ABSTRACT. With growing evidence of deep learning-based sensing technologies, artificial intelligence-based decision-making algorithms, and big geospatial data analytics, there is an essential demand for comprehending cognitive Internet of Things. In this research, prior findings were cumulated indicating that advancement of connected sensors, cloud technologies, big data analytics, machine learning algorithms, and ubiquitous sensing systems have enabled cognitive Internet of Things. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and April 2021, with search terms including “cognitive Internet of Things,” “cognitive computing technologies,” and “cognitive sensor networks.” As we analyzed research published between 2015 and 2021, only 142 papers met the eligibility criteria. By eliminating controversial or unclear findings (insufficient/irrelevant data), results unsubstantiated by replication, too imprecise or undetailed content, and studies having quite similar titles, we decided on 28, mainly empirical, sources. Subsequent analyses should develop on cloud-assisted cognitive Internet of Things.

Keywords: cognition; Internet of Things; deep learning; sensing technology; big data

How to cite: Blake, R., and Frajtova Michalikova, K. (2021). “Deep Learning-based Sensing Technologies, Artificial Intelligence-based Decision-Making Algorithms, and Big Geospatial Data Analytics in Cognitive Internet of Things,” Analysis and Metaphysics 20: 159–173. doi: 10.22381/am20202111.

Received 24 May 2021 • Received in revised form 22 December 2021
Accepted 26 December 2021 • Available online 30 December 2021