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Object-aware multi-criteria decision-making approach using the heuristic data-driven theory for intelligent transportation systems.

Mekala, M.S.; Eyad, Elyan; Srivastava, Gautam

Authors

Gautam Srivastava



Abstract

Sharing up-to-date information about the surrounding measured by On-Board Units (OBUs) and Roadside Units (RSUs) is crucial in accomplishing traffic efficiency and pedestrians safety towards Intelligent Transportation Systems (ITS). Transferring measured data demands >10Gbit/s transfer rate and >1GHz bandwidth though the data is lost due to unusual data transfer size and impaired line of sight (LOS) propagation. Most existing models concentrated on resource optimization instead of measured data optimization. Subsequently, RSU-LiDARs have become increasingly popular in addressing object detection, mapping and resource optimization issues of Edge-based Software-Defined Vehicular Orchestration (ESDVO). In this regard, we design a two-step data-driven optimization approach called Object-aware Multi-criteria Decision-Making (OMDM) approach. First, the surroundings-measured data by RSUs and OBUs is processed by cropping object-enabled frames using YoLo and FRCNN at RSU. The cropped data likely share over the environment based on the RSU Computation-Communication method. Second, selecting the potential vehicle/device is treated as an NP-hard problem that shares information over the network for effective path trajectory and stores the cosine data at the fog server for end-user accessibility. In addition, we use a nonlinear programming multi-tenancy heuristic method to improve resource utilization rates based on device preference predictions (Like detection accuracy and bounding box tracking) which elaborately concentrate in future work. The simulation results agree with the targeted effectiveness of our approach, i.e., mAP (>71%) with processing delay (< 3.5 x 106bits/slot), and transfer delay (< 3Sms). Our simulation results indicate that our approach is highly effective.

Citation

MEKALA, M.S., EYAD, E. and SRIVASTAVA, G. 2023. Object-aware multi-criteria decision-making approach using the heuristic data-driven theory for intelligent transportation systems. In Proceedings of the 10th IEEE (Institute of Electrical and Electronics Engineers) Data science and advanced analytics international conference 2023 (DSAA 2023), 9-13 October 2023, Thessaloniki, Greece. Piscataway: IEEE [online], 10302554. Available from: https://doi.org/10.1109/DSAA60987.2023.10302554

Conference Name 10th IEEE (Institute of Electrical and Electronics Engineers) Data science and advanced analytics international conference 2023 (DSAA 2023)
Conference Location Thessaloniki, Greece
Start Date Oct 9, 2023
End Date Oct 13, 2023
Acceptance Date Jul 10, 2023
Online Publication Date Nov 6, 2023
Publication Date Dec 31, 2023
Deposit Date Dec 15, 2023
Publicly Available Date Dec 15, 2023
Publisher Institute of Electrical and Electronics Engineers (IEEE)
DOI https://doi.org/10.1109/DSAA60987.2023.10302554
Keywords Edge computing; RSU selection; Cyber-physical systems; Object detection; Path trajectory; Multi-criteria decision making method
Public URL https://rgu-repository.worktribe.com/output/2174520

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