Smart manufacturing and smart factories depend on automation and robotics, whereas human–robot collaboration (HRC) contributes to increasing the effectiveness and productivity of today’s and future factories. Industrial robots especially in HRC settings can be hazardous if safety is not addressed properly. In this review, we look at the collaboration levels of HRC and what safety actions have been used to address safety. One hundred and ninety-three articles were identified from which, after screening and eligibility stages, 46 articles were used for the extraction stage. Predefined parameters such as: devices, algorithms, collaboration level, safety action, and standards used for HRC were extracted. Despite close human and robot collaboration, 25% of all reviewed studies did not use any safety actions, and more than 50% did not use any standard to address safety issues. This review shows HRC trends and what kind of functionalities are lacking in today’s HRC systems. HRC systems can be a tremendously complex process; therefore, proper safety mechanisms must be addressed at an early stage of development.

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Performing a good-quality indoor localization of a mobile target is a challenging task, which can be affected by many factors such as radio wave behavior, the nature of the experimental environment, and available infrastructure. (1) Background: An indoor localization experiment using an Internet of Things (IoT) wireless sensor network (WSN) testbed is performed, in order to study the influence of transmission power level on the quality of received signals, and consequently, the estimated target positional coordinates. (2) Methods: A received signal strength indicator (RSSI) range-based localization system using a geometrical constrained weighted least squares (WLS) estimator multilateration technique is selected to validate the influence of the transmission power level on the performance of the localization algorithm. (3) Results: Fair localization quality was obtained at the highest transmission power level instead of the proposed transmission power level. (4) Conclusion: Additional factors are discussed to fully represent the required operational transmission power for a better localization quality, along with suggested improvements of the infrastructure configuration as a future work.

The availability of data is an important aspect of any research as it determines the likelihood of the study's commencement, completion, and success. The Internet of Things and Wireless Sensor Networks technologies have been attracting a huge amount of researchers for more than two decades, without having a consolidated or unified source, identifying and describing available Internet of Things and Wireless sensor network testbed facilities.  In this paper, a dataset including 41 distinct testbed facilities is described. These testbed facilities are classified according to their key features such as Device Under Test (DUT) type, mobility, access level, facility count, connection/interaction interfaces along with other criteria. The systematic review process resulting in the gathered data set consisted of three filtering phases applied to relevant articles published between the years 2011 and 2021 as obtained from the Web of Science and SCOPUS databases.

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This research aims to provide a high-level software framework for IoT-Edge-Cloud computational continuum-based applications with support for mobile IoT and DevOps integration utilizing the Edge computing paradigms. This is achieved by dividing the system in a modular fashion and providing a loosely coupled service and module descriptions for usage in the respective system layers for flexible and yet trustworthy implementation. The article describes the software architecture for a DevOps-enabled Edge computing solution in the IoT-Edge-Cloud computational continuum with the support for flexible and mobile IoT solutions. The proposed framework is validated on an intelligent transport system use case in the rolling stock domain and showcases the improvements gained by using the proposed IoT-Edge-Cloud continuum framework.