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Full-Text Articles in Engineering
Iot Camera System For Monitoring Strawberry Fields, Simon Schoennauer
Iot Camera System For Monitoring Strawberry Fields, Simon Schoennauer
Master's Theses
A wireless imaging system for monitoring strawberry fields provides enough quality image data for computer vision algorithms to make meaningful yield predictions. This report contains a design for a wireless sensor network modified with mesh networking techniques to extend coverage range and a solar energy harvesting system to improve sensor node lifetime. A two hop system with six nodes is implemented in a laboratory environment validating the communication systems integrity over an 800’ range. Moving from a primary battery system to solar energy harvesting increases the module lifetime indefinitely.
Traffic Privacy Study On Internet Of Things – Smart Home Applications, Ayan Patel
Traffic Privacy Study On Internet Of Things – Smart Home Applications, Ayan Patel
Master's Theses
Internet of Things (IoT) devices have been widely adopted in many different applications in recent years, such as smart home applications. An adversary can capture the network traffic of IoT devices and analyze it to reveal user activities even if the traffic is encrypted. Therefore, traffic privacy is a major concern, especially in smart home applications. Traffic shaping can be used to obfuscate the traffic so that no meaningful predictions can be drawn through traffic analysis. Current traffic shaping methods have many tunable variables that are difficult to optimize to balance bandwidth overheads and latencies. In this thesis, we study …
Development And Characterization Of An Iot Network For Agricultural Imaging Applications, Jacob D. Wahl
Development And Characterization Of An Iot Network For Agricultural Imaging Applications, Jacob D. Wahl
Master's Theses
Smart agriculture is an increasingly popular field in which the technology of wireless sensor networks (WSN) has played a large role. Significant research has been done at Cal Poly and elsewhere to develop a computer vision (CV) and machine learning (ML) pipeline to monitor crops and accurately predict crop yield numbers. By autonomously providing farmers with this data, both time and money are saved. During the past development of a prediction pipeline, the primary focuses were CV and ML processing while a lack of attention was given to the collection of quality image data. This lack of focus in previous …