Machine Learning Based Data Reduction in WSN for Smart Agriculture Nowadays, the agriculture domain faces a lot of challenges for a better usage of its natural resources. For this purpose, and for the increasing danger of climate change, there is a need to locally monitor meteorological data and soil conditions to help make quicker and more adapted decision for the culture. Wireless Sensor Networks (WSN) can serve as a monitoring system for those types of features. However, WSN suffer from the limited energy resources of the motes which shorten the lifetime of the overall network. Every mote periodically captures the monitored feature and sends the data to the sink for further analysis depending on a certain sampling rate. This process of sending big amount of data causes a high energy consumption of the sensor node and an important bandwidth usage on the network. In this paper, a Machine Learning based Data Reduction Algorithm (MLDR) is introduced. MLDR focuses on environmental data for the benefits of agriculture. MLDR is a data reduction approach which reduces the amount of transmitted data to the sink by adding some machine learning techniques at the sensor node level by keeping data availability and accuracy at the sink. This data reduction helps reduce the energy consumption and the bandwidth usage and it enhances the use of the medium while maintaining the accuracy of the information. This approach is validated through simulations on MATLAB using real temperature datasets from WeatherUnderground sensor network. Results show that the amount of sent data is reduced by more than 70% while maintaining a very good accuracy with a variance that did not surpass 2 degrees.

Data Reconstruction for CyberPhysical Landslide Detection System Wireless Sensor Network (WSN) systems are often used to collect data from the environment for predicting landslide occurrence. However, due to the instability of wireless communications in WSN systems, data loss could occur often. To reconstruct the missing soil moisture data, heterogeneous data with spatiotemporal relations are used, such as soil moisture and rainfall intensity. In the spatial reconstruction method, we take into consideration not only the distance but also the difference of rainfall intensity between two locations. In the temporal reconstruction method, soil moisture will decrease because of evaporation if there is no rainfall. We use historical evaporation rate to reconstruct missing soil moisture data when there is no rainfall. If there is rain, we take a period of sensed soil moisture data to reconstruct any missing data. As a result, we can improve the accuracy of data reconstruction by selecting either spatial or temporal reconstruction results that have a smaller estimation error. The Root Mean Squared Error of spatiotemporal reconstruction is below 2%, even when there is 80% random missing soil moisture data. This is due to both spatiotemporal considerations, as well as, the use of heterogeneous data (rainfall intensity).