An IoT Tree Health Indexing Method Using Heterogeneous Neural Network

Abstract

Urban trees provide essential ecosystem services on regulating temperature and humidity, filtering urban pollutants, and improving air quality. However, the increasing number of urban trees put pressure on maintenance and public safety. The total compensatory value of the trees, consisting of inspection, maintenance, and settlement of tree damages, is more than $2 trillion USD. At this point in time, there is no known research on manifesting guidance on automated tree health assessment. The Internetof-Things (IoT) proliferates the deployment of wireless sensors and networks. A concept of the IoT trees is raised to implement various sensors on the trees for automated health monitoring and assessment. In this paper, an urban tree health index (UTHI) is first developed to indicate the health of urban IoT trees. The index will facilitate preventive measures on urban trees. To construct the indexing model, seven ( 7) dynamic (timeseries) features and seven (7) static features are extracted to explore the ambient effects on urban tree health. Afterward, a heterogeneous neural network (HNN) for UTHI modeling is developed to adopt the heterogeneous feature structure. In HNN, the dynamic features are analyzed in the gated recurrent unit (GRU) layer and the static features are analyzed in a hidden layer. The novel fusion layer then aggregates the outputs computed from those layers and further explores unseen correlations among all features. The experimental result verifies that the HNN-based modeling achieves high accuracy and model fitness with the error rate of less than 5%. In addition, the HNN achieves 34% to 66% improvement of accuracy in comparison with the other machine learning algorithms. The supremacy of the developed model is that all indexing features can be predefined or monitored by the IoT sensors, thus rendering an automated and economic urban tree management.INDEX TERMS Tree health assessment, heterogeneous neural network, modeling.

Building an indoor air quality monitoring system based on the architecture of the Internet of Things

“…29 Effects of fine particulate matters on AQI values. MATLAB was also used to simulate the effects of fine particulate matters and CO in the environment on the air quality sub-indices CO 2 or fine particulate matters exceed the standard, the Arduino Uno board will activate the ventilation unit and air purifier through the infrared emitter to reduce the concentration [26][27][28].…”

ICS: Interoperable Communication System for Inter-Domain Routing in Internet-of-Things

“…In this study, the author has investigated the use of random linear network coding targeting to improve the throughput performance in IoT. A unique study presented by Wu et al [28] discusses the use of tree concept in presenting sensor network over IoT considering case study of healthcare assessment. In this work, a unique tree structured is developed with indexing policies where a neural network is applied in order to perform feature engineering for facilitating interoperable operation in IoT.…”

IoT‐based cyber‐physical communication architecture: challenges and research directions

“…v. Find suitable cross-domain application: There is a significant research gap between practical application and academic research in the IoT-based CPSs. In biomedical engineering, the IoT can be used for signal transmission whereas they are not well familiar in this emerging technology [36][37][38]. Similarly, the machine learning tools such as deep learning and reinforcement learning can be used for CPS state estimation in data-driven approach [39][40][41].…”