Matches in SemOpenAlex for { <https://semopenalex.org/work/W2996262564> ?p ?o ?g. }
- W2996262564 endingPage "181732" @default.
- W2996262564 startingPage "181721" @default.
- W2996262564 abstract "The Internet of Things (IoT) is widely regarded as a key component of the Internet of the future and thereby has drawn significant interests in recent years. IoT consists of billions of intelligent and communicating “things”, which further extend borders of the world with physical and virtual entities. Such ubiquitous smart things produce massive data every day, posing urgent demands on quick data analysis on various smart mobile devices. Fortunately, the recent breakthroughs in deep learning have enabled us to address the problem in an elegant way. Deep models can be exported to process massive sensor data and learn underlying features quickly and efficiently for various IoT applications on smart devices. In this article, we survey the literature on leveraging deep learning to various IoT applications. We aim to give insights on how deep learning tools can be applied from diverse perspectives to empower IoT applications in four representative domains, including smart healthcare, smart home, smart transportation, and smart industry. A main thrust is to seamlessly merge the two disciplines of deep learning and IoT, resulting in a wide-range of new designs in IoT applications, such as health monitoring, disease analysis, indoor localization, intelligent control, home robotics, traffic prediction, traffic monitoring, autonomous driving, and manufacture inspection. We also discuss a set of issues, challenges, and future research directions that leverage deep learning to empower IoT applications, which may motivate and inspire further developments in this promising field." @default.
- W2996262564 created "2019-12-26" @default.
- W2996262564 creator A5024846253 @default.
- W2996262564 creator A5035121918 @default.
- W2996262564 creator A5042326001 @default.
- W2996262564 creator A5055118203 @default.
- W2996262564 creator A5056041589 @default.
- W2996262564 creator A5058697648 @default.
- W2996262564 creator A5071037763 @default.
- W2996262564 date "2019-01-01" @default.
- W2996262564 modified "2023-10-16" @default.
- W2996262564 title "A Survey on Deep Learning Empowered IoT Applications" @default.
- W2996262564 cites W1498436455 @default.
- W2996262564 cites W1575271337 @default.
- W2996262564 cites W1947481528 @default.
- W2996262564 cites W2005286252 @default.
- W2996262564 cites W2025241138 @default.
- W2996262564 cites W2025768430 @default.
- W2996262564 cites W2028941259 @default.
- W2996262564 cites W2045127238 @default.
- W2996262564 cites W2057755039 @default.
- W2996262564 cites W2064675550 @default.
- W2996262564 cites W2083238230 @default.
- W2996262564 cites W2097117768 @default.
- W2996262564 cites W2099866409 @default.
- W2996262564 cites W2100495367 @default.
- W2996262564 cites W2117515799 @default.
- W2996262564 cites W2117699769 @default.
- W2996262564 cites W2117731089 @default.
- W2996262564 cites W2119112357 @default.
- W2996262564 cites W2134447997 @default.
- W2996262564 cites W2136922672 @default.
- W2996262564 cites W2156017603 @default.
- W2996262564 cites W2157331557 @default.
- W2996262564 cites W2160532515 @default.
- W2996262564 cites W2165991108 @default.
- W2996262564 cites W2170102584 @default.
- W2996262564 cites W2194775991 @default.
- W2996262564 cites W2202505358 @default.
- W2996262564 cites W2309512289 @default.
- W2996262564 cites W2317595875 @default.
- W2996262564 cites W2320517083 @default.
- W2996262564 cites W2322341507 @default.
- W2996262564 cites W2340811421 @default.
- W2996262564 cites W2345255852 @default.
- W2996262564 cites W2468676150 @default.
- W2996262564 cites W2557738935 @default.
- W2996262564 cites W2558891025 @default.
- W2996262564 cites W2559767995 @default.
- W2996262564 cites W2560645261 @default.
- W2996262564 cites W2563686712 @default.
- W2996262564 cites W2565548788 @default.
- W2996262564 cites W2573587735 @default.
- W2996262564 cites W2593182953 @default.
- W2996262564 cites W2610332124 @default.
- W2996262564 cites W2623902153 @default.
- W2996262564 cites W2625731802 @default.
- W2996262564 cites W2653246566 @default.
- W2996262564 cites W2763068163 @default.
- W2996262564 cites W2782812883 @default.
- W2996262564 cites W2784246882 @default.
- W2996262564 cites W2786070938 @default.
- W2996262564 cites W2786134370 @default.
- W2996262564 cites W2789896367 @default.
- W2996262564 cites W2790688089 @default.
- W2996262564 cites W2792616458 @default.
- W2996262564 cites W2795613961 @default.
- W2996262564 cites W2796802878 @default.
- W2996262564 cites W2799126587 @default.
- W2996262564 cites W2799932776 @default.
- W2996262564 cites W2800230615 @default.
- W2996262564 cites W2805454539 @default.
- W2996262564 cites W2805491853 @default.
- W2996262564 cites W2806282362 @default.
- W2996262564 cites W2808064527 @default.
- W2996262564 cites W2890209713 @default.
- W2996262564 cites W2891691179 @default.
- W2996262564 cites W2896880663 @default.
- W2996262564 cites W2898652425 @default.
- W2996262564 cites W2899462042 @default.
- W2996262564 cites W2902644322 @default.
- W2996262564 cites W2902759202 @default.
- W2996262564 cites W2917805266 @default.
- W2996262564 cites W2928726480 @default.
- W2996262564 cites W2929932977 @default.
- W2996262564 cites W2962736495 @default.
- W2996262564 cites W2963037989 @default.
- W2996262564 cites W2963087201 @default.
- W2996262564 cites W2963363070 @default.
- W2996262564 cites W2963974064 @default.
- W2996262564 cites W2964248614 @default.
- W2996262564 cites W3098217967 @default.
- W2996262564 cites W3100857292 @default.
- W2996262564 doi "https://doi.org/10.1109/access.2019.2958962" @default.
- W2996262564 hasPublicationYear "2019" @default.
- W2996262564 type Work @default.
- W2996262564 sameAs 2996262564 @default.
- W2996262564 citedByCount "65" @default.