An Edge-based Situ-aware Framework for Energy-Efficient 5G Vehicular Networks and Traffic Congestion Mitigation
Vehicular edge computing (VEC) is a promising technology composed of distributed computation and 5G communications to deliver the Quality of Service (QoS) to intelligent vehicles. Battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs) heavily rely on electricity to perform regular functions or support different kinds of movements. As a result, battery management is a critical issue to BEVs and HEVs. Meanwhile, edge servers in the network need to do load balancing as well. We want the energy consumption in the whole vehicular networks to be minimized and the efficiency can be maximized by the assistance of edge computing.
To address this problem, an edge-based situation-aware framework is proposed to reduce energy consumption and mitigate traffic congestion. The framework is composed of three (3) components: 1) vehicular energy saver – turn off the unnecessary applications based on the LSTM model’s forecast in driver’s situation, 2) edge server load balancer – share the loading of requests to the neighbor and put the idle servers to sleep, and 3) traffic congestion mitigator – placing realistic rewards on suggested rerouting road sections.
The proposed framework is extensively experimented and simulated. The results show that, 1) our proposed framework can save electricity from the perspective of service requests, 2) offloading algorithm is effective in load-balancing, thus, saving more energy and, 3) more intelligent agents cooperate with the congestion rerouting policy.
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