LoRaWAN Behaviour Analysis through Dataset Traffic Investigation

Integration of Data and Predictive Models for the Evaluation of Air Quality and Noise in Urban Environments

This work addresses assessing air quality and noise in urban environments by integrating predictive models and Internet of Things technologies. For this, a model generated heat maps for PM2.5 and noise levels, incorporating traffic data from open sources for precise contextualization. This approach reveals significant correlations between high pollutant/noise concentrations and their proximity to industrial zones and traffic routes. The predictive models, including convolutional neural networks and decision trees, demonstrated high accuracy in predicting pollution and noise levels, with correlation values such as R2 of 0.93 for PM2.5 and 0.90 for noise. These findings highlight the need to address environmental issues in urban planning comprehensively. Furthermore, the study suggests policies based on the quantitative results, such as implementing low-emission zones and promoting green spaces, to improve urban environmental management. This analysis offers a significant contribution to scientific understanding and practical applicability in the planning and management of urban environments, emphasizing the relevance of an integrated and data-driven approach to inform effective policy decisions in urban environmental management.

Exploring LoRaWAN Traffic: In-Depth Analysis of IoT Network Communications

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.

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning

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.

A Remote Monitoring System for Rodent Infestation Based on LoRaWAN

Rodent infestations are a common problem that can result in several issues, including diseases, damage to property, and crop loss. Conventional methods of controlling rodent infestations often involve using mousetraps and applying rodenticides manually, leading to high manpower expenses and environmental pollution. To address this issue, we introduce a system for remotely monitoring rodent infestations using Internet of Things (IoT) nodes equipped with Long Range (LoRa) modules. The sensing nodes wirelessly transmit data related to rodent activity to a cloud server, enabling the server to provide real-time information. Additionally, this approach involves using images to auxiliary detect rodent activity in various buildings. By capturing images of rodents and analyzing their behavior, we can gain insight into their movement patterns and activity levels. By visualizing the recorded information from multiple nodes, rodent control personnel can analyze and address infestations more efficiently. Through the digital and quantitative sensing technology proposed at this stage, it can serve as a new objective indicator before and after the implementation of medication or other prevention and control methods. The hardware cost for the proposed system is approximately USD 43 for one sensor module and USD 17 for one data collection gateway (DCG). We also evaluated the power consumption of the sensor module and found that the 3.7 V 18,650 Li-ion batteries in series can provide a battery life of two weeks. The proposed system can be combined with rodent control strategies and applied in real-world scenarios such as restaurants and factories to evaluate its performance.

Planning and Optimization of Software-Defined and Virtualized IoT Gateway Deployment for Smart Campuses

The Internet of Things (IoT) is based on objects or “things” that have the ability to communicate and transfer data. Due to the large number of connected objects and devices, there has been a rapid growth in the amount of data that are transferred over the Internet. To support this increase, the heterogeneity of devices and their geographical distributions, there is a need for IoT gateways that can cope with this demand. The SOFTWAY4IoT project, which was funded by the National Education and Research Network (RNP), has developed a software-defined and virtualized IoT gateway that supports multiple wireless communication technologies and fog/cloud environment integration. In this work, we propose a planning method that uses optimization models for the deployment of IoT gateways in smart campuses. The presented models aimed to quantify the minimum number of IoT gateways that is necessary to cover the desired area and their positions and to distribute IoT devices to the respective gateways. For this purpose, the communication technology range and the data link consumption were defined as the parameters for the optimization models. Three models are presented, which use LoRa, Wi-Fi, and BLE communication technologies. The gateway deployment problem was solved in two steps: first, the gateways were quantified using a linear programming model; second, the gateway positions and the distribution of IoT devices were calculated using the classical K-means clustering algorithm and the metaheuristic particle swarm optimization. Case studies and experiments were conducted at the Samambaia Campus of the Federal University of Goiás as an example. Finally, an analysis of the three models was performed, using metrics such as the silhouette coefficient. Non-parametric hypothesis tests were also applied to the performed experiments to verify that the proposed models did not produce results using the same population.

A Communication Framework for Image Transmission through LPWAN Technology

Analyzing the conditions of use and selecting which technology is more efficient to apply is required when transmitting information through wireless networks.The Internet of Things (IoT) has gained traction in industry and academia as a paradigm in which information and communication technologies merge to deliver unique solutions by detecting, actuating, calculating, and sharing massive volumes of data via embedded systems. In this scenario, Low-Power Wide-Area Networks (LPWAN) appear to be an attractive solution for node connectivity. Typical IoT solutions demand flexible restrictions for wireless communication networks in terms of data rates and latency in exchange for having larger communication ranges and low energy consumption. Nonetheless, as the amount of data and data speeds demanded for particular applications increase, such as image transmissions, IoT network connectivity deteriorates. This paper proposes a communication architecture for image transmission across LPWAN networks utilizing LoRa modulation. The framework combines image processing techniques (classification, compressive sensing (CS), and reconstruction) with an investigation of LoRa modulation parameters using a Software-Defined Radio (SDR) environment. The results show that is possible to communicate an image of 128×128 pixels with four packets and one frequency channel in 2.51 s.