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oai:gredos.usal.es:10366/1441572026-03-25T12:57:12Zcom_10366_122575com_10366_4512com_10366_3823com_10366_4508com_10366_4504col_10366_156515col_10366_4509

00925njm 22002777a 4500 dc Öztürk, Mehmet author 2020 [EN]The increase in the number of connected devices on the Internet of Things (IoT), interactions and the amount of data raises a number of issues. Two major problems are limitations in terms of network latency and bandwidth. While cloud-based infrastructures give us access to scalable, on-demand storage and processing services that can scale to the requirements of the Internet of Things (IoT), these centralized resources can create unacceptable delays and performance problems for devices that have latency-sensitive applications, such as health monitoring and emergency response applications. This article has been created for the PhD thesis that aims to create a deep tech architecture for intelligent IoT systems. Ozturh. M. (2020). A deep tech architecture for intelligent IoT systems. En Sara Rodríguez González, Fernando de la Prieta Pintado, José Alberto García Coria, Roberto Casado Vara (eds.) The role of artificial intelligence and distributed computing in IOT Applications, pp. 41-54 http://hdl.handle.net/10366/144157 10.14201/0AQ02874154 Edge Computing Machine Learning Deep Tech Internet of Things IoT A deep tech architecture for intelligent IoT systems