TY - GEN
T1 - Origin
T2 - 2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021
AU - Mishra, Cyan Subhra
AU - Sampson, Jack
AU - Kandemir, Mahmut Taylan
AU - Narayanan, Vijaykrishnan
N1 - Publisher Copyright:
© 2021 EDAA.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - There is an increasing demand for performing machine learning tasks, such as human activity recognition (HAR) on emerging ultra-low-power internet of things (IoT) platforms. Recent works show substantial efficiency boosts from performing inference tasks directly on the IoT nodes rather than merely transmitting raw sensor data. However, the computation and power demands of deep neural network (DNN) based inference pose significant challenges when executed on the nodes of an energy-harvesting wireless sensor network (EH-WSN). Moreover, managing inferences requiring responses from multiple energy-harvesting nodes imposes challenges at the system level in addition to the constraints at each node. This paper presents a novel scheduling policy along with an adaptive ensemble learner to efficiently perform HAR on a distributed energy-harvesting body area network. Our proposed policy, Origin, strategically ensures efficient and accurate individual inference execution at each sensor node by using a novel activity-aware scheduling approach. It also leverages the continuous nature of human activity when coordinating and aggregating results from all the sensor nodes to improve final classification accuracy. Further, Origin proposes an adaptive ensemble learner to personalize the optimizations based on each individual user. Experimental results using two different HAR data-sets show Origin, while running on harvested energy, to be at least 2.5% more accurate than a classical battery-powered energy aware HAR classifier continuously operating at the same average power.
AB - There is an increasing demand for performing machine learning tasks, such as human activity recognition (HAR) on emerging ultra-low-power internet of things (IoT) platforms. Recent works show substantial efficiency boosts from performing inference tasks directly on the IoT nodes rather than merely transmitting raw sensor data. However, the computation and power demands of deep neural network (DNN) based inference pose significant challenges when executed on the nodes of an energy-harvesting wireless sensor network (EH-WSN). Moreover, managing inferences requiring responses from multiple energy-harvesting nodes imposes challenges at the system level in addition to the constraints at each node. This paper presents a novel scheduling policy along with an adaptive ensemble learner to efficiently perform HAR on a distributed energy-harvesting body area network. Our proposed policy, Origin, strategically ensures efficient and accurate individual inference execution at each sensor node by using a novel activity-aware scheduling approach. It also leverages the continuous nature of human activity when coordinating and aggregating results from all the sensor nodes to improve final classification accuracy. Further, Origin proposes an adaptive ensemble learner to personalize the optimizations based on each individual user. Experimental results using two different HAR data-sets show Origin, while running on harvested energy, to be at least 2.5% more accurate than a classical battery-powered energy aware HAR classifier continuously operating at the same average power.
UR - http://www.scopus.com/inward/record.url?scp=85111018828&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111018828&partnerID=8YFLogxK
U2 - 10.23919/DATE51398.2021.9474017
DO - 10.23919/DATE51398.2021.9474017
M3 - Conference contribution
AN - SCOPUS:85111018828
T3 - Proceedings -Design, Automation and Test in Europe, DATE
SP - 1414
EP - 1419
BT - Proceedings of the 2021 Design, Automation and Test in Europe, DATE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 February 2021 through 5 February 2021
ER -