A Bayesian Packet Sharing Approach for Noisy IoT Scenarios
A Bayesian Packet Sharing Approach for Noisy IoT Scenarios
复制标题
适用于嘈杂物联网场景的贝叶斯数据包共享方法
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Marco Leo
中科院分区：
文献类型：
--
作者：
A. Vegni;V. Loscrí;A. Neri;Marco Leo
云计算和物联网（IoT）代表了两种不同的技术，它们在我们的日常生活中被大量采用，在未来的互联网中发挥着基础性作用。需要处理的一个重要挑战是传感设备产生的大量数据，这使得控制发送无用数据变得非常重要。为了应对这一挑战，人们对避免发送高时空相关数据的预测方法越来越感兴趣。置信传播（BP）算法是一种对任意图形模型执行近似推理的方法，在物联网的背景下变得越来越流行。通过利用BP算法，我们可以得到有效的方法，以大大减少传输的消息的数量，同时保持高的数据吞吐量在全球信息系统。在本文中，我们提出了一个BP方法在一个层次结构简单的节点，网关和数据中心。我们评估的错误边界，并提出了一个纠正机制，以保持一定的质量的全球信息的架构考虑。
Cloud computing and Internet of Things (IoT) represent two different technologies that are massively being adopted in our daily life, playing a fundamental role in the future Internet. One important challenge that need to be handled is the enormous amount of data generated by sensing devices, that make the control of sending useless data very important. In order to face with this challenge, there is a increasing interest about predictive approaches to avoid to send high spatio-temporal correlated data. Belief Propagation (BP) algorithm is a method of performing approximate inference on arbitrary graphical models that is becoming increasingly popular in the context of IoT. By exploiting BP, we can derive effective methods to drastically reduce the number of transmitted messages, while keeping high the data throughput in the global information system. In this paper, we propose a BP approach in a hierarchical architecture with simple nodes, gateways and data centers. We evaluate the error bounding and propose a corrective mechanism to keep a certain quality of the global information in the architecture considered.