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

Architecture and communication protocol to monitor and control water quality and irrigation in agricultural environments

[ES] La introducción de soluciones tecnológicas en la agricultura permite reducir el uso de recursos y aumentar la producción de los cultivos. Además, la calidad del agua de regadío se puede monitorizar para asegurar la seguridad de los productos para el consumo humano. Sin embargo, la localización remota de la mayoría de los campos presenta un problema para proveer de cobertura inalámbrica a los nodos sensores y actuadores desplegados en los campos y los canales de agua para regadío. El trabajo presentado en esta tesis aborda el problema de habilitar la comunicación inalámbrica entre los dispositivos electrónicos desplegados para la monitorización de la calidad del agua y el campo a través de un protocolo de comunicación y arquitectura heterogéneos. La primera parte de esta tesis introduce los sistemas de agricultura de precisión (PA) y la importancia de la monitorización de la calidad del agua y el campo. Asimismo, las tecnologías que permiten la comunicación inalámbrica en sistemas PA y el uso de soluciones alternativas como el internet de las cosas bajo tierra (IoUT) y los vehículos aéreos no tripulados (UAV) se introducen también. Después, se realiza un análisis en profundidad del estado del arte respecto a los sensores para la monitorización del agua, el campo y las condiciones meteorológicas, así como sobre las tecnologías inalámbricas más empleadas en PA. Además, las tendencias actuales y los desafíos de los sistemas de internet de las cosas (IoT) para regadío, incluyendo las soluciones alternativas introducidas anteriormente, han sido abordados en detalle. A continuación, se presenta la arquitectura propuesta para el sistema, la cual incluye las áreas de interés para las actividades monitorización que incluye las áreas de los canales y el campo. A su vez, la descripción y los algoritmos de operación de los nodos sensores contemplados para cada área son proporcionados. El siguiente capítulo detalla el protocolo de comunicación heterogéneo propuesto, incluyendo los mensajes y alertas del sistema. Adicionalmente, se presenta una nueva topología de árbol para redes híbridas LoRa/WiFi multisalto. Las funcionalidades específicas adicionales concebidas para la arquitectura propuesta están descritas en el siguiente capítulo. Éstas incluyen algoritmos de agregación de datos para la topología propuesta, un esquema de las amenazas de seguridad para los sistemas PA, algoritmos de ahorro de energía y tolerancia a fallos, comunicación bajo tierra para IoUT y el uso de drones para adquisición de datos. Después, los resultados de las simulaciones para las soluciones propuestas anteriormente son presentados. Finalmente, se tratan las pruebas realizadas en entornos reales para el protocolo heterogéneo presentado, las diferentes estrategias de despliegue de los nodos empleados, el consumo energético y la función de cuantificación de fruta. Estas pruebas demuestran la validez de la arquitectura y protocolo de comunicación heterogéneos que se han propuesto.[CA] La introducció de solucions tecnològiques en l'agricultura permet reduir l'ús de recursos i augmentar la producció dels cultius. A més, la qualitat de l'aigua de regadiu es pot monitoritzar per assegurar la qualitat dels productes per al consum humà. No obstant això, la localització remota de la majoria dels camps presenta un problema per a proveir de cobertura sense fils als nodes sensors i actuadors desplegats als camps i els canals d'aigua per a regadiu. El treball presentat en aquesta tesi tracta el problema d'habilitar la comunicació sense fils entre els dispositius electrònics desplegats per a la monitorització de la qualitat de l'aigua i el camp a través d'un protocol de comunicació i arquitectura heterogenis. La primera part d'aquesta tesi introdueix els sistemes d'agricultura de precisió (PA) i la importància de la monitorització de la qualitat de l'aigua i el camp. Així mateix, també s'introdueixen les tecnologies que permeten la comunicació sense fils en sistemes PA i l'ús de solucions alternatives com l'Internet de les coses sota terra (IoUT) i els vehicles aeris no tripulats (UAV). Després, es realitza una anàlisi en profunditat de l'estat de l'art respecte als sensors per a la monitorització de l'aigua, el camp i les condicions meteorològiques, així com sobre les tecnologies sense fils més emprades en PA. S'aborden les tendències actuals i els reptes dels sistemes d'internet de les coses (IoT) per a regadiu, incloent les solucions alternatives introduïdes anteriorment. A continuació, es presenta l'arquitectura proposada per al sistema, on s'inclouen les àrees d'interès per a les activitats monitorització en els canals i el camp. Finalment, es proporciona la descripció i els algoritmes d'operació dels nodes sensors contemplats per a cada àrea. El següent capítol detalla el protocol de comunicació heterogeni proposat, així como el disseny del missatges i alertes que el sistema proposa. A més, es presenta una nova topologia d'arbre per a xarxes híbrides Lora/WiFi multi-salt. Les funcionalitats específiques addicionals concebudes per l'arquitectura proposada estan descrites en el següent capítol. Aquestes inclouen algoritmes d'agregació de dades per a la topologia proposta, un esquema de les alertes de seguretat per als sistemes PA, algoritmes d'estalvi d'energia i tolerància a fallades, comunicació per a IoUT i l'ús de drons per a adquisició de dades. Després, es presenten els resultats de les simulacions per a les solucions proposades. Finalment, es duen a terme les proves en entorns reals per al protocol heterogeni dissenyat. A més s'expliquen les diferents estratègies de desplegament dels nodes empleats, el consum energètic, així com, la funció de quantificació de fruita. Els resultats d'aquetes proves demostren la validesa de l'arquitectura i protocol de comunicació heterogenis propost en aquesta tesi.[EN] The introduction of technological solutions in agriculture allows reducing the use of resources and increasing the production of the crops. Furthermore, the quality of the water for irrigation can be monitored to ensure the safety of the produce for human consumption. However, the remote location of most fields presents a problem for providing wireless coverage to the sensing nodes and actuators deployed on the fields and the irrigation water canals. The work presented in this thesis addresses the problem of enabling wireless communication among the electronic devices deployed for water quality and field monitoring through a heterogeneous communication protocol and architecture. The first part of the dissertation introduces Precision Agriculture (PA) systems and the importance of water quality and field monitoring. In addition, the technologies that enable wireless communication in PA systems and the use of alternative solutions such as Internet of Underground Things (IoUT) and Unmanned Aerial Vehicles (UAV) are introduced as well. Then, an in-depth analysis on the state of the art regarding the sensors for water, field and meteorology monitoring and the most utilized wireless technologies in PA is performed. Furthermore, the current trends and challenges for Internet of Things (IoT) irrigation systems, including the alternate solutions previously introduced, have been discussed in detail. Then, the architecture for the proposed system is presented, which includes the areas of interest for the monitoring activities comprised of the canal and field areas. Moreover, the description and operation algorithms of the sensor nodes contemplated for each area is provided. The next chapter details the proposed heterogeneous communication protocol including the messages and alerts of the system. Additionally, a new tree topology for hybrid LoRa/WiFi multi-hop networks is presented. The specific additional functionalities intended for the proposed architecture are described in the following chapter. It includes data aggregation algorithms for the proposed topology, an overview on the security threats of PA systems, energy-saving and fault-tolerance algorithms, underground communication for IoUT, and the use of drones for data acquisition. Then, the simulation results for the solutions previously proposed are presented. Finally, the tests performed in real environments for the presented heterogeneous protocol, the different deployment strategies for the utilized nodes, the energy consumption, and a functionality for fruit quantification are discussed. These tests demonstrate the validity of the proposed heterogeneous architecture and communication protocol.García García, L. (2021). Architecture and communication protocol to monitor and control water quality and irrigation in agricultural environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17422

AICropCAM: Deploying classification, segmentation, detection, and counting deep-learning models for crop monitoring on the edge

Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with \u3e43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations

Development and Characterization of an IoT Network for Agricultural Imaging Applications

Smart agriculture is an increasingly popular field in which the technology of wireless sensor networks (WSN) has played a large role. Significant research has been done at Cal Poly and elsewhere to develop a computer vision (CV) and machine learning (ML) pipeline to monitor crops and accurately predict crop yield numbers. By autonomously providing farmers with this data, both time and money are saved. During the past development of a prediction pipeline, the primary focuses were CV and ML processing while a lack of attention was given to the collection of quality image data. This lack of focus in previous research presented itself as incomplete and inefficient processing models. This thesis work attempts to solve this image acquisition problem through the initial development and design of an Internet of Things (IoT) prototype network to collect consistent image data with no human interaction. The system is developed with the goals of being low-power, low-cost, autonomous, and scalable. The proposed IoT network nodes are based on the ESP32 SoC and communicate over-the-air with the gateway node via Bluetooth Low Energy (BLE). In addition to BLE, the gateway node periodically uplinks image data via Wi-Fi to a cloud server to ensure the accessibility of collected data. This research develops all functionality of the network, comprehensively characterizes the power consumption of IoT nodes, and provides battery life estimates for sensor nodes. The sensor node developed consumes a peak current of 150mA in its active state and sleeps at 162µA in its standby state. Node-to-node BLE data transmission throughput of 220kbps and node-tocloud Wi-Fi data transmission throughput of 709.5kbps is achieved. Sensor node device lifetime is estimated to be 682 days on a 6600mAh LiPo battery while acquiring five images per day. This network can be utilized by any application that requires a wireless sensor network (WSN), high data rates, low power consumption, short range communication, and large amounts of data to be transmitted at low frequency intervals

Optimal Policy Derivation for Transmission Duty-Cycle Constrained LPWAN

Low-power wide-area network (LPWAN) technologies enable Internet of Things (IoT) devices to efficiently and robustly communicate over long distances, thus making them especially suited for industrial environments. However, the stringent regulations on the usage of certain industrial, scientific, and medical bands in many countries in which LPWAN operate limit the amount of time IoT motes can occupy the shared bands. This is particularly challenging in industrial scenarios, where not being able to report some detected events might result in the failure of critical assets. To alleviate this, and by mathematically modeling LPWAN-based IoT motes, we have derived optimal transmission policies that maximize the number of reported events (prioritized by their importance) while still complying with current regulations. The proposed solution has been customized for two widely known LPWAN technologies: 1) LoRa and 2) Sigfox. Analytical results reveal that our solution is feasible and performs remarkably close to the theoretical limit for a wide range of network activity patterns

A Fog-based Smart Agriculture System to Detect Animal Intrusion

Smart agriculture is one of the most promising areas where IoT-enabled technologies have the potential to substantially improve the quality and quantity of the crops and reduce the associated operational cost. However, building a smart agriculture system presents several challenges, including high latency and bandwidth consumption associated with cloud computing, frequent Internet disconnections in rural areas, and the need to keep costs low for farmers. This paper presents an end-to-end, fog-based smart agriculture infrastructure that incorporates edge computing and LoRa-based communication to address these challenges. Our system is deployed to transform traditional agriculture land of rural areas into smart agriculture. We address the top concern of farmers - animals intruding - by proposing a solution that detects animal intrusion using low-cost PIR sensors, cameras, and computer vision. In particular, we propose three different sensor layouts and a novel algorithm for predicting animals' future locations. Our system can detect animals before they intrude into the field, identify them, predict their future locations, and alert farmers in a timely manner. Our experiments show that the system can effectively and quickly detect animal intrusions while maintaining a much lower cost than current state-of-the-art systems.Comment: 9 pages, 16 figure

Agricultural Production System Based On IOT

Internet of things (IoT) is not a single word, but it has gathered billions of devices in the same lane. The Internet of things has given the lives of things. Machines have a sense now like a human. It works remotely as the program has been settled inside the chip. The system has become so smart and reliable. The Internet of things has brought out changes in most of the sectors of humankind. Meanwhile, agriculture is the main strength of a country. The more the production of agricultural products increased, the world will be more completeness from food shortage. The production of agriculture can be increased when the IoT system can be entirely implemented in the agricultural sector. Most of the approaches for IoT based agriculture have been reviewed in this paper. Related to IoT based agriculture, most of the architecture and methodology have been interpreted and have been critically analyzed based on previous related work of the researchers. This paper will be able to provide a complete idea with the architecture and methodology in the field of IoT based agriculture. Moreover, the challenges for agricultural IoT are discussed with the methods provided by the researche

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

Artificial intelligence (AI) has recently been used frequently, especially concerning the Internet of Things (IoT). However, IoT devices cannot work alone, assisted by Low Power Wide Area Network (LPWAN) for long-distance communication and Short-Range Network for a short distance. However, few reviews about AI can help LPWAN and Short-Range Network. Therefore, the author took the opportunity to do this review. This study aims to review LPWAN and Short-Range Networks AI papers in systematically enhancing IoT performance. Reviews are also used to systematically maximize LPWAN systems and Short-Range networks to enhance IoT quality and discuss results that can be applied to a specific scope. The author utilizes selected reporting items for systematic review and meta-analysis (PRISMA). The authors conducted a systematic review of all study results in support of the authors' objectives. Also, the authors identify development and related study opportunities. The author found 79 suitable papers in this systematic review, so a discussion of the presented papers was carried out. Several technologies are widely used, such as LPWAN in general, with several papers originating from China. Many reports from conferences last year and papers related to this matter were from 2020-2021. The study is expected to inspire experimental studies in finding relevant scientific papers and become another review