Abstract-It is commonly assumed that in a smart city there will be thousands of mostly mobile/wireless smart devices (e.g. sensors, smart-phones, etc.) that will continuously generate big amounts of data. Data will have to be collected and processed in order to extract knowledge out of it, to feed users' and smart city applications. A typical approach to process such big amounts of data is to i) gather all the collected data on the cloud through wireless pervasive networks, and ii) perform data analysis operations exploiting machine learning techniques. However, according to many studies, this centralised cloud-based approach may not be sustainable from a networking point of view. The joint effect of data-intensive users' multimedia applications and smart cities monitoring and control applications may result in severe network congestions making applications hardly usable. To cope with this problem, in this paper we propose a distributed machine learning approach that does not require to move data in a centralised cloud platform, but processes it directly where it is collected. Specifically, we exploit Hypothesis Transfer Learning (HTL) to build a distributed machine learning framework. In our framework we train a series of partial models, each "residing" in a location where a subset of the dataset is generated. We then refine the partial models by exchanging them between locations, thus obtaining a unique complete model. Using an activity classification task on a reference dataset as a concrete example, we show that the classification accuracy of the HTL model is comparable with that of a model built out of the complete dataset, but the cost in term of network overhead is dramatically reduced. We then perform a sensitiveness analysis to characterise how the overhead depends on key parameters. It is also worth noticing that the HTL approach is suitable for applications dealing with privacy sensitive data, as data can stay where they are generated, and do not need to be transferred to third parties, i.e., to a cloud provider, to extract knowledge out of it.
In this paper, we extend the analysis initiated in REF in order to study the accuracy vs. network traffic trade off.
Hypothesis Transfer Learning for Efficient Data Computing in Smart Cities Environments
2016 IEEE International Conference on Smart Computing (SMARTCOMP)
2016 IEEE International Conference on Smart Computing (SMARTCOMP)
Computer Science