一种面向最小能耗自适应汇聚路由判定算法

doi:10.19665/j.issn1001-2400.2022.02.002

西安电子科技大学学报 ›› 2022, Vol. 49 ›› Issue (2): 11-20.doi: 10.19665/j.issn1001-2400.2022.02.002

一种面向最小能耗自适应汇聚路由判定算法

孙泽宇^1,^2,³(),兰岚^1,³(),曾操^1,³(),廖桂生^1,³()

1.西安电子科技大学雷达信号处理国家重点实验室,陕西西安 710071
2.洛阳理工学院计算机与信息工程学院,河南洛阳 471023
3.西安电子科技大学信息感知协同创新中心,陕西西安 710071

收稿日期:2021-10-28 出版日期:2022-04-20 发布日期:2022-05-31
通讯作者: 廖桂生
作者简介:孙泽宇(1977—),男,教授,西安电子科技大学博士研究生,E-mail: lylgszy@163.com;|兰岚(1993—),女,副教授,博士,E-mail: lanlan@xidian.edu.cn;|曾操(1979—),男,教授,博士,E-mail: czeng@mail.xidian.edu.cn
基金资助:
国家自然科学基金(61771015);国家自然科学基金(61931016);国家自然科学基金(62101402);河南省科技厅科技攻关重点项目(212102210374);河南省自然科学基金面上项目(202300410286);河南省教育厅高等学校重点资助项目(19A520006);河南省教育厅高等学校重点资助项目(20A520027);中国航空基金(2020000108101);中国博士后基金(2021M702547);中国博士后面上基金(2021TQ0261)

Adaptive sink-routing decision algorithm for minimum-energy consumption

SUN Zeyu^1,^2,³(),LAN Lan^1,³(),ZENG Cao^1,³(),LIAO Guisheng^1,³()

1. National Key Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China
2. School of Computer and Information Engineering,Luoyang Institute of Science and Technology,Luoyang 471023,China
3. Collaborative Innovation Center of Information Sensing,Xidian University,Xi’an 710071,China

Received:2021-10-28 Online:2022-04-20 Published:2022-05-31
Contact: Guisheng LIAO

摘要/Abstract

摘要：

在无线传感器网络数据传输过程中,由于大量数据在信道内积累,将会产生频繁数据冲突与碰撞,无故地消耗了过多的网络能量。为此,提出了一种面向最小能耗自适应汇聚路由判定算法。首先,该算法借助于汇聚增益计算了数据融合度介于最小值与最大值之间时,数据融合度与节点距离相关参数之间的比例关系;其次,讨论了3种距离相关系数情况下,节点之间的关联关系,证明了多节点不进行数据融合时,下一跳节点数据融合度存在的条件;然后,通过数据压缩能量比函数关系,讨论了数据在链路上进行压缩与解压以及数据在源节点和Sink节点进行压缩与解压时,全网能量消耗的计算过程;最后,在上述分析基础上,利用能量转换模型计算了任意节点之间欧氏距离满足的必要条件,同时分析了这种算法的实现过程。仿真实验表明,这种算法与其他算法在网络能量开销和网络平均时延上进行对比,其数值分别减少了约10.29%和12.57%,达到了优化汇聚路由的目的,从而证验了这种算法的有效性和实效性。

关键词: 无线传感器网络, 汇聚路由, 最小能耗, 网络能量, 平均时延

Abstract:

During the data transmission in wireless sensing networks (WSNs),the data accumulation in the communication link will lead to frequent data collisions,which further consumes the network energy.To address this problem,we propose an adaptive sink-routing decision algorithm for minimum-energy consumption (ASD-MC).First,when the data fusion degree is between the maximum and minimum value,the aggregation gain is exploited to calculate the proportional relationship between the data fusion degree and node distance-related parameters.Then,we discuss the correlation among different nodes with three distance correlation coefficients.When multiple nodes do not conduct data fusion,the condition for the existence of data fusion degree is proved for the next-hop node.In addition,according to the functional relationship of data compression energy ratio,we consider the data compression and decompression process on the link both at the source node and at the sink node.Then the procedure for the calculation of the network energy consumption is provided.Based on the above analysis,we employ the energy conversion model to derive the necessary condition for the Euclidean distance between any two nodes.Furthermore,the procedure for the implementation of our proposed algorithm is also presented.Finally,simulation results show that,compared with existing algorithms,our proposed algorithm could reduce the network energy consumption and the average network delay by 10.29% and 12.57%,respectively,which verifies the effectiveness and validity of the ASD-MC algorithm.

Key words: wireless sensor networks, sink routing, minimum-energy consumption, network energy, average delay

中图分类号:

TP393

孙泽宇,兰岚,曾操,廖桂生. 一种面向最小能耗自适应汇聚路由判定算法[J]. 西安电子科技大学学报, 2022, 49(2): 11-20.

SUN Zeyu,LAN Lan,ZENG Cao,LIAO Guisheng. Adaptive sink-routing decision algorithm for minimum-energy consumption[J]. Journal of Xidian University, 2022, 49(2): 11-20.

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参考文献 19

[1]	SUN Z Y, LI L X, XING X F, et al. A Novel Nodes Deployment Assignment Scheme with Data Association Attributed in Wireless Sensor Networks[J]. Journal of Internet Technology, 2019, 20(2):509-520.
[2]	YANG G S, LIANG T T, HE X Y, et al. Global and Local Reliability-Based Routing Protocol for Wireless Sensor Networks[J]. IEEE Internet of Things Journal, 2019, 6(2):3620-3632. doi: 10.1109/JIOT.2018.2889379
[3]	孙泽宇, 伍卫国, 王换招, 等. 概率模型下的一种优化覆盖算法[J]. 软件学报, 2016, 27(5):1285-1300.
	SUN Zeyu, WU Weiguo, WANG Huanzhao, et al. Optimized Coverage Algorithm in Probability Model[J]. Journal of Software, 2016, 27(5):1285-1300.
[4]	HE X Y, PENG Z W, WANG J T, et al. Generic and Efficient Connectivity Determination for IoT Applications[J]. IEEE Internet of Things Journal, 2020, 7(6):5291-5301. doi: 10.1109/JIOT.2020.2976685
[5]	李明, 胡江平, 曹晓莉. 异构传感网成本优化的节点部署策略[J]. 西安电子科技大学学报, 2021, 48(4):11-19.
	LI Ming, HU Jiangping, CAO Xiaoli. Minimum Cost of Node Deployment Strategy for Heterogeneous Sensor Networks[J]. Journal of Xidian University, 2021, 48(4):11-19.
[6]	HU Z D, WU H L, WANG Z D. Energy Balanced Adaptive Clustering Routing Protocol for Heterogeneous Wireless Sensor Networks[J]. International Journal of Wireless and Mobile Computing, 2019, 16(3):264-271. doi: 10.1504/IJWMC.2019.099867
[7]	XIANG X M, LIU W, LIU A F, et al. Adaptive Duty Cycle-control-based Opportunistic Routing Scheme to Reduce Delay in Cyber Physical Systems[J]. International Journal of Distributed Sensor Networks, 2019, 15(4):1-21.
[8]	SUN Z Y, LV Z G, HOU Y, et al. MR-DFM:AMulti-Path Routing Algorithm Based on Data Fusion Mechanism in Sensor Networks[J]. Computer Science and Information Systems, 2019, 16(3):867-890. doi: 10.2298/CSIS180917031S
[9]	REN H Z, XU Z C, LIANG W F, et al. Efficient Algorithms for Delay-aware NFV-Enabled Multicasting in Mobile Edge Clouds with Resource Sharing[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 31(9):2050-2066. doi: 10.1109/TPDS.2020.2983918
[10]	WANG T, ZENG J D, LAI Y X, et al. Date Collection from WSNs to the Cloud Based on Mobile Fog Elements[J]. Future Generation Computer Systems, 2020, 105:864-872. doi: 10.1016/j.future.2017.07.031
[11]	XIE K, WANG L L, WANG X, et al. Low Cost and High Accuracy Data Gathering in WSNs with Matrix Completion[J]. IEEE Transactions on Mobile Computing, 2018, 17(7):1595-1608. doi: 10.1109/TMC.2017.2775230
[12]	LIU X X, WANG T, JIA W, et al. Quick Convex Hull-based Rendezvous Planning for Delay-harsh Mobile Data Gathering in Disjoint Sensor Networks[J]. IEEE Transaction on Systems Man Cybernetics-Systems, 2021, 51(6):3844-3854.
[13]	孙泽宇, 李传锋, 阎奔. 传感网中基于压缩感知的丢包匹配数据收集算法[J]. 电子学报, 2020, 48(4):723-733. doi: 10.3969/j.issn.0372-2112.2020.04.014
	SUN Zeyu, LI Chuanfeng, YAN Ben. CS-MDGA A Packet Loss Matching Data Gathering Algorithm in Sensor Networks Based on Compressive Sensing[J]. Acta Elecctronica Sinica, 2020, 48(4):723-733. doi: 10.3969/j.issn.0372-2112.2020.04.014
[14]	YUAN Y C, LIU W, WANG T, et al. Compressive Sensing Based Clustering Joint Annular Routing Data Gathering Scheme for Wireless Sensor Networks[J]. IEEE Access, 2019, 7:114639-114658. doi: 10.1109/ACCESS.2019.2935462
[15]	黄影, 华雨晴. 能量与路径约束的无线传感器网络路由优化[J]. 西安电子科技大学学报, 2020, 47(3):113-120.
	HUANG Ying, HUA Yuqing. Routing Optimization of the Wireless Sensor Networks with Energy and Path Constraints[J]. Journal of Xidian University, 2020, 47(3):113-120.
[16]	甄岩, 赵虎. 层次型软件定义无线传感器网络资源调度策略[J]. 西安电子科技大学学报, 2019, 46(4):87-98.
	ZHEN Yan, ZHAO Hu. Resource Scheduling Strategy in Hierarchical Software Defined Wireless Sensor Networks[J]. Journal of Xidian University, 2019, 46(4):87-98.
[17]	LIU X X, QIU T, WANG T. Load-balanced Data Dissemination for Wireless Sensor Networks:A Nature-Inspired Approach[J]. IEEE Internet of Things Journal, 2019, 6(6):9256-9265. doi: 10.1109/JIOT.2019.2900763
[18]	汪祥莉. 无线传感器网络中高能效路由技术的研究[D]. 武汉: 武汉理工大学, 2011.
[19]	ZHANG J X, YAN R Q. CentralizedEnergy-efficient Clustering Routing Protocol for Mobile Nodes in Wireless Sensor Networks[J]. IEEE Communications Letters, 2019, 23(7):1215-1218. doi: 10.1109/LCOMM.2019.2917193

参数	数值	参数	数值
监测区域1/m²	200×200	E_elec/(nJ·bit^-1)	50
监测区域2/m²	400×400	ε_fs/(pJ·bit^-1·m²)	5
感知半径R_s/m	5	ε_mp/(pJ·bit^-1·m⁴)	0.001 3
通信半径R_c/m	10	E_da/J	0.2
节点初始能量/J	3	k/bit	200
节点数量	2 100	d₀/m	87

一种面向最小能耗自适应汇聚路由判定算法

Adaptive sink-routing decision algorithm for minimum-energy consumption

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