Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse

Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things(IoT)technology,which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making.In the process,anomaly detection for wireless sensor data plays an important role.However,the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness,correlations,and concept drift,which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and efficiency.First,the data stream is usually generated quickly,which means that the data stream is infinite and enormous.Hence,any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space.Second,there exist correlations among different data streams,and traditional algorithms hardly consider these correlations.Third,the underlying data generation process or distribution may change over time.Thus,traditional anomaly detection algorithms with no model update will lose their effects.Considering these issues,a novel method(called DLSHiForest)based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient detection.Comprehensive experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach.Experimental results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency.

Methods for the Efficient Energy Management in a Smart Mini Greenhouse

To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption.One of the possible ways is proposed in this research.A multi-level hierarchical system for energy efficiency management of the enterprise is designed,it is based on the modular principle providing rapid modernization.The novelty of the work is the development of new and improvement of the existing methods and models,in particular:1)models for dynamic analysis of IT tools for data acquisition and processing(DAAP)in multilevel energy management systems,which are based on Petri nets;2)method of synthesis of DAAP tools in energy efficiency management information systems(EEMIS)of the enterprise which provides a reduction in hardware and time costs from 10%to 40%;3)method of conflict-free data exchange determining the minimum memory speed for the synthesis of realtime exchanges,it reduces the cost and energy consumption;4)method of calculating the signal of postsynaptic excitation of neural elements decreases the processing time of technological data two or more times.The proposed methods,models and tools have been tested while implementing the EEMIS of the intelligent mini-greenhouse,as a result,energy efficiency has increased by 12%-25%(depending on season and peculiarities of growing plants).

Zhijiang’s Harmonious Urban-Rural Development Project

ANFUSI Town in Yichang City,northwest of Zhijiang City,is a national demonstration town of urbanization and rural development in Hubei and site of a large industrial park.The reporter went there to see the work in progress on building a 4,000-square-meter smart greenhouse with a bucolic landscape and an aquatic product factory equipped to produce surimi.In 2000,there were just a few small local businesses in Anfusi.

Plant functional remote sensing and smart farming applications

Plants have the distinctive 3D spatial structure that varies among organs,species and communities,and the spatial structure changes as they interact with their environments.The functions linked to fundamental biological activities such as transpiration,photosynthesis,and growth are also affected by the spatial structure and the environment.In order to promote smart farming using information and communication technology(ICT),it is necessary to measure and utilize information at the cell-organ of plants to the individual and the community levels and the environments in two or even three dimensions.Therefore,this paper introduced the outline of remote sensing of plant functioning and examples of the 3D remote sensing from relatively short distances using drones and ground Lidar.The quality control of rice in the paddy field and chlorophyll fluorescence imaging for photosynthetic diagnosis were also introduced.In addition,a field smart farm and a smart greenhouse,which heavily utilize ICT,built at Takasaki University of Health and Welfare in Gunma,Japan,were also introduced.