Towards smarter cities: Learning from Internet of Multimedia Things-generated big data

Abstract

In today’s technological era, smart devices connected through the IoT and giant IoT infrastructures are playing a vital role in making daily life easier and simpler than it ever was. Numerous sensors including IoT-interconnected multimedia sensors communicating with each other generate a huge amount of data. In particular, IoT multimedia sensors play a vital role for green cities, providing secure and efficient analytics to monitor routine activities. Big data generated by these sensors contain dense information that needs to be processed for various applications such as summarization, security, and privacy. The heterogeneity and complexity of video data is the biggest hurdle and a pretty number of techniques are already developed for the efficient processing of big video data. IoT big data processing is an emerging field and many researchers are enthusiastic to contribute in making the cities smarter. Among all these methods, deep learning-based techniques are dominant over existing traditional multimedia data processing algorithms with convincing results emerged recently. This special issue targets the current problems in smart cities development and provides future challenges in this domain and invite researchers working in IoT domain to make cities smarter. It also focuses on some related technologies comprising Internet of Multimedia Things (IoMTs) and machine learning for big data. Furthermore, it covers deep learning-based solutions for real-time data processing, learning from big data, distributed learning paradigms with embedded processing, and efficient inference.