Reference : Cloud-Assisted Device Clustering for Lifetime Prolongation in Wireless IoT Networks
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
Cloud-Assisted Device Clustering for Lifetime Prolongation in Wireless IoT Networks
Bhandari, Sabin []
Sharma, Shree Krishna mailto [University of Western Ontario, Canada]
Wang, Xianbin []
Proc. IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) 2017
2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)
30 April-3 May 2017
Windsor, ON
[en] IoT ; Device clustering ; Cloud computing ; Lifetime prolongation ; IoT-Cloud convergence
[en] One of the crucial challenges in the recently emerging Internet of Things (IoT) applications is how to handle the massive heterogeneous data generated from a large number of resource-constrained sensors. In this context, cloud computing has emerged as a promising paradigm due to its enormous storage and computing capabilities, thus leading to the IoT-Cloud convergence. In such a framework, IoT devices can be grouped into several clusters and each cluster head can send the aggregated information to the cloud via a gateway for further processing. Although a number of clustering methods have been proposed for the conventional Wireless Sensor Networks (WSNs), it is important to consider specific IoT characteristics while adapting these techniques for wireless IoT networks. One of the important features of IoT networks that can be exploited while developing clustering techniques is the collaborations among heterogeneous IoT devices. In this regard, the network-wide knowledge at the cloud center can be useful to provide information about the device relations to the IoT gateway. Motivated by this, we propose and evaluate a cloud-assisted device interaction-aware clustering scheme for heterogeneous IoT networks. The proposed method considers the joint impact of residual energy and device closeness factor for the effective selection of cluster heads. Our results show that the proposed clustering scheme can significantly prolong the network lifetime, and enhance the overall throughput of a wireless IoT network.
Researchers ; Professionals

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