An AI-enabled lightweight data fusion and load optimization approach for Internet of Things.

Autor: Jan MA; Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan., Zakarya M; Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan., Khan M; ComNets Lab, Department of Computer Science, New York University, Abu Dhabi, United Arab Emirates., Mastorakis S; College of Information Science & Technology, University of Nebraska Omaha, USA., Menon VG; Department of Computer Science and Engineering, SCMS School of Engineering and Technology, Ernakulam 683576, India., Balasubramaniam V; School of Science, Engineering and Information Technology, Federation University, Mount Helen, VIC 3350, Australia., Ur Rehman A; Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan.
Jazyk: angličtina
Zdroj: Future generations computer systems : FGCS [Future Gener Comput Syst] 2021 Sep; Vol. 122, pp. 40-51. Date of Electronic Publication: 2021 Apr 08.
DOI: 10.1016/j.future.2021.03.020
Abstrakt: 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.
Competing Interests: Author Statement: All the authors declare no conflict of interest for the research conducted in this paper.
Databáze: MEDLINE
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