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    Performance Analysis of Unmanned Aerial Vehicle Assisted Wireless IoT Sensors Based on Air-to-Ground Communication Model for Smart Farming
    (2023-01-01)
    Duangsuwan, Sarun
    ;
    Promwong, Sathaporn
    We used an unmanned aerial vehicle (UAV) and IoT as a new platform for soil moisture monitoring based on the air-to-ground (A2G) communication model. We investigated an energyefficient UAV trajectory by considering the power outage probability and transmission rate for UAV-assisted wireless IoT sensor connectivity. We considered the closed-form power outage probability for IoT sensors located within the coverage zone of a UAV drone small cell. We conducted experiments in the Napier and Ruzi grass farms with IoT sensors for detecting soil moisture along a drip line irrigation system located in the fields. The power outage probability is given for different UAV heights and transmission rates, which contributes to reliable communication with IoT sensors.
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    PigNet: Failure-tolerant pig activity monitoring system using structural vibration
    (2021-05-18)
    Bonde, Amelie
    ;
    Codling, Jesse R.
    ;
    Naruethep, Kanittha
    ;
    Dong, Yiwen
    ;
    Siripaktanakon, Wachirawich
    Automated monitoring of livestock behavior can help farmers economically by detecting changes in animal welfare. Prior approaches use video, which requires light and high storage capability, or motion detection, which has difficulty separating subtle activities. Wearable sensors can address these issues but are vulnerable to destruction by the animals. To the best of our knowledge, we present the first system that uses structural vibration to track animal behavior, and the first system to automatically detect piglet nursing. PigNet uses vibration sensors attached to a pig pen to sense the unique vibration patterns and changes in structural response caused by the animals' movement and position within the pen. Combined with our knowledge of pig behavior, we use this physical knowledge of vibration characteristics to detect pig activities and track piglet growth in a real farm environment. Our system is designed to be robust to the harsh environment, which can create unpredictable noise, as well as physically damage or disconnect sensor nodes. When deployed in a real-world farm environment, our system was able to achieve a daily pen-level status profile of up to 90% accuracy, which tracks nursing activity, sow lying activity, and changes in piglet growth over the weeks-long pre-weaning period.
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    Performance Analysis of Power Outage Probability for Drone based IoT Connectivity Network
    (2019-12-01)
    Duangsuwan, Sarun
    ;
    Chusongsang, Anna
    ;
    Promwong, Sathaporn
    In this paper, we present a performance analysis of power outage probability of using on drone communicated with an internet of things (IoT) connectivity network. We obtain that there are several IoT devices are connected with a drone as simultaneously. Therefore, the power outage probability (POP) is analyzed based on the verifying of drone height which ensures the best performance in term of connectivity network. Experimental results confirm that this analysis is very useful for drone usage as drone small cell or hot-spot to distribute the transmitting power to coverage all IoT devices.