Big Data Management Algorithms, Deep Learning-Based Object Detection Technologies, and Geospatial Simulation and Sensor Fusion Tools in the Internet of Robotic Things

Abstract

The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.

From data inference to evidence fusion: A methodology for studying computer ethics at different levels

“…Specifically, questions related to computer ethics and morality issues were found to have a significant positive impact. This highlights the importance of education and awareness campaigns surrounding computer ethics to stimulate interest and participation in the field [5, 6, 42]. The findings demonstrate the dynamic evolution of computer ethics, emphasizing the shift from individual‐centric perspectives to network ethics.…”

“…They can identify and understand ethical issues within large data sets, such as detecting harmful content, hate speech, or misinformation [7]. The synergy of multisensor fusion navigation algorithms and feature information‐efficient representation networks can bring a new dimension to the automated analysis of computer ethics issues [5, 6, 43]. By combining sensor data with feature extraction through neural networks, it becomes possible to gain insights into users' digital behavior and its ethical implications.…”

“…Developed by Arthur P. Dempster in the 1960s, this theory has found applications in various fields such as artificial intelligence, decision-making, and information fusion. Andronie et al [5] delved into the most recent literature concerning the Internet of Robotic Things. In their analysis, they integrated insights related to the utilization of deep learning-based object detection technologies, algorithms for managing substantial data sets, geospatial simulation, and sensor fusion tools.…”

“…navigation algorithms and feature information-efficient representation networks can bring a new dimension to the automated analysis of computer ethics issues [5,6,43]. By combining sensor data with feature extraction through neural networks, it becomes possible to gain insights into users' digital behavior and its ethical implications.…”

Are we ready for hotel robots after the pandemic? A profile analysis

“…Authors show that strong intelligence and empathetic behavior in service robots and AI devices can increase their acceptance by consumers. Andronie et al (2023) provided a systematic review of recent literature on the Internet of Robotic Things (IoRT) to explore how big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools optimize remote sensing robots. The review identified computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology as important for IoRT.…”