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arXiv 1907.11367cs.NIcs.DCcs.LG
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物联网数据聚合技术

Data Aggregation Techniques for Internet of Things

Chongqing University of Posts and Telecommunications(重庆邮电大学);School of Communication and Information Engineering(通信与信息工程学院)

Sunny Sanyal

摘要:

本文的目标是为不同场景下的大规模物联网网络设计高效的数据聚合框架,以支持物联网分析层的正常运行。本文包括现代算法框架,如非凸优化,机器学习,随机矩阵扰动理论和联邦滤波,以及现代计算基础设施,如雾计算和云计算。这种雄心勃勃的设计的开发涉及许多开放的挑战,该提案设想了物联网数据聚合的三个主要开放挑战:第一,由于有限的功率和计算能力,物联网节点的严重资源约束,第二,高度不确定(不可靠)的原始物联网数据不适合决策,第三,关键应用程序的网络延迟和隐私问题。本文提出了三个独立的新颖的方法,不同的情况下,以解决一个或多个上述开放的挑战。第一种方法侧重于节能路由;讨论了基于固定和移动IoT节点的设备到设备通信的集群协议。第二种方法侧重于处理不确定的原始物联网数据;提出了一种物联网数据聚合方案,以提高原始物联网数据的质量。最后,第三种方法侧重于由于医疗物联网设备(IoMT)的通信开销和隐私问题而导致的功率损耗;描述了用于大规模IoMT设备的基于预测的数据聚合框架。

英文摘要:

The goal of this dissertation is to design efficient data aggregation frameworks for massive IoT networks in different scenarios to support the proper functioning of IoT analytics layer. This dissertation includes modern algorithmic frameworks such as non convex optimization, machine learning, stochastic matrix perturbation theory and federated filtering along with modern computing infrastructure such as fog computing and cloud computing. The development of such an ambitious design involves many open challenges, this proposal envisions three major open challenges for IoT data aggregation: first, severe resource constraints of IoT nodes due to limited power and computational ability, second, the highly uncertain (unreliable) raw IoT data is not fit for decisionmaking and third, network latency and privacy issue for critical applications. This dissertation presents three independent novel approaches for distinct scenarios to solve one or more aforementioned open challenges. The first approach focuses on energy efficient routing; discusses a clustering protocol based on device to device communication for both stationary and mobile IoT nodes. The second approach focuses on processing uncertain raw IoT data; presents an IoT data aggregation scheme to improve the quality of raw IoT data. Finally, the third approach focuses on power loss due to communication overhead and privacy issues for medical IoT devices (IoMT); describes a prediction based data aggregation framework for massive IoMT devices.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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