{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:36:34Z","timestamp":1774629394638,"version":"3.50.1"},"reference-count":188,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,4,25]],"date-time":"2020-04-25T00:00:00Z","timestamp":1587772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Smart, secure and energy-efficient 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 efforts 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-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different 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.<\/jats:p>","DOI":"10.3390\/jsan9020021","type":"journal-article","created":{"date-parts":[[2020,4,27]],"date-time":"2020-04-27T04:15:29Z","timestamp":1587960929000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9350-6056","authenticated-orcid":false,"given":"Martins O.","family":"Osifeko","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Lynnwood Rd, Hatfield, Pretoria 0028, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4026-687X","authenticated-orcid":false,"given":"Gerhard P.","family":"Hancke","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Lynnwood Rd, Hatfield, Pretoria 0028, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6413-3924","authenticated-orcid":false,"given":"Adnan M.","family":"Abu-Mahfouz","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Lynnwood Rd, Hatfield, Pretoria 0028, South Africa"},{"name":"Council for Scientific and Industrial Research, Pretoria 0001, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3619","DOI":"10.1109\/ACCESS.2017.2779844","article-title":"A survey on 5g networks for the internet of things: Communication technologies and challenges","volume":"6","author":"Akpakwu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19084","DOI":"10.1109\/ACCESS.2017.2749415","article-title":"Cognitive radio based sensor network in smart grid: Architectures, applications and communication technologies","volume":"5","author":"Ogbodo","year":"2017","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Desai, M., and Phadke, A. (2017, January 24\u201326). Internet of Things based vehicle monitoring system. Proceedings of the 2017 Fourteenth International Conference on Wireless and Optical Communications Networks (WOCN), Mumbai, India.","DOI":"10.1109\/WOCN.2017.8065840"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Parpala, R.C., and Iacob, R. (2017). Application of IoT concept on predictive maintenance of industrial equipment. Proc. MATEC Web Conf.","DOI":"10.1051\/matecconf\/201712102008"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/JIOT.2014.2311513","article-title":"Cognitive Internet of Things: A New Paradigm Beyond Connection","volume":"1","author":"Wu","year":"2014","journal-title":"IEEE Int. Things J."},{"key":"ref_6","unstructured":"Sangaiah, A.K., Thangavelu, A., and Meenakshi Sundaram, V. (2018). Beyond Automation: The Cognitive IoT. Artificial Intelligence Brings Sense to the Internet of Things. Cognitive Computing for Big Data Systems Over IoT: Frameworks, Tools and Applications, Springer International Publishing."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Osuwa, A.A., Ekhoragbon, E.B., and Fat, L.T. (2017, January 16\u201317). Application of artificial intelligence in Internet of Things. Proceedings of the 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), Girne, Cyprus.","DOI":"10.1109\/CICN.2017.8319379"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1109\/TVT.2010.2043968","article-title":"A survey of artificial intelligence for cognitive radios","volume":"59","author":"He","year":"2010","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.dcan.2017.10.002","article-title":"Machine learning for Internet of Things data analysis: A survey","volume":"4","author":"Mahdavinejad","year":"2017","journal-title":"Digital Commun. Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"22489","DOI":"10.1109\/ACCESS.2018.2825282","article-title":"A Survey of Decision-Theoretic Models for Cognitive Internet of Things (CIoT)","volume":"6","author":"Zaheer","year":"2018","journal-title":"IEEE Access"},{"key":"ref_11","unstructured":"Al-Garadi, M.A., Mohamed, A., Al-Ali, A., Du, X., and Guizani, M. (2018). A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1109\/COMST.2018.2844341","article-title":"Deep Learning for IoT Big Data and Streaming Analytics: A Survey","volume":"20","author":"Mohammadi","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.jnca.2018.08.002","article-title":"Wireless energy harvesting: Empirical results and practical considerations for Internet of Things","volume":"121","author":"Sanislav","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_14","unstructured":"Tuna, G., Gungor, V.C., Gulez, K., Hancke, G., and Gungor, V. (2013). Energy harvesting techniques for industrial wireless sensor networks. Industrial Wireless Sensor Networks: Applications, Protocols, Standards, and Products, Taylor & Francis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1109\/JIOT.2017.2742663","article-title":"Internet of hybrid energy harvesting things","volume":"5","author":"Akan","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sharma, H., Haque, A., and Jaffery, Z.A. (2018). Modeling and optimisation of a solar energy harvesting system for wireless sensor network nodes. J. Sens. Actuator Netw., 7.","DOI":"10.3390\/jsan7030040"},{"key":"ref_17","unstructured":"Kraemer, F.A., Ammar, D., Braten, A.E., Tamkittikhun, N., and Palma, D. (, January 22\u201325October). Solar energy prediction for constrained IoT nodes based on public weather forecasts. Proceedings of the Seventh International Conference on the Internet of Things, Linz, Austria."},{"key":"ref_18","unstructured":"Patil, S., Vijayalashmi, M., and Tapaskar, R. (2017). Solar energy monitoring system using IOT. Indian J. Sci. Res., 149\u2013156. Available online: https:\/\/go.gale.com\/ps\/anonymous?id=GALE%7CA521163122&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=09762876&p=AONE&sw=w."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111","DOI":"10.3390\/jsan1020111","article-title":"An efficient medium access control protocol with parallel transmission for wireless sensor networks","volume":"1","author":"Arifuzzaman","year":"2012","journal-title":"J. Sens. Actuator Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/MCOM.2019.1700811","article-title":"Sleep scheduling for unbalanced energy harvesting in industrial wireless sensor networks","volume":"57","author":"Mukherjee","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.procs.2016.04.093","article-title":"Event Prediction in an IoT Environment Using Na\u00efve Bayesian Models","volume":"83","author":"Karakostas","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"28603","DOI":"10.3390\/s151128603","article-title":"A novel scheme for an energy efficient Internet of Things based on wireless sensor networks","volume":"15","author":"Rani","year":"2015","journal-title":"Sensors"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, F., R\u00fcdiger, C., and Yuce, M.R. (2017). Real-time performance of a self-powered environmental IoT sensor network system. Sensors, 17.","DOI":"10.3390\/s17020282"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hesse, H.C., Schimpe, M., Kucevic, D., and Jossen, A. (2017). Lithium-ion battery storage for the grid\u2014A review of stationary battery storage system design tailored for applications in modern power grids. Energies, 10.","DOI":"10.3390\/en10122107"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dias, G.M., Bellalta, B., and Oechsner, S. (2016, January 12\u201314). Using data prediction techniques to reduce data transmissions in the IoT. Proceedings of the 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), Reston, VA, USA.","DOI":"10.1109\/WF-IoT.2016.7845518"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1109\/JSAC.2019.2904357","article-title":"Data transmission reduction schemes in WSNs for efficient IoT systems","volume":"37","author":"Jarwan","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1109\/TGCN.2018.2873783","article-title":"Cognitive-LPWAN: Towards intelligent wireless services in hybrid low power wide area networks","volume":"3","author":"Chen","year":"2018","journal-title":"IEEE Trans. Green Commun. Netw."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Onumanyi, A.J., Abu-Mahfouz, A.M., and Hancke, G.P. (2019). Cognitive Radio in Low Power Wide Area Network for IoT Applications: Recent Approaches, Benefits and Challenges. IEEE Trans. Ind. Inform.","DOI":"10.1109\/INDIN41052.2019.8972333"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nikoletseas, S., Raptis, T.P., Souroulagkas, A., and Tsolovos, D. (2017). Wireless power transfer protocols in sensor networks: Experiments and simulations. J. Sens. Actuator Netw., 6.","DOI":"10.3390\/jsan6020004"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1109\/TMC.2012.161","article-title":"Energy provisioning in wireless rechargeable sensor networks","volume":"12","author":"He","year":"2012","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shi, Y., Xie, L., Hou, Y.T., and Sherali, H.D. (2011, January 10\u201315). On renewable sensor networks with wireless energy transfer. Proceedings of the 2011 Proceedings IEEE INFOCOM, Shanghai, China.","DOI":"10.1109\/INFCOM.2011.5934919"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lai, W.-Y., and Hsiang, T.-R. (2019). Wireless Charging Deployment in Sensor Networks. Sensors, 19.","DOI":"10.3390\/s19010201"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Perez, S., Fuertes, J.A.C., and Coupechoux, M. (2017, January 8\u201313). ODMAC++: An IoT communication manager based on energy harvesting prediction. Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292360"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.jnca.2019.01.015","article-title":"Energy management in harvesting enabled sensing nodes: Prediction and control","volume":"132","author":"Ashraf","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rodrigues, L.M., Montez, C., Budke, G., Vasques, F., and Portugal, P. (2017). Estimating the lifetime of wireless sensor network nodes through the use of embedded analytical battery models. J. Sens. Actuator Netw., 6.","DOI":"10.3390\/jsan6020008"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, M., Wang, J., Lin, K., Wu, D., Wan, J., Peng, L., and Youn, C.-H. (2016, January 5\u20139). M-plan: Multipath planning based transmissions for IoT multimedia sensing. Proceedings of the 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus.","DOI":"10.1109\/IWCMC.2016.7577081"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wei, Z., Liu, F., Lyu, Z., Ding, X., Shi, L., and Xia, C. (2018). Reinforcement learning for a novel mobile charging strategy in wireless rechargeable sensor networks. International Conference on Wireless Algorithms, Systems, and Applications, Springer.","DOI":"10.1007\/978-3-319-94268-1_40"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3126495","article-title":"Adaptive power management in solar energy harvesting sensor node using reinforcement learning","volume":"16","author":"Shresthamali","year":"2017","journal-title":"Acm Trans. Embed. Comput. Syst. (Tecs)"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"21126","DOI":"10.1109\/ACCESS.2017.2755588","article-title":"Battery management in a green fog-computing node: A reinforcement-learning approach","volume":"5","author":"Conti","year":"2017","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Murad, A., Kraemer, F.A., Bach, K., and Taylor, G. (2019, January 16\u201320). Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning. Proceedings of the 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), Umea, Sweden.","DOI":"10.1109\/SASO.2019.00015"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"15576","DOI":"10.1109\/ACCESS.2018.2810115","article-title":"Joint optimization of energy consumption and packet scheduling for mobile edge computing in cyber-physical networks","volume":"6","author":"Yang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chien, W.-C., Cho, H.-H., Chen, C.-Y., Chao, H.-C., and Shih, T.K. (2015, January 9\u201312). An efficient charger planning mechanism of WRSN using simulated annealing algorithm. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, China.","DOI":"10.1109\/SMC.2015.452"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nabil, M.S., Elkhatib, M.M., and Tammam, A. (2019, January 16\u201318). Fuzzy Power Management for Internet of Things (IOT) Wireless Sensor Nodes. Proceedings of the 2019 36th National Radio Science Conference (NRSC), Port Said, Egypt.","DOI":"10.1109\/NRSC.2019.8734645"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4319","DOI":"10.3233\/JIFS-169752","article-title":"Fuzzy system for monitoring energy consumption of wireless sensor network nodes","volume":"35","author":"Hua","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Smart, G., Atkinson, J., Mitchell, J., Rodrigues, M., and Andreopoulos, Y. (2016, January 16\u201318). Energy harvesting for the Internet-of-Things: Measurements and probability models. Proceedings of the 2016 23rd International Conference on Telecommunications (ICT), Thessaloniki, Greece.","DOI":"10.1109\/ICT.2016.7500416"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.peva.2017.03.004","article-title":"Markov fluid queue model of an energy harvesting IoT device with adaptive sensing","volume":"111","author":"Tunc","year":"2017","journal-title":"Perform. Eval."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1016\/j.jnca.2012.07.023","article-title":"An autonomic bio-inspired algorithm for wireless sensor network self-organization and efficient routing","volume":"35","author":"Ribeiro","year":"2012","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_48","first-page":"1","article-title":"An architectural blueprint for autonomic computing","volume":"31","author":"Computing","year":"2006","journal-title":"Ibm White Pap."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ndiaye, M., Hancke, G.P., and Abu-Mahfouz, A.M. (2017). Software defined networking for improved wireless sensor network management: A survey. Sensors, 17.","DOI":"10.3390\/s17051031"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MCI.2013.2264251","article-title":"An Intelligent Self-Organization Scheme for the Internet of Things","volume":"8","author":"Ding","year":"2013","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bassoli, M., Bianchi, V., and Munari, I.D. (2018). A plug and play IoT Wi-Fi smart home system for human monitoring. Electronics, 7.","DOI":"10.3390\/electronics7090200"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Solano, A., Dormido, R., Duro, N., and S\u00e1nchez, J.M. (2016). A self-provisioning mechanism in OpenStack for IoT devices. Sensors, 16.","DOI":"10.3390\/s16081306"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.jocs.2017.04.014","article-title":"Adaptive data rate control in low power wide area networks for long range IoT services","volume":"22","author":"Kim","year":"2017","journal-title":"J. Comput. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"10344","DOI":"10.1109\/JIOT.2019.2938486","article-title":"Optimization of Sensor Deployment for Industrial Internet of Things Using a Multiswarm Algorithm","volume":"6","author":"Hasan","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Lanza-Guti\u00e9rrez, J.M., Caball\u00e9, N., G\u00f3mez-Pulido, J.A., Crawford, B., and Soto, R. (2019). Toward a Robust Multi-Objective Metaheuristic for Solving the Relay Node Placement Problem in Wireless Sensor Networks. Sensors, 19.","DOI":"10.3390\/s19030677"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Oyewobi, S.S., Hancke, G.P., Abu-Mahfouz, A.M., and Onumanyi, A.J. (2019). An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things. Sensors (Basel), 19.","DOI":"10.3390\/s19061395"},{"key":"ref_57","unstructured":"K\u00fchn, F., Hellbr\u00fcck, H., and Fischer, S.A. (2020, April 04). Model-based Approach for Self-healing IoT Systems. Available online: https:\/\/dl.acm.org\/doi\/abs\/10.1145\/582128.582134."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e4263","DOI":"10.1002\/cpe.4263","article-title":"Provenance aware run-time verification of things for self-healing Internet of Things applications","volume":"31","author":"Aktas","year":"2019","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s10489-016-0849-0","article-title":"DSHMP-IOT: A distributed self healing movement prediction scheme for internet of things applications","volume":"46","author":"Zamanifar","year":"2017","journal-title":"Appl. Intell."},{"key":"ref_60","first-page":"89","article-title":"An Architecture for Self-healing in Internet of Things","volume":"2015","author":"Ribeiro","year":"2015","journal-title":"UBICOMM"},{"key":"ref_61","first-page":"1","article-title":"Network optimizations in the Internet of Things: A review","volume":"22","author":"Srinidhi","year":"2019","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"11307","DOI":"10.3390\/s120811307","article-title":"A self-optimizing scheme for energy balanced routing in wireless sensor networks using sensorant","volume":"12","author":"Ali","year":"2012","journal-title":"Sensors"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MSP.2018.2825478","article-title":"IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?","volume":"35","author":"Xiao","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.comcom.2017.10.006","article-title":"SEIRA: An effective algorithm for IoT resource allocation problem","volume":"119","author":"Tsai","year":"2018","journal-title":"Comput. Commun."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.procs.2014.05.416","article-title":"Enabling self-learning in dynamic and open IoT environments","volume":"32","author":"Preuveneers","year":"2014","journal-title":"Procedia Comput. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2470","DOI":"10.1109\/TCSI.2017.2716358","article-title":"Self-optimizing IoT wireless video sensor node with in-situ data analytics and context-driven energy-aware real-time adaptation","volume":"64","author":"Cao","year":"2017","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_67","first-page":"3","article-title":"Power modelling of sensors for IoT using reinforcement learning","volume":"10","author":"Kumar","year":"2018","journal-title":"Int. J. Adv. Intell. Paradig."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Iwendi, C., Maddikunta, P.K.R., Gadekallu, T.R., Lakshmanna, K., Bashir, A.K., and Piran, M.J. (2020). A metaheuristic optimization approach for energy efficiency in the IoT networks. Softw. Pract. Exp.","DOI":"10.1002\/spe.2797"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.future.2017.10.023","article-title":"A new fuzzy logic based node localization mechanism for Wireless Sensor Networks","volume":"93","author":"Amri","year":"2017","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Cuka, M., Elmazi, D., Obukata, R., Ozera, K., Oda, T., and Barolli, L. (2017, January 27\u201329). An integrated intelligent system for IoT device selection and placement in opportunistic networks using fuzzy logic and genetic algorithm. Proceedings of the 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), Taipei, Taiwan.","DOI":"10.1109\/WAINA.2017.178"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Nazari Cheraghlou, M., Khadem-Zadeh, A., and Haghparast, M. (2019). A New Hybrid Fault Tolerance Approach for Internet of Things. Electronics, 8.","DOI":"10.3390\/electronics8050518"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"5063261","DOI":"10.1155\/2016\/5063261","article-title":"Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach","volume":"2016","author":"Julie","year":"2016","journal-title":"Scientific World J."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s11036-015-0592-5","article-title":"Optimal energy strategy for node selection and data relay in WSN-based IoT","volume":"20","author":"Luo","year":"2015","journal-title":"Mob. Netw. Appl."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/MSEC.2018.2884860","article-title":"A Cognitive Protection System for the Internet of Things","volume":"17","author":"Siegel","year":"2019","journal-title":"IEEE Secur. Priv."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Abdul-Ghani, H.A., and Konstantas, D. (2019). A comprehensive study of security and privacy guidelines, threats, and countermeasures: An IoT perspective. J. Sens. Actuator Netw., 8.","DOI":"10.3390\/jsan8020022"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Abu-Mahfouz, A.M., and Hancke, G.P. (2013, January 9\u201312). Evaluating ALWadHA for providing secure localisation for wireless sensor networks. Proceedings of the 2013 Africon, Pointe-Aux-Piments, Mauritius.","DOI":"10.1109\/AFRCON.2013.6757656"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1016\/j.procs.2016.04.239","article-title":"A Simple Security Architecture for Smart Water Management System","volume":"83","author":"Ntuli","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_78","unstructured":"O\u2019connor, C. (2020, April 05). Security in the Era of Cognitive IT-the Risks and Mitigations to Be Aware of. Available online: https:\/\/www.ibm.com\/blogs\/internet-of-things\/security-cognitive-iot\/."},{"key":"ref_79","first-page":"102352","article-title":"Cognitive security: A comprehensive study of cognitive science in cybersecurity","volume":"48","author":"Andrade","year":"2019","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Andrade, R., Torres, J., and Cadena, S. (2019). Cognitive security for incident management process. International Conference on Information Technology & Systems, Springer.","DOI":"10.1007\/978-3-030-11890-7_59"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Ioannou, C., and Vassiliou, V. (2019, January 29\u201331). Classifying Security Attacks in IoT Networks Using Supervised Learning. Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece.","DOI":"10.1109\/DCOSS.2019.00118"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/MWC.2017.1800079","article-title":"Active learning for wireless IoT intrusion detection","volume":"25","author":"Yang","year":"2018","journal-title":"IEEE Wirel. Commun."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.-L., Iorkyase, E., Tachtatzis, C., and Atkinson, R. (2016, January 11\u201313). Threat analysis of IoT networks using artificial neural network intrusion detection system. Proceedings of the 2016 International Symposium on Networks, Computers and Communications (ISNCC), Yasmine Hammamet, Tunisia.","DOI":"10.1109\/ISNCC.2016.7746067"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., and Alazab, A. (2019). A Novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks. Electronics, 8.","DOI":"10.3390\/electronics8111210"},{"key":"ref_85","first-page":"704","article-title":"Feature selection in network intrusion detection using metaheuristic algorithms","volume":"4","author":"Khorram","year":"2018","journal-title":"Int. J. Adv. Res. Ideas Innov. Technol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"31711","DOI":"10.1109\/ACCESS.2019.2903723","article-title":"Intrusion detection for IoT based on improved genetic algorithm and deep belief network","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Mahalle, P.N., Thakre, P.A., Prasad, N.R., and Prasad, R. (2013, January 24\u201327). A fuzzy approach to trust based access control in internet of things. Proceedings of the Wireless VITAE 2013, Atlantic City, NJ, USA.","DOI":"10.1109\/VITAE.2013.6617083"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sysarc.2019.01.017","article-title":"Fuzzy pattern tree for edge malware detection and categorization in IoT","volume":"97","author":"Dovom","year":"2019","journal-title":"J. Syst. Archit."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1007\/s00779-015-0874-8","article-title":"A new online anomaly learning and detection for large-scale service of internet of thing","volume":"19","author":"Wang","year":"2015","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"5494","DOI":"10.1109\/ACCESS.2017.2696031","article-title":"IoTRiskAnalyzer: A probabilistic model checking based framework for formal risk analytics of the Internet of Things","volume":"5","author":"Mohsin","year":"2017","journal-title":"IEEE Access"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Cheng, S., Cai, Z., and Li, J. (2017). Approximate sensory data collection: A survey. Sensors, 17.","DOI":"10.3390\/s17030564"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"5069","DOI":"10.1109\/ACCESS.2017.2679207","article-title":"A Cluster-Based Data Fusion Technique to Analyze Big Data in Wireless Multi-Sensor System","volume":"5","author":"Din","year":"2017","journal-title":"IEEE Access"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/MWC.2017.1600404","article-title":"Cognitive-radio-based internet of things: Applications, architectures, spectrum related functionalities, and future research directions","volume":"24","author":"Khan","year":"2017","journal-title":"IEEE Wirel. Commun."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"910","DOI":"10.3390\/a8040910","article-title":"Effective data acquisition protocol for multi-hop heterogeneous wireless sensor networks using compressive sensing","volume":"8","author":"Khedr","year":"2015","journal-title":"Algorithms"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Jaloudi, S. (2019). Communication protocols of an industrial internet of things environment: A comparative study. Future Internet, 11.","DOI":"10.3390\/fi11030066"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Dos Santos, Y.L., and Canedo, E.D. (2019). On the design and implementation of an IoT based architecture for reading ultra high frequency tags. Information, 10.","DOI":"10.3390\/info10020041"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"4470","DOI":"10.3390\/s150204470","article-title":"Architecture of a service-enabled sensing platform for the environment","volume":"15","author":"Kotsev","year":"2015","journal-title":"Sensors"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1504\/IJSNET.2018.088366","article-title":"Localised information fusion techniques for location discovery in wireless sensor networks","volume":"26","author":"Hancke","year":"2018","journal-title":"Int. J. Sens. Netw."},{"key":"ref_99","unstructured":"Abdelgawad, A.M. (2011). Resourse-Aware Data Fusion Algorithms for Wireless Sensor Networks. [Ph.D. Thesis, University of Louisiana at Lafayette]."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Dobslaw, F., Gidlund, M., and Zhang, T. (2015, January 24\u201328). Challenges for the use of data aggregation in industrial Wireless Sensor Networks. Proceedings of the 2015 IEEE International Conference on Automation Science and Engineering (CASE), Gothenburg, Sweden.","DOI":"10.1109\/CoASE.2015.7294052"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"4264","DOI":"10.1109\/JSEN.2015.2416208","article-title":"Particle Swarm Optimization-Based Clustering by Preventing Residual Nodes in Wireless Sensor Networks","volume":"15","author":"Rejinaparvin","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s13638-019-1374-8","article-title":"Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks","volume":"2019","author":"Wan","year":"2019","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1504\/IJWMC.2018.091139","article-title":"A fault tolerance based route optimisation and data aggregation using artificial intelligence to enhance performance in wireless sensor networks","volume":"14","author":"Menaria","year":"2018","journal-title":"Int. J. Wirel. Mob. Comput."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Cheng, S., Li, J., and Cai, Z. (2013, January 14\u201319). O(\u03b5)-Approximation to physical world by sensor networks. Proceedings of the 2013 Proceedings IEEE INFOCOM, Turin, Italy.","DOI":"10.1109\/INFCOM.2013.6567121"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Djelouat, H., Amira, A., and Bensaali, F. (2018). Compressive sensing-based IoT applications: A review. J. Sens. Actuator Netw., 7.","DOI":"10.3390\/jsan7040045"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MCOM.2017.1600218CM","article-title":"Efficient energy management for the internet of things in smart cities","volume":"55","author":"Ejaz","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"235","DOI":"10.3390\/jsan2020235","article-title":"IETF standardization in the field of the internet of things (IoT): A survey","volume":"2","author":"Ishaq","year":"2013","journal-title":"J. Sens. Actuator Netw."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Xu, C., Yang, H.H., Wang, X., and Quek, T.Q. (2019, January 15\u201318). On peak age of information in data preprocessing enabled IoT networks. Proceedings of the 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco.","DOI":"10.1109\/WCNC.2019.8885690"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.comnet.2015.12.023","article-title":"Urban planning and building smart cities based on the Internet of Things using Big Data analytics","volume":"101","author":"Rathore","year":"2016","journal-title":"Comput. Netw."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.future.2019.02.005","article-title":"An energy efficient IoT data compression approach for edge machine learning","volume":"96","author":"Azar","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Karjee, J., Rath, H.K., and Pal, A. (2018, January 6\u20138). Efficient Data Prediction, Reconstruction and Estimation in Indoor IoT Networks. Proceedings of the 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), Barcelona, Spain.","DOI":"10.1109\/FiCloud.2018.00042"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Chang, H., Feng, J., and Duan, C. (2019). Reinforcement learning-based data forwarding in underwater wireless sensor networks with passive mobility. Sensors, 19.","DOI":"10.3390\/s19020256"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"2964","DOI":"10.3390\/s150202964","article-title":"A Data Fusion Method in Wireless Sensor Networks","volume":"15","author":"Izadi","year":"2015","journal-title":"Sensors"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Kumar, G.R., Mangathayaru, N., and Narsimha, G. (2016, January 22\u201324). Design of novel fuzzy distribution function for dimensionality reduction and intrusion detection. Proceedings of the 2016 International Conference on Engineering & MIS (ICEMIS), Agadir, Morocco.","DOI":"10.1109\/ICEMIS.2016.7745346"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.comnet.2019.01.024","article-title":"Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT","volume":"151","author":"Thangaramya","year":"2019","journal-title":"Comput. Netw."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1109\/JSYST.2018.2873591","article-title":"A strategy for elimination of data redundancy in internet of things (IoT) based wireless sensor network (wsn)","volume":"13","author":"Kumar","year":"2018","journal-title":"IEEE Syst. J."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"10015","DOI":"10.1109\/ACCESS.2018.2804623","article-title":"Real-time probabilistic data fusion for large-scale IoT applications","volume":"6","author":"Akbar","year":"2018","journal-title":"IEEE Access"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Alsharif, M.H., Kelechi, A.H., Yahya, K., and Chaudhry, S.A. (2020). Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends. Symmetry, 12.","DOI":"10.3390\/sym12010088"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1109\/TMSCS.2018.2864297","article-title":"Smart, Secure, Yet Energy-Efficient, Internet-of-Things Sensors","volume":"4","author":"Akmandor","year":"2018","journal-title":"IEEE Trans. Multi-Scale Comput. Syst."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Ferrando, R., and Stacey, P. (2017, January 17\u201318). Classification of device behaviour in internet of things infrastructures: Towards distinguishing the abnormal from security threats. Proceedings of the 1st International Conference on Internet of Things and Machine Learning, Liverpool, UK.","DOI":"10.1145\/3109761.3109791"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Kaliya, N., and Hussain, M. (2017, January 7\u20139). Framework for privacy preservation in iot through classification and access control mechanisms. Proceedings of the 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, India.","DOI":"10.1109\/I2CT.2017.8226166"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Akbar, A., Carrez, F., Moessner, K., and Zoha, A. (2015, January 14\u201316). Predicting complex events for pro-active IoT applications. Proceedings of the 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), Milan, Italy.","DOI":"10.1109\/WF-IoT.2015.7389075"},{"key":"ref_123","unstructured":"Da Rosa Righi, R., Correa, E., Gomes, M.M., and da Costa, C.A. (2018). Enhancing performance of IoT applications with load prediction and cloud elasticity. Future Gener. Comput. Syst., in press."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"30257","DOI":"10.1007\/s11042-018-7005-2","article-title":"Network attack prediction method based on threat intelligence for IoT","volume":"78","author":"Zhang","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/LCOMM.2018.2875978","article-title":"Bringing Deep Learning at the Edge of Information-Centric Internet of Things","volume":"23","author":"Khelifi","year":"2019","journal-title":"IEEE Commun. Lett."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Lawrence, T., and Zhang, L. (2019). IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices. Sensors, 19.","DOI":"10.3390\/s19245541"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1109\/TCSI.2017.2735490","article-title":"A reconfigurable streaming deep convolutional neural network accelerator for Internet of Things","volume":"65","author":"Du","year":"2017","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"18042","DOI":"10.1109\/ACCESS.2017.2747560","article-title":"Network traffic classifier with convolutional and recurrent neural networks for Internet of Things","volume":"5","author":"Carro","year":"2017","journal-title":"IEEE Access"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"13439","DOI":"10.1109\/ACCESS.2018.2810264","article-title":"CS-CNN: Enabling robust and efficient convolutional neural networks inference for Internet-of-Things applications","volume":"6","author":"Shen","year":"2018","journal-title":"IEEE Access"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Njima, W., Ahriz, I., Zayani, R., Terre, M., and Bouallegue, R. (2019). Deep CNN for Indoor Localization in IoT-Sensor Systems. Sensors, 19.","DOI":"10.3390\/s19143127"},{"key":"ref_131","unstructured":"Disabato, S., Roveri, M., and Alippi, C. (2019). Distributed Deep Convolutional Neural Networks for the Internet-of-Things. arXiv."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Pius Owoh, N., Singh, M.M., and Zaaba, Z.F. (2018). Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors. Sensors, 18.","DOI":"10.3390\/s18072134"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Cruciani, F., Cleland, I., Nugent, C., Mccullagh, P., Synnes, K., and Hallberg, J. (2018). Automatic annotation for human activity recognition in free living using a smartphone. Sensors, 18.","DOI":"10.3390\/s18072203"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2015.11.044","article-title":"Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging","volume":"185","author":"Zabalza","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Su, S., Sun, Y., Gao, X., Qiu, J., and Tian, Z. (2019). A correlation-change based feature selection method for IoT equipment anomaly detection. Applied Sciences, 9.","DOI":"10.3390\/app9030437"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"N\u00f5mm, S., and Bah\u015fi, H. (2018, January 17\u201320). Unsupervised anomaly based botnet detection in IoT networks. Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA.","DOI":"10.1109\/ICMLA.2018.00171"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Ejaz, W., and Anpalagan, A. (2019). Dimension Reduction for Big Data Analytics in Internet of Things. Internet of Things for Smart Cities, Springer.","DOI":"10.1007\/978-3-319-95037-2"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"2282","DOI":"10.1109\/JIOT.2017.2730360","article-title":"Probabilistic recovery of incomplete sensed data in IoT","volume":"5","author":"Fekade","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1109\/JIOT.2015.2490162","article-title":"NDCMC: A Hybrid Data Collection Approach for Large-Scale WSNs Using Mobile Element and Hierarchical Clustering","volume":"3","author":"Zhang","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JIOT.2017.2756025","article-title":"Recursive principal component analysis-based data outlier detection and sensor data aggregation in IoT systems","volume":"4","author":"Yu","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3390\/make1010002","article-title":"Learning to teach reinforcement learning agents","volume":"1","author":"Fachantidis","year":"2019","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Willig, A., Matusovsky, Y., and Kind, A. (2017). Relayer-Enabled Retransmission Scheduling in 802.15. 4e LLDN\u2014Exploring a Reinforcement Learning Approach. J. Sens. Actuator Netw., 6.","DOI":"10.3390\/jsan6020006"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Derhab, A., Guerroumi, M., Gumaei, A., Maglaras, L., Ferrag, M.A., Mukherjee, M., and Khan, F.A. (2019). Blockchain and random subspace learning-based IDS for SDN-enabled industrial IoT security. Sensors, 19.","DOI":"10.3390\/s19143119"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Chincoli, M., and Liotta, A. (2018). Self-learning power control in wireless sensor networks. Sensors, 18.","DOI":"10.3390\/s18020375"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Escolar, S., Caruso, A., Chessa, S., del Toro, X., Villanueva, F.J., and L\u00f3pez, J.C. (2018, January 25\u201328). Statistical energy neutrality in IoT hybrid energy-harvesting networks. Proceedings of the 2018 IEEE Symposium on Computers and Communications (ISCC), Natal, Brazil.","DOI":"10.1109\/ISCC.2018.8538532"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"2234","DOI":"10.1109\/JIOT.2018.2828943","article-title":"A dynamic programming algorithm for high-level task scheduling in energy harvesting IoT","volume":"5","author":"Caruso","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Fraternali, F., Balaji, B., and Gupta, R. (2018, January 4\u20137). Scaling configuration of energy harvesting sensors with reinforcement learning. Proceedings of the 6th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems, Shenzhen, China.","DOI":"10.1145\/3279755.3279760"},{"key":"ref_149","unstructured":"Dulac-Arnold, G., Mankowitz, D., and Hester, T. (2019). Challenges of Real-World Reinforcement Learning. arXiv."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1109\/JIOT.2018.2872440","article-title":"Reinforcement learning-based multiaccess control and battery prediction with energy harvesting in IoT systems","volume":"6","author":"Chu","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"128014","DOI":"10.1109\/ACCESS.2019.2939735","article-title":"Reinforcement Learning for Adaptive Resource Allocation in Fog RAN for IoT With Heterogeneous Latency Requirements","volume":"7","author":"Nassar","year":"2019","journal-title":"IEEE Access"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"9868","DOI":"10.1109\/JIOT.2019.2932775","article-title":"Secure and Efficient K Nearest Neighbor Query Over Encrypted Uncertain Data in Cloud-IoT Ecosystem","volume":"6","author":"Guo","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1109\/JIOT.2019.2945425","article-title":"IoT Load Classification and Anomaly Warning in ELV DC Picogrids Using Hierarchical Extended k\u2014Nearest Neighbors","volume":"7","author":"Quek","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Chen, Y., Lu, L., Yu, X., and Li, X. (2019). Adaptive Method for Packet Loss Types in IoT: An Naive Bayes Distinguisher. Electronics, 8.","DOI":"10.3390\/electronics8020134"},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Khan, A., Khan, M.N., and Anwar, S. (2014, January 11\u201312). A novel learning method to classify data streams in the internet of things. Proceedings of the 2014 National Software Engineering Conference, Rawalpindi, Pakistan.","DOI":"10.1109\/NSEC.2014.6998242"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Coello Coello, C.A., Brambila, S.G., Gamboa, J.F., Tapia, M.G.C., and G\u00f3mez, R.H. (2019). Evolutionary multiobjective optimization: Open research areas and some challenges lying ahead. Complex Intell. Syst.","DOI":"10.1007\/s40747-019-0113-4"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1007\/s13202-018-0447-2","article-title":"A comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs","volume":"8","author":"Khosravanian","year":"2018","journal-title":"J. Pet. Explor. Prod. Technol."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Liu, X., Qin, Z., and Gao, Y. (2019, January 20\u201324). Resource allocation for edge computing in iot networks via reinforcement learning. Proceedings of the ICC 2019-2019 IEEE International Conference on Communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761385"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1002\/9780470496916.ch2","article-title":"Single-solution based metaheuristics","volume":"74","author":"Talbi","year":"2009","journal-title":"Metaheuristics Des. Implement."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Leivadeas, A., Kesidis, G., Ibnkahla, M., and Lambadaris, I. (2019). VNF placement optimization at the edge and cloud. Future Internet, 11.","DOI":"10.3390\/fi11030069"},{"key":"ref_161","first-page":"78","article-title":"A tabu search method for load balancing in fog computing","volume":"16","author":"Jimeno","year":"2018","journal-title":"Int. J. Artif. Intell"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1002\/cpe.3105","article-title":"Towards scheduling for Internet-of-Things applications on clouds: A simulated annealing approach","volume":"27","author":"Moschakis","year":"2015","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Sreenivasamurthy, S., and Obraczka, K. (2018, January 25\u201328). Clustering for load balancing and energy efficiency in IoT applications. Proceedings of the 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Milwaukee, WI, USA.","DOI":"10.1109\/MASCOTS.2018.00038"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1007\/s11276-019-02083-7","article-title":"Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications","volume":"26","author":"Rani","year":"2020","journal-title":"Wirel. Netw."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"2329","DOI":"10.1007\/s11276-019-02121-4","article-title":"Genetic algorithm based adaptive offloading for improving IoT device communication efficiency","volume":"26","author":"Hussain","year":"2020","journal-title":"Wirel. Netw."},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Habib, M., Aljarah, I., Faris, H., and Mirjalili, S. (2020). Multi-objective Particle Swarm Optimization for Botnet Detection in Internet of Things. Evolutionary Machine Learning Techniques, Springer.","DOI":"10.1007\/978-981-32-9990-0_10"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1994","DOI":"10.1007\/s11036-019-01333-4","article-title":"A Novel Hierarchical Data Aggregation with Particle Swarm Optimization for Internet of Things","volume":"24","author":"Yin","year":"2019","journal-title":"Mob. Netw. Appl."},{"key":"ref_168","unstructured":"Scott, S. (2020, April 05). How Swarm Intelligence Is Making Simple Tech Much Smarter. Available online: https:\/\/singularityhub.com\/2018\/02\/08\/how-swarm-intelligence-is-making-simple-tech-much-smarter\/#sm.0000exyrw0h6feo6ygy1c415091lk."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCOM.2017.1700454","article-title":"Energy Neutral Internet of Drones","volume":"56","author":"Long","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"2396","DOI":"10.11591\/ijece.v6i5.pp2396-2402","article-title":"Trust-Based Privacy for Internet of Things","volume":"6","author":"Suryani","year":"2016","journal-title":"Int. J. Electr. Comput. Eng. (Ijece)"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"1710","DOI":"10.3906\/elk-1801-100","article-title":"Reliable data gathering in the Internet of Things using artificial bee colony","volume":"26","author":"Yousefi","year":"2018","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Chen, Z.-H., and Tsai, C.-W. (2018, January 17\u201319). An Effective Metaheuristic Algorithm for Intrusion Detection System. Proceedings of the 2018 IEEE International Conference on Smart Internet of Things (SmartIoT), Xi\u2019an, China.","DOI":"10.1109\/SmartIoT.2018.00036"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Dos Santos, W.G., Costa, W.S., Faber, M.J., Silva, J.A., Rocha, H.R., and Segatto, M.E. (2019, January 11\u201313). Sensor Allocation in a Hybrid Star-Mesh IoT Network using Genetic Algorithm and K-Medoids. Proceedings of the 2019 IEEE Latin-American Conference on Communications (LATINCOM), Salvador, Brazil.","DOI":"10.1109\/LATINCOM48065.2019.8937958"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Cuka, M., Elmazi, D., Ikeda, M., Matsuo, K., and Barolli, L. (2019). IoT Node Selection and Placement: A New Approach Based on Fuzzy Logic and Genetic Algorithm. Conference on Complex, Intelligent, and Software Intensive Systems, Springer.","DOI":"10.1007\/978-3-030-22354-0_3"},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Choochotkaew, S., Yamaguchi, H., Higashino, T., Shibuya, M., and Hasegawa, T. (2017, January 5\u20137). EdgeCEP: Fully-distributed complex event processing on IoT edges. Proceedings of the 2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS), Ottawa, ON, Canada.","DOI":"10.1109\/DCOSS.2017.14"},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Da Silva Fr\u00e9, G.L., Silva, J.d., Reis, F.A., and Mendes, L.D.P. (2015, January 14\u201317). Particle swarm optimization implementation for minimal transmission power providing a fully-connected cluster for the Internet of Things. Proceedings of the 2015 International Workshop on Telecommunications (IWT), Santa Rita do Sapucai, Brazil.","DOI":"10.1109\/IWT.2015.7224573"},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Zhao, H.-Y., Wang, J.-C., Guan, X., Wang, Z., He, Y.-H., and Xie, H.-L. (2019, January 14\u201317). Ant Colony Based Energy Consumption Optimization for Mobile IoT Networks. Proceedings of the 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Atlanta, GA, USA.","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00041"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/02564602.2017.1391136","article-title":"Hybrid arificial bee colony algorithm for an energy efficient internet of things based on wireless sensor network","volume":"34","author":"Muhammad","year":"2017","journal-title":"IETE Tech. Rev."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"12686","DOI":"10.1109\/ACCESS.2019.2892798","article-title":"An energy efficient Internet of Things network using restart artificial bee colony and wireless power transfer","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"25","DOI":"10.3991\/ijoe.v11i7.4762","article-title":"Hybrid Metaheuristics and their Implementations","volume":"11","author":"Ding","year":"2015","journal-title":"Int. J. Online Eng."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"992","DOI":"10.3390\/en10070992","article-title":"Bluetooth 5 energy management through a fuzzy-pso solution for mobile devices of internet of things","volume":"10","author":"Pau","year":"2017","journal-title":"Energies"},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Rahman, S., Al Mamun, S., Ahmed, M.U., and Kaiser, M.S. (2016, January 3\u20135). PHY\/MAC layer attack detection system using neuro-fuzzy algorithm for IoT network. Proceedings of the 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India.","DOI":"10.1109\/ICEEOT.2016.7755150"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/s13638-018-1128-z","article-title":"An intrusion detection method for internet of things based on suppressed fuzzy clustering","volume":"2018","author":"Liu","year":"2018","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Shukla, R.M., and Munir, A. (2016, January 19\u201321). A computation offloading scheme leveraging parameter tuning for real-time IoT devices. Proceedings of the 2016 IEEE International Symposium on Nanoelectronic and Information Systems (iNIS), Gwalior, India.","DOI":"10.1109\/iNIS.2016.055"},{"key":"ref_185","unstructured":"Lapowsky, I. (2020, April 05). How Cambridge Analytica Sparked the Great Privacy Awakening. Available online: https:\/\/www.wired.com\/story\/cambridge-analytica-facebook-privacy-awakening\/."},{"key":"ref_186","unstructured":"Gdpr (2020, April 05). General Data Protection Regulation (GDPR)\u2013Official Legal Text. Available online: https:\/\/gdpr-info.eu\/."},{"key":"ref_187","unstructured":"Epsrc (2020, April 04). Databox Project. Available online: https:\/\/www.databoxproject.uk\/."},{"key":"ref_188","unstructured":"Ibm (2020, April 05). Data Privacy Protection Solutions. Available online: https:\/\/www.ibm.com\/security\/privacy."}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/9\/2\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:51:16Z","timestamp":1760363476000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/9\/2\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,25]]},"references-count":188,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["jsan9020021"],"URL":"https:\/\/doi.org\/10.3390\/jsan9020021","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,25]]}}}