摘要
无线传感器网络涉及现代传感器、微电子、无线通信、嵌入式计算、分布式信息处理等多个学科领域,以数据为中心,具有自组织、自调节的特点,是构建普适计算新环境、实现普适控制的新兴交叉研究领域,可广泛用于军事、农业、交通、建筑和灾难应急救援指挥等领域。节点位置信息对于无线传感器网络至关重要,不仅是监测应用进一步采取措施和做出决策的基础,而且还是目标跟踪、基于地理位置的路由机制、网络管理等关键技术研究与实现的基础。
针对传统基于RSSI的定位算法多为理论研究、需要对仿真环境进行理想化设置、不能实际应用或者实际测试结果与仿真结果差距较大的特点,本文面向实际应用需求,从环境衰减因素模型、人员定位模型、锚节点优选策略和角度权重函数四方面改进了传统的基于RSSI定位算法,提出2个改进算法,实现了算法复杂度与定位性能上的折衷。本文主要研究成果包括如下三个方面:
①提出了基于锚节点优选策略和角度权重函数的加权三边测量定位算法。算法利用优选锚节点策略筛选参与定位计算的锚节点,去除不符合要求的锚节点;依据角度权重函数综合多次定位结果以获得具有较高精度的目标节点位置,仿真、实验结果证明了算法的定位精度较原三边测量定位算法有了较大改善,且算法对硬件要求低,易于实现。
②提出了基于空间补偿模型和环境衰减因素模型的定位算法。针对人员充当网络移动节点而与锚节点处于不同高度的特点,设计了空间补偿模型,缩小了其对定位精度的影响;传统基于RSSI的定位技术由于采用无线信号传播理论模型测距,实际应用时会产生较大的测距误差,通过大量实验数据分析,本文构建了环境衰减因素模型,有效解决了环境对测距精度的影响。
③面向军事防御环境监测应用,构建了基于RSSI定位算法的应用原型系统——基于无线传感器网络的多模态区域态势感知系统。设计了基于加权三边测量定位算法、人员定位模型和环境衰减因素模型的定位策略,在硬件平台(CC2430)及软件平台(自行开发的WSN stack)上实现了该定位策略。系统性能测试表明,该定位策略对硬件要求低、可实用性强,较好的满足了系统需求。系统实现了便携式指挥、人员定位、环境信息感知、移动用户生理监护、多模式交互(语音、图像、文字等方式)等功能,为无线传感器网络技术的进一步实用化做出了探索性尝试。
Integrated modern sensor, micro-electronics, wireless communication, embedded computing, distributed information processing and other technologies, wireless sensor network (WSN) is an emerging research direction. Compared with the traditional networks, WSN is a data-centric wireless network with self-organizing and self-regulating characteristics, and can be widely used in military, agriculture, traffic, construction, emergent rescue and many other kinds of applications. Location information of each sensor node is essential to WSN. It is not only the basic of taking further measures and making decision-making, but also the research foundation of other key technologies (for example target tracking, routing supporting and network management) in WSN.
Most of typical localization algorithms based on RSSI only have theoretical research, and the simulation environment needs idealized setting. They can’t apply in actual environment or the actual test results are significantly different with the simulation results. Facing on the limitations of typical localization, lightweight localization technology research is carried out from four aspects in this thesis: environment attenuation factor model, person localization model, anchor selection strategy and angle weighted function. The important research results are as follows:
①A weighted trilateration localization algorithm based on anchor node selection strategy and angle weighted function is put forward. The algorithm uses anchor node selection strategy to choose appropriate anchors, integrates multiple localization results based on angle weight to get high precision. It achieves higher localization accuracy and enjoys advantage on hardware.
②A lightweight localization algorithm based on space compensated model and environment degradation factor model is put forward. The space compensated model is designed in order to minimize the localization error arosed by that the target node (the patrol soldier) and anchor node are in different planes. The existing RSSI-based localization technology which always uses wireless signal propagation theory model makes great error. By a large number of experimental data, the paper constructs the environment degradation factor model which decreases the error greatly.
③Considering military patrol application environments, a lightweight localization application prototype system - multi-modal regional situation awareness prototype system based on WSN is built. Integrated weighted trilateration localization algorithm, compensated model and environment degradation factor model, a new localization strategy is put forward. The strategy is implemented on hardware platform (CC2430) and software platform (WSN stack). System performance tests full proof that the localization strategy has low hardware requirement and good localization accuracy. Also, the system can collect environmental information, monitor user’s physiological information and interact with monitor center through multi-mode (voice, images and text).
引文
[1] K.J.Astrom. Control–The Hidden Technology[R].Beijing. 2004.
[2] M.Weiser. The Computer for the 21st Century[J]. Scientific America, 1991, 265 (3): 66-75.
[3]徐光祐,史元春,谢伟凯.普适计算[J].计算机学报, 2004, 26 (9): 9.
[4]石为人,周彬,许磊.普适计算:人本计算[J].计算机应用, 2005, 25 (7): 1479-1484. [5 W.R.Shi, L.Xu, S.X.Yang. A hierarchical coordination model based on task priority in pervasive computing environment[J]. Dynamics of Continuous Discrete and Impulsive Systems-Series B-Applications & Algorithms, 2006, 13 (2): 3663-3667.
[6]孙利民,李建中,陈渝,朱红松.无线传感器网络[M].北京:清华大学出版社, 2005.
[7] J.A.Byrne. 21 ideas for 21's century[J]. Business Week, 1999, 8: 78-167.
[8] I.F.Akyildiz, Weilian.Su, Y.Sankarasubramaniam, et al. A survey on sensor networks[J]. IEEE Communications Magazine, 2002, 40 (8): 102~114.
[9]于海斌,曾鹏.智能无线传感器网络系统[M].科学出版社, 2006.
[10] B.Hofmann-Wellenhof, H.Lichtenegger, J.Collins. Global Positioning Systems: Theory and Practice[M]. Springer Verlag, 2001.
[11] M.G.Rabbat, R.D.Nowak. Decentralized Source localization and Tracking[C]. In IEEE International Conference on Acoustics, Speech And Signal Processing Montreal, 2004: 921-924.
[12] Nirupanma.Bulusu, John.Heidemann, Deborah.Estrin. Adaptive beacon placement[C]. In Proceedings of the 21st international Conference on Distributed Computing Systems, 2001:489-498.
[13] William J. Kaiser. Low Power Wireless Integrated Microsensors[EB/01]. http://www.janet.ucla. edu/ WINS/lwim-innovative.Htm.
[14]国务院.国家中长期科学与技术发展规划纲要[EB/01]. http://news.sina.com.cn/.
[15] R.Vijay, A.Kansal, J.Hsu, et al. Design considerations for solar energy harvesting wireless embedded systems[C]. In Proceedings of 2005 Fourth International Symposium on Information Processing in Sensor Networks, 2005:457-562.
[16] J.A.Paradiso, T.Starner. Energy scavenging for mobile and wireless electronics[J]. Pervasive Computing, 2005, 4 (1): 18-27.
[17] L.Mateu. Energy Harvesting from Passive Human Power[D]. Polytechnical University of Catalonia 2004.
[18] S.J.Roundy. Energy scavenging for wireless sensor nodes with a focus on vibration to electricity conversion[D]. UC Berkeley 2003.
[19] H.Kim, H.Cha. Multithreading optimization techniques for sensor network operating systems[C]. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Delft, Netherlands, 2007:293-308.
[20] P.A.Levis. TinyOS: An open operating system for wireless sensor networks (invited seminar)[C]. In Nara, Japan, 2006.
[21] C.C.HAN, R.KUMAR, R.SHEA, et al. A Dynamic Operating System for Sensor Nodes[C]. In Proceedings of the 3rd International Conference on Mobile Systems,Applications and Services, 2005:163-176.
[22] S.BHATTI, J.CARLSON, H.DAI, et al. MANTIS OS: An embedded multithreaded operating system for wireless micro sensor platforms[C]. In ACM kluwer Mobile Networks & Applications Journal, special Issue on Wireless Sensor Networks, 2005:231-245.
[23] SenSpire:针对无线传感网络节点设计的综合软件开发平台[EB/OL]. In http://eagle.zju.edu.cn/home/eos/senspire/.
[24]王万里,郑扣根,姚翔等.无线网络传感器及其微型操作系统的研究[J].计算机应用研究, 2005, 26(09):32-38.
[25] I.Chalermek, G.Ramesh, E.Deborah, et al. Directed diffusion for wireless sensor networking[J]. IEEE/ACM Transactions on Networking, 2003, 11 (1): 2-16.
[26] A.Manjeshwar, D.P.Agrawal. TEEN: a routing protocol for enhanced efficiency in wireless sensor networks[C]. In Proceedings 15th International Parallel and Distributed Processing Symposium, 2001:2009-2015.
[27] T.V.Dam, K.Langendoen. An adaptive energy-efficient MAC protocol for wireless sensor networks[C]. In SenSys'03: Proceedings of the First International Conference on Embedded Networked Sensor Systems, Los Angeles, CA, United states, 2003: 171-180.
[28] H.S.Aghdasi, M.Abbaspour, M.E.Moghadam. An energy-efficient and high-quality MAC protocol for image transmission in wireless sensor networks[C]. In 2008 4th IEEE International Conference on Circuits and Systems for Communications, Shanghai, China, 2008: 838-842.
[29] K.Oh-Heum, S.Ha-Joo. Localization through Map Stitching in Wireless Sensor Networks[J]. Parallel and Distributed Systems, IEEE Transactions on, 2008, 19 (1): 93-105.
[30] Y.Shang, W.Ruml, Y.Zhang, et al. Localization from mere connectivity[C]. In Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Annapolis, MD, United states, 2003: 201-212.
[31] A.Savvides, C.C.Han,M.B.Srivastava. Dynamic finge-grained localization in ad-hoc networks of sensors[C]. In Proceedings of 7th Annual International Conference on Mobile Computing and Networking, 2001: 166-179.
[32] P.Bahl, V.N.Padmanabhan. RADAR: An in-building RF-based user location and tracking system[C]. In IEEE INFOCOM, Tel Aviv, Isr, 2000: 775-784.
[33] N.B.Priyantha, A.Chakraborty, H.Balakrishnan. The cricket location-support system[C]. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking, 2000:32-43.
[34] D.Niculescu, B.Nath. Ad hoc positioning system (APS)[C]. In EEE Global Telecommunications Conference, San Antonio, TX, United states, 2001: 2926-2931.
[35] XF.Shen, Z.Wang, P.Jiang, et al. Connectivity and RSSI based localization scheme for wireless sensor networks[C]. In Lecture Notes in Computer Science, Hefei, China, 2005: 578-587.
[36] T.He, C.Huang, B.Blum, et al. Range-Free Localization Schemes for Large Scale Sensor Networks[C]. In Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, 2003:81-95.
[37] L.Doherty, K.S.J.pister, L.El Ghaoui. Convex position estimation in wireless sensor networks[C]. In INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, 2001: 1655-1663.
[38] Priyantha N B, Balakrishnam H, Demaine E, Teller S. Anchor-free distributed localization in sensor networks. Technical Report MIT-LCS-TR-892, MIT Lab for Computer Science, April 2003.
[39] Pentland A. Machine Understanding of Human Action. In: Proc. Of the 7th Int’l Forum on Frontier of Telecommunication Technology. Tokyo: ARPA Press, 1995, 757-764.
[40] C.Savarese, J.Rabaey, K.Langendoen. Robust positioning algorithms for distributed ad-hoc wireless sensor networks[C]. In Proceedings of the USEN IX Technical Annual Conference, 2002: 317-327.
[41]许磊,石为人.一种无线传感器网络分步求精节点定位算法[J] .仪器仪表学报, 2008, 29 (2): 314-319.
[42] S.Misra, G.Xue. CluRoL: Clustering based Robust Localization in Wireless Sensor Networks[C]. In IEEE Military Communications Conference, 2007: 1-7.
[43] Y.Shang, W.Ruml, Y.Zhang, et al. Localization from mere connectivity[C]. In Annapolis, MD, United states, 2003:201-212.
[44] Harter A, Jones A, Hooper A. A New Location Technique for the Active Office. IEEE Personal Communications, 1997, 4(5):42-47.
[45] Harter A, Hopper A. A Distributed Location System for the Active Office. IEEE Network, 1994, 8(1):62-70.
[46] X.Li, H.Shi, Y.Shang. A map-growing localization algorithm for ad-hoc wireless sensornetworks[C]. In Proceedings of the International Conference on Parallel and Distributed Systems, Newport Beach, CA, United states, 2004: 395-402.
[47] N.Patwari, J.N.Ash, S.Kyperountas, et al. Locating the nodes: cooperative localization in wireless sensor networks[J]. IEEE Signal Processing Magazine, 2005, 22 (4): 54-69.
[48]段渭军,王建刚,王福豹.无线传感器网络节点定位系统与算法的研究和发展[J].信息与控制,2006,35(2):2392245.
[49] Girod L, Estrin D. Robust range estimation using acoustic and multimodal sensing [C]. //In: Proc. of the IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems (IROS 01). Vol.3, Maui: IEEE Robotics and Automation Society, 2001.
[50] Theodore S.Rappaport, Wireless Communications, Principles & Practice[M].Prentice Hall, 1999.
[51] Niculescu D, Nath B. Ad hoc positioning system (APS) using AoA[C]. //In: Proc. of the IEEE INFOCOM 2003. Vol.3, San Francisco: IEEE Computer and Communications Societies, 2003.
[52] Bahl P, Padmanabhan V N. RADAR: An in-building RF-based user location and tracking system[C]. Proceeding of the IEEE INFOCOM, 2000. New York: IEEE Computer and Communications Society , 2000:775-784.