Real Time Performance Testing of LoRa-LPWAN Based Environmental Monitoring UAV System

Aerial drones are emerging in industrial and environmental monitoring as they are effective tools that are able to reach far and isolated areas. However, the regularity communication developments have not grown as fast as the technology needs. Either due to the lack of communication coverage or power inefficiency. As a result, some other solution should be proposed such as the internet of things. Internet of Things technology has a great potential of becoming a leading industry since it makes objects able to communicate with each other. IOT/M2M (Internet of Things/Machine-to-machine) communication could be used in a wide range of applications such as environmental surveillance and monitoring systems. These systems could be fixed ends or moving ends like an Unmanned Ariel vehicle (UAV). In this case, LoRa/LPWAN (Long Range Communication) / (Low Power Wide Area Network) is selected to be the best candidate, since it provides a wide coverage area and power efficient systems. This thesis develops and tests a communication scheme prototype for environmental UAV monitoring system using LoRa-LPWAN. Also, a functional testbed for testing the prototype is proposed as well. The prototype was tested in different environmental sites such as line-of-sight and non-line-of-sight environments. The developed scheme performs successfully in harsh environments and its readings were fully documented throughout this thesis

Internet of things and LoRaWAN enabled future smart farming

It is estimated that to keep pace with the predicted population growth over the next decades, agricultural processes involving food production will have to increase their output up to 70 percent by 2050. "Precision" or "smart" agriculture is one way to make sure that these goals for future food supply, stability, and sustainability can be met. Applications such as smart irrigation systems can utilize water more efficiently, optimizing electricity consumption and costs of labor; sensors on plants and soil can optimize the delivery of nutrients and increase yields. To make all this smart farming technology viable, it is important for it to be low-cost and farmer-friendly. Fundamental to this IoT revolution is thus the adoption of low-cost, long-range communication technologies that can easily deal with a large number of connected sensing devices without consuming excessive power. In this article, a review and analysis of currently available long-range wide area network (LoRaWAN)-enabled IoT application for smart agriculture is presented. LoRaWAN limitations and bottlenecks are discussed with particular focus on their effects on agri-tech applications. A brief description of a testbed in development is also given, alongside a review of the future research challenges that this will help to tackle

Optimal deployment of mobile gateways in LoRaWAN environments

The recent growth of the Internet of Things (IoT) has given rise to new applications and technologies. Of these technologies, LoRa is the one that has stood out recently due to its ability to transmit packets over long distances at low energy costs. In addition to this, this technology also uses unlicensed frequency bands, and all these factors make it possible to build low energy cost networks with large coverage areas at low monetary cost. This makes LoRa very appealing for environments where multiple square kilometers need to be covered for monitoring, such as agriculture. This thesis focuses primarily on positioning gateways in a Lo- RaWAN in order to achieve energy fairness in the network.The target in question is an environmental sensor network that monitors conditions inside tree canopies in an orange orchard in the Algarve, south of Portugal.The peculiar characteristics of these orange trees, with heights up to 3.5 m and very dense foliage, makes it a very challenging environment for radio waves propagation and causes a rapid drop in signal quality. The power consumption of the end-nodes of the network is defined by 7 combinations of spreading factor and bandwidth (0 to 6) where 0 represents the slowest and most reliable transmission at the cost of higher power consumption while 6 represents the opposite. The combination of bandwidth and spreading factor is denominated data rate. Environmental factors can negatively impact the quality of LoRa packets and the necessary power adjustments of the end-node to overcome this, and increase signal reliability, can easily define whether a device is able to transmit for 1 year or 10! The main factors that can affect signal quality are obstruction, distance and meteorology. In the case study, of these 3 factors, obstruction affects transmission quality the most. Most of the literature suggests solutions within the framework of optimizing the datarate optimization algorithm (ADR). ADR aims to minimize energy consumption while ensuring the best possible packet transmission rate and achieves this by changing the data rate based on the quality of the last 20 packets received.However, this optimization is done directly to individual end-nodes and does not solve the problem of energy fairness over the whole network because, regardless of how optimized this algorithm is, the algorithm cannot transcend the physical constraints imposed by the devices and the technology itself. Distance and obstruction will always be obstacles to signal quality. Since these physical constraints will always be present in a network and the solutions proposed by the literature only improve performance at the level of individual devices, this ends up creating a large lifetime discrepancy between devices depending on their placement. In the case of LHT65s, the discrepancy in device life expectancy is high. For example the difference between using a data rate of 0 or 5 is about 10 years. The solution proposed in this thesis to overcome this problem is to precompute the optimal position for the gateways in order to guarantee the highest life expectancy for the network. Given a number of available positions for the gateways and having a certain number of gateways less than the number of positions, the goal is to compute the optimal positioning of the gateways in order to maximize the overall network life expectancy by ensuring a fair energy consumption among different end-nodes. The first step in this process was to collect information about signal quality from a real case LoRaWAN deployment. This allowed to better understand the constraints and problems associated with its implementation. This was done using 25 LTH65 devices, 1 RAK 7244 gateway and Chirpstack as the framework to manage the network. Regarding the study of the algorithm before applying it to the practical case, a simulator was used to collect data. The simulator chosen for the development of the application was OMNet++, which besides being easier to use is also better documented than the other options considered. This simulator also offers a graphical interface with great detail that allows you to easily observe the behavior of the network. Using the Flora module it was simulated a LoRaWAN network with the structure suggested by the LoRa Alliance® with 25 devices using Oulu’s path loss model. The information obtained from this simulation was used as input and test for the algorithm that was compiled by CPLEX. In each simulation about 10,000 packets were sent per device and each experiment was repeated 30 times. The results show that the optimization model has the ability to identify the best placement for the gateway given a predefined locations and network geometry. This is due to the fact that the algorithm identifies the lowest value in the highest energy consumption per packet, and minimizing this value creates a balance of consumption among the devices and consequently extends the life expectancy of the network. It can then be concluded that this methodology is indeed efficient for deployments where changing network devices cannot be done frequently. Although it is not easy to relocate gateways in already implemented networks, but in new environments where monitoring and optimization are requirements, and these new environments are built considering the network structure, we can use this methodology since it has proven to be able to improve network life expectancy.O recente crescimento da Internet das Coisas (IoT) deu origem a novas aplicac¸ ˜oes e tecnologias. Destas tecnologias, a LoRa ´e a que se tem destacado recentemente devido `a sua capacidade de transmitir pacotes a longas distˆancias a baixos custos energ´eticos. Al´em disso, esta tecnologia tamb´em utiliza bandas de frequˆencia n˜ao licenciadas, e todos estes factores tornam poss´ıvel a construc¸ ˜ao de redes de baixo custo energ´etico com grandes ´areas de cobertura a baixo custo monet´ario. Isto torna LoRa muito apelativo para ambientes onde v´arios quil´ometros quadrados precisam de ser cobertos para monitorizac¸ ˜ao, tais como a agricultura. Esta tese centra-se principalmente no posicionamento de gateways numa rede LoRaWAN, a fim de alcançar a energy fairness na rede.(...)This work was supported by FCT (Foundation for Science and Technology) from Portugal within CEOT (Center for Electronic, Optoelectronic and Telecommunications) and UID/MULTI/00631/2020 project

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated

Implementing Efficient and Multi-Hop Image Acquisition In Remote Monitoring IoT systems using LoRa Technology

Remote sensing or monitoring through the deployment of wireless sensor networks (WSNs) is considered an economical and convenient manner in which to collect information without cumbersome human intervention. Unfortunately, due to challenging deployment conditions, such as large geographic area, and lack of electricity and network infrastructure, designing such wireless sensor networks for large-scale farms or forests is difficult and expensive. Many WSN-appropriate wireless technologies, such as Wi-Fi, Bluetooth, Zigbee and 6LoWPAN, have been widely adopted in remote sensing. The performance of these technologies, however, is not sufficient for use across large areas. Generally, as the geographical scope expands, more devices need to be employed to expand network coverage, so the number and cost of devices in wireless sensor networks will increase dramatically. Besides, this type of deployment usually not only has a high probability of failure and high transmission costs, but also imposes additional overhead on system management and maintenance. LoRa is an emerging physical layer standard for long range wireless communication. By utilizing chirp spread spectrum modulation, LoRa features a long communication range and broad signal coverage. At the same time, LoRa also has low power consumption. Thus, LoRa outperforms similar technologies in terms of hardware cost, power consumption and radio coverage. It is also considered to be one of the promising solutions for the future of the Internet of Things (IoT). As the research and development of LoRa are still in its early stages, it lacks sufficient support for multi-packet transport and complex deployment topologies. Therefore, LoRa is not able to further expand its network coverage and efficiently support big data transfers like other conventional technologies. Besides, due to the smaller payload and data rate in LoRa physical design, it is more challenging to implement these features in LoRa. These shortcomings limit the potential for LoRa to be used in more productive application scenarios. This thesis addresses the problem of multi-packet and multi-hop transmission using LoRa by proposing two novel protocols, namely Multi-Packet LoRa (MPLR) and Multi-Hop LoRa (MHLR). LoRa's ability to transmit large messages is first evaluated in this thesis, and then the protocols are well designed and implemented to enrich LoRa's possibilities in image transmission applications and multi-hop topologies. MPLR introduces a reliable transport mechanism for multi-packet sensory data, making its network not limited to the transmission of small sensor data only. In collaboration with a data channel reservation technique, MPLR is able to greatly mitigate data collisions caused by the increased transmission time in laboratory experiments. MHLR realizes efficient routing in LoRa multi-hop transmission by utilizing the power of machine learning. The results of both indoor and outdoor experiments show that the machine learning based routing is effective in wireless sensor networks

Internet of underground things in precision agriculture: Architecture and technology aspects

The projected increases in World population and need for food have recently motivated adoption of information technology solutions in crop fields within precision agriculture approaches. Internet Of Underground Things (IOUT), which consists of sensors and communication devices, partly or completely buried underground for real-time soil sensing and monitoring, emerge from this need. This new paradigm facilitates seamless integration of underground sensors, machinery, and irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. In this paper, state-of-the-art communication architectures are reviewed, and underlying sensing technology and communication mechanisms for IOUT are presented. Moreover, recent advances in the theory and applications of wireless underground communication are also reported. Finally, major challenges in IOUT design and implementation are identified

Measuring Environmental Effects on LoRa Radios in Cold Weather using 915 MHz

The “Internet of Things” (IoT) is a commonplace term in today’s society. Measuring our homes, cities, and farm land, IoT devices and sensor networks help give insight with new amounts and types of data collected. With several billion devices projected to be in use by 2020, an inexpensive, reliable solution for communication is needed. Typically, this would mean Wi-Fi, and in the home that works; but what if the devices are several miles away? LoRaWAN has sought to fll this need, however, appears unreliable given changes in atmospheric conditions. Experiments in Europe have shown a complete communication breakdown when temperatures reach 60◦C, below the 80◦ hardware specifcation. The researcher’s own observations and preliminary test also show inconsistencies in terms of range and received signal strength. This thesis describes the process of designing a physical server-client architecture using a Dragino LoRa client and Raspberry Pis. Using hobbyist-available materials, three LoRa nodes were created to test the effects of cold weather, along with rain and snow. The nodes were deployed from January to the beginning of March, recording communications, weather data, and the received signal strength of packets. This data was then analyzed for factors that affected communication most. The experiment was split into two phases, one for recording the natural environmental conditions, the other, for measuring environmental conditions when heat is applied directly to the LoRa radio chip. Visual representation and statistical correlations were used to determine the relationship between temperature and humidity inside and outside a node, the intensity of rain and snow, and the temperature of the radio chip, compared to the received signal strength (RSS) and received packet ratio (RPR). From the comparisons made, humidity appears to be a leading predictor in LoRa communication reliability. This is followed by temperature, then the amount of rain, and fnally snow. The temperature of the radio chip, from ambient to 60◦C does not seem to affect signal strength or communication in a noticeably impactful way. This shows an indication that communication failure is caused by problems with the antenna or the micro-controller, a distinction other experiments have not made, however the exact distinction between antenna and micro-controller was outside the scope of this study

Energy efficiency in LoRaWAN

Abstract. Low-power wide-area networks (LPWANs) are emerging rapidly as a fundamental Internet of Things (IoT) technology because of features like low-power consumption, long-range connectivity, and the ability to support massive numbers of users. With its high growth rate, Long Range (LoRa) is becoming the most adopted LPWAN technology. Sensor nodes are typically powered by batteries, and many network applications, which expect end-devices to operate reliably for a prolonged time. Each sensor node or actuator consumes a distinct current for a different period of time, depending on its operational state. To model a self-sufficient sensor nodes network, it is of the utmost importance to investigate the energy consumption of class-A end-devices in a LoRa Wide Area Network (LoRaWAN) with the impact of respective physical and MAC layers. Several latest published research works have analyzed the energy consumption model of a sensor node in different transmission (confirmed or unconfirmed) modes and also examined the network performance of LoRaWAN under uplink outage probabilities. This research work investigates the energy cost of the LoRaWAN, deploying hundreds of sensor nodes to transmit information messages. The proposed scheme is evaluated by considering the average power consumption of end-device powered by 2400 mAh battery. Furthermore, the energy efficiency of an unconfirmed transmission network is examined to provide the optimal number of sensor nodes for each spreading factor

LoRaWAN simulation and analysis for performance enhancement of realistic networks

The Internet of Things (IoT) is becoming an ubiquitous technology, with new devices, solutions and applications being developed at an ever-increasing rate. Fundamental to the IoT revolution is the adoption of wireless protocols purposely designed to enable low-cost, long-range communication for numerous connected devices. Low Power Wide Area Networks (LPWANs) are wide area wireless telecommunication networks designed specifically for IoT applications. They allow for long-range communication at a low bit-rate among connected items, such as battery-powered sensors. However, with these benefits come also a number of drawbacks, including the limited data rate available and the reliance on low power channel access methods which can negatively impact performance in a highly dense network. The purpose of the research contained in this work is to measure the performance in terms of Quality-of-Service (QoS), Packet Delivery Ratio (PDR) and scalability of one LPWAN in particular, Long Range Wide Area Network (LoRaWAN), as well as providing possible improvements that current and future network owners can put into practice. LoRaWAN simple channel access protocol, based on pure Additive Links On-line Hawaii Area (ALOHA) is intended to reduce cost, complexity, and energy consumption while increasing transmission range. However, it also severely limits the scalability of the technology, making it more prone to packet collision, despite LoRaWAN being particularly resilient to self-interference, thanks to the underlining, proprietary Long Range (LoRa) modulation. In this thesis, LoRaWAN technology is evaluated through both software simulation and experimental deployments, with the goal of gaining a deeper understanding of the technology to then create better models and better performing deployments. The innovations and novel results presented throughout will accelerate the pervasiveness of LPWAN networks such as LoRaWAN, and ultimately their effectivness. Despite being developed in 2015, LoRa and LoRaWAN have both not been fully characterised, particularly in regard to large-scale behaviour. This is partly due to the low feasibility of deploying vast networks. To address this, the first recorded instance of anurban digital twin of 20 devices LoRaWAN network was deployed and analysed. The available simulation models, despite being successfully used in various research studies, are also not fully complete, and a deeper understanding of the technology is required to fix some remaining open issues. To give additional insight into their operation as well as practical improvements that can be carried out to maximise performance, both from a consumer and an industrial standpoint, existing LoRa and LoRaWAN modules for the network simulator NS-3 are enhanced and used throughout the work presented. Scalability and Quality-of-Service improvements are also presented, based on the knowledge gaps found in current LoRaWAN research and the results of the simulations performed. In particular, improvements on PDR up to 10% are reported using novel techniques of downlink independent optimisation, and new insight on the positioning of gateways to achieve maximum scalability in a two-gateways network are also highlighted