TY - GEN
T1 - Middleware Framework for IoT Services Integration
AU - Lomotey, Richard K.
AU - Pry, Joseph
AU - Sriramoju, Sumanth
AU - Kaku, Emmanuel
AU - Deters, Ralph
N1 - Funding Information:
This study was partly funded by the Foundation for Research, Science and Technology (New Zealand, contract CO1X0308) and Cemagref (France, Lyon). The authors are grateful to Jean-Yves Champagne for the help and flume facilities provided by the Laboratoire de Mécanique des Fluides, Institut National des Sciences Appliquées, Villeurbanne, France.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/7
Y1 - 2017/9/7
N2 - In the era of the Internet of Things (IoT), data from sensors can give insightful enterprise information through analytics. As a result, several enterprises are adopting sensors and other wireless technologies for their needs. However, some challenges exist within the IoT space. Most of the devices in use have varied device semantics and protocol variations which can limit interoperability. As a result, data and process integration can be hindered. In this work, we propose a middleware with both machine-to-infrastructure (M2I) and machine-to-machine (M2M) capabilities which addresses these problems based on mapping techniques between the heterogeneous device semantics and providing a common interface for merging protocol variations. When a device is discoverable, our middleware uses an enhanced environment-context features to match the appropriate communication protocol. This aids in the pushing of data from within-range sensors to a cloud-hosted infrastructure.
AB - In the era of the Internet of Things (IoT), data from sensors can give insightful enterprise information through analytics. As a result, several enterprises are adopting sensors and other wireless technologies for their needs. However, some challenges exist within the IoT space. Most of the devices in use have varied device semantics and protocol variations which can limit interoperability. As a result, data and process integration can be hindered. In this work, we propose a middleware with both machine-to-infrastructure (M2I) and machine-to-machine (M2M) capabilities which addresses these problems based on mapping techniques between the heterogeneous device semantics and providing a common interface for merging protocol variations. When a device is discoverable, our middleware uses an enhanced environment-context features to match the appropriate communication protocol. This aids in the pushing of data from within-range sensors to a cloud-hosted infrastructure.
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U2 - 10.1109/AIMS.2017.20
DO - 10.1109/AIMS.2017.20
M3 - Conference contribution
AN - SCOPUS:85032296178
T3 - Proceedings - 2017 IEEE 6th International Conference on AI and Mobile Services, AIMS 2017
SP - 89
EP - 92
BT - Proceedings - 2017 IEEE 6th International Conference on AI and Mobile Services, AIMS 2017
A2 - Tata, Samir
A2 - Mao, Zhi-Hong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on AI and Mobile Services, AIMS 2017
Y2 - 25 June 2017 through 30 June 2017
ER -