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

doi:10.12263/DZXB.20201348

电子学报 ›› 2021, Vol. 49 ›› Issue (10): 1982-1992.DOI: 10.12263/DZXB.20201348

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

重庆大学计算机学院，重庆 400044

收稿日期:2020-11-30 修回日期:2021-03-16 出版日期:2021-11-30
- 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
- 基金资助:
- 国家自然科学基金 (62072064); 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)

SIC-Based Data Aggregation Scheduling with Cross-Layer Concurrent Transmission for Internet of Things

JIAO Xian-long, GUO Song-tao, LI Yong, LI Yan-tao, XIANG Chao-can

School of Computer Science, Chongqing University, Chongqing 400044, China

Received:2020-11-30 Revised:2021-03-16 Online:2021-11-30 Published:2021-10-25
- Supported by:
- National Natural Science Foundation of China (62072064); Key Program of Natural Science Foundation of Chongqing Municipality, China (cstc2020jcyj-zdxmX0026); Open Project Fund of Zhejiang Lab (2021LC0AB01); General Program of Natural Science Foundation of Chongqing Municipality, China (cstc2019jcyj-msxmX0110)

摘要/Abstract

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%.

关键词: 相继干扰消除, 跨层并发传输, 数据聚合调度, 最短路径树, 物联网

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.

Key words: successive interference cancellation, cross-layer concurrent transmission, data aggregation scheduling, shortest path tree, Internet of Things

中图分类号:

TP393

焦贤龙, 郭松涛, 黎勇, 李艳涛, 向朝参. 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, LI Yan-tao, XIANG Chao-can. SIC-Based Data Aggregation Scheduling with Cross-Layer Concurrent Transmission for Internet of Things[J]. Acta Electronica Sinica, 2021, 49(10): 1982-1992.

图/表 10

图1 应用于智慧城市的物联网模型

图2 数据聚合树构造示例

图3 基于SIC的链路调度示例

表1 实验参数设置

实验参数	取值范围
部署区域大小	200m $×$ 200m
网络规模n	100~400
传输半径r	40~100m
阈值 $β$	0.8~2
噪声功率N₀	1~4W
信号衰减系数 $α$	2~3.2
最大设备度 $Δ$	30~42

表1 实验参数设置

实验参数	取值范围
部署区域大小	200m $×$ 200m
网络规模n	100~400
传输半径r	40~100m
阈值 $β$	0.8~2
噪声功率N₀	1~4W
信号衰减系数 $α$	2~3.2
最大设备度 $Δ$	30~42

图4 不同网络规模下三种算法的性能变化

图5 不同传输半径下三种算法的性能变化

图6 不同阈值下三种算法的性能变化

图7 不同信号衰减系数下三种算法的性能变化

图8 不同噪声功率下三种算法的性能变化

图9 不同最大设备度下三种算法的性能变化

参考文献 37

1	李森森, 黄一才, 郁滨, 等. 基于PUF的低开销物联网安全通信方案[J]. 电子学报, 2019, 47(4): 812－817.
	LiS S, HuangY C, YuB, et al. A PUF-based low cost secure communication scheme for IoT[J]. Acta Electronica Sinica, 2019, 47(4): 812－817. (in Chinese)
2	王巍, 赵继军, 彭力, 等. 基于UAV的移动物联网远距离通信节能策略研究[J]. 电子学报, 2018, 46(12): 2914－2921.
	WangW, ZhaoJ J, PengL, et al. Research on the energy saving strategy for long distance communication of mobile internet of things based on UAVs[J]. Acta Electronica Sinica, 2018, 46(12): 2914－2921. (in Chinese)
3	OmoniwaB, HussainR, AdilM, et al. An optimal relay scheme for outage minimization in fog-based Internet-of-Things (IoT) networks[J]. IEEE Internet of Things Journal, 2019, 6(2): 3044－3054.
4	AnsereJ A, HanG, WangH, et al. A reliable energy efficient dynamic spectrum sensing for cognitive radio IoT networks[J]. IEEE Internet of Things Journal, 2019, 6(4): 6748－6759.
5	黄美根, 郁滨. 软件定义WSN规则一致更新研究[J]. 电子学报, 2019, 47(9): 1965－1971.
	HuangM G, YuB. Research on consistent rule update in software-defined WSN[J]. Acta Electronica Sinica, 2019, 47(9): 1965－1971. (in Chinese)
6	王巍, 彭力, 赵继军, 等. 移动物联网非完整约束中继的协同任务规划[J]. 电子学报, 2019, 47(6):1251－1259.
	WangW, PengL, ZhaoJ J, et al. Cooperative task planning of ground relay with nonholonomic constraints in mobile Internet of Things based on UAVs[J]. Acta Electronica Sinica, 2019, 47(6): 1251－1259. (in Chinese)
7	张德干, 葛辉, 刘晓欢, 等. 一种基于Q-Learning策略的自适应移动物联网路由新算法[J]. 电子学报, 2018, 46(10): 2325－2332.
	ZhangD G, GeH, LiuX H, et al. A Kind of New Routing Algorithm with Adaptivity for Mobile IOT Based on Q-Learning[J]. Acta Electronica Sinica, 2018, 46(10): 2325－2332.
8	ChenX, HuX, ZhuJ. Minimum data aggregation time problem in wireless sensor networks[A]. Proceedings of Mobile Ad-Hoc Sensor Network (MSN) [C]. Berlin, Germany: Springer, 2005. 133－142.
9	XuX, LiX, MaoX, et al. A delay-efficient algorithm for data aggregation in multihop wireless sensor networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2011, 22(1): 163－175.
10	JiaoX, LouW, WangX, et al. Data aggregation scheduling in uncoordinated duty-cycled wireless sensor networks under protocol interference model[J]. Ad Hoc & Sensor Wireless Networks, 2012, 15(2): 315－338.
11	XuX, LiX Y, SongM. Efficient aggregation scheduling in multihop wireless sensor networks with SINR constraints[J]. IEEE Transactions on Mobile Computing, 2013, 12(12): 2518－2528.
12	TangJ, JiaoX, XiaoW. Minimum-latency data aggregation in duty-cycled wireless sensor networks under physical interference model[A]. Proceedings of IEEE Wireless and Optical Communication Conference[C]. Washington, DC, USA: IEEE, 2013. 309－314.
13	BagaaM, YounisM, DjenouriD, et al. Distributed low-latency data aggregation scheduling in wireless sensor networks[J]. ACM Transactions on Sensor Networks, 2015, 11(3): 1－36.
14	YousefiH, MalekimajdM, AshouriM, et al. Fast aggregation scheduling in wireless sensor networks[J]. IEEE Transactions on Wireless Communications, 2015, 14(6): 3402－3414.
15	ChenQ, GaoH, CaiZ, et al. Energy-collision aware data aggregation scheduling for energy harvesting sensor networks[A]. Proceedings of IEEE INFOCOM[C]. Washington, DC, USA: IEEE, 2018. 117－125.
16	ChenK, GaoH, CaiZ, et al. Distributed energy-adaptive aggregation scheduling with coverage guarantee for battery-free wireless sensor networks[A]. Proceedings of IEEE INFOCOM[C]. Washington, DC, USA: IEEE, 2019. 1018－1026.
17	JiaoX, LouW, GuoS, et al. Delay efficient scheduling algorithms for data aggregation in multi-channel asynchronous duty-cycled WSNs[J]. IEEE Transactions on Communications, 2019, 67(9): 6179－6192.
18	NguyenT D, ZalyubovskiyV, LeD T, et al. Break-and-join tree construction for latency-aware data aggregation in wireless sensor networks[J]. Wireless Networks, 2020, 26: 5255－5269.
19	PlotnikovR, ErzinA, ZalyubovskiyV. GLS and VNS based heuristics for conflict-free minimum-latency aggregation scheduling in WSN[J]. Optimization Methods and Software, 2020: 1－23.
20	ZhengB, WenM, WanC, et al. Secure NOMA based two-way relay networks using artificial noise and full duplex[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(7): 1426－1440.
21	YueX, LiuY, KangS, et al. Modeling and analysis of two-way relay non-orthogonal multiple access systems[J]. IEEE Transactions on Communications, 2018, 66(9): 3784－3795.
22	KilziA, FarahJ, NourC A, et al. Mutual successive interference cancellation strategies in NOMA for enhancing the spectral efficiency of CoMP systems[J]. IEEE Transactions on Communications, 2020, 68(2): 1213－1226.
23	AkhtarM W, HassanS A, SaleemS, et al. STBC-Aided Cooperative NOMA with Timing Offsets, Imperfect Successive Interference Cancellation, and Imperfect Channel State Information[J]. IEEE Transactions on Vehicular Technology, 2020, 69(10): 11712－11727.
24	ArefM A, JayaweeraS K. Deep learning-aided successive interference cancellation for MIMO-NOMA[A]. Proceedings of IEEE GLOBECOM[C]. Washington, DC, USA: IEEE, 2020. 1－5.
25	YangC, WangX, XiaB, et al. Joint interference cancellation in cache-and SIC-enabled networks[J]. IEEE Transactions on Communications, 2018, 66(9): 4155－4169.
26	JiangC, QinX, YuanX, et al. Cross-layer optimization for multi-hop wireless networks with successive interference cancellation[J]. IEEE Transactions on Wireless Communications, 2016, 15(8): 5819－5831.
27	LvS, ZhuangW, XuM, et al. Understanding the scheduling performance in wireless networks with successive interference cancellation[J]. IEEE Transactions on Mobile Computing, 2013, 12(8): 1625－1639.
28	AmarlingamM, PrasadK, RajalakshmiP, et al. A novel low-complexity compressed data aggregation method for energy-constrained IoT networks[J]. IEEE Transactions on Green Communications and Networking, 2020, 4(3): 717－730.
29	CuiJ, BoussettaK, ValoisF. Classification of data aggregation functions in wireless sensor networks[J]. Computer Networks, 2020, 178: 107342.
30	LiuX, YuJ, LiF, et al. Data aggregation in wireless sensor networks: from the perspective of security[J]. IEEE Internet of Things Journal, 2020, 7(7): 6495－6512.
31	ZhouL, GeC, HuS, et al. Energy-efficient and privacy-preserving data aggregation algorithm for wireless sensor networks[J]. IEEE Internet of Things Journal, 2020, 7(5): 3948－3957.
32	SaleemA, KhanA, MalikS, et al. FESDA: Fog-enabled secure data aggregation in smart grid IoT network[J]. IEEE Internet of Things Journal, 2020, 7(7): 6132－6142.
33	YangY, GuoS, LiuG, et al. Joint source coding rate allocation and flow scheduling for data aggregation in collaborative sensing networks[J]. Computer Networks, 2020, 175: 107269.
34	高云全. 物联网环境下数据聚合关键技术研究[D]. 北京:北京邮电大学,2019.
	GaoY Q. Research on Key Technologies of Data Aggregation in Internet of Things Environment[D]. Beijing,China: Beijing University of Post and Telecommunications, 2019. (in Chinese)
35	PatelP, HoltzmanJ. Analysis of a simple successive interference cancellation scheme in a DS/CDMA system[J]. IEEE Journal on Selected Areas in Communications, 1994, 12(5): 796－807.
36	HrbekS J, ShivaramaiahN C, AkosD M. Filtering and quantization effects on GNSS successive interference cancellation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(2):924－936.
37	GrabnerM J, LiX, FuS. An adaptive BLAST successive interference cancellation method for high data rate perfect space-time coded MIMO systems[J]. IEEE Transactions on Vehicular Technology, 2020, 69(2):1542－1553.

[1]	李玮, 张雨希, 谷大武, 张金煜, 朱晓铭, 刘春, 蔡天培, 李嘉耀. 轻量级密码MANTIS的唯密文故障分析[J]. 电子学报, 2022, 50(4): 967-976.
[2]	陈书仪, 刘亚丽, 林昌露, 李涛, 董永权. 面向物联网的轻量级可验证群组认证方案[J]. 电子学报, 2022, 50(4): 990-1001.
[3]	孟超, 周倩, 郭林, 王攀, 孙知信. 基于相关性传输模型的无线链路质量估计方法及路由优化算法[J]. 电子学报, 2022, 50(10): 2409-2424.
[4]	陈亮, 李峰, 任保全, 杨建喜. 软件定义物联网研究综述[J]. 电子学报, 2021, 49(5): 1019-1032.
[5]	黄美根, 郁滨. 软件定义WSN规则一致更新研究[J]. 电子学报, 2019, 47(9): 1965-1971.
[6]	徐诚, 何杰, 张晓彤, 姚翠, 段世红, 齐悦. IMU/TOA融合人体运动追踪性能评估方法[J]. 电子学报, 2019, 47(8): 1748-1754.
[7]	王巍, 彭力, 赵继军, 常存喜, 黄晓丹, 田立勤. 移动物联网非完整约束中继的协同任务规划[J]. 电子学报, 2019, 47(6): 1251-1259.
[8]	李森森, 黄一才, 郁滨, 鲍博武. 基于PUF的低开销物联网安全通信方案[J]. 电子学报, 2019, 47(4): 812-817.
[9]	唐晓庆, 谢桂辉, 佘亚军, 俞杨. 基于MCU的无源Wi-Fi散射通信方法[J]. 电子学报, 2019, 47(10): 2069-2075.
[10]	杨晓东, 陈益强, 于汉超, 张迎伟, 钟习, 胡子昂, 刘弘. 面向帕金森病的多模态异构协同感知方法[J]. 电子学报, 2018, 46(3): 659-664.
[11]	王巍, 赵继军, 彭力, 黄晓丹, 李林茂, 魏丁丁. 基于UAV的移动物联网远距离通信节能策略研究[J]. 电子学报, 2018, 46(12): 2914-2922.
[12]	张德干, 葛辉, 刘晓欢, 张晓丹, 李文斌. 一种基于Q-Learning策略的自适应移动物联网路由新算法[J]. 电子学报, 2018, 46(10): 2325-2332.
[13]	李勐, 王晓峰, 崔莉. 一种物联网设备自动描述方法[J]. 电子学报, 2016, 44(5): 1055-1063.
[14]	田野, 袁博, 李廷力. 物联网海量异构数据存储与共享策略研究[J]. 电子学报, 2016, 44(2): 247-257.
[15]	陈珍萍, 黄友锐, 唐超礼, 曲立国. 物联网感知层低能耗时间同步方法研究[J]. 电子学报, 2016, 44(1): 193-199.

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

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 37

相关文章 15

编辑推荐

Metrics