing, and analytics. Dynamic route optimization algorithms minimize time, distance, and fuel use based on real-time bin data. Extensive pilot studies in 10 different locations across Lahore, Pakistan, validated the system, processing over 200 million data points. The results showed a 32% improvement in route efficiency, a 29% decrease in fuel consumption and emissions, a 33% increase in waste processing throughput, and 18% vehicle maintenance savings versus conventional practices. This demonstrates quantifiable benefits across operational, economic, and sustainability dimensions. The proposed IoT-enabled waste management system represents a significant advancement towards sustainable and ecologically responsible waste practices in smart cities worldwide. This research provides a replicable model for holistic smart city solutions integrating sensing, algorithms, and analytics to transition civic operations towards data-driven, efficient paradigms. It represents a significant advancement in sustainable waste practices for smart cities worldwide. Further work could apply emerging technologies like automation and artificial intelligence to create waste management 3.0.

On the Simulation of LoRaWAN Networks: A Focus on Reproducible Parameter Configuration

In the past decade, with the rapid proliferation of Internet of Things (IoT) devices new applications have emerged in diverse fields such as agriculture, smart cities, and healthcare. In this context, LoRaWAN, a Low-Power Wide Area Network protocol, has gained significant adoption. Its advantages, including low-power consumption and long-range communication capabilities, make it an ideal solution for IoT communication. Simulation tools have played a crucial role in facilitating experimentation and advancing the implementation of the LoRaWAN protocol. However, effectively modelling these experiments present a significant challenge. Through a comprehensive survey of the literature on LoRaWAN, we have identified variations in parameter configuration among different works and observed a lack of sufficient information in most of them, thereby impeding result reproducibility. This paper offers a comprehensive overview of LoRaWAN technology, encompassing its architecture and underlying technologies such as LoRa and LPWAN. Furthermore, we present and compare the main simulation tools currently available for LoRaWAN, discuss the parameters that significantly impact the performance of a LoRaWAN network, and explore how researchers have configured these parameters to model their simulation experiments.

Scalability and Performance Analysis of LoRaWAN Networks for IoT Implementation: Comprehensive Review and Research Agenda

The surge in IoT devices has created the need for a scalable and high-performance network infrastructure capable of handling massive data flows and diverse application requirements. Among the various wireless technologies available, with long-range communication capabilities, low power consumption, and cost-effectiveness, the Low-Power Wide-Area Network (LPWAN) has emerged as a highly popular choice for numerous applications. This literature review aims to provide a comprehensive overview of recent developments in scalability and performance analysis of LoRaWAN networks for IoT implementation by reviewing existing research and identifying key challenges and opportunities for the research agenda. In the research on LoRaWAN performance in IoT, five characteristics have been proven and become factors for using LoRaWAN in IoT: low energy power usage, wide coverage, low cost, stable data transmission speed, and system reliability. However, not many have discussed its security. Based on the literature review, LoRaWAN does have some characteristics that make it suitable for large-scale IoT applications. This review will contribute to advancing knowledge in this field, enabling the development of scalable and high-performance IoT systems to meet the demands of the growing IoT ecosystem

Resource Allocation for LoRaWAN Network Slicing: Multi-Armed Bandit-based Approaches

Wireless sensor networks have become increasingly popular in recent years due to the growing demand for Internet of Things (IoT) applications, including LoRaWAN networks. However, the effective allocation of resources remains a crucial challenge in LoRaWAN networks due to the limited bandwidth and the diverse demands for multiple services. This paper presents three novel resource allocation solutions for LoRaWAN network slicing to address this challenge. These solutions are based on the Multi-Armed Bandit (MAB) algorithm, which is known for balancing the exploration of available actions with the exploitation of optimal decisions. Our objective is to dynamically and efficiently allocate resources to network slices by treating the resource allocation as a MAB problem. This approach aims to maximize Packet Delivery Rate (PDR) performance while ensuring each service’s Service Level Agreement (SLA). The first solution, UCB-MAB, uses the Upper Confidence Bound (UCB) strategy to balance exploration and exploitation to improve network performance. The second solution, Q-UCB-MAB, continuously updates Q-values using the Q-learning update equation and incorporates the UCB strategy for further optimization. Finally, the third solution, ARIMA-UCB-MAB, leverages the predicted reward value from the Autoregressive Integrated Moving Average (ARIMA) model within the UCB framework to enhance network performance. Our results demonstrate that all three solutions offer efficient resource allocation in terms of PDR and SLA satisfaction. Specifically, the ARIMA-UCB-MAB solution outperforms the two other solutions.

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.

RSSI-based fingerprint localization in LoRaWAN networks using CNNs with squeeze and excitation blocks

The ability to offer long-range, high scalability, sustainability, and low-power wireless communication, are the key factors driving the rapid adoption of the LoRaWAN technology in large-scale Internet of Things applications. This situation has created high demand to incorporate location estimation capabilities into large-scale IoT applications to meaningfully interpret physical measurements collected from IoT devices. As a result, research aimed at investigating node localization in LoRaWAN networks is on the rise. The poor localization performance of classical range-based localization approaches in LoRaWAN networks is due to the long-range nature of LoRaWAN and the rich scattering nature of outdoor environments, which affects signal transmission. Because of the ability of fingerprint-based localization methods to effectively learn useful positional information even from noisy RSSI data, this work proposes a fingerprinting-based branched convolutional neural network (CNN) localization method enhanced with squeeze and excitation (SE) blocks to localize a node in LoRaWAN using RSSI data. Results from the experiments conducted to evaluate the performance of the proposed method using a publicly available LoRaWAN dataset prove its effectiveness and robustness in localizing a node with satisfactory results even with a 30% reduction in both the principal component analysis (PCA) variances on the training data and the size of the original sample. A localization accuracy of 284.57 m mean error on the test area was achieved using the Powed data representation, which represents an 8.39% increase in localization accuracy compared to the currently best-performing fingerprint method in the literature, evaluated using the same LoRaWAN dataset.

End-to-End Emulation of LoRaWAN Architecture and Infrastructure in Complex Smart City Scenarios Exploiting Containers.

In a LoRaWAN network, the backend is generally distributed as Software as a Service (SaaS) based on container technology, and recently, a containerized version of the LoRaWAN node stack is also available. Exploiting the disaggregation of LoRaWAN components, this paper focuses on the emulation of complex end-to-end architecture and infrastructures for smart city scenarios, leveraging on lightweight virtualization technology. The fundamental metrics to gain insights and evaluate the scaling complexity of the emulated scenario are defined. Then, the methodology is applied to use cases taken from a real LoRaWAN application in a smart city with hundreds of nodes. As a result, the proposed approach based on containers allows for the following: (i) deployments of functionalities on diverse distributed hosts; (ii) the use of the very same SW running on real nodes; (iii) the simple configuration and management of the emulation process; (iv) affordable costs. Both premise and cloud servers are considered as emulation platforms to evaluate the resource request and emulation cost of the proposed approach. For instance, emulating one hour of an entire LoRaWAN network with hundreds of nodes requires very affordable hardware that, if realized with a cloud-based computing platform, may cost less than USD 1.

LoRaWAN-based hybrid internet of wearable things system implementation for smart healthcare

This study introduces the design and development of an Internet of Wearable Things-based Hybrid Healthcare Monitoring System (IoWT-HHMS) for smart medical applications. The system incorporates smart wearable sensing units for real-time, remote monitoring of vital health parameters such as Blood Pressure (BP), Heart Rate (HR), and Body Temperature (BT). A key innovation is the development of a hybrid wireless network communication mechanism within the IoWT-HHMS, utilising the FiPy microcontroller. This mechanism supports both short- and long-range connectivity and integrates an algorithm for efficient data acquisition and updating to the IoT platform. The IoWT-HHMS has undergone extensive testing and validation across various scenarios, including sensor functionality, performance of Wi-Fi and LoRaWAN networks, hybrid network connectivity, and accuracy assessment using the Datacake dashboard. The tests evaluated crucial aspects such as communication reliability, power consumption, and latency. The results demonstrate the system's high stability and accuracy in reading health parameters. Comparisons with reference devices reveal impressive accuracy levels for Systolic BP (SBP), Diastolic BP (DBP), HR, and BT, recording 96.37 %, 95.17 %, 97 %, and 98.57 % accuracy, respectively. Both Wi-Fi and LoRaWAN networks proved reliable in indoor and outdoor settings, maintaining data transmission over distances up to 1.5 km without data loss. In conclusion, the developed IoWT-HHMS shows great promise for efficient and effective real-time remote monitoring of patients' health conditions using an innovative hybrid wireless network communication mechanism.

Advancing Performance in LoRaWAN Networks: The Circular Region Grouped Bit-Slot LoRa MAC Protocol

LoRaWAN is an emerging Low-Power Wide-Area Network (LPWAN) technology, widely adopted in various Internet of Things (IoT) applications due to its long transmission range, low power consumption, and robust anti-interference capabilities. However, using the ALOHA medium access control (MAC) protocol in LoRaWAN significantly reduces the packet delivery rate, particularly in high-density networks where end devices (EDs) access the network randomly. It seriously affects the overall network performance. This paper introduces the Circular Region Grouped Bit-Slot CGBS-LoRa MAC protocol to address this challenge. The protocol takes a proactive approach by allocating transmission parameters for end devices, executing regional segmentation based on the distance between EDs and the gateway using different spreading factors. Concurrently, improving the ALOHA access method ensures the efficient communication of EDs in the region. Experimental results demonstrate that our proposed protocol markedly improves the scalability of LoRa networks and minimizes device collisions compared to three other MAC protocols. Even as the LoRaWAN network expands, the proposed protocol maintains a high packet delivery rate and low latency.

LoRA Gateway Coverage and Capacity Analysis for Supporting Monitoring Passive Infrastructure Fiber Optic in Urban Area

In the era of digital transformation, telecommunications infrastructure has become the backbone of global connectivity. Optical Distribution Cabinet (ODC) is a crucial part of an optical network that distributes signals to various points in the network. Maintenance and monitoring of ODCs have become essential to ensure optimal availability and performance. However, conventional approaches are often expensive and difficult to implement. The objective of this study is to develop a LoRaWAN network with the purpose of determining the required number of gateways. Additionally, the research aims to devise an IoT-basedODC device monitoring system within the FTTH network, utilizing data from PT. Telkom Witel Bandung. The approach involves employing simulation techniques through the Atoll apps v 3.40. Multiple calculation stages are applied to expect RSSI and SINR parameters within an area spanning 188.96 km². The study employs a frequency of 920 MHz, a bandwidth of 125 kHz, and a spreading factor of 10. The data analysis includes RSSI and SINR signals. As a result of calculations and planning simulations, this study recommended the use of nine gateways and achieved an RSSI parameter of -70. 35 dBm and a SINR parameter of 17. 33 dBm.

Advance Path Loss Model for Distance Estimation Using LoRaWAN Network’s Received Signal Strength Indicator (RSSI)

Advance Path Loss Model for Distance Estimation Using LoRaWAN Network’s Received Signal Strength Indicator (RSSI)