{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/118675"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/118675","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Low-Power Wireless Sensor Node with Edge Computing for Pig Behavior Classifications","abstract":"A wireless sensor node (WSN) system, capable of sensing animal motion and transmitting motion data wirelessly, is an effective and efficient way to monitor pigs' activity. However, the raw sensor data sampling and transmission consumes lots of power such that WSNs' battery have to be frequently charged or replaced. The proposed work solves this issue through WSN edge computing solution, in which a Random Forest Classifier (RFC) is trained and implemented into WSNs. The implementation of RFC on WSNs does not save power, but the RFC predicts animal behavior such that WSNs can adaptively adjust the data sampling frequency to reduce power consumption. In addition, WSNs can transmit less data by sending RFC predictions instead of raw sensor data to save power. The proposed RFC classifies common animal activities: eating, drinking, laying, standing, and walking with a F-1 score of 93%. The WSN power consumption is reduced by 25% with edge computing intelligence, compare to WSN power that samples and transmits raw sensor data periodically at 10 Hz.","abstract_html":"A wireless sensor node (WSN) system, capable of sensing animal motion and transmitting motion data wirelessly, is an effective and efficient way to monitor pigs' activity. However, the raw sensor data sampling and transmission consumes lots of power such that WSNs' battery have to be frequently charged or replaced. The proposed work solves this issue through WSN edge computing solution, in which a Random Forest Classifier (RFC) is trained and implemented into WSNs. The implementation of RFC on WSNs does not save power, but the RFC predicts animal behavior such that WSNs can adaptively adjust the data sampling frequency to reduce power consumption. In addition, WSNs can transmit less data by sending RFC predictions instead of raw sensor data to save power. The proposed RFC classifies common animal activities: eating, drinking, laying, standing, and walking with a F-1 score of 93%. The WSN power consumption is reduced by 25% with edge computing intelligence, compare to WSN power that samples and transmits raw sensor data periodically at 10 Hz.","abstract_has_math":false,"creators":["Xu, Yuezhong"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Ha, Dong S."],"committee_members":["Hudait, Mantu K.","Ha, Sook Shin"],"year":2024,"date_issued":"2024-04-25","date_published":"2024-04-25","updated_at":"2026-07-24T05:56:51Z","subjects":["Wireless Sensor Node (WSN)","Edge Computing","Random Forest Classifier (RFC)","Bluetooth Low Energy (BLE)","Bio-sensing."],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:39879"],"render_values":[{"text":"vt_gsexam:39879","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/118675","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Ha, Dong S."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Hudait, Mantu K.","Ha, Sook Shin"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Xu, Yuezhong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-04-26T08:00:54Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-04-26T08:00:54Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-04-25"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Wireless Sensor Node (WSN)","Edge Computing","Random Forest Classifier (RFC)","Bluetooth Low Energy (BLE)","Bio-sensing."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:39879"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/118675"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A wireless sensor node (WSN) system, capable of sensing animal motion and transmitting motion data wirelessly, is an effective and efficient way to monitor pigs' activity. However, the raw sensor data sampling and transmission consumes lots of power such that WSNs' battery have to be frequently charged or replaced. The proposed work solves this issue through WSN edge computing solution, in which a Random Forest Classifier (RFC) is trained and implemented into WSNs. The implementation of RFC on WSNs does not save power, but the RFC predicts animal behavior such that WSNs can adaptively adjust the data sampling frequency to reduce power consumption. In addition, WSNs can transmit less data by sending RFC predictions instead of raw sensor data to save power. The proposed RFC classifies common animal activities: eating, drinking, laying, standing, and walking with a F-1 score of 93%. The WSN power consumption is reduced by 25% with edge computing intelligence, compare to WSN power that samples and transmits raw sensor data periodically at 10 Hz."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["A wireless sensor node (WSN) system that detects animal movement and wirelessly transmits this data is a valuable tool for monitoring pigs' activity. However, the process of sampling and transmitting raw sensor data consumes a significant amount of power, leading to frequent recharging or replacement of WSN batteries. To address this issue, our proposed solution integrates edge computing into WSNs, utilizing a Random Forest Classifier (RFC). The RFC is trained and deployed within the WSNs to predict animal behavior, allowing for adaptive adjustment of data sampling frequency to reduce power consumption. Additionally, by transmitting RFC predictions instead of raw sensor data, WSNs can conserve power by transmitting less data. Our RFC can accurately classify common animal activities, such as eating, drinking, laying, standing, and walking, achieving an F-1 score of 93%. With the integration of edge computing intelligence, WSN power consumption is reduced by 25% compared to traditional WSNs that periodically sample and transmit raw sensor data at 10 Hz."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Low-Power Wireless Sensor Node with Edge Computing for Pig Behavior Classifications"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Ha, Dong S."],"dc:contributor.committeemember":["Hudait, Mantu K.","Ha, Sook Shin"],"dc:contributor.department":["Electrical Engineering"],"dc:creator":["Xu, Yuezhong"],"dc:date.accessioned":["2024-04-26T08:00:54Z"],"dc:date.available":["2024-04-26T08:00:54Z"],"dc:date.issued":["2024-04-25"],"dc:description.abstract":["A wireless sensor node (WSN) system, capable of sensing animal motion and transmitting motion data wirelessly, is an effective and efficient way to monitor pigs' activity. However, the raw sensor data sampling and transmission consumes lots of power such that WSNs' battery have to be frequently charged or replaced. The proposed work solves this issue through WSN edge computing solution, in which a Random Forest Classifier (RFC) is trained and implemented into WSNs. The implementation of RFC on WSNs does not save power, but the RFC predicts animal behavior such that WSNs can adaptively adjust the data sampling frequency to reduce power consumption. In addition, WSNs can transmit less data by sending RFC predictions instead of raw sensor data to save power. The proposed RFC classifies common animal activities: eating, drinking, laying, standing, and walking with a F-1 score of 93%. The WSN power consumption is reduced by 25% with edge computing intelligence, compare to WSN power that samples and transmits raw sensor data periodically at 10 Hz."],"dc:description.abstractgeneral":["A wireless sensor node (WSN) system that detects animal movement and wirelessly transmits this data is a valuable tool for monitoring pigs' activity. However, the process of sampling and transmitting raw sensor data consumes a significant amount of power, leading to frequent recharging or replacement of WSN batteries. To address this issue, our proposed solution integrates edge computing into WSNs, utilizing a Random Forest Classifier (RFC). The RFC is trained and deployed within the WSNs to predict animal behavior, allowing for adaptive adjustment of data sampling frequency to reduce power consumption. Additionally, by transmitting RFC predictions instead of raw sensor data, WSNs can conserve power by transmitting less data. Our RFC can accurately classify common animal activities, such as eating, drinking, laying, standing, and walking, achieving an F-1 score of 93%. With the integration of edge computing intelligence, WSN power consumption is reduced by 25% compared to traditional WSNs that periodically sample and transmit raw sensor data at 10 Hz."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:39879"],"dc:identifier.uri":["https://hdl.handle.net/10919/118675"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Wireless Sensor Node (WSN)","Edge Computing","Random Forest Classifier (RFC)","Bluetooth Low Energy (BLE)","Bio-sensing."],"dc:title":["Low-Power Wireless Sensor Node with Edge Computing for Pig Behavior Classifications"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-24T05:56:51Z"}