Distributed Unsupervised Pattern Learning for Constrained Sensor Network
Zhiwen Chen, Qiong Hao · 2022
Wireless Sensor Networks are collections of typically low-powered sensors, communicating data to a central computer over a multi-hop wireless network. Transmitting all data to the centre is a potential solution in such a distributed scenario, but may cause data overload, consumes battery power for nodes and allows strong data analytics algorithms to be applied. To avoid this, distributed methods, where each sensor within the network try to learn a global picture, are proposed to learn a global picture collaboratively. We focus on developing an asynchronous distributed clustering algorithm, which balances communication cost and clustering quality. Empirical experiments show the efficiency of our methods.