Osifeko, Martins O.Hancke, Gerhard P.Abu-Mahfouz, Adnan Mohammed2020-11-092020-11-092020-04-25Osifeko, M.O., Hancke, G.P. & Abu-Mahfouz, A.M. 2020, 'Artificial intelligence techniques for cognitive sensing in future IoT : state-of-the-art, potentials, and challenges', Journal of Sensor and Actuator Networks, vol. 9, art. 21, pp. 1-31.2224-2708 (online)10.3390/jsan9020021http://hdl.handle.net/2263/76942Smart, secure and energy-e cient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research e orts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-e cient data collection processes. In this article, we provide a survey of di erent Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for di erent FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard.en© 2020 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Artificial intelligence-based techniquesCognitive sensingSmart energy managementCognitive securityIntelligent data collectionFuture Internet of Things (FIoT)Article