基于相继干扰消除和跨层并发传输的物联网数据聚合调度

焦贤龙; 郭松涛; 黎勇; 李艳涛; 向朝参

doi:10.12263/DZXB.20201348

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基于相继干扰消除和跨层并发传输的物联网数据聚合调度

学术论文 | 更新时间：2025-12-08

- 基于相继干扰消除和跨层并发传输的物联网数据聚合调度
- SIC-Based Data Aggregation Scheduling with Cross-Layer Concurrent Transmission for Internet of Things
- 电子学报 2021年49卷第10期页码：1982-1992
- 作者机构：
  
  重庆大学计算机学院，重庆 400044
- 作者简介：
  
  [ "焦贤龙男，1982年生，江西丰城人，博士.分别于2003年、2005年和2011年在国防科技大学获得学士、硕士和博士学位. 现为重庆大学计算机学院助理研究员.主要研究方向包括物联网和边缘计算. E-mail:xljiao@cqu.edu.cn" ]
  [ "郭松涛（通信作者）男，1975年生，河南西平人，博士.分别于1999年、2003年和2008年在重庆大学获得学士、硕士和博士学位. 现为重庆大学计算机学院教授.主要研究方向包括智能边缘计算、无线与移动网络、深度学习与图像处理. E-mail:guosongtao@cqu.edu.cn" ]
  [ "黎勇男，1982年生，重庆酉阳人，博士. 2012年12月获厦门大学工学博士学位. 现为重庆大学计算机学院副教授.主要研究方向包括信息论与编码、计算机视觉、量子密钥分发、医学大数据. E-mail:yongli@cqu.edu.cn" ]
  [ "李艳涛男，1984年生，河南济源人，博士. 2012年12月获重庆大学工学博士学位. 现为重庆大学计算机学院研究员.主要研究方向包括无线网络、传感器系统、移动计算安全. E-mail:liyantao@cqu.edu.cn" ]
  [ "向朝参男，1987年生，四川达州人，博士. 2014年6月获解放军理工大学博士学位. 现为重庆大学计算机学院副教授.主要研究方向包括人工智能、城市计算、物联网、移动智能感知、大数据. E-mail:xiangchaocan@cqu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62072064;61872447;61771081;62072061);重庆市自然科学基金重点项目(cstc2020jcyj-zdxmX0026);之江实验室课题开放基金(2021LC0AB01);重庆市自然科学基金面上项目(cstc2019jcyj-msxmX0110)
- DOI：10.12263/DZXB.20201348
  中图分类号： TP393;
- 收稿：2020-11-30，
  
  修回：2021-03-16，
  
  纸质出版：2021-10-25
- 稿件说明：
移动端阅览
焦贤龙,郭松涛,黎勇等.基于相继干扰消除和跨层并发传输的物联网数据聚合调度[J].电子学报,2021,49(10):1982-1992.

JIAO Xian-long,GUO Song-tao,LI Yong,et al.SIC-Based Data Aggregation Scheduling with Cross-Layer Concurrent Transmission for Internet of Things[J].ACTA ELECTRONICA SINICA,2021,49(10):1982-1992.
焦贤龙,郭松涛,黎勇等.基于相继干扰消除和跨层并发传输的物联网数据聚合调度[J].电子学报,2021,49(10):1982-1992. DOI： 10.12263/DZXB.20201348.

JIAO Xian-long,GUO Song-tao,LI Yong,et al.SIC-Based Data Aggregation Scheduling with Cross-Layer Concurrent Transmission for Internet of Things[J].ACTA ELECTRONICA SINICA,2021,49(10):1982-1992. DOI： 10.12263/DZXB.20201348.

摘要

近年来物联网在许多军事和民用领域（灾后恢复、环境监控和军事对抗等）展现出蓬勃的应用前景，而在实际应用中，为了维护终端数据的新鲜度，必须以尽可能低的时延来完成数据聚合调度，从而为用户提供及时准确的数据服务.但是，受信号干扰的影响，最低时延数据聚合调度问题已被证明是NP（Non-deterministic Polynomial）难问题，而如何设计低时延的数据聚合调度算法是物联网领域的研究热点.现有面向传统物联网（如无线传感网）的解决方案通常采用逐层调度方法和干扰避免技术来实现，减少了可并发传输的链路数目，不利于降低数据聚合时延.值得关注的是，相继干扰消除（Successive Interference Cancellation

SIC）技术作为一种简单而强大的多包接收技术，是研究者近年来取得的重大突破，而如何结合SIC 技术来设计物联网低时延数据聚合调度算法具有非常重要的理论研究意义.因此，本文以最大程度地增加可并发传输的链路数目为目标，利用跨层并发传输的思想来进行数据聚合调度，并结合SIC技术来实现链路调度，提出了一种新颖的低时延数据聚合调度算法.实验结果表明，与现有算法相比，本文所提算法在数据聚合时延优化方面最多可达43.8%.

Abstract

Recent years have witnessed booming application of Internet of Things to many military and civilian fields (disaster recovery

environmental monitoring

military confrontation

and so on). In practical application

in order to maintain the freshness of terminal data

data aggregation scheduling must be completed with the lowest possible delay

so as to provide users with timely and accurate data services. However

affected by signal interference

the minimum-delay data aggregation scheduling problem has been proved to be NP-hard

and how to design a low-delay data aggregation scheduling algorithm is a hot topic in the field of Internet of Things. Most of existing solutions for traditional Internet of Things (such as wireless sensor networks) usually adopt the layer-by-layer scheduling method and the interference-avoidance technology

which is not conducive to improve data aggregation delay due to the reduced number of concurrent transmission links. It is worth noting that successive interference cancellation (SIC) technology

as a kind of simple and powerful multi-packet receiving technology

is a major breakthrough made by researchers in recent years. How to combine SIC technology to design low-delay data aggregation scheduling algorithms for Internet of Things has very important theoretical research significance. Therefore

this paper utilizes the idea of cross-layer concurrent transmission to schedule the data aggregation process

incorporates the SIC technology to schedule the data aggregation links

and proposes a novel delay-efficient data aggregation scheduling algorithm

with the aim of increasing the number of concurrent transmission links to the most extent. Simulation results show that

our algorithm can improve the data aggregation delay by at most 43.8% compared with the existing algorithm.

关键词

Keywords

references

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