多源异构传感器数据融合和算力优化研究  

Research on heterogeneous data fusion and arithmetic optimization in multi-sensor systems

作　　者：丁凯 蒋超越 陶铭 谢仁平 DING Kai;JIANG Chaoyue;TAO Ming;XIE Renping(School of Computer Science and Technology,Dongguan University of Technology,Dongguan 523808,China;Guangdong Laboratory of Artificial Intelligence and Digital Economy(Shenzhen),Shenzhen 518107,China)

机构地区：[1]东莞理工学院计算机科学与技术学院,广东东莞523808 [2]人工智能与数字经济广东省实验室(深圳),广东深圳518107

出　　处：《物联网学报》2024年第4期23-33,共11页Chinese Journal on Internet of Things

基　　金：国家自然科学基金资助项目(No.62001113);广东省基础与应用基础研究基金项目(No.2021A1515010656)。

摘　　要：多传感器系统通过整合多种传感器数据,实现了全面且精准的环境感知,然而,如何有效融合异构数据并实现实时处理的高效性,仍然是当前研究的热点和难点问题。为此,围绕多源异构传感器的数据融合和算力优化展开研究,提出了一种创新的解决方案。首先,基于主/从架构设计数据融合系统,解决多源异构数据处理难题;其次,构建了“云—边—端”3层架构,利用边缘服务器分担云服务器的计算压力,权衡任务调度策略,实现网络资源与计算资源的协同管理;最后,针对任务的时延与能耗需求进行建模,在资源约束下构建最小化系统成本的优化问题,将问题转化为马尔可夫决策过程(MDP,Markov decision process),使用深度确定性策略梯度(DDPG,deep deterministic policy gradient)算法进行求解。仿真结果表明,所提出的架构和调度策略在降低时延和能耗方面表现优异,为多传感器系统中的高效数据融合与算力优化提供了新思路。Multi-sensor systems integrate diverse sensor data to achieve comprehensive and accurate environmental perception.However,how to effectively fuse heterogeneous data and realize the efficiency of real-time processing is still a hot and difficult issue in current research.Therefore,focusing on data fusion and arithmetic optimization of multi-source heterogeneous sensors,an innovative solution was proposed.Firstly,a data fusion system based on master-slave architecture was designed to solve the problem of multi-source heterogeneous data processing.Secondly,a three-layer“cloudedge-end”architecture was implemented,leveraging edge servers to offload computational pressure from cloud servers,optimizing task scheduling strategies,and enabling coordinated management of network and computing resources.Finally,the delay and energy consumption requirements of tasks were modeled,and the optimization problem of minimizing system cost was constructed under resource constraints,which was transformed into Markov decision process(MDP)and solved with deep deterministic policy gradient(DDPG)algorithm.Simulation experiments show that the proposed architecture and scheduling algorithm exhibit excellent performance in reducing both latency and energy consumption,and provide a new idea for efficient data fusion and arithmetic optimization in multi-sensor systems.

关 键 词：多源异构数据 数据融合 传感器 算力优化 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]