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This automation enables numerous useful real-life use-cases, such as smart transport, smart living, smart cities, and so on. However, recent industry surveys reflect that data-related challenges are responsible for slower growth of IoT in recent years. For this reason, this article presents a systematic and comprehensive survey on IoT Big Data (IoTBD) with the aim to identify the uncharted challenges for IoTBD. This article analyzes the state-of-the-art academic works in IoT and big data management across various domains and proposes a taxonomy for IoTBD management. Then, the survey explores the IoT portfolio of major cloud vendors and provides a classification of vendor services for the integration of IoT and IoTBD on their cloud platforms. After that, the survey identifies the IoTBD challenges in terms of 13 V\u2019s challenges and envisions IoTBD as \u201cBig Data 2.0.\u201d Then the survey provides comprehensive analysis of recent works that address IoTBD challenges by highlighting their strengths and weaknesses to assess the recent trends and future research directions. Finally, the survey concludes with discussion on open research issues for IoTBD.<\/jats:p>","DOI":"10.1145\/3419634","type":"journal-article","created":{"date-parts":[[2020,12,6]],"date-time":"2020-12-06T22:23:20Z","timestamp":1607293400000},"page":"1-59","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":100,"title":["A Survey on IoT Big Data"],"prefix":"10.1145","volume":"53","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0037-0613","authenticated-orcid":false,"given":"Maggi","family":"Bansal","sequence":"first","affiliation":[{"name":"Thapar Institute of Engineering and Technology, Patiala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inderveer","family":"Chana","sequence":"additional","affiliation":[{"name":"Thapar Institute of Engineering and Technology, Patiala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siobh\u00e1n","family":"Clarke","sequence":"additional","affiliation":[{"name":"Trinity College Dublin, Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,12,6]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2017.06.013"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON\u201916)","author":"Alduais N. 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[n.d.]. AWS IoT Button. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/aws.amazon.com\/iotbutton\/.  AWS. [n.d.]. AWS IoT Button. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/aws.amazon.com\/iotbutton\/."},{"key":"e_1_2_1_10_1","unstructured":"AWS. [n.d.]. AWS IoT Core. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/aws.amazon.com\/iot-core\/.  AWS. [n.d.]. AWS IoT Core. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/aws.amazon.com\/iot-core\/."},{"key":"e_1_2_1_11_1","unstructured":"AWS. [n.d.]. AWS IoT Greengrass. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/aws.amazon.com\/greengrass\/.  AWS. [n.d.]. AWS IoT Greengrass. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/aws.amazon.com\/greengrass\/."},{"key":"e_1_2_1_12_1","unstructured":"AWS. [n.d.]. Rachio Case Study. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/aws.amazon.com\/solutions\/case-studies\/rachio.  AWS. [n.d.]. Rachio Case Study. 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Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/cloud.google.com\/customers\/ather-energy.  Google Cloud. [n.d.]. Ather Energy: Driving the future of mobility in India with BigQuery and Cloud IoT Core. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/cloud.google.com\/customers\/ather-energy."},{"key":"e_1_2_1_31_1","unstructured":"Google Cloud. [n.d.]. Deep Sky Vineyard: Pairing wine with the IoT. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/cloud.google.com\/customers\/deep-sky-vineyard\/.  Google Cloud. [n.d.]. Deep Sky Vineyard: Pairing wine with the IoT. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/cloud.google.com\/customers\/deep-sky-vineyard\/."},{"key":"e_1_2_1_32_1","unstructured":"Google Cloud. [n.d.]. Energyworx: Building an energy data management solution using Google Cloud Platform. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/cloud.google.com\/customers\/energyworx.  Google Cloud. [n.d.]. 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Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/cloud.google.com\/solutions\/iot."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/GrC.2007.76"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2016.05.002"},{"key":"e_1_2_1_60_1","unstructured":"Michael Haupt. 2016. \u201cData is the New Oil\u201d\u2014A Ludicrous Proposition. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/medium.com\/project-2030\/data-is-the-new-oil-a-ludicrous-proposition-1d91bba4f294.  Michael Haupt. 2016. \u201cData is the New Oil\u201d\u2014A Ludicrous Proposition. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/medium.com\/project-2030\/data-is-the-new-oil-a-ludicrous-proposition-1d91bba4f294."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700246"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2015.7248401"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2801559"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107208"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISITIA.2016.7828696"},{"key":"e_1_2_1_66_1","unstructured":"IBM. [n.d.]. Watson Internet of Things. Retrieved from https:\/\/2.zoppoz.workers.dev:443\/https\/www.ibm.com\/internet-of-things\/solutions\/iot-platform\/watson-iot-platform.  IBM. [n.d.]. Watson Internet of Things. 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