Neighbor Discovery in a Wireless Sensor Network: Multipacket Reception Capability and Physical-Layer Signal Processing
Jeongho Jeon, Anthony Ephremides
备注：
The material in this paper was presented in part at the 48th Annual Allerton Conference on Communication, Control, and Computing (Monticello, IL), Sept. 2010
发表信息：
JOURNAL OF COMMUNICATIONS AND NETWORKS, VOL. 14, NO. 5, OCTOBER 2012
在随机部署的网络中，例如传感器网络，每个节点的一个重要问题是发现它的\textit{neighbor}节点，以便可以建立节点之间的连通性。在本文中，我们考虑这个问题，通过将物理层参数的对比，大多数以前的工作，假设一个碰撞通道。具体地，如果接收信号的强度相对于干扰足够高，则成功解码节点发送的导频信号。因此，每个节点必须从未知数量的接收信号的叠加中提取信号参数信息。这个问题自然属于随机集理论（Random Set Theory，简称RMM）的范畴，它通过将\textit{sets}而不是值分配给随机结果来推广标准概率论。在本文中的贡献是双重的：首先，我们介绍了现实的影响物理层的考虑，在评估性能的\textit{logical}发现算法，这样的介绍是必要的准确评估算法如何执行。其次，考虑到环境的\textit{double}不确定性（即不知道邻域个数以及不知道单个信号的参数），本文采用了自适应的观点，并证明了它相对于经典匹配滤波检测方法的优越性。
英文摘要：
In randomly deployed networks, such as sensor networks, an important problem for each node is to discover its \textit{neighbor} nodes so that the connectivity amongst nodes can be established. In this paper, we consider this problem by incorporating the physical layer parameters in contrast to the most of the previous work which assumed a collision channel. Specifically, the pilot signals that nodes transmit are successfully decoded if the strength of the received signal relative to the interference is sufficiently high. Thus, each node must extract signal parameter information from the superposition of an unknown number of received signals. This problem falls naturally in the purview of random set theory (RST) which generalizes standard probability theory by assigning \textit{sets}, rather than values, to random outcomes. The contributions in the paper are twofold: first, we introduce the realistic effect of physical layer considerations in the evaluation of the performance of \textit{logical} discovery algorithms; such an introduction is necessary for the accurate assessment of how an algorithm performs. Secondly, given the \textit{double} uncertainty of the environment (that is, the lack of knowledge of the number of neighbors along with the lack of knowledge of the individual signal parameters), we adopt the viewpoint of RST and demonstrate its advantage relative to classical matched filter detection method.
Panda: Neighbor Discovery on a Power Harvesting Budget
对象跟踪应用越来越受欢迎，并且将很快利用能量收集（EH）低功率节点，其将主要消耗功率用于邻居发现（ND）（即，识别通信范围内的节点）。虽然ND协议是为传感器网络开发的，但新兴的EH低功耗收发器所带来的挑战并未得到解决。因此，我们设计了一个ND协议的代表EH原型的特点：TI eZ 430-RF 2500-SEH。我们提出了一个广义的ND模型，该模型考虑了独特的原型特征（即，用于发送/接收的能量成本以及收发器状态切换时间/成本）。然后，我们提出了功率感知邻居发现异步（熊猫）协议，其中节点之间的睡眠，接收和发送状态转换。我们分析了\name，并选择其参数，以最大限度地提高ND率受到均匀的功率预算。我们还提出了Panda-D，设计用于非均匀EH节点。我们使用原型进行广泛的测试台评估并研究各种设计权衡。我们证明了一个小的差异（小于2%）之间的实验和分析结果，从而证实了建模假设。此外，我们表明，熊猫提高ND率高达3倍相比，相关协议。最后，我们证明了Panda-D在非均匀功率收获下运行良好。
