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.

本论文旨在为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确运行，研究内容包括非凸优化、机器学习、随机矩阵扰动理论和联邦滤波等现代算法框架，以及雾计算和云计算等现代计算基础架构。本研究提出了三种独立的新方法，分别解决了 IoT 数据聚合中的三个主要挑战：能源效率路由、处理不确定的原始 IoT 数据、预测基于丢失的通信开销和隐私问题的医疗 IoT 设备的数据聚合框架。