Citation: L˘ az˘ aroiu, G.; Andronie, M.; Iatagan, M.; Geam ˘ anu, M.; S , tef˘ anescu, R.; Dijm ˘ arescu, I. Deep Learning-Assisted Smart Process Planning, Robotic Wireless Sensor Networks, and Geospatial Big Data Management Algorithms in the Internet of Manufacturing Things. ISPRS Int. J. Geo-Inf. 2022, 11, 277. https://doi.org/10.3390/ ijgi11050277 Academic Editor: Wolfgang Kainz Received: 22 February 2022 Accepted: 26 April 2022 Published: 27 April 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Geo-Information Review Deep Learning-Assisted Smart Process Planning, Robotic Wireless Sensor Networks, and Geospatial Big Data Management Algorithms in the Internet of Manufacturing Things George Lăzăroiu 1, * , Mihai Andronie 1 , Mariana Iatagan 1 , Marinela Geamănu 1 , Roxana S , tefănescu 2 and Irina Dijmărescu 3 1 Department of Economic Sciences, Spiru Haret University, 030045 Bucharest, Romania; mihai_a380@spiruharet.ro (M.A.); se_iataganm@spiruharet.ro (M.I.); geamanu_marinela@yahoo.com.au (M.G.) 2 Department of Juridical Sciences and Economic Sciences, Spiru Haret University, 500152 Bras , ov, Romania; roxana.stefanescu@spiruharet.ro 3 Grigore Alexandrescu Children’s Emergency Hospital, 011743 Bucharest, Romania; irinaandronie@yahoo.com * Correspondence: george.lazaroiu@spiruharet.ro Abstract: The purpose of our systematic review is to examine the recently published literature on the Internet of Manufacturing Things (IoMT), and integrate the insights it configures on deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms by employing Preferred Reporting Items for Systematic Reviews and Meta- analysis (PRISMA) guidelines. Throughout October 2021 and January 2022, a quantitative literature review of aggregators such as ProQuest, Scopus, and the Web of Science was carried out, with search terms including “deep learning-assisted smart process planning + IoMT”, “robotic wireless sensor networks + IoMT”, and “geospatial big data management algorithms + IoMT”. As the analyzed research was published between 2018 and 2022, only 346 sources satisfied the eligibility criteria. A Shiny app was leveraged for the PRISMA flow diagram to comprise evidence-based collected and handled data. Major difficulties and challenges comprised identification of robust correlations among the inspected topics, but focusing on the most recent and relevant sources and deploying screening and quality assessment tools such as the Appraisal Tool for Cross-Sectional Studies, Dedoose, Distiller SR, the Mixed Method Appraisal Tool, and the Systematic Review Data Repository we integrated the core outcomes related to the IoMT. Future research should investigate dynamic scheduling and production execution systems advanced by deep learning-assisted smart process planning, data-driven decision making, and robotic wireless sensor networks. Keywords: Internet of Manufacturing Things; deep learning-assisted smart process planning; robotic wireless sensor network; geospatial big data management; machine learning algorithm; Industry 4.0 1. Introduction The purpose of our systematic review is to examine the recently published literature on the Internet of Manufacturing Things (IoMT) and integrate the insights it configures on deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms. Real-time performance supervision, inspection, and control of IoMT-based industrial systems [1–9] necessitate smart sensors, devices, and actuators [10–18] in terms of manufacturing optimization through geospatial big data management algorithms. By inspecting the most recent (2018–2022) and relevant (Web of Science, Scopus, and ProQuest) sources, our paper has endeavored to prove that IoMT aims to improve shop floor operations, logistics, and production [19–28], decreasing machine downtime and system failure, and optimizing data acquisition and product quality [29–38] through geospatial big data management algorithms. The actuality and novelty of our ISPRS Int. J. Geo-Inf. 2022, 11, 277. https://doi.org/10.3390/ijgi11050277 https://www.mdpi.com/journal/ijgi