Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2020 Nov 29;20(23):6820. doi: 10.3390/s20236820.
To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive research has been conducted on RCC-based computation offloading, while the studies on VCC-based computation offloading are relatively rare. In fact, due to the dynamic and uncertainty of on-board resource, the VCC-based computation offloading is more challenging then the RCC one, especially under the vehicular scenario with expensive inter-vehicle communication or poor communication environment. To solve this problem, we propose to leverage the VCC's computation resource for computation offloading with a perception-exploitation way, which mainly comprise resource discovery and computation offloading two stages. In resource discovery stage, upon the action-observation history, a Long Short-Term Memory (LSTM) model is proposed to predict the on-board resource utilizing status at next time slot. Thereafter, based on the obtained computation resource distribution, a decentralized multi-agent Deep Reinforcement Learning (DRL) algorithm is proposed to solve the collaborative computation offloading with VCC and RCC. Last but not least, the proposed algorithms' effectiveness is verified with a host of numerical simulation results from different perspectives.
为满足计算密集型车载应用的爆炸式增长，我们研究了认知车联网 (CVN) 中的计算卸载问题。具体来说，在我们的方案中，联合考虑了车载云计算 (VCC) 和远程云计算 (RCC) 支持的计算卸载。到目前为止，已经对基于 RCC 的计算卸载进行了广泛的研究，而基于 VCC 的计算卸载的研究相对较少。事实上，由于车载资源的动态性和不确定性，VCC 支持的计算卸载比 RCC 更具挑战性，尤其是在车载场景中存在昂贵的车车间通信或较差的通信环境时。为了解决这个问题，我们提出了一种感知利用的方法，利用 VCC 的计算资源进行计算卸载，主要包括资源发现和计算卸载两个阶段。在资源发现阶段，根据动作观测历史，提出了一种长短期记忆 (LSTM) 模型来预测下一时间段的车载资源利用状态。然后，根据获得的计算资源分布，提出了一种分散式多智能体深度强化学习 (DRL) 算法来解决 VCC 和 RCC 的协同计算卸载问题。最后，但并非最不重要的是，从不同角度的大量数值模拟结果验证了所提出算法的有效性。
