物联网高能效组播路由研究及在牧区牲畜监测中的应用

项目名称： 物联网高能效组播路由研究及在牧区牲畜监测中的应用

项目编号： No.61262087

项目类型： 地区科学基金项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 杨文忠

作者单位： 新疆大学

项目金额： 46万元

中文摘要： 物联网作为信息产业革命的第三次浪潮，有着广阔的应用空间，然而现有的物联网通信协议大都是单播路由协议且存在着对电源能量要求较高、带宽不足等问题，高能效物联网组播协议研究则较少。网络编码可通过减少分组传输次数和压缩数据量的方式来提高物联网的能效性，机会路由在一定程度上可以克服物联网等无线网络链路的不可靠性，整合网络编码和机会路由可实现局部牧区内高能效机会组播路由。利用社会网络和图谱理论可以准确合理地划分牧区为若干个局部牧区，通过预测牧区间移动节点的联系时间和位置等信息，可进一步实现局部牧区间高能效机会组播路由。这种局部牧区间和牧区内的两级高能效组播路由协议可用于牧区牲畜监测等应用。实验仿真以及新疆天山牧区实地验证平台的建立可从实践上检验所提物联网高能效组播路由协议及其在牧区牲畜监测中的应用的有效性，其次本课题对物联网高能效组播信理论与应用也将是有益探索，对提高物联网的进一步应用有重要意义。

中文关键词： 物联网；高能效组播；网络编码；机会路由；位置预测

英文摘要： Internet of Things representing the third wave of information revolution, has a broad application space, however, the existing communication protocols of the Internet of Things are almost unicast routing protocol and these protocols also have some problems,such as high power energy requirement and insufficient bandwidth, the research of energy efficiency multicast protocol is also less. Network coding can be adopted to improve the energy efficiency of Internet of Things by reduceing the number of packet transmission and compressing the amount of data, Opportunistic routing can overcome the unreliability of Internet of Things wireless network link to a certain extent, the integration of network coding and opportunistic routing can achieve energy efficiency opportuniistic multicast routing protocol in the local pastoral area. Social network and graph matrices can accurated and reasonably partion the whole pastoral area into some local pastoral areas, by forecasting the contact time and location of mobile node between local pastoral areas, The energy efficient opportunistic multicast routintg protocol between local pastoral areas can alos be formulated. The two layered energy efficient multicast routing protocols of the intra local pastoral areas and the inter local pastoral areas can be used in livestock monitorin

英文关键词： Internet of things；High energy-efficient multicast；Networking coding；Opportunistic routing；Location predict