Volume 15 , Issue 1 , PP: 115-132, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mohanapriya .M 1 * , V. Anusuya 2 , K. Aravindhan 3 , N. Krishnaveni 4 , R. Santhosh 5 , D. Gowthami 6
Doi: https://doi.org/10.54216/JCIM.150110
In VANETs, user equipment (UE) schedules tasks by prioritizing them based on urgency and resource availability to ensure timely and efficient communication and processing. Effective task scheduling and resource allocation in VANET are crucial for maintaining low latency, high reliability, and optimal resource utilization for real-time vehicular communications. However, existing works often face limitations such as inadequate handling of dynamic network conditions, leading to increased latency and suboptimal resource usage. In this paper, we introduced a precise model by proposing Optimizing Task Offloading in Vehicular Network named as OTO framework. Initially, UEs are clustered using an Improved Fuzzy Algorithm (IFA) to reduce latency and energy consumption, with optimal clusters determined by a cluster validity index. Clustering considers distance, location, RSSI, link stability, and trust values, and cluster heads (CH) chosen based on distance, trust, and link stability. Following this, tasks from UE are classified using a Hybrid Deep Learning (HDL) algorithm, with LiteCNN for classification into emergency and non-emergency tasks and LiteLSTM for scheduling to reduce the weight matrix and overfitting. Dual scheduling based on task length, delay sensitivity, QoS, priority, resource consumption, and queue length reduces execution time and latency. Finally, the scheduled tasks are allocated to the optimal edge server based on task load, resource availability, waiting time, and distance using the RL-based Multi-agent Deep Reinforcement Learning (MA-DRL) algorithm, where edge servers act as sellers and users as buyers, reducing latency due to high convergence. In order to, evaluate and prove the efficacy of proposed OTO framework, we performed comparative analysis in terms of several performance metrics where our proposed OTO model outperforms other existing approaches.
Task Scheduling , Game Theory , Resource Allocation , Task Classification , Hybrid Deep Learning (HDL) , Multi-agent Deep Reinforcement Learning (MA-DRL)
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