- Title
- An AI-enabled lightweight data fusion and load optimization approach for internet of things
- Creator
- Jan, Mian; Zakarya, Muhammad; Khan, Muhammad; Mastorakis, Spyridon; Balasubramanian, Venki
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176668
- Identifier
- vital:15151
- Identifier
-
https://doi.org/10.1016/j.future.2021.03.020
- Identifier
- ISBN:0167-739X (ISSN)
- Abstract
- In the densely populated Internet of Things (IoT) applications, sensing range of the nodes might overlap frequently. In these applications, the nodes gather highly correlated and redundant data in their vicinity. Processing these data depletes the energy of nodes and their upstream transmission towards remote datacentres, in the fog infrastructure, may result in an unbalanced load at the network gateways and edge servers. Due to heterogeneity of edge servers, few of them might be overwhelmed while others may remain less-utilized. As a result, time-critical and delay-sensitive applications may experience excessive delays, packet loss, and degradation in their Quality of Service (QoS). To ensure QoS of IoT applications, in this paper, we eliminate correlation in the gathered data via a lightweight data fusion approach. The buffer of each node is partitioned into strata that broadcast only non-correlated data to edge servers via the network gateways. Furthermore, we propose a dynamic service migration technique to reconfigure the load across various edge servers. We assume this as an optimization problem and use two meta-heuristic algorithms, along with a migration approach, to maintain an optimal Gateway-Edge configuration in the network. These algorithms monitor the load at each server, and once it surpasses a threshold value (which is dynamically computed with a simple machine learning method), an exhaustive search is performed for an optimal and balanced periodic reconfiguration. The experimental results of our approach justify its efficiency for large-scale and densely populated IoT applications. © 2021 Elsevier B.V. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**.
- Publisher
- Elsevier B.V.
- Relation
- Future Generation Computer Systems Vol. 122, no. (2021), p. 40-51
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright ©2021 Elsevier B.V. All rights reserved.
- Subject
- 0803 Computer Software; 0805 Distributed Computing; 0806 Information Systems; Data fusion; Evolutionary algorithms; Gateway-Edge configuration; Internet of Things; Load optimization; Service migration
- Reviewed
- Funder
- This work was partially supported by a pilot award from the Center for Research in Human Movement Variability, USA and the NIH, USA ( P20GM109090 ) and the National Science Foundation, USA under award CNS-2016714 .
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