1,686 research outputs found

    Blockchain Solutions for Multi-Agent Robotic Systems: Related Work and Open Questions

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    The possibilities of decentralization and immutability make blockchain probably one of the most breakthrough and promising technological innovations in recent years. This paper presents an overview, analysis, and classification of possible blockchain solutions for practical tasks facing multi-agent robotic systems. The paper discusses blockchain-based applications that demonstrate how distributed ledger can be used to extend the existing number of research platforms and libraries for multi-agent robotic systems.Comment: 5 pages, FRUCT-2019 conference pape

    Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

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    The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

    Block-Based Development of Mobile Learning Experiences for the Internet of Things

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    The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor's standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.Spanish National Research Agency (AEI) - ERDF fund

    The Penetration of Internet of Things in Robotics: Towards a Web of Robotic Things

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    As the Internet of Things (IoT) penetrates different domains and application areas, it has recently entered also the world of robotics. Robotics constitutes a modern and fast-evolving technology, increasingly being used in industrial, commercial and domestic settings. IoT, together with the Web of Things (WoT) could provide many benefits to robotic systems. Some of the benefits of IoT in robotics have been discussed in related work. This paper moves one step further, studying the actual current use of IoT in robotics, through various real-world examples encountered through a bibliographic research. The paper also examines the potential ofWoT, together with robotic systems, investigating which concepts, characteristics, architectures, hardware, software and communication methods of IoT are used in existing robotic systems, which sensors and actions are incorporated in IoT-based robots, as well as in which application areas. Finally, the current application of WoT in robotics is examined and discussed

    Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities

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    [EN] Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2-4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.This research was funded by the Spanish Science and Innovation Ministry grant number MICINN: CICYT project PRECON-I4: "Predictable and dependable computer systems for Industry 4.0" TIN2017-86520-C3-1-R.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2020). Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities. Sensors. 20(1):1-18. https://doi.org/10.3390/s20010112S118201Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., & Noguera, J. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors, 15(8), 18080-18101. doi:10.3390/s150818080Cao, J., Song, C., Peng, S., Xiao, F., & Song, S. (2019). Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles. Sensors, 19(18), 4021. doi:10.3390/s19184021González García, C., Meana-Llorián, D., Pelayo G-Bustelo, B. C., Cueva Lovelle, J. M., & Garcia-Fernandez, N. (2017). Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes. Future Generation Computer Systems, 76, 301-313. doi:10.1016/j.future.2016.12.033Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1-10. doi:10.1016/j.jii.2017.04.005Li, S., Xu, L. D., & Zhao, S. (2014). The internet of things: a survey. Information Systems Frontiers, 17(2), 243-259. doi:10.1007/s10796-014-9492-7Zdraveski, V., Mishev, K., Trajanov, D., & Kocarev, L. (2017). ISO-Standardized Smart City Platform Architecture and Dashboard. IEEE Pervasive Computing, 16(2), 35-43. doi:10.1109/mprv.2017.31Dastjerdi, A. V., & Buyya, R. (2016). Fog Computing: Helping the Internet of Things Realize Its Potential. Computer, 49(8), 112-116. doi:10.1109/mc.2016.245Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. IEEE Internet of Things Journal, 1(1), 22-32. doi:10.1109/jiot.2014.2306328Hancke, G., Silva, B., & Hancke, Jr., G. (2012). The Role of Advanced Sensing in Smart Cities. Sensors, 13(1), 393-425. doi:10.3390/s130100393Chen, Y. (2016). Industrial information integration—A literature review 2006–2015. Journal of Industrial Information Integration, 2, 30-64. doi:10.1016/j.jii.2016.04.004Lim, G. H., Suh, I. H., & Suh, H. (2011). Ontology-Based Unified Robot Knowledge for Service Robots in Indoor Environments. 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    Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

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    The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

    The concept of collaborative engineering: a systematic literature review

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    Collaborative engineering is not a new subject but it assumes a new importance in the Industry 4.0 (I4.0). There are other concepts frequently mismatched with collaboration. Thus, the main objective of this paper is to put forward a collaborative engineering concept, along its sub concepts, supported by an extensive systematic literature review. A critical analysis and discussion about the fundamental importance of learning, and the central human role in collaboration, in the I4.0, is presented, based on the main insights brought through the literature review. This study also enables to realize about the importance of collaboration in the current digitalization era, along with the importance of recent approaches and technology for enabling or promoting collaboration. Main current practices of human centered and autonomous machine-machine approaches and applications of collaboration in engineering, namely in manufacturing and management, are presented, along with main difficulties and further open research opportunities on collaboration.This work was supported by the Fundação para a Ciência e a Tecnologia [UIDB/00319/2020, UIDB/50014/2020, and EXPL/EME-SIS/1224/2021]
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