Environmental Monitoring Based on Fog Computing Paradigm and Internet of Things

Providing accurate real-time environmental monitoring services is a meaningful but difficult task. Whereas recent developments in the Internet of Things and the fog computing paradigm have brought new opportunities to improve the service, achieving accurate environmental monitoring faces several challenges. First, data collected by the Internet of Things varies in temporal-spatial distribution, quality, and relevance to objectives. Second, due to the limited communication cost and stability, local real-time monitoring systems can be hardly achieved by adopting centralized cloud paradigm solutions in the real world. Third, the amount of data at a single edge node of fog computing paradigm is usually not sufficient for accurate environmental monitoring. In this paper, we propose a framework for environmental monitoring based on fog computing that uses multi-source heterogeneous data collected from the Internet of Things sensors. At each edge node, we employ local sub-classifiers to analyze the collected data and afterwards, a deep neural network based model to aggregate results from sub-classifiers. For the homologous sub-classifiers at different edge nodes, we design a federated learning mechanism to update sub-classifiers in concert by model transmission. We use multi-source heterogeneous data collected in Beijing to evaluate the proposed fog computing framework. Experimental results show that our federated learning mechanism has almost the same performance compared with centralized data fusion mechanism when training rounds are increased.