A WSN-Based Prediction Model ofMicroclimate in a Greenhouse Using Extreme
Learning Approaches
Qi Liu*, Dandan Jin*, Jian Shen*, Zhangjie Fu**, Nigel Linge**** College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu,
China** Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science and Technology,Nanjing, Jiangsu, China*** The University of Salford, Salford, Greater Manchester, UK
qi.liu@nuist.edu.cn, 1005949332@qq.com, s_shenjian@126.com, wwwfzj@126.com, n.linge@salford.ac.uk

Abstract—Monitoring and controlling microclimate in a greenhouse becomes one of the research hotspots in the field ofagrometeorology, where the application of Wireless Sensor Networks (WSN) recently attracts more attentions due to its features ofself-adaption, resilience and cost-effectiveness. Present microclimate monitoring and control systems achieve their prediction bymanipulating captured environmental factors and traditional neural network algorithms; however, these systems suffer thechallenges of quick prediction (e.g. hourly and even minutely) when a WSN network is deployed. In this paper, a novel prediction
method based on an Extreme Learning Machine (ELM) algorithm and KELM (Kernel based ELM) is proposed to predict thetemperature and humidity in a practical greenhouse environment in Nanjing, China. Indoor temperature and humidity are measured
as data samples via WSN nodes. According to the results, our approach (0.0222s) has shown significant improvement on the training
speed than Back Propagation (BP) (0.7469s), Elman (11.3307s) and Support Vector Machine (SVM) (19.2232s) models, the accuracy
rate of our model is higher than those models. In the future, research on faster learning speed of the ELM and KELM based neuralnetwork model will be conducted.
Keyword—Wireless Sensor Networks; Kernel based Extreme Learning Machine; Greenhouse Microclimate; Prediction Model
Qi Liu (M’11) received his BSc degree in Computer Science and Technology from Zhuzhou Institute of Technology, China in 2003, andhis MSc and PhD in Data Telecommunications and Networks from the University of Salford, UK in 2006 and 2010. His research interests
include context awareness, data communication in MANET and WSN, and smart grid. His recent research work focuses on intelligentagriculture and meteorological observation systems based on WSN.

Dandan Jin received her bachelor's degree in Software Engineering from Nanjing University of Information, Science and Technology in2015, and she will continue to pursue a master's degree in computer science and technology at the Nanjing University of Information
Science and Technology. Her research interests include smart grid and greenhouse microclimate modeling.

Jian Shen received his bachelor's degree in Electronic Science and Technology Specialty from Nanjing University of Information,
Science and Technology in 2007, and he received his masters and PhD in Information and communication from CHOSUN University,South Korean in 2009 and 2012. His research interests includes Computer network security, information security, mobile computing andnetwork, wireless ad hoc network.

Zhangjie Fu received his BS in education technology from Xinyang Normal University, China, in 2006; received his MS in education
technology from the College of Physics and Microelectronics Science, Hunan University, China, in 2008; obtained his PhD in computerscience from the College of Computer, Hunan University, China, in 2012. Currently, he works as an assistant professor in College ofComputer and Software, Nanjing University of Information Science and Technology, China. His research interests include cloudcomputing, digital forensics, network and information security.

Nigel Linge received his BSc degree in Electronics from the University of Salford, UK in 1983, and his PhD in Computer Networks fromthe University of Salford, UK, in 1987. He was promoted to Professor of Telecommunications at the University of Salford, UK in 1997.
His research interests include location based and context aware information systems, protocols, mobile systems and applications ofnetworking technology in areas such as energy and building monitoring.