Kolomvatsos, K. (2018) Time-optimized management of IoT nodes. Ad Hoc Networks, 69, pp. 1-14. (doi: 10.1016/j.adhoc.2017.10.011)
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Abstract
The vision of Internet of Things (IoT) aims to offer a vast infrastructure of numerous interconnected devices usually called IoT nodes. The infrastructure consists of the basis of pervasive computing applications. Applications can be built with the participation of the IoT nodes that interact in very dynamic environments. In this setting, one can identify the need for applying updates in the software/firmware of the autonomous nodes. Updates may include software extensions and patches significant for the efficient functioning of the IoT nodes. Legacy methodologies involve centralized models where complex algorithms and protocols are adopted for the distribution of the updates to the nodes. This paper proposes a distributed approach where each node is responsible to initiate and conclude the update process. We envision that each node monitors specific performance metrics (related to the node itself and/or the network) and based on a time-optimized scheme identifies the appropriate time to perform the update process.We propose the adoption of a finite horizon optimal stopping scheme. Our stopping model originates in the Optimal Stopping Theory (OST) and takes into account multiple performance metrics. The aim is to have the nodes capable of identifying when their performance and the performance of the network are of high quality. In that time, nodes could be able to efficiently conclude the update process. We provide a set of formulations and the analysis of our problem. Extensive experiments and a comparison assessment reveal the advantages of the proposed solution.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Kolomvatsos, Dr Kostas |
| Authors: | Kolomvatsos, K. |
| College/School: | College of Science and Engineering > School of Computing Science |
| Journal Name: | Ad Hoc Networks |
| Publisher: | Elsevier |
| ISSN: | 1570-8705 |
| ISSN (Online): | 1570-8713 |
| Published Online: | 23 October 2017 |
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