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Besides, anomalies may occur occasionally in most applications, while the majority of time durations may reflect a <jats:italic>healthy<\/jats:italic> situation. In this setting, the range, rather than an accurate value of sensory data, should be more interesting to domain applications, and the range is represented in terms of the category of sensory data. To decrease the energy consumption of <jats:italic>IoT<\/jats:italic> networks, this paper proposes an energy-efficient sensory data gathering mechanism, where the category of sensory data is processed by adopting the compressed sensing algorithm. The sensory data are forecasted through a data prediction model in the cloud, and sensory data of an <jats:italic>IoT<\/jats:italic> node is necessary to be routed to the cloud for the synchronization purpose, only when the category provided by this <jats:italic>IoT<\/jats:italic> node is different from the category of the forecasted one in the cloud. Experiments are conducted and evaluation results demonstrate that our approach performs better than state-of-the-art techniques, in terms of the network traffic and energy consumption.<\/jats:p>","DOI":"10.1186\/s13677-020-00166-x","type":"journal-article","created":{"date-parts":[[2020,4,5]],"date-time":"2020-04-05T21:02:41Z","timestamp":1586120561000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Energy-efficient sensory data gathering based on compressed sensing in IoT networks"],"prefix":"10.1186","volume":"9","author":[{"given":"Xinxin","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangbing","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taj","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,5]]},"reference":[{"key":"166_CR1","doi-asserted-by":"publisher","unstructured":"Veres M, Moussa M (2019) Deep learning for intelligent transportation systems: A survey of emerging trends. 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They have seen the manuscript and approved to submit to your journal. We confirm that the content of the manuscript has not been published or submitted for publication elsewhere.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"19"}}