TY - GEN
T1 - Evaluating Performance of In-Situ Distributed Processing on IoT Devices by Developing a Workspace Context Recognition Service
AU - Talusan, Jose Paolo
AU - Tiausas, Francis
AU - Stirapongsasuti, Sopicha
AU - Nakamura, Yugo
AU - Mizumoto, Teruhiro
AU - Yasumoto, Keiichi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - With the number of IoT devices expected to exceed 50 billion in 2023, edge and fog computing paradigms are beginning to attract attention as a way to process the massive amounts of raw data being generated. However, these paradigms do not consider the processing capabilities of the existing commodity IoT devices in the wild. In order to solve this challenge, we are developing a new middleware platform called IFoT, which processes various sensor data while considering Quality of Service (QoS) by utilizing the computational resources of heterogeneous IoT devices within an area. This allows smart services to be created and processed in parallel by various IoT devices. In this paper, we show the effectiveness of the IFoT based approach of constructing services. We designed and implemented a workspace context recognition service, utilizing environmental sensor data processed in a distributed manner according to the IFoT framework. We evaluate the QoS of IFoT middleware and its feasibility when used on commodity devices such as the Raspberry Pi, through the service.
AB - With the number of IoT devices expected to exceed 50 billion in 2023, edge and fog computing paradigms are beginning to attract attention as a way to process the massive amounts of raw data being generated. However, these paradigms do not consider the processing capabilities of the existing commodity IoT devices in the wild. In order to solve this challenge, we are developing a new middleware platform called IFoT, which processes various sensor data while considering Quality of Service (QoS) by utilizing the computational resources of heterogeneous IoT devices within an area. This allows smart services to be created and processed in parallel by various IoT devices. In this paper, we show the effectiveness of the IFoT based approach of constructing services. We designed and implemented a workspace context recognition service, utilizing environmental sensor data processed in a distributed manner according to the IFoT framework. We evaluate the QoS of IFoT middleware and its feasibility when used on commodity devices such as the Raspberry Pi, through the service.
UR - http://www.scopus.com/inward/record.url?scp=85067990562&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067990562&partnerID=8YFLogxK
U2 - 10.1109/PERCOMW.2019.8730693
DO - 10.1109/PERCOMW.2019.8730693
M3 - Conference contribution
AN - SCOPUS:85067990562
T3 - 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019
SP - 633
EP - 638
BT - 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019
Y2 - 11 March 2019 through 15 March 2019
ER -