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• W2964248614 abstract "In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature." @default.
• W2964248614 created "2019-07-30" @default.
• W2964248614 creator A5008695053 @default.
• W2964248614 creator A5015767232 @default.
• W2964248614 creator A5034181427 @default.
• W2964248614 creator A5057916222 @default.
• W2964248614 date "2018-01-01" @default.
• W2964248614 modified "2023-10-16" @default.
• W2964248614 title "Deep Learning for IoT Big Data and Streaming Analytics: A Survey" @default.
• W2964248614 cites W114517082 @default.
• W2964248614 cites W1152476827 @default.
• W2964248614 cites W12599678 @default.
• W2964248614 cites W1480583224 @default.
• W2964248614 cites W1498436455 @default.
• W2964248614 cites W1536680647 @default.
• W2964248614 cites W1735317348 @default.
• W2964248614 cites W1932198206 @default.
• W2964248614 cites W1967900521 @default.
• W2964248614 cites W1972223082 @default.
• W2964248614 cites W1977295820 @default.
• W2964248614 cites W1981474079 @default.
• W2964248614 cites W1984020445 @default.
• W2964248614 cites W1998917233 @default.
• W2964248614 cites W2001626625 @default.
• W2964248614 cites W2007272376 @default.
• W2964248614 cites W2014847057 @default.
• W2964248614 cites W2021354639 @default.
• W2964248614 cites W2025241138 @default.
• W2964248614 cites W2028501442 @default.
• W2964248614 cites W2041785419 @default.
• W2964248614 cites W2047507037 @default.
• W2964248614 cites W2053637704 @default.
• W2964248614 cites W2060969833 @default.
• W2964248614 cites W2062017159 @default.
• W2964248614 cites W2064675550 @default.
• W2964248614 cites W2068849277 @default.
• W2964248614 cites W2071712938 @default.
• W2964248614 cites W2076063813 @default.
• W2964248614 cites W2081052120 @default.
• W2964248614 cites W2092850413 @default.
• W2964248614 cites W2095569536 @default.
• W2964248614 cites W2097117768 @default.
• W2964248614 cites W2100495367 @default.
• W2964248614 cites W2108069432 @default.
• W2964248614 cites W2110767187 @default.
• W2964248614 cites W2113325037 @default.
• W2964248614 cites W2116769344 @default.
• W2964248614 cites W2117239687 @default.
• W2964248614 cites W2118023920 @default.
• W2964248614 cites W2123585936 @default.
• W2964248614 cites W2134295053 @default.
• W2964248614 cites W2144461192 @default.
• W2964248614 cites W2147062276 @default.
• W2964248614 cites W2150355110 @default.
• W2964248614 cites W2155893237 @default.
• W2964248614 cites W2157881433 @default.
• W2964248614 cites W2157954477 @default.
• W2964248614 cites W2158054309 @default.
• W2964248614 cites W2164364358 @default.
• W2964248614 cites W2193121729 @default.
• W2964248614 cites W2194775991 @default.
• W2964248614 cites W2200092826 @default.
• W2964248614 cites W2259801182 @default.
• W2964248614 cites W2261465687 @default.
• W2964248614 cites W2270470215 @default.
• W2964248614 cites W2285660444 @default.
• W2964248614 cites W2285924575 @default.
• W2964248614 cites W2289252105 @default.
• W2964248614 cites W2291859485 @default.
• W2964248614 cites W2293587938 @default.
• W2964248614 cites W2293983046 @default.
• W2964248614 cites W2295959395 @default.
• W2964248614 cites W2302255633 @default.
• W2964248614 cites W2308155233 @default.
• W2964248614 cites W2325939864 @default.
• W2964248614 cites W2406349003 @default.
• W2964248614 cites W2414162830 @default.
• W2964248614 cites W2414564754 @default.
• W2964248614 cites W2444471228 @default.
• W2964248614 cites W2461621749 @default.
• W2964248614 cites W2470368200 @default.
• W2964248614 cites W2473418344 @default.
• W2964248614 cites W2492586371 @default.
• W2964248614 cites W2501369945 @default.
• W2964248614 cites W2509232319 @default.
• W2964248614 cites W2511063986 @default.
• W2964248614 cites W2514727156 @default.
• W2964248614 cites W2515357728 @default.
• W2964248614 cites W2515751428 @default.
• W2964248614 cites W2526868004 @default.
• W2964248614 cites W2530443992 @default.
• W2964248614 cites W2533328922 @default.
• W2964248614 cites W2545300838 @default.
• W2964248614 cites W2548765505 @default.
• W2964248614 cites W2551195109 @default.
• W2964248614 cites W2553245249 @default.
• W2964248614 cites W2555541806 @default.
• W2964248614 cites W2559968629 @default.

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