Environmental Parameters Monitoring And Control System In Horticulture Greenhouse Using The Internet Of Things: Case Of IPRC Musanze

Efficient management of greenhouse farming is a challenge to ensure high yield production. This is a great challenge to farmers who do not have a reliable mechanism to ensure the optimum environmental conditions for their crops. Farmers are opting to look for solutions from technologies such as Machine to Machine and Internet of Things. This paper proposes a wireless sensor network architecture for real-time greenhouse environmental parameters monitoring to achieve technology- based farming at a low management cost. Uncontrolled temperature, humidity, light intensity and soil moisture content, are among the major parameters that contribute to the deterioration of plants in the green house. The system employs the temperature and Humidity sensor DHT11, a light sensor LDR and soil moisture sensor to detect the environment parameters inside the greenhouse. A low-cost Wi-Fi microchip, with built -in TCP/IP networking software called as ESP8266, has been used to help connect the microntroller with the internet wirelessly. Sensed data is monitored on-site using a Liquid Crystal Display. The ThingSpeak Cloud platform has been used to assure the remote monitoring of the sensed data, and further analytics can be done through it. Actuators namely the solenoid valve, cooling fan, and heating bulb are immediately triggered in case the limit level of the environmental parameters been sensed, has been exceeded. The Global System for Mobile Communication has been used to provide notification to the farmers cell phone farmers in case of critical conditions.  The results of the system are provided in form of waveforms observed through the ThingSpeak for the sensed parameters, others are in form of notification through LCD and GSM, and the actions performed by the solenoid valve, cooling fan and Heating bulb in case the sensed environment data goes beyond the required level

City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201

Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges

open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture

Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The case of Road/Bridge Construction and Maintenance

Road/bridge construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2), mainly due to extensive use of heavy-duty diesel construction equipment and large-scale earthworks and earthmoving operations. Heavy equipment is a costly resource and its underutilization could result in significant budget overruns. A practical way to cut emissions is to reduce the time equipment spends doing non-value-added activities and/or idling. Recent research into the monitoring of automated equipment using sensors and Internet-of-Things (IoT) frameworks have leveraged machine learning algorithms to predict the behavior of tracked entities. In this project, end-to-end deep learning models were developed that can learn to accurately classify the activities of construction equipment based on vibration patterns picked up by accelerometers attached to the equipment. Data was collected from two types of real-world construction equipment, both used extensively in road/bridge construction and maintenance projects: excavators and vibratory rollers. The validation accuracies of the developed models were tested of three different deep learning models: a baseline convolutional neural network (CNN); a hybrid convolutional and recurrent long shortterm memory neural network (LSTM); and a temporal convolutional network (TCN). Results indicated that the TCN model had the best performance, the LSTM model had the second-best performance, and the CNN model had the worst performance. The TCN model had over 83% validation accuracy in recognizing activities. Using deep learning methodologies can significantly increase emission estimation accuracy for heavy equipment and help decision-makers to reliably evaluate the environmental impact of heavy civil and infrastructure projects. Reducing the carbon footprint and fuel use of heavy equipment in road/bridge projects have direct and indirect impacts on health and the economy. Public infrastructure projects can leverage the proposed system to reduce the environmental cost of infrastructure project

A module placement scheme for fog-based smart farming applications

As in Industry 4.0 era, the impact of the internet of things (IoT) on the advancement of the agricultural sector is constantly increasing. IoT enables automation, precision, and efficiency in traditional farming methods, opening up new possibilities for agricultural advancement. Furthermore, many IoT-based smart farming systems are designed based on fog and edge architecture. Fog computing provides computing, storage, and networking services to latency-sensitive applications (such as Agribots-agricultural robots-drones, and IoT-based healthcare monitoring systems), instead of sending data to the cloud. However, due to the limited computing and storage resources of fog nodes used in smart farming, designing a modules placement scheme for resources management is a major challenge for fog based smart farming applications. In this paper, our proposed module placement algorithm aims to achieve efficient resource utilization of fog nodes and reduce application delay and network usage in Fog-based smart farming applications. To evaluate the efficacy of our proposal, the simulation was done using iFogSim. Results show that the proposed approach is able to achieve significant reductions in latency and network usage

On-farm evaluation of integrated pest management of thrips and whiteflies in herb cuttings in Ethiopia : report to the Ministry of Agriculture and Rural Development

Integrated Pest Management reduces the use of chemicals and therewith the impact of greenhouse horticulture on the environment. It improves working conditions and enables access of Ethiopian products on the world market. In response to such concerns the Ethiopian Horticulture Producers and Exporters Organization (EPHEA) has taken the initiative to develop a Code of Practice, of which Integrated Pest Management forms an integral part. The development of this Integrated Pest Management approach is supported through the Ethiopia-Netherlands Horticulture Partnership Programme

Sistem Monitoring Smart Greenhouse pada Lahan Terbatas Berbasis Internet of Things (IoT)

Beberapa faktor lingkungan yang sangat berpengaruh pada proses pertumbuhan dan kesuburan tanaman adalah Faktor suhu, air, kelembapan tanah, kelembapan udara, dan cahaya. Pengembangan metode bercocok tanam cerdas semakin luas didukung dengan teknologi Greenhouse yang mana kondisi iklim bercocok tanam dapat direkayasa. Tidak menutup kemungkinan bahwa tanaman yang tidak cocok ditanam di Indonesia dapat di tanam didalam Greenhouse. Untuk itulah dikembangkan sebuah sistem “Rancang Bangun Smart Greenhouse berbasis IoT (Internet of Things)” berbasis agroteknologi. Sistem Monitoring Smart Greenhouse berhasil diimplementasikan dengan membaca kondisi Suhu, pH Tanah, Kelembaban Tanah dan Udara, dan data tersebut dikirimkan ke server untuk ditampilkan ke pengguna sistem. Data yang diperoleh dari Smart Greenhouse diolah menggunakan operator logika untuk mengendalikan perangkat-perangkat outputan didalam Smart Greenhouse yaitu Lampu, Kipas Masukan, Kipas Keluaran, Mist Maker dan Pompa Air. Sistem ini dapat memudahkan pengguna untuk memonitoring dan mengendalikan suhu, air, kelembapan tanah, kelembapan udara, dan cahaya didalam Smart Greenhouse yang sesuai dengan kebutuhan tanaman

Smart Internet of Things Modular Micro Grow Room Architecture

This article proposes the Internet of Things-based self-sustaining modular grow room architecture for optimising the seed germination and seedling development process. The architecture is scalable and flexible as it can be adapted to particular environments, scopes, requirements and plant types; it is modular as the host room can contain one or more smaller-scale grow rooms, each of them controlling their own micro-environment independently. One of the main goals of the research was to develop such a system that could be deployed efficiently, with minimal costs and energy footprint, which would enable its practical usage primarily in private self-sustainable households. The usage of widely available and inexpensive components, open source code, and free cloud services all enabled us to reach such a goal. Besides simple automation mostly described by existing solutions, the architecture proposed within this article offers remote control and data processing and visualisation, data trend tracking, smart optimisation, and actuator control, and event notifications