Design and Implementation of CI/CD over LoRaWAN : Continuous Integration and Deployment in LoRaWAN Edge Computing Applications

The recent rise of IoT devices in commercial and industrial spaces has created a demand for energy-efficient and reliable communication solutions. Communication solutions used on IoT devices vary depending on the applications. Wireless Low Power Wide Area Network (LPWAN) technologies have proven benefits, including long-range, low power, and low-cost communication alternatives for IoT devices. These benefits come at the cost of limitations, such as lower data rates. At the same time, the demand for faster, cheaper, and more reliable software deployment is becoming more critical than ever before. This thesis aims to find a way of having an automated process where software could be remotely deployed into LoRa nodes and investigate whether it is possible to implement a DevOps pipeline with both Continuous Integration (CI) and Continuous Deployment (CD) over LoRaWAN. For this thesis, an IoT LoRaWAN Edge computing application was chosen to determine how to design and implement a CI/CD pipeline to ensure a dependable and a continuous software deployment to the LoRaWAN nodes. Designing and implementing a Continuous Deployment pipeline for this IoT application was made possible with the integration of DevOps tools like GitHub and a TeamCity automation server. Additionally, a series of scripts have been designed and developed for this case, including automated tests, integration to cloud services, and file fragmentation and defragmentation tools. For software deployment and verification to the LoRaWAN network, a program was designed to communicate with the LoRaWAN network server over the WebSocket communication protocol. The implementation of DevOps in LoRaWAN applications is affected by the limitations of the LoRaWAN protocol. This thesis argues that these limitations can be eliminated using modular software and file fragmentation techniques. The implementation presented in this work can be extended for various time-critical use cases. The solution presented in this thesis also opens the door to combining LoRaWAN with other LPWAN technologies, like NB-IoT, that can be activated on demand

Reserved Parking Validation

A common situation that we can testify every day: fossil fuel cars occupying electric cars charge only places, and handy capped reserved places, occupied with cars without the proper authorization. This is something that plagues our society, where the values and moral are forgotten, and our duties and rights are lost in the day-to-day life. There are more and more cars moving, every day, to the city center, where the lack of available parking, together with the lack of proper public transportation creates a chaotic situation. Also, the large proliferation of electric cars, that is not accompanied by a proportional availability of electric chargers, raises issues, where these cars’ drivers are not allowed to charge their vehicles, most of the times, because they are being used as abusive parking. This dissertation has the goal to identify and propose a universal solution, with low implementation and maintenance costs, that allows a fast and unambiguous validation of authorization of a user, for parking in a reserved parking space

Ag-IoT for crop and environment monitoring: Past, present, and future

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task. OBJECTIVE: Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring. METHODS: It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms

Flexible Devices for Arctic Ecosystems Observations

Devices for observing the environment range from basic sensor systems, like step-counters, through wild-life cameras, with limited processing capabilities, to more capable devices with significant processing, memory and storage resources. Individual usage domains can benefit from a range of functionalities in these devices including flexibility in prototyping, on- device analytics, network roaming, reporting of data, and keeping the devices and services available in spite of failures and disconnections. The problem is that either the devices are too resource limited to support the range of functionalities, or they use too much energy. An important usage domain is COAT – Climate-Ecological Obser- vatory for Arctic Tundra. Presently, best practice includes deploying wild-life cameras in the Arctic Tundra, and visiting them to manually collect the recorded observations. This is a problem because such devices can only be rarely visited, and manual approaches to fetching data and storing it do not scale with regards to number of cameras, handling of human mistakes, and with freshness of observations. We present a prototype for observing the environment composed of a general purpose computer, a Raspberry PI, in combination with an ARM-based microcontroller. The combination enables us to create a more energy efficient prototype while supporting the needed functionality. The prototype improves on currently applied methods of observing the Arctic tundra. The prototype automatically observes the arctic tundra through camera, humidity and temperature sensors. It monitors itself for failures. The data is stored locally on the prototype until it can be automatically reports to a backend service over a wireless network. We have conducted experiments that show that task scheduling can reduce power consumption, and we identify some additional points that need to be addressed before we can run the device for long periods on battery power

A Smart Waste Management System Framework Using IoT and LoRa for Green City Project

Waste management is a pressing concern for society, requiring substantial labor resources and impacting various social aspects. Green cities strive for achieving a net zero-carbon footprint, including efficient waste management. The waste management system deals with three problems that are interrelated: a) the timely checking of the status of bins to prevent overflow; b) checking the precise location of bins; and c) finding the optimal route to the filled bins. The existing systems fail to satisfy all three problem areas with a single solution. To track the overflow of the bin, the proposed model uses ultrasonic sensors, which are complemented with LoRa to transmit the exact location of the bins in a real-time environment. The existing models are not that efficient at calculating the exact bin-filled status along with the precise location of the bins. The Floyd-Warshall algorithm in the proposed model optimizes waste collection using the Floyd-Warshall algorithm to determine the shortest path. Leveraging low-cost IoT technologies, specifically LoRa modules for data transfer, our solution offers benefits such as simplicity, affordability, and ease of replacement. By employing the Floyd-Warshall algorithm with a time complexity of O (n^3), our method efficiently determines the most optimal waste pickup route, saving time and resources. This study presents a smart waste management solution utilising Arduino UNO microcontrollers, ultrasonic sensors, and LoRaWAN to measure waste levels accurately. The proposed strategy aims to create clean and pollution-free cities by addressing the problem of waste distribution caused by poor collection techniques

Variable link performance due to weather effects in a long-range, low-power LoRa sensor network

When aiming for the wider deployment of low-power sensor networks, the use of sub-GHz frequency bands shows a lot of promise in terms of robustness and minimal power consumption. Yet, when deploying such sensor networks over larger areas, the link quality can be impacted by a host of factors. Therefore, this contribution demonstrates the performance of several links in a real-world, research-oriented sensor network deployed in a (sub)urban environment. Several link characteristics are presented and analysed, exposing frequent signal deterioration and, more rarely, signal strength enhancement along certain long-distance wireless links. A connection is made between received power levels and seasonal weather changes and events. The irregular link performance presented in this paper is found to be genuinely disruptive when pushing sensor-networks to their limits in terms of range and power use. This work aims to give an indication of the severity of these effects in order to enable the design of truly reliable sensor networks

A Systematic Review of IoT Solutions for Smart Farming

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.info:eu-repo/semantics/publishedVersio