Sensors-enabled autonomous computational intelligence vehicle model with QoS-driven communication services

Edge computing is integrated with various physical and computational components in autonomous networks to develop transportation systems. These systems can gather data from the intended field and send it to computing servers for data analysis and sharing with network users. Integration of vehicles with sensor technology improves the automotive industry for safety and road monitoring. However, load balancing and communication stability represent some of the major research challenges for vehicle communication because of the inherent limitations of sensor technologies in emerging networks. Most of the solutions have been presented based on centralized computing which effectively increases the data flow, nonetheless, distributed channels and heterogeneous devices continue to pose significant quality of services issues. Such problems lower the optimization criteria and reflect communication structure around the network edges. This research presents an autonomous computing model with mobile edge technology and a cooperative intelligent transportation network algorithm. Firstly, mobile data collectors are identified with computational intelligence to forward the relaying data and balance the traffic overhead. Secondly, edged-enabled authentic procedures are defined for improving integrity and privacy in a trust-lacking environment. In terms of network throughput, data latency, network lifetime, and retransmission load under varying data rates and vehicle sensor scenarios, the significance of the proposed algorithm is assessed, and performance results demonstrate that it outperforms state-of-the-art schemes.