基于粒子群与模拟退火协同优化的农田物联网混合多跳路由算法

doi:10.12133/j.smartag.2020.2.3.202007-SA001

摘要/Abstract

摘要：

农业无线传感器网络对农田土壤、环境和作物生长的多源异构信息的获取起关键作用。针对传感器在农田中非均匀分布且受到能量制约等问题，本研究提出了一种基于粒子群和模拟退火协同优化的农田物联网混合多跳路由算法（PSMR）。首先，通过节点剩余能量和节点度加权选择簇首，采用成簇结构实现异构网络高效动态组网。然后通过簇首间多跳数据结构解决簇首远距离传输能耗过高问题，利用粒子群与模拟退火协同优化方法提高算法收敛速度，实现sink节点加速采集簇首中的聚合数据。对算法的仿真试验结果表明，PSMR算法与基于能量有效负载均衡的多路径路由策略方法（EMR）相比，无线传感器网络生命周期提升了57%；与贪婪外围无状态路由算法（GPSR-A）相比，在相同的网络生命周期内，第1个死亡传感器节点推迟了两轮，剩余能量标准差减少了0.04 J，具有良好的网络能耗均衡性。本研究提出的PSMR算法通过簇首间多跳降低远端簇首额外能耗，提高了不同距离簇首的能耗均衡性能，为实现大规模农田复杂环境的长时间、高效、稳定地数据采集监测提供了技术基础，可提高农业物联网的资源利用效率。

关键词: 大规模农田, 粒子群优化, 模拟退火, 无线传感器网络, 路由优化, 混合型网络结构, 数据传输链路, 物联网

Abstract:

Agricultural wireless sensor networks plays a key role in obtaining multi-source heterogeneous big data of farmland soil, environment and crop growth. The increasing network scale brings challenges to the application of agricultural Internet of Things. In order to solve the problem that sensors are not uniformly distributed in farmland and constrained by energy, a collaborative optimization hybrid multi hop routing algorithm, particle simulated multipath routing (PSMR) based on particle swarm optimization and simulated annealing was proposed. Firstly, cluster heads were selected by node residual energy and node degree weighting, and cluster structure was used to realize efficient dynamic networking of heterogeneous networks. Then, the multi hop data structure between cluster heads was used to solve the problem of high energy consumption in long-distance transmission of cluster heads. Particle swarm optimization and simulated annealing were used to improve the convergence speed, and sink nodes could accelerate the collection of aggregated data in cluster heads. The simulation results showed that compared with the energy-efficient load balancing multipath routing scheme (EMR), the network lifetime of PSMR algorithm was increased by 57%. EMR selected the data transmission link with low energy consumption and small delay by calculating the weight of total link hops and transmission energy consumption. Compared with greedy perimeter stateless routing-algorithm (GPSR-A algorithm), which could ensure the shortest data transmission distance and lower network transmission delay, the first dead sensor node was delayed for two rounds in the same network life cycle, and the residual energy standard deviation was reduced by 0.04 J, which had good network energy consumption balance. PSMR algorithm could reduce the extra energy consumption of remote cluster heads by multi hop between cluster heads, and improved the energy balance performance of cluster heads with different distances. It can provide technical basis for long-term, efficient and stable data acquisition and monitoring of large-scale farmland complex environment, and improve the resource utilization efficiency of agricultural Internet of Things.

Key words: large-scale farmland, particle swarm optimization, simulated annealing, wireless sensor networks, routing optimization, hybrid network architecture, data transmission link, Internet of Things

中图分类号:

TN212

孙浩然, 孙琳, 毕春光, 于合龙. 基于粒子群与模拟退火协同优化的农田物联网混合多跳路由算法[J]. 智慧农业（中英文）, 2020, 2(3): 98-107.

SUN Haoran, SUN Lin, BI Chunguang, YU Helong. Hybrid Multi-Hop Routing Algorithm for Farmland IoT based on Particle Swarm and Simulated Annealing Collaborative Optimization Method[J]. Smart Agriculture, 2020, 2(3): 98-107.

参考文献

1	赵春江. 智慧农业发展现状及战略目标研究[J]. 智慧农业, 2019, 1(1): 1-7.
1	ZHAO C. State-of-the-art and recommended developmental strategic objectives of smart agriculture[J]. Smart Agriculture, 2019, 1(1):1-7.
2	SHANTHI G, SUNDARAMBAL M. FSO-PSO based multihop clustering in WSN for efficient medical building management system[J]. Cluster Computing, 2019, 22(4): 12157-12168.
3	MIAO Y, YUAN L, WU H, et al. Optimization of energy heterogeneous cluster-head selection in farmland WSN[J]. Applied Mechanics & Materials, 2014, 441: 1010-1015.
4	葛文杰, 赵春江. 农业物联网研究与应用现状及发展对策研究[J]. 农业机械学报, 2014, 45(7): 222-230.
4	GE W, ZHAO C. State-of-the-art and developing strategies of agricultural Internet of Things[J]. Transactions of the CSAM, 2014, 45(7): 222-230.
5	孔繁涛, 朱孟帅, 孙坦. 现代信息技术在农业领域的应用分析与建议——互联网企业进军农业引发的思考[J]. 智慧农业, 2019, 1(4): 31-41.
5	KONG F, ZHU M, SUN T. Application analysis and suggestions of modern information technology in agriculture: Thoughts on Internet enterprises entering agriculture[J]. Smart Agriculture, 2019, 1(4): 31-41.
6	PANAG T S, DHILLON J S. Maximal coverage hybrid search algorithm for deployment in wireless sensor networks[J]. Wireless Networks, 2019, 25: 637-652.
7	TANG Y, LIAN H, LI L, et al. A randomness detection method of ZigBee protocol in a wireless sensor network[J]. Sensors, 2018, 18(11): ID 3962.
8	WU H, LI Q, ZHU H, et al. Directional sensor placement in vegetable greenhouse for maximizing target coverage without occlusion[J]. Wireless Networks, 2020, 26(2): 4677-4687.
9	何勇, 聂鹏程, 刘飞. 农业物联网与传感仪器研究进展[J]. 农业机械学报, 2013, 44(10): 216-226.
9	HE Y, NIE P, LIU F. Advancement and trend of internet of things in agriculture and sensing instrument[J]. Transactions of the CSAM, 2013, 44(10): 216-226.
10	MONDAL A, MISRA I S, BOSE S. Building a low cost solution using wireless sensor network for agriculture application[C]// 2017 International Conference on Innovations in Electronics, Signal Processing and Communication (IESC). Piscataway, New York, USA: IEEE, 2017.
11	MITTAL N, SINGH U, SOHI B S. A novel energy efficient stable clustering approach for wireless sensor networks[J]. Wireless Personal Communications, 2017, 95: 2947-2971.
12	MANN P S, SINGH S. Artificial bee colony metaheuristic for energy-efficient clustering and routing in wireless sensor networks[J]. Soft Computing, 2017, 21: 6699-6712.
13	JOHN J, RODRIGUES P. MOTCO: Multi-objective Taylor crow optimization algorithm for cluster head selection in energy aware Wireless Sensor Network[J]. Mobile Networks and Applications, 2019, 24(5): 1509-1525.
14	VERMA S, SOOD N, SHARMA A K. Genetic algorithm-based optimized cluster head selection for single and multiple data sinks in heterogeneous Wireless Sensor Network[J]. Applied Soft Computing, 2019, 85: ID 105788.
15	VERMA S, SOOD N, SHARMA A K. A novelistic approach for energy efficient routing using single and multiple data sinks in heterogeneous wireless sensor network[J]. Peer-to-Peer Networking and Applications, 2019, 12(5): 1-27.
16	CHANDIRASEKARAN D, JAYABARATHI T. Cat swarm algorithm in wireless sensor networks for optimized cluster head selection: A real time approach[J]. Cluster Computing, 2017, 22: 11351-11361.
17	樊志平, 金政哲, 谢冬青. 无线传感器网络中基于能量效率的多路径路由算法[J]. 计算机工程与科学, 2012, 34(7): 12-17.
17	FAN Z, JIN Z, XIE D. An energy-efficient multipath routing scheme for Wireless Sensor Network[J]. Computer Engineering & Science, 2012, 34(7): 12-17.
18	NOUH S, GETA Z. Energy aware GPSR routing protocol in a WSN[J]. Zede Journal, 2009, 26: 1-9.
19	孙想, 吴保国, 吴华瑞, 等. 能量高效的农田无线传感器网络拓扑关联路由算法[J]. 农业机械学报, 2015, 46(8): 232-238.
19	SUN X, WU B, WU H, et al. Topology based energy efficient routing algorithm in farmland Wireless Sensor Network[J]. Transactions of the CSAM, 2015, 46(8): 232-238.
20	SUJATHA B, JILO C T, RAO C S. Energy-efficient data route-in-network aggregation with secure EEDRINA[C]// Proceedings of International Conference on Computational Intelligence and Data Engineering. Dordrecht. Netherlands: Springer B. V., 2018.
21	汪进鸿, 韩宇星. 用于作物表型信息边缘计算采集的认知无线传感器网络分簇路由算法[J].智慧农业(中英文), 2020, 2(2): 28-47.
21	WANG J, HAN Y. Cognitive radio sensor networks clustering routing algorithm for crop phenotypic information edge collection[J]. Smart Agriculture, 2020, 2(2): 28-47.
22	刘伟, 杜佳鸿, 贾素玲, 等. 能量有效的无线传感器网络分簇路由协议[J]. 北京航空航天大学学报, 2019, 45(1): 53-59.
22	LIU W, DU J, JIA S, et al. Energy efficient clustering routing protocol for wireless sensor networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(1): 53-59.
23	MEHRA P S, DOJA M N, ALAM B. Fuzzy based enhanced cluster head selection (FBECS) for WSN[J]. Journal of King Saud University-Scienc, 2018, 32(1): 390-401.
24	ATTEA B A, KHALIL E A. A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks[J]. Applied Soft Computing, 2012, 12(7): 1950-1957.
25	陈伟宏, 李仁发. 无线传感器网络仿真技术综述[J]. 控制工程, 2014, 21(2): 149-155.
25	CHEN W, LI R. Simulation technologies analysis of Wireless Sensor Networks[J]. Control Engineering of China, 2014, 21(2): 149-155.

[1]	秦英栋, 贾文珅. 基于NB-IoT网络的兔舍环境实时监测系统[J]. 智慧农业(中英文), 2023, 5(1): 155-165.
[2]	李莉, 李民赞, 刘刚, 张漫, 汪懋华. 中国大田作物智慧种植目标、关键技术与区域模式[J]. 智慧农业(中英文), 2022, 4(4): 26-34.
[3]	赵瑞雪, 杨晨雪, 郑建华, 李娇, 王剑. 农业智能知识服务研究现状及展望[J]. 智慧农业(中英文), 2022, 4(4): 105-125.
[4]	韩冷, 何雄奎, 王昌陵, 刘亚佳, 宋坚利, 齐鹏, 刘理民, 李天, 郑义, 林桂海, 周战, 黄康, 王忠, 查海涅, 张国山, 周国涛, 马勇, 伏浩, 聂宏远, 曾爱军, 张炜. 智慧果园构建关键技术装备及展望[J]. 智慧农业(中英文), 2022, 4(3): 1-11.
[5]	郭志明, 王郡艺, 宋烨, 邹小波, 蔡健荣. 果蔬品质劣变传感检测与监测技术研究进展[J]. 智慧农业(中英文), 2021, 3(4): 14-28.
[6]	徐立鸿, 刘辉辉, 徐赫, 蔚瑞华, 蔡文韬. 南方蓝莓智能温室促早熟生产多因子协调控制技术[J]. 智慧农业(中英文), 2021, 3(4): 86-98.
[7]	黄凯, 舒磊, 李凯亮, 杨星, 朱艳, 汪小旵, 苏勤. 太阳能杀虫灯物联网节点的防盗防破坏设计及展望[J]. 智慧农业(中英文), 2021, 3(1): 129-143.
[8]	洪葳, 胥保华, 刘升平. 蜂群多特征长期监测系统设计与试验研究[J]. 智慧农业（中英文）, 2020, 2(2): 105-114.
[9]	杨选将, 李华龙, 李淼, 胡泽林, 廖建军, 刘先旺, 郭盼盼, 岳旭东. 蜂群箱体关键参数在线监测系统与性能测试[J]. 智慧农业（中英文）, 2020, 2(2): 115-125.
[10]	汪进鸿, 韩宇星. 用于作物表型信息边缘计算采集的认知无线传感器网络分簇路由算法[J]. 智慧农业（中英文）, 2020, 2(2): 28-47.
[11]	朱登胜, 方慧, 胡韶明, 王文权, 周延锁, 王红艳, 刘飞, 何勇. 农机远程智能管理平台研发及其应用[J]. 智慧农业（中英文）, 2020, 2(2): 67-81.
[12]	杨星, 舒磊, 黄凯, 李凯亮, 霍志强, 王彦飞, 王心怡, 卢巧玲, 张亚成. 太阳能杀虫灯物联网故障诊断特征分析及潜在挑战[J]. 智慧农业（中英文）, 2020, 2(2): 11-27.
[13]	李凯亮, 舒磊, 黄凯, 孙元昊, 杨帆, 张宇, 霍志强, 王彦飞, 王心怡, 卢巧玲, 张亚成. 太阳能杀虫灯物联网研究现状与展望[J]. 智慧农业, 2019, 1(3): 13-28.
[14]	张海峰, 李杨, 张宇, 宋丽娟, 唐立新, 毕洪文. 基于云服务的棚室蔬菜智能终端系统设计与实现——以黑龙江省为研究案例[J]. 智慧农业, 2019, 1(3): 87-99.
[15]	张晓涵, 尹长川, 吴华瑞. 面向大规模农田生境监测的无线传感器网络节能优化策略[J]. 智慧农业, 2019, 1(2): 55-63.