Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support

Internet of things (IoT) systems generate a large volume of data all the time. How to choose and transfer which data are essential for decision-making is a challenge. This is especially important for low-cost and low-power designs, for example Long-Range Wide-Area Network (LoRaWan)-based IoT systems, where data volume and frequency are constrained by the protocols. This paper presents an unsupervised learning approach using Laplacian scores to discover which types of sensors can be reduced, without compromising the decision-making. Here, a type of sensor is a feature. An IoT system is designed and implemented for a plant-monitoring scenario. We have collected data and carried out the Laplacian scores. The analytical results help choose the most important feature. A comparative study has shown that using fewer types of sensors, the accuracy of decision-making remains at a satisfactory level.

[1]  Noman Islam,et al.  A review of wireless sensors and networks' applications in agriculture , 2014, Comput. Stand. Interfaces.

[2]  Marios D. Dikaiakos,et al.  ADMin: Adaptive monitoring dissemination for the Internet of Things , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[3]  Rosdiadee Nordin,et al.  Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review , 2017, Sensors.

[4]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

[5]  Rui Huang,et al.  Manifold-based constraint Laplacian score for multi-label feature selection , 2018, Pattern Recognit. Lett..

[6]  Jugal K. Kalita,et al.  Network Anomaly Detection: Methods, Systems and Tools , 2014, IEEE Communications Surveys & Tutorials.

[7]  James Brusey,et al.  Edge Mining the Internet of Things , 2013, IEEE Sensors Journal.

[8]  Sven Nomm,et al.  Dimensionality Reduction for Machine Learning Based IoT Botnet Detection , 2018, 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV).

[9]  Haibin Zhang,et al.  Connecting Intelligent Things in Smart Hospitals Using NB-IoT , 2018, IEEE Internet of Things Journal.

[10]  Frank Englert,et al.  Reduce the Number of Sensors: Sensing Acoustic Emissions to Estimate Appliance Energy Usage , 2013, BuildSys@SenSys.

[11]  Rongtao Xu,et al.  Design and Implementation of Smart Irrigation System Based on LoRa , 2017, 2017 IEEE Globecom Workshops (GC Wkshps).

[12]  Jan Pavlík,et al.  Internet of Things (IoT) in Agriculture - Selected Aspects , 2016 .

[13]  Peter A. Flach,et al.  Feature Construction and Calibration for Clustering Daily Load Curves from Smart-Meter Data , 2016, IEEE Trans. Ind. Informatics.

[14]  Jiguo Yu,et al.  IoT Applications on Secure Smart Shopping System , 2017, IEEE Internet of Things Journal.

[15]  Xiaodong Wang,et al.  Distributed Compressive Sensing Augmented Wideband Spectrum Sharing for Cognitive IoT , 2018, IEEE Internet of Things Journal.

[16]  Ali Dehghantanha,et al.  A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks , 2019, IEEE Transactions on Emerging Topics in Computing.

[17]  Marios D. Dikaiakos,et al.  Low-Cost Adaptive Monitoring Techniques for the Internet of Things , 2018, IEEE Transactions on Services Computing.

[18]  M. P. Jyothi,et al.  IOT Based Monitoring System in Smart Agriculture , 2017, 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT).

[19]  Jaganathan Venkatesh,et al.  Modular and Personalized Smart Health Application Design in a Smart City Environment , 2018, IEEE Internet of Things Journal.

[20]  Raghavendra B. Deshmukh,et al.  A Machine Condition Monitoring Framework Using Compressed Signal Processing , 2020, Sensors.

[21]  Qi Wang,et al.  Nonnegative Laplacian embedding guided subspace learning for unsupervised feature selection , 2019, Pattern Recognit..

[22]  Qinghua Zheng,et al.  Adaptive Unsupervised Feature Selection With Structure Regularization , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Yuval Elovici,et al.  N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders , 2018, IEEE Pervasive Computing.

[24]  Khalid Benabdeslem,et al.  Soft-constrained Laplacian score for semi-supervised multi-label feature selection , 2015, Knowledge and Information Systems.

[25]  Kaibin Huang,et al.  Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networks , 2018, IEEE Transactions on Wireless Communications.

[26]  Mustapha Lebbah,et al.  Hierarchical Laplacian Score for unsupervised feature selection , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).