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- Title
RF-BBFT: a random forest based multimedia big data routing technique for social opportunistic IoT networks.
- Authors
Nigam, Ritu; Jain, Satbir; Sharma, Deepak Kumar
- Abstract
Opportunistic Routing (OR) is an emerging and promising data communication technique in social Opportunistic IoT networks (social OppIoTs). The OR becomes more critical if data is multimedia big data (MBD) produced from multidimensional distributed mobile nodes. The research work carried out in this paper suggests an intelligent routing mechanism named RF-BBFT, which uses Machine Learning (ML) algorithm, namely, random forest (RF), to make smart routing decisions. The RF model is trained by utilizing traits like direct bonding metrics, node popularity, power consumption within a node, speed, and node location. Simulation results conducted through the ONE simulator demonstrate that RF-BBFT performs substantially better than BBFT concerning the successful transmissions, average latency, and average buffer time. The proposed RF-BBFT outperforms BBFT and MLProph by 14.69% and 15.42% respectively, in terms of the delivery success rate.
- Publication
Multimedia Tools & Applications, 2023, Vol 82, Issue 25, p39815
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-023-15734-x