能量异构的无线传感器网络节点协作传输策略研究
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摘要
无线传感器网络应用领域广阔,具有低成本、动态拓扑性强等特点,其研究应用越来越受关注。由于成本、体积等限制,传感器节点能量有限,如何从各方面有效降低能耗,延长网络生命周期,一直是无线传感器网络的研究重点。而在网络总能耗中,无线传输能耗占了很大一部分比例,而将虚拟MIMO (多输入多输出)传输方式应用于无线传感器网络中,将比普通传输方式更为有效地降低传输能耗。
对虚拟MIMO的研究主要集中在节点协作传输策略的设计和能量消耗、系统容量等方面的研究,而以往大部分协作传输策略研究都是针对于同构网络,对异构无线传感器网络的研究更为贴近现实。现有传输策略在虚拟MIMO传输的设计、簇头及协作节点的选择上依然存在不足,本文针对这些不足之处,提出了能量有效的基于异构无线传感器网络的节点协作传输策略。本文的主要贡献在于以下几个方面：
(1)对虚拟MIMO原理进行研究,对簇间虚拟MIMO的传输方式进行设计,在编码、转发、合并和译码等方式上,选择更为适合无线传感器网络的方法。并对普通传输方式与虚拟MIMO传输方式的能耗进行分析,对网络中各节点能耗进行建模分析。
(2)针对能量异构的无线传感器网络,提出了一个能量更为有效的节点协作传输策略——ASVM(Advanced SEP and Virtual MIMO)策略。该策略结合适应于能量异构的分簇算法SEP协议和虚拟MIMO传输方式,并在其基础上进行改进。在分簇阶段,由能量无限的sink节点收集各节点信息,并对其进行分簇,在簇头和协作节点的选择上,考虑到了高级节点应具备更大概率以及剩余能量和距离等因素,使得网络能量得到均衡。在传输阶段,采用普通传输和虚拟MIMO传输相结合的传输方式,选择能耗较小的方式进行传输。结果表明,该策略在减少能耗、延长网络生命周期及提高信息接收量方面具有较大优势。
Wireless sensor network is widely used in many fields because of its low cost, dynamic topology and other characteristics. The researchers pay more and more attention on its application. Due to the cost, volume and other restrictions, the energy of sensor node is limited. Therefore, how to reduce the energy consumption effectively and extend the network lifetime, is always the emphasis in the research of wireless sensor network. The energy consumption in the wireless transmission accounts for a large proportion of total energy consumption. If we apply the virtual MIMO (Multiple Input Multiple Output) transmission in the wireless sensor network, it will be more effective to reduce the transmission energy than ordinary transmission.
The focus of the virtual MIMO research is the design of node cooperative transmission strategy and the analysis of energy consumption and system capacity. In the past, most researches of cooperative transmission strategy are based on isomorphism network. However, the research of heterogeneous wireless sensor network is more close to reality. There still remain deficiencies on the design of virtual MIMO transmission and the choice of cluster head and cooperative node in the existing transmission strategy. Aimed at these deficiencies, the energy effective strategy of node cooperative transmission based on the heterogeneous wireless sensor network is put forward in this paper. The main contribution is as follows:
(1) Research on the principle of virtual MIMO transmission; design the virtual MIMO transmission method between the clusters; As for the on coding, forwarding, combing and decoding, choose more suitable method for wireless sensor network; compare the energy consumption of virtual MIMO transmission with ordinary transmission; modeling analysis each node's energy consumption in the network.
(2) A node cooperative transmission strategy with more effective energy, named ASVM (Advanced SEP and Virtual MIMO), for the heterogeneous wireless sensor network is put forward in this paper. This strategy combines the SEP protocol (suitable in heterogeneous wireless sensor network) and virtual MIMO transmission. What's more, the method is improved based on it in this paper. On the clustering stage, sink node with infinite energy collects the information from sensor nodes, and then clusters it. When choosing the cluster head and cooperative node, we consider the factor that senior node should have greater probability and more surplus energy and longer distance to balance the network energy. On the transmission stage, we apply the combination of ordinary transmission and virtual MIMO transmission, and choose the less energy consumption method. The results show that the proposed strategy has more advantages in reducing energy consumption, prolonging the network life cycle and improving the information receive quantity.
对虚拟MIMO的研究主要集中在节点协作传输策略的设计和能量消耗、系统容量等方面的研究,而以往大部分协作传输策略研究都是针对于同构网络,对异构无线传感器网络的研究更为贴近现实。现有传输策略在虚拟MIMO传输的设计、簇头及协作节点的选择上依然存在不足,本文针对这些不足之处,提出了能量有效的基于异构无线传感器网络的节点协作传输策略。本文的主要贡献在于以下几个方面：
(1)对虚拟MIMO原理进行研究,对簇间虚拟MIMO的传输方式进行设计,在编码、转发、合并和译码等方式上,选择更为适合无线传感器网络的方法。并对普通传输方式与虚拟MIMO传输方式的能耗进行分析,对网络中各节点能耗进行建模分析。
(2)针对能量异构的无线传感器网络,提出了一个能量更为有效的节点协作传输策略——ASVM(Advanced SEP and Virtual MIMO)策略。该策略结合适应于能量异构的分簇算法SEP协议和虚拟MIMO传输方式,并在其基础上进行改进。在分簇阶段,由能量无限的sink节点收集各节点信息,并对其进行分簇,在簇头和协作节点的选择上,考虑到了高级节点应具备更大概率以及剩余能量和距离等因素,使得网络能量得到均衡。在传输阶段,采用普通传输和虚拟MIMO传输相结合的传输方式,选择能耗较小的方式进行传输。结果表明,该策略在减少能耗、延长网络生命周期及提高信息接收量方面具有较大优势。
Wireless sensor network is widely used in many fields because of its low cost, dynamic topology and other characteristics. The researchers pay more and more attention on its application. Due to the cost, volume and other restrictions, the energy of sensor node is limited. Therefore, how to reduce the energy consumption effectively and extend the network lifetime, is always the emphasis in the research of wireless sensor network. The energy consumption in the wireless transmission accounts for a large proportion of total energy consumption. If we apply the virtual MIMO (Multiple Input Multiple Output) transmission in the wireless sensor network, it will be more effective to reduce the transmission energy than ordinary transmission.
The focus of the virtual MIMO research is the design of node cooperative transmission strategy and the analysis of energy consumption and system capacity. In the past, most researches of cooperative transmission strategy are based on isomorphism network. However, the research of heterogeneous wireless sensor network is more close to reality. There still remain deficiencies on the design of virtual MIMO transmission and the choice of cluster head and cooperative node in the existing transmission strategy. Aimed at these deficiencies, the energy effective strategy of node cooperative transmission based on the heterogeneous wireless sensor network is put forward in this paper. The main contribution is as follows:
(1) Research on the principle of virtual MIMO transmission; design the virtual MIMO transmission method between the clusters; As for the on coding, forwarding, combing and decoding, choose more suitable method for wireless sensor network; compare the energy consumption of virtual MIMO transmission with ordinary transmission; modeling analysis each node's energy consumption in the network.
(2) A node cooperative transmission strategy with more effective energy, named ASVM (Advanced SEP and Virtual MIMO), for the heterogeneous wireless sensor network is put forward in this paper. This strategy combines the SEP protocol (suitable in heterogeneous wireless sensor network) and virtual MIMO transmission. What's more, the method is improved based on it in this paper. On the clustering stage, sink node with infinite energy collects the information from sensor nodes, and then clusters it. When choosing the cluster head and cooperative node, we consider the factor that senior node should have greater probability and more surplus energy and longer distance to balance the network energy. On the transmission stage, we apply the combination of ordinary transmission and virtual MIMO transmission, and choose the less energy consumption method. The results show that the proposed strategy has more advantages in reducing energy consumption, prolonging the network life cycle and improving the information receive quantity.
引文
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[2]D. Culler, D. Estrin, M. Srivastava. Overview of sensor networks [J]. Computer,2004,37(8): 41-49.
[3]N. Akshay, M.P. Kumar, B. Harish, et al. An efficient approach for sensor deployments in wireless sensor network [C]. Proceedings of IEEE International Emerging Trend in Robotics and Communication Technologies,2010:350-355.
[4]J.N. Laneman, G.W. Womell, D.N.C. Tse. An efficient protocol for realizing cooperative diversity in wireless networks [C]. Proceedings of IEEE International Symposium on Information Theory,2001:294-294.
[5]A. Nosratinia, T.E. Hunter, A. Hedayat. Cooperative communication in wireless networks [J]. Communications Magazine,2004,42(10):74-80.
[6]肖海林,欧阳缮,聂在平MIMO无线通信系统研究现状及其技术路线[J].电信科学,2009,6(3)：94～98.
[7]王怡莹,王云生,薛方,等MIMO无线技术的研究现状[J].中国新通信,2006,23(12)：55～58.
[8]S. Cui, A.J. Coldsimth, A. Bahai. Modulation optimization under energy constraints [C]. Proceedings of IEEE International Conference on Communications,2003:2805-2811.
[9]S. Cui, A.J. Coldsimth, A. Bahai. Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks [J]. IEEE Journal on Selected Areas in Communications,2004, 22(6):1089-1098.
[10]C.T. Cheng, C.K. Tse, F.C.M. Lau. A delay-aware data collection network structure for wireless sensor networks [J]. Sensors Journal,2011,11(3):699-710.
[11]H. Chen, H. Wu, N. Tzeng. Grid-based approach for working node selection in wireless sensor networks [C]. Proceedings of IEEE International Conference on Communications, 2004:3673-3678.
[12]G.J. Pottie, W.J. Kaiser. Wireless integrated network sensor [J]. Communication of the ACM, 2000,43(5):51-58.
[13]狄亮.无线传感器网络路由协议研究[D].南京邮电大学,2010.
[14]潘玉峰.无线传感器网络通信与路由的研究[D].西安电子科技大学,2007.
[15]易艳明.无线传感器网络平台开发与组网实现[D].南京理工大学,2007.
[16]K.A. Delin, S.P. Jackson. The sensor web:A new instrument concept [C]. Proceedings of SPIE-the International Society for Optical Engineering,2001:1-9.
[17]T. Arici, Y. Altunbasak. Adaptive sensing for environment monitoring using wireless sensor networks [C]. Proceedings of IEEE Wireless Communications and Networking Conference, 2004:2347-2352.
[18]W. Main, D. Culler, J. Polastre, et al. Wireless sensor networks for habitat monitoring [C]. Proceedings of ACM International Workshop on Wireless Sensor Networks and Applications, 2002:88-97.
[19]J. Yuan, W. Yu. Joint source coding, routing and power allocation in wireless sensor networks [J]. IEEE Transactions on Communications,2008,56(6):886-896.
[20]B. Sibbald. Use computerized systems to cut adverse drug events [J]. Canadian Medical Association Journal,2001,164(13):1878-1883.
[21]J.S. Baek, J.S. Seo. A weighted STBC-block adaptive frequency domain equalization for single-carrier systems in frequency-selective time-varying channels [C]. Wireless Communi-cation and Networking Conference,2007:1455-1460.
[22]G.D. Abowd, J.R.G. Sterbenz. Final report on the interagency workshop on research issues for smart environments [C]. Proceedings of IEEE International Conference on Personal Communications,2000:36-40.
[23]J. Hill, R. Szewezyk, A. Wo, et al. System architecture direction for networked sensors [C]. Proceedings of the 9th International Conference on Architectural Support for Programming Languages and Operating Systems,2000:93-104
[24]I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, et al. A survey on sensor networks [J]. Communications Magazine,2002,40(8):102-114.
[25]D. Niyato, E. Hossain. Competitive pricing in heterogeneous wireless sensor network:issues and approaches [J]. Network, Dec 2008,22(6):4-11.
[26]潘巨龙,闻育.无线传感器网络的异构性研究[J].航空计算技术,2007,37(2)：124～127.
[27]Y. Mark, K. Nandakishore, S. Harkirat, et al. Exploiting heterogeneity in sensor networks [C]. Proceedings of the IEEE International Conference on Computer Communication,2005: 878-890.
[28]G. Smaragdakis, I. Matta, A. Bestavros. SEP:A stable election protocol for clustered heterogeneous wireless sensor networks [C]. Proceedings of the IEEE International Conference on the Workshop on SANPA,2004:251-261.
[29]卿利,朱清新,王明文.异构传感器网络的分布式能量有效成簇算法[J].软件学报,2006,17(3)：481～489.
[30]N.J. Laneman. Cooperative diversity in wireless networks:Algorithms and architectures [C]. Proceedings of the Conference on Electrical Engineering and Computer Science,2002: 179-187.
[31]李艳华.无线传感器网络协作式MIMO技术研究[D].湖南大学,2009.
[32]付卫红,杨小牛,刘乃安,等.宽带无线通信中的MIMO系统[J].电子科技大学学报,2007,36(2)：176～178.
[33]龙承志.能量有效的异质无线传感器网络节点协作问题研究[D].北京邮电大学,2009.
[34]刘侃如,刘威,杨宗凯,等.无线传感器网络中一种基于分簇的能量高效虚拟MIMO传输策略[J].小型微型计算机系统,2008,29(2)：193-198.
[35]李艳华,彭蔓蔓,曾凡仔.无线传感器网络中基于链的虚拟MIMO策略[J].计算机工程,2009,35(19)：132～134.
[36]Y. Yong, W. Chen. Virtual MIMO protocol based on clustering for wireless sensor network [C]. Proceedings of 10th IEEE Symposium on Computers and Communications,2005: 335-340.
[37]D.D.N. Bevan, R. Tanner, C.R. Ward. Space-time coding for capacity enhancement in future generation wireless communications networks [J]. Electronics Letters,2000,15(3):801-811.
[38]G.J. Foshini. Layered space-time architecture for wireless communications in a fading environment when using multiple antennas [J]. Bell Labs Technical Journal,1996,1(2): 41-59.
[39]A.F. Naguib, N. Seshadri, R.A. Galderband. Space-time coding and signal-processing for high-data-rate wireless communications [J]. IEEE Signal Processing Magazine,2000,17(3): 77-91.
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