Design of Remote Datalogger Connection and Live Data Tweeting System

Low-Impact Development (LID) is an attempt to sustainably respond to the potential hazards posed by urban expansion. Green roofs are an example of LID design meant to reduce the amount of runoff from storm events that are becoming more intense and less predictable while also providing insulation to buildings. LID has not yet been widely adopted as it is often a more expensive alternative to conventional infrastructure (Bowman et. al., 2009). However, its benefits are apparent. The University of Arkansas Honors College awarded a grant to research the large green roof atop Hillside Auditorium. One part of this grant is aimed at educating the public on the benefits LID infrastructure and encourage its development. To accomplish this task, a Raspberry Pi was programmed to operate in tandem with a Campbell Scientific CR1000 datalogger to collect, organize and tweet data to the public under the moniker, “Rufus the Roof.” It is believed that personifying the roof allows data to be conveyed in an entertaining manner that promotes education and public engagement in the LID design. The Raspberry Pi was initially intended to collect data and publish tweets automatically on a live basis. However, automation was not realized due to time constraints and challenges in establishing connection to the datalogger. Instead, a system was developed that allowed the remote transfer of environmental data files from a datalogger on the green roof. Along with remote file transfer protocol, several Python scripts were written that enabled tweets to be published by the Raspberry Pi. The design was successful. Manual remote file transfer and tweeting was achieved. Full automation remains to be achieved, but the Python scripts are built with the capability to operate automatically. The conditions are in place for future development of the project in order to achieve full autonomy. A fully automated system could open the doors for more widespread public engagement in the value and benefits of Low-Impact Development initiatives

Proposal of architecture for IoT solution for monitoring and management of plantations

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 presents a systematic review of the existing literature on smart farming with IoT. The systematic review reveals an evolution in the way data are processed by IoT solutions in recent years. Traditional approaches mostly used data in a reactive manner. In contrast, recent approaches allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis. Based on the finds of the systematic review, this work proposes an architecture of an IoT solution that enables monitoring and management of crops in real time. The proposed architecture allows the usage of big data and machine learning to process the collected data. A prototype is implemented to validate the operation of the proposed architecture and a security risk assessment of the implemented prototype is carried out. The implemented prototype successfully validates the proposed architecture. The architecture presented in this work allows the implementation of IoT solutions in different scenarios of farming, such as indoor and outdoor

Development of a wireless sensor network for agricultural monitoring for Internet of Things (IoT)

Monitoring of the agricultural environment has become an important area of control and protection which provides real-time system and control communication with the physical world. This thesis focuses on Development ofa wireless Sensor Network for agricultural monitoring for Internet of things (IoT) to monitor environmental condition. Among the various technologies for Agriculture monitoring, Wireless Sensor Networks (WSNs) are perceived as an amazing one to gather and process information in the agricultural area with low-cost and low-energy consumption. WSN is capable of providing processed field data in real time from sensors which are physically distributed in the field. Agriculture and farming are one of the industries which have a late occupied their regards for WSNs, looking for this financially acute innovation to improve its production and upgrade agribusiness yield standard. Wireless Sensor Networks (WSNs) have pulled in a lot consideration in recent years.The proposed system uses WSN sensors to capture and track information pertaining to crop growth condition outside and inside greenhouses. 6LowPAN network protocol is used for low power consumption and for transmitting and receiving of data packets.This thesis introduces the agricultural monitoring system's hardware design, system architecture, and software process control. Agriculture monitoring system set-up is based on Contiki OS while device testing is carried out using real-time farm information and historical dat

Lightweight edge-based networking architecture for low-power IoT devices

Abstract. The involvement of low power Internet of Things (IoT) devices in the Wireless Sensor Networks (WSN) allow enhanced autonomous monitoring capability in many application areas. Recently, the principles of edge computing paradigm have been used to cater onsite processing and managing actions in WSNs. However, WSNs deployed in remote sites require human involvement in data collection process since internet accessibility is still limited to population dense areas. Nowadays, researchers propose UAVs for monitoring applications where human involvement is required frequently. In this thesis work, we introduce an edge-based architecture which create end-to-end secure communication between IoT sensors in a remote WSN and central cloud via UAV, which assist the data collection, processing and managing procedures of the remote WSN. Since power is a limited resource, we propose Bluetooth Low Energy (BLE) as the communication media between UAV and sensors in the WSN, where BLE is considered as an ultra-low power radio access technology. To examine the performance of the system model, we have presented a simulation analysis considering three sensor nodes array types that can realize in the practical environment. The impact of BLE data rate, impact of speed of the UAV, impact of distance between adjacent sensors and impact of data generation rate of the sensor node have been analysed to examine the performance of system. Moreover, to observe the practical functionality of the proposed architecture, prototype implementation is presented using commercially available off-the-shelf devices. The prototype of the system is implemented assuming ideal environment

Environmental monitoring using a drone-enabled wireless sensor network

Water quality monitoring traditionally occurs via resource intensive field surveys, such as when a researcher manually collects data in a stream. Limiting factors such as time, money, and accessibility often result in less oversight of impaired water bodies, significantly threatening ecosystemic health and related ecosystem services. According to the United States Environmental Protection Agency, 84% of rivers and streams within the United States remain unassessed, resulting in significant lapses in available data. Such lapses prohibit efficient and effective monitoring, restoration, and conservation efforts throughout the United States. The objective of this project was to employ an unmanned aerial vehicle to remotely collect data regarding water quality from a wireless sensor network. The site under analysis was Boones Run, a tributary of the South Fork of the Shenandoah River near Elkton, Virginia. This project served as a proof-of-concept that communication with a wireless sensor node has the capability to be deployed to collect data in remote areas efficiently and effectively. This system would be useful in areas where accessibility is difficult, and transmission of data for processing is not readily available due to the lack of network connectivity. Initial analysis of environmental data gathered by hand indicated that surrounding land use had a significant impact on Boones Run water quality. This conclusion was reached given the trends seen in dissolved oxygen, water temperature, pH, and conductivity data from upstream to downstream over time. The completion of this project also lead to the successful data flow amongst all parts in the wireless sensor network. Three sensors soldered to a breadboard and connected to an Arduino Uno were able to gather data and send it to a Raspberry Pi 0. The Raspberry Pi 0 acted as a temporary storage device for the data before it was sent wirelessly to a Raspberry Pi 3 acting as an access point. The Raspberry Pi 3 device was mounted to an unmanned aerial vehicle so it could be flown over the node to decrease data collection time as well as adding the ability to collect data from places that are otherwise difficult for humans to access

Agricultural Management through Wireless Sensors and Internet of Things

Agriculture plays a significant role in most countries and there is an enoromous need for this industry to become “Smart”. The Industry is now moving towards agricultural modernization by using modern smart technologies to find solutions for effective utilization of scarce resources there by meeting the ever increasing consumtion needs of global population. With the advent of Internet of Things and Digital transformation of rural areas, these technologies can be leveraged to remotely monitor soil moisture, crop growth and take preventive measures to detect crop damages and threats. Utilize artificial intelligence based analytics to quickly analyze operational data combined with 3rd party information, such as weather services, expert advises etc., to provide new insights and improved decision making there by enabling farmers to perform “Smart Agriculture”. Remote management of agricultural activities and their automation using new technologies is the area of focus for this research activity. A solar powered remote management and automation system for agricultural activities through wireless sensors and Internet of Things comprising, a hardware platform based on Raspberry Pi Micro controller configured to connect with a user device and accessed through the internet network. The data collection unit comprises a set of wireless sensors for sensing agricultural activities and collecting data related to agricultural parameters; the base station unit comprising: a data logger; a server; and a software application for processing, collecting, and sending the data to the user device. The user device ex: mobile, tablet etc. can be connected to an internet network, whereby an application platform (mobile-app) installed in the user device facilitates in displaying a list of wireless sensor collected data using Internet of Things and a set of power buttons. This paper is a study and proposal paper which discusses the factors and studies that lead towards this patent pending invention, AGRIPI

Machine learning for IoT-based smart farming

Agriculture balances food requirements for mankind, and the supply of essential raw materials for many industries is the fundamental occupation in India. Smart farming allows analyzing the growth of crops and the parameters which influence crop growth and supports farmers in their activities, it is more profitable and reduces irrigation wastages. The proposed model is a smart farming system that analyzes the influence of parameters on crop growth and predicts the soil condition using a machine learning algorithm. Temperature, Ph, humidity, gas, and water level are the few most essential parameters to determine the quantity of water required and to find hazardous gas in any agriculture field. This system comprises temperature, pH, humidity, smoke detector, and water level sensor, deployed in an agricultural field, sends data through a microprocessor, developing an IoT device with cloud. In this study, we present a model that predicts soil series with regard to land type and, in accordance with the prediction, suggests appropriate crops. For soil land classification and crop prediction application is developed using KNN algorithms. Three steps are necessary for its implementation: the first is data collecting using sensors placed in an agricultural field, the second is data cleaning and storage, and the third is predictive processing utilizing the ML technique. The results obtained through the algorithms are sent to the cloud, which helps in decision-making in advance

Ubiquitous Environment Control System: An Internet-of- Things–Based Decentralized Autonomous Measurement and Control System for a Greenhouse Environment

A low-cost and flexible system for environmental measurement and control in greenhouses based on decentralized autonomous technics, Ubiquitous Environment Control System (UECS), was proposed in 2004. The UECS is composed of autonomous nodes as the minimum units of measurement and control. The nodes can connect with each other through Ethernet or Wi-Fi and can communicate information regardless of manufacturer or model. To realize automation and efficiency of protected horticultural production, two consortia for UECS development and extension were established. During the last 10 years, the UECS has been used to apply environment control in large-scale greenhouses and plant factories. The stability and utility of the UECS have been demonstrated and verified in these practical cultivations. Current research and development are being carried out to install information and communication technology (ICT) systems to improve productivity in existing small- to medium-scale greenhouses in Japan. The flexibility and concept of the UECS have been very effective to enable sophisticated environmental control technology to be applied to small- and medium-scale greenhouses. In this chapter, self-fabricated UECS, the renewal of old environmental control systems using the UECS, and Sub-GHz radio band use for communicating UECS nodes among distributed greenhouses are discussed

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 of an open source agricultural mobile data collector system

The information is important in every decision area. The Big Data philosophy lead to collect every possible data. Nowadays these applications are more and more successful in the following agricultural areas: different parts of food industry, extension services, precision agriculture. While studying the use of these new ICT technologies can be concluded that different types of services offer different possibilities. Firstly we compared the possible mainboards and sensors. General information about the existing mobile main boards. We compared the Atmel AVR, the Raspberry PI and the LEGO Mindstorms NXT. We choosed the Arduino system board. We described the main system architecture and connection possibilites. We found the temperature sensor widely useable. The software was also briefly mentioned. We can say there are several advantages of the Arduino. The whole system can be upgradeable, and there are several Arduino based mainboards and sensors too. Nowadays the block programming support are increasing (etc. MIT Appinventor), but there are disadvantages too. The system has several limitation: the number of the connected sensor, the connection type, the system energy supply, the data loss, the creation of user friendly interface and the system failure tolerability.</jats:p