传感器网络中局部性认知多信道通信机制与协议研究

Wireless sensor networks has attracted much interest due to its versatility and wide applicability, and huge market potential. The performance of wireless sensor networks system such as throughput and packet reception ratio and so on may be decreased by localized interference or collision of the networks. In our previous work, we found that the impact of communication coming from the internal and external interference of the networks can be greatly reduced by multi-channel mechanisms and the performance of the networks can be accordingly improved. The main scientific issues of multi-channel communication include: (1) How to easily find the sensor networks are suffering from the interference or not and how to distinguish the interference coming from the internal or external networks by reasonably using localized link quality estimation.(2) How to exactly get the mechanism of the channel assignment in localized multi-channel communication and how to evaluate the performance of multi-channel scheme of the networks. Based on the above consideration, we will focus on the research of the localized multi-channel assignment and communication protocol including the link quality estimation and the interference classification method. The main issues of this proposal are as follows: Firstly, a two-step and twice prediction model will be built to analyze the link quality and interference of the networks for multi-channel scheme. We will use the ARIMA model to predict future points in the time series data such as RSSI, LQI, throughput and packet reception ratio and so on, which is the first step. Then the other step is that the Logistic model will be used to predict the probability of the interference of the networks for the first time and the same model will be used to predict the type of the interference for the second time. Secondly, the characteristic of localized multi-channel assignment will be analyzed and the optimized prediction and assignment of multi-channel model will be established. The model of reinforcement learning theory on cognitive wireless snesor networks will be used to balance the end to end transmission process and then the channel assignment will be performed based on this model. Thirdly, the time slot and balance mechanism of the communication in the sub net will be analyzed and a localized multi-channel transmission model will be built to implement a time division multi-channel protocol. The cost of the multi-channel communication will be considered, and the boundary of the sub net at the location of multi-channel performed will be also analyzed to optimize the performance of the whole networks. Finally, we will test the theoretical research results on our heterogeneous testbed and then perform the results on our actual environmental monitoring applications to evaluate the performance of the multi-channel assignment scheme and protocol.

在无线传感器网络系统中，经常出现因局部链路干扰及数据冲突而导致网络性能降低的问题，本课题研究可认知的局部性多信道通信机制与协议，以有效降低上述网间和网内干扰对网络通信的影响，提高网络传输性能。主要科学问题包括：(1)如何合理地利用局部性链路质量估计方法，实现对链路干扰的识别；(2)局部性多信道传输的信道分配预测以及传输方法。本申请围绕以上科学问题，以可认知的局部性多信道传输方法为核心，研究传感网局部多信道链路质量估计、干扰识别方法以及最优局部多信道通信机制，主要内容包括：(1)建立基于两步两次预测法的局部链路质量估计及干扰识别模型；(2)分析局部网络信道分配特性，提出结合强化学习算法建立最优局部信道认知分配的预测模型；(3)研究节点传输时隙与子网数据流量的权衡机制，建立局部网络多信道传输模型，并形成一种分时局部多信道传输协议。理论研究成果将在测试平台上进行测试，并在实际系统中加以验证。

无线传感器网络通信链路的干扰与数据包冲突是导致网络传输丢包率提升，吞吐率降低的主要原因，从而会进一步引起网络内节点能量消耗的增加，降低网络整体的工作寿命。本课题重点研究了传感网的局部性多信道通信机制与协议，有效降低网络的网内和网间干扰，提高网络传输性能。具体的研究内容包括：无线传感器网络干扰分类识别机制、通信负载状态识别方法、可变带宽信道分配方法以及差异化比特错误率估计方法，相关研究成果发表在国内一级期刊和学报上。基于降低干扰方法的研究，为加强研究内容的实用性，本课题还研究了在传感器网络应用中的异步多信道网络邻居发现机制以及动态多模通信自适应组网方法，相关研究成果分别发表于国内一级期刊和国际期刊。在实际传感器网络系统的应用中，为进一步提升网络工作的性能，降低网络能量消耗，本课题还研究了基于在线模型驱动的数据获取方法以及监测时间序列数据的高斯过程建模与多步预测方法，相关成果分别发表于国内一级学报以及国际会议。. 综上，本课题针对传感器网络多信道通信机制与方法以及网络数据处理方法，展开了研究并取得了良好的研究成果，完成了既定研究目标并有所突破，同时也为进一步的研究工作提供了较好的基础，有利于研究工作的深入发展。