| Title | IoT in Smart Cities: Exploring Information Theoretic and Deep Learning Models to Improve Parking Solutions |
|---|---|
| ID_Doc | 43558 |
| Authors | Sharma, PK; Raglin, A |
| Title | IoT in Smart Cities: Exploring Information Theoretic and Deep Learning Models to Improve Parking Solutions |
| Year | 2019 |
| Published | |
| Abstract | The smart city is an emerging concept which appears to be a promising approach not only in urban environment to accommodate the growing needs and to improve the quality of life, but also in military environment for installing and integrating network-connected sensors into the everyday operations. This demands the use of information technologies, digitization, and urban traffic planning for better mobility. Internet of Things (IoT) devices (wireless sensors, actuators, meters, etc.) come in handy to collect and analyze data. Because military bases share many of the same characteristics as small cities, this work in progress sets out to develop a machine learning model to predict the parking availability in IoT environment for smart cities. As IoT offers data nuances challenges, present work explores a recently introduced information theoretic technique, Chisini Jensen Shannon Divergence (CJSD), known for their utility to tease apart data classes with discernible differences. We found that while clustering methods were unable to detect visually identifiable clusters, family of CJSDs was able to capture underlying data regularity and give statistically significant improvement over deep learning model. This is the first application of information theoretic models in IoT environment in the research literature. |
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