Sensory network development for autonomous irrigation system

ได้รับทุนอุดหนุนการวิจัยจากมหาวิทยาลัยเทคโนโลยีสุรนารี ประจำปี 255

Design and development of automatic watering system using fuzzy controller

ได้ทุนอุดหนุนการวิจัยจากมหาวิทยาลัยเทคโนโลยีสุรนารี ปีงบประมาณ พ.ศ.255

Wireless sensor network modeling using modified recurrent neural network: Application to fault detection

Wireless Sensor Networks (WSNs) consist of a large number of sensors, which in turn have their own dynamics. They interact with each other and the base station, which controls the network. In multi-hop wireless sensor networks, information hops from one node to another and finally to the network gateway or base station. Dynamic Recurrent Neural Networks (RNNs) consist of a set of dynamic nodes that provide internal feedback to their own inputs. They can be used to simulate and model dynamic systems such as a network of sensors. In this dissertation, a dynamic model of wireless sensor networks and its application to sensor node fault detection are presented. RNNs are used to model a sensor node, the node\u27s dynamics, and the interconnections with other sensor network nodes. A neural network modeling approach is used for sensor node identification and fault detection in WSNs. The input to the neural network is chosen to include previous output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of a backpropagation-type neural network. The input to the neural network (NN) and the topology of the network are based on a general nonlinear sensor model. A simulation example, including a comparison to the Kalman filter method, has demonstrated the effectiveness of the proposed scheme. The simulation with comparison to the Kalman filtering technique was carried out on a network with 15 sensor nodes. A fault such as drift was introduced and successfully detected with the modified recurrent neural net model with no early false alarm that could have resulted when using the Kalman filtering approach. In this dissertation, we also present the real-time implementation of a neural network-based fault detection for WSNs. The method is implemented on a TinyOS operating system. A collection tree network is formed, and multi-hoping data is sent to the base station root. Nodes take environmental measurements every N seconds while neighboring nodes overhear the measurement as it is being forwarded to the base station for recording it. After nodes complete M and receive/store M measurements from each neighboring node, recurrent neural networks are used to model the sensor node, the node\u27s dynamics, and the interconnections with neighboring nodes. The physical measurement is compared to the predicted value and to a given threshold of error to determine a sensor fault. The process of neural network training can be repeated indefinitely to maintain self-aware network fault detection. By simply overhearing network traffic, this implementation uses no extra bandwidth or radio broadcast power. The only costs of the approach are the battery power required to power the receiver for overhearing packets and the processor time to train the RNN

Environmental Sensor Anomaly Detection Using Learning Machines

The problem of quality assurance/quality control (QA/QC) for real-time measurements of environmental and water quality variables has been a field explored by many in recent years. The use of in situ sensors has become a common practice for acquiring real-time measurements that provide the basis for important natural resources management decisions. However, these sensors are susceptible to failure due to such things as human factors, lack of necessary maintenance, flaws on the transmission line or any part of the sensor, and unexpected changes in the sensors\u27 surrounding conditions. Two types of machine learning techniques were used in this study to assess the detection of anomalous data points on turbidity readings from the Paradise site on the Little Bear River, in northern Utah: Artificial Neural Networks (ANNs) and Relevance Vector Machines (RVMs). ANN and RVM techniques were used to develop regression models capable of predicting upcoming Paradise site turbidity measurements and estimating confidence intervals associated with those predictions, to be later used to determine if a real measurement is an anomaly. Three cases were identified as important to evaluate as possible inputs for the regression models created: (1) only the reported values from the sensor from previous time steps, (2) reported values from the sensor from previous time steps and values of other water types of sensors from the same site as the target sensor, and (3) adding as inputs the previous readings from sensors from upstream sites. The decision of which of the models performed the best was made based on each model\u27s ability to detect anomalous data points that were identified in a QA/QC analysis that was manually performed by a human technician. False positive and false negative rates for a range of confidence intervals were used as the measure of performance of the models. The RVM models were able to detect more anomalous points within narrower confidence intervals than the ANN models. At the same time, it was shown that incorporating as inputs measurements from other sensors at the same site as well as measurements from upstream sites can improve the performance of the models