{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:51:06Z","timestamp":1743022266827,"version":"3.40.3"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030436049"},{"type":"electronic","value":"9783030436056"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-43605-6_13","type":"book-chapter","created":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T00:03:27Z","timestamp":1584489807000},"page":"219-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Toward Holistic Integration of Computing and Wireless Networking"],"prefix":"10.1007","author":[{"given":"Kwang-Cheng","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingze","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixiang","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qimei","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,18]]},"reference":[{"issue":"12","key":"13_CR1","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1109\/LAWP.2019.2942819","volume":"18","author":"R Adeogun","year":"2019","unstructured":"Adeogun, R.: Calibration of stochastic radio propagation models using machine learning. IEEE Antennas Wirel. Propag. Lett. 18(12), 2538\u20132542 (2019)","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"13_CR2","volume-title":"Readings in Distributed Artificial Intelligence","author":"AH Bond","year":"2014","unstructured":"Bond, A.H., Gasser, L.: Readings in Distributed Artificial Intelligence. Morgan Kaufmann, San Mateo (2014)"},{"key":"13_CR3","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1109\/JIOT.2019.2955035","volume":"7","author":"M Camelo","year":"2019","unstructured":"Camelo, M., Claeys, M., Latr\u00e9, S.: Parallel reinforcement learning with minimal communication overhead for IoT environments. IEEE Internet Things J. 7, 1387\u20131400 (2019)","journal-title":"IEEE Internet Things J."},{"key":"13_CR4","doi-asserted-by":"publisher","first-page":"6505","DOI":"10.1109\/ACCESS.2017.2783682","volume":"6","author":"B Chen","year":"2017","unstructured":"Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., Yin, B.: Smart factory of Industry 4.0: key technologies, application case, and challenges. IEEE Access 6, 6505\u20136519 (2017)","journal-title":"IEEE Access"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Chen, K.C., Hung, H.M.: Wireless robotic communication for collaborative multi-agent systems. In: ICC 2019-2019 IEEE International Conference on Communications (ICC), pp. 1\u20137. IEEE (2019)","DOI":"10.1109\/ICC.2019.8761140"},{"issue":"2","key":"13_CR6","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/MNET.2018.1800011","volume":"33","author":"KC Chen","year":"2018","unstructured":"Chen, K.C., Zhang, T., Gitlin, R.D., Fettweis, G.: Ultra-low latency mobile networking. IEEE Netw. 33(2), 181\u2013187 (2018)","journal-title":"IEEE Netw."},{"issue":"4","key":"13_CR7","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MVT.2017.2752758","volume":"12","author":"I Chih-Lin","year":"2017","unstructured":"Chih-Lin, I., Sun, Q., Liu, Z., Zhang, S., Han, S.: The big-data-driven intelligent wireless network: architecture, use cases, solutions, and future trends. IEEE Veh. Technol. Mag. 12(4), 20\u201329 (2017)","journal-title":"IEEE Veh. Technol. Mag."},{"issue":"2","key":"13_CR8","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1109\/JIOT.2018.2872442","volume":"6","author":"Q Cui","year":"2018","unstructured":"Cui, Q., et al.: Big data analytics and network calculus enabling intelligent management of autonomous vehicles in a smart city. IEEE Internet Things J. 6(2), 2021\u20132034 (2018)","journal-title":"IEEE Internet Things J."},{"key":"13_CR9","unstructured":"Demazeau, Y.: From interactions to collective behaviour in agent-based systems. In: Proceedings of the 1st European Conference on Cognitive Science, Saint-Malo. Citeseer (1995)"},{"issue":"6","key":"13_CR10","doi-asserted-by":"publisher","first-page":"6486","DOI":"10.1016\/j.eswa.2010.11.097","volume":"38","author":"M Elango","year":"2011","unstructured":"Elango, M., Nachiappan, S., Tiwari, M.K.: Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms. Expert Syst. Appl. 38(6), 6486\u20136491 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"13_CR11","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/TCOMM.2018.2878025","volume":"67","author":"A Ferdowsi","year":"2019","unstructured":"Ferdowsi, A., Saad, W.: Deep learning for signal authentication and security in massive Internet-of-Things systems. IEEE Trans. Commun. 67(2), 1371\u20131387 (2019)","journal-title":"IEEE Trans. Commun."},{"issue":"10","key":"13_CR12","doi-asserted-by":"publisher","first-page":"8118","DOI":"10.1109\/TIE.2017.2701778","volume":"64","author":"X Ge","year":"2017","unstructured":"Ge, X., Han, Q.L.: Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism. IEEE Trans. Ind. Electron. 64(10), 8118\u20138127 (2017)","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"4","key":"13_CR13","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1109\/LWC.2018.2805902","volume":"7","author":"D He","year":"2018","unstructured":"He, D., Liu, C., Quek, T.Q., Wang, H.: Transmit antenna selection in MIMO wiretap channels: a machine learning approach. IEEE Wirel. Commun. Lett. 7(4), 634\u2013637 (2018)","journal-title":"IEEE Wirel. Commun. Lett."},{"issue":"11","key":"13_CR14","doi-asserted-by":"publisher","first-page":"10433","DOI":"10.1109\/TVT.2017.2751641","volume":"66","author":"Y He","year":"2017","unstructured":"He, Y., et al.: Deep-reinforcement-learning-based optimization for cache-enabled opportunistic interference alignment wireless networks. IEEE Trans. Veh. Technol. 66(11), 10433\u201310445 (2017)","journal-title":"IEEE Trans. Veh. Technol."},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Hsiao, J.H., Chen, K.C.: Communication methodology to control a distributed multi-agent system. In: ICC 2019\u20132019 IEEE International Conference on Communications (ICC), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/ICC.2019.8761869"},{"key":"13_CR16","unstructured":"Huang, L., Bi, S., Zhang, Y.J.: Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks. IEEE Trans. Mob. Comput., 1 (2019)"},{"issue":"2","key":"13_CR17","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1080\/00207543.2014.999958","volume":"54","author":"D Ivanov","year":"2016","unstructured":"Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., Ivanova, M.: A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory Industry 4.0. Int. J. Prod. Res. 54(2), 386\u2013402 (2016)","journal-title":"Int. J. Prod. Res."},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Jiang, Z., He, Z., Chen, S., Molisch, A.F., Zhou, S., Niu, Z.: Inferring remote channel state information: Cram\u00e9r-Rae lower bound and deep learning implementation. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/GLOCOM.2018.8648140"},{"issue":"1","key":"13_CR19","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s40684-016-0015-5","volume":"3","author":"HS Kang","year":"2016","unstructured":"Kang, H.S., et al.: Smart manufacturing: past research, present findings, and future directions. Int. J. Precis. Eng. Manuf.-Green Technol. 3(1), 111\u2013128 (2016). https:\/\/doi.org\/10.1007\/s40684-016-0015-5","journal-title":"Int. J. Precis. Eng. Manuf.-Green Technol."},{"issue":"10","key":"13_CR20","doi-asserted-by":"publisher","first-page":"4983","DOI":"10.1109\/TWC.2019.2931570","volume":"18","author":"J Kim","year":"2019","unstructured":"Kim, J., Hwang, G.: Adaptive bandwidth allocation based on sample path prediction with gaussian process regression. IEEE Trans. Wirel. Commun. 18(10), 4983\u20134996 (2019)","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Ko, E., Chen, K.C.: Wireless communications meets artificial intelligence: an illustration by autonomous vehicles on Manhattan streets. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/GLOCOM.2018.8647380"},{"issue":"4","key":"13_CR22","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s11023-007-9079-x","volume":"17","author":"S Legg","year":"2007","unstructured":"Legg, S., Hutter, M.: Universal intelligence: a definition of machine intelligence. Mind. Mach. 17(4), 391\u2013444 (2007). https:\/\/doi.org\/10.1007\/s11023-007-9079-x","journal-title":"Mind. Mach."},{"issue":"6","key":"13_CR23","doi-asserted-by":"publisher","first-page":"10119","DOI":"10.1109\/JIOT.2019.2935543","volume":"6","author":"L Lei","year":"2019","unstructured":"Lei, L., Xu, H., Xiong, X., Zheng, K., Xiang, W., Wang, X.: Multiuser resource control with deep reinforcement learning in IoT edge computing. IEEE Internet Things J. 6(6), 10119\u201310133 (2019)","journal-title":"IEEE Internet Things J."},{"issue":"10","key":"13_CR24","doi-asserted-by":"publisher","first-page":"4665","DOI":"10.1109\/TII.2018.2842821","volume":"14","author":"L Li","year":"2018","unstructured":"Li, L., Ota, K., Dong, M.: Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Trans. Ind. Inf. 14(10), 4665\u20134673 (2018)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"1","key":"13_CR25","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1109\/JIOT.2019.2951509","volume":"7","author":"L Li","year":"2020","unstructured":"Li, L., Xu, Y., Yin, J., Liang, W., Li, X., Chen, W., Han, Z.: Deep reinforcement learning approaches for content caching in cache-enabled D2D networks. IEEE Internet Things J. 7(1), 544\u2013557 (2020)","journal-title":"IEEE Internet Things J."},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Lin, C.Y., Chen, K.C., Wickramasuriya, D., Lien, S.Y., Gitlin, R.D.: Anticipatory mobility management by big data analytics for ultra-low latency mobile networking. In: 2018 IEEE International Conference on Communications (ICC), pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/ICC.2018.8422231"},{"key":"13_CR27","doi-asserted-by":"publisher","first-page":"4276","DOI":"10.1109\/TII.2019.2908210","volume":"15","author":"CC Lin","year":"2019","unstructured":"Lin, C.C., Deng, D.J., Chih, Y.L., Chiu, H.T.: Smart manufacturing scheduling with edge computing using multi-class deep Q network. IEEE Trans. Ind. Inform. 15, 4276\u20134284 (2019)","journal-title":"IEEE Trans. Ind. Inform."},{"key":"13_CR28","doi-asserted-by":"publisher","first-page":"7957","DOI":"10.1109\/TVT.2019.2920284","volume":"68","author":"X Liu","year":"2019","unstructured":"Liu, X., Liu, Y., Chen, Y., Hanzo, L.: Trajectory design and power control for multi-UAV assisted wireless networks: a machine learning approach. IEEE Trans. Veh. Technol. 68, 7957\u20137969 (2019)","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"17","key":"13_CR29","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1049\/el.2019.1864","volume":"55","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Tong, K.F., Wong, K.K.: Reinforcement learning based routing for energy sensitive wireless mesh IoT networks. Electron. Lett. 55(17), 966\u2013968 (2019)","journal-title":"Electron. Lett."},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Liu, Z., Dai, J., Wu, B., Lin, H.: Communication-aware motion planning for multi-agent systems from signal temporal logic specifications. In: 2017 American Control Conference (ACC), pp. 2516\u20132521. IEEE (2017)","DOI":"10.23919\/ACC.2017.7963331"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Mamdouh, M., Elrukhsi, M.A.I., Khattab, A.: Securing the Internet of Things and wireless sensor networks via machine learning: a survey. In: 2018 International Conference on Computer and Applications (ICCA), pp. 215\u2013218. IEEE, Beirut, August 2018","DOI":"10.1109\/COMAPP.2018.8460440"},{"issue":"9","key":"13_CR32","doi-asserted-by":"publisher","first-page":"6062","DOI":"10.1109\/TWC.2017.2718526","volume":"16","author":"DD Nguyen","year":"2017","unstructured":"Nguyen, D.D., Nguyen, H.X., White, L.B.: Reinforcement learning with network-assisted feedback for heterogeneous RAT selection. IEEE Trans. Wirel. Commun. 16(9), 6062\u20136076 (2017)","journal-title":"IEEE Trans. Wirel. Commun."},{"issue":"11","key":"13_CR33","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1109\/JPROC.2019.2941458","volume":"107","author":"J Park","year":"2019","unstructured":"Park, J., Samarakoon, S., Bennis, M., Debbah, M.: Wireless network intelligence at the edge. Proc. IEEE 107(11), 2204\u20132239 (2019)","journal-title":"Proc. IEEE"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Pinto, E.M.d.L., Lachowski, R., Pellenz, M.E., Penna, M.C., Souza, R.D.: A machine learning approach for detecting spoofing attacks in wireless sensor networks. In: 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), pp. 752\u2013758. IEEE, Krakow, May 2018","DOI":"10.1109\/AINA.2018.00113"},{"issue":"5","key":"13_CR35","doi-asserted-by":"publisher","first-page":"2500","DOI":"10.1109\/TII.2018.2874693","volume":"15","author":"A Rahman","year":"2018","unstructured":"Rahman, A., Jin, J., Cricenti, A.L., Rahman, A., Kulkarni, A.: Communication-aware cloud robotic task offloading with on-demand mobility for smart factory maintenance. IEEE Trans. Ind. Inf. 15(5), 2500\u20132511 (2018)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"13_CR36","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.cie.2018.03.039","volume":"125","author":"YR Shiue","year":"2018","unstructured":"Shiue, Y.R., Lee, K.C., Su, C.T.: Real-time scheduling for a smart factory using a reinforcement learning approach. Comput. Ind. Eng. 125, 604\u2013614 (2018)","journal-title":"Comput. Ind. Eng."},{"key":"13_CR37","unstructured":"Stein, S., Williamson, S.A., Jennings, N.R.: Decentralised channel allocation and information sharing for teams of cooperative agents. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 231\u2013238. International Foundation for Autonomous Agents and Multiagent Systems (2012)"},{"key":"13_CR38","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.jmsy.2018.01.006","volume":"48","author":"F Tao","year":"2018","unstructured":"Tao, F., Qi, Q., Liu, A., Kusiak, A.: Data-driven smart manufacturing. J. Manuf. Syst. 48, 157\u2013169 (2018)","journal-title":"J. Manuf. Syst."},{"issue":"4","key":"13_CR39","doi-asserted-by":"publisher","first-page":"3844","DOI":"10.1109\/LRA.2019.2929983","volume":"4","author":"Veniamin Tereshchuk","year":"2019","unstructured":"Tereshchuk, V., Stewart, J., Bykov, N., Pedigo, S., Devasia, S., Banerjee, A.G.: An efficient scheduling algorithm for multi-robot task allocation in assembling aircraft structures. arXiv preprint arXiv:1902.08905 (2019)","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"1","key":"13_CR40","doi-asserted-by":"publisher","first-page":"4","DOI":"10.20965\/ijat.2017.p0004","volume":"11","author":"KD Thoben","year":"2017","unstructured":"Thoben, K.D., Wiesner, S., Wuest, T.: \u201cIndustrie 4.0\u201d and smart manufacturing-a review of research issues and application examples. Int. J. Autom. Technol. 11(1), 4\u201316 (2017)","journal-title":"Int. J. Autom. Technol."},{"key":"13_CR41","doi-asserted-by":"publisher","first-page":"82027","DOI":"10.1109\/ACCESS.2019.2923425","volume":"7","author":"S Wang","year":"2019","unstructured":"Wang, S., Shin, Y.: Efficient routing protocol based on reinforcement learning for magnetic induction underwater sensor networks. IEEE Access 7, 82027\u201382037 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"13_CR42","doi-asserted-by":"publisher","first-page":"3159805","DOI":"10.1155\/2016\/3159805","volume":"12","author":"S Wang","year":"2016","unstructured":"Wang, S., Wan, J., Li, D., Zhang, C.: Implementing smart factory of Industrie 4.0: an outlook. Int. J. Distrib. Sens. Netw. 12(1), 3159805 (2016)","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Zhang, X., Wang, W.: Reinforcement learning based congestion control in satellite Internet of Things. In: 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1\u20136. IEEE, Xi\u2019an, October 2019","DOI":"10.1109\/WCSP.2019.8928132"},{"issue":"2","key":"13_CR44","doi-asserted-by":"publisher","first-page":"2061","DOI":"10.1109\/JIOT.2018.2878435","volume":"6","author":"Y Wei","year":"2019","unstructured":"Wei, Y., Yu, F.R., Song, M., Han, Z.: Joint optimization of caching, computing, and radio resources for fog-enabled IoT using natural actor-critic deep reinforcement learning. IEEE Internet Things J. 6(2), 2061\u20132073 (2019)","journal-title":"IEEE Internet Things J."},{"issue":"7","key":"13_CR45","doi-asserted-by":"publisher","first-page":"071018","DOI":"10.1115\/1.4036350","volume":"139","author":"D Wu","year":"2017","unstructured":"Wu, D., Jennings, C., Terpenny, J., Gao, R.X., Kumara, S.: A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J. Manuf. Sci. Eng. 139(7), 071018 (2017)","journal-title":"J. Manuf. Sci. Eng."},{"issue":"5","key":"13_CR46","doi-asserted-by":"publisher","first-page":"8215","DOI":"10.1109\/JIOT.2019.2919225","volume":"6","author":"H Wu","year":"2019","unstructured":"Wu, H., Zhang, Z., Jiao, C., Li, C., Quek, T.Q.S.: Learn to sense: a meta-learning-based sensing and fusion framework for wireless sensor networks. IEEE Internet Things J. 6(5), 8215\u20138227 (2019)","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"13_CR47","first-page":"23","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest, T., Weimer, D., Irgens, C., Thoben, K.D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23\u201345 (2016)","journal-title":"Prod. Manuf. Res."},{"key":"13_CR48","doi-asserted-by":"crossref","unstructured":"Xia, F., Song, H., Xu, C.: Securing the wireless environment of IoT. In: 2018 IEEE International Conference of Safety Produce Informatization (IICSPI), pp. 315\u2013318. IEEE, Chongqing, December 2018","DOI":"10.1109\/IICSPI.2018.8690435"},{"key":"13_CR49","doi-asserted-by":"publisher","first-page":"157807","DOI":"10.1109\/ACCESS.2019.2950055","volume":"7","author":"W Xia","year":"2019","unstructured":"Xia, W., Di, C., Guo, H., Li, S.: Reinforcement learning based stochastic shortest path finding in wireless sensor networks. IEEE Access 7, 157807\u2013157817 (2019)","journal-title":"IEEE Access"},{"key":"13_CR50","doi-asserted-by":"crossref","unstructured":"Xuan, P., Lesser, V., Zilberstein, S.: Communication decisions in multi-agent cooperation: model and experiments. In: Proceedings of the Fifth International Conference on Autonomous Agents, pp. 616\u2013623. ACM (2001)","DOI":"10.1145\/375735.376469"},{"issue":"3","key":"13_CR51","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/TGCN.2019.2907913","volume":"3","author":"C Yang","year":"2019","unstructured":"Yang, C., Chin, K.W., He, T., Liu, Y.: On sampling time maximization in wireless powered Internet of Things. IEEE Trans. Green Commun. Netw. 3(3), 641\u2013650 (2019)","journal-title":"IEEE Trans. Green Commun. Netw."},{"issue":"4","key":"13_CR52","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1109\/LWC.2019.2904486","volume":"8","author":"F Yao","year":"2019","unstructured":"Yao, F., Jia, L.: A collaborative multi-agent reinforcement learning anti-jamming algorithm in wireless networks. IEEE Wirel. Commun. Lett. 8(4), 1024\u20131027 (2019)","journal-title":"IEEE Wirel. Commun. Lett."},{"issue":"1","key":"13_CR53","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/LWC.2017.2757490","volume":"7","author":"H Ye","year":"2017","unstructured":"Ye, H., Li, G.Y., Juang, B.H.: Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wirel. Commun. Lett. 7(1), 114\u2013117 (2017)","journal-title":"IEEE Wirel. Commun. Lett."},{"issue":"6","key":"13_CR54","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1109\/JSAC.2019.2904329","volume":"37","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Wang, T., Liew, S.C.: Deep-reinforcement learning multiple access for heterogeneous wireless networks. IEEE J. Sel. Areas Commun. 37(6), 1277\u20131290 (2019)","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"6","key":"13_CR55","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1109\/JSAC.2019.2904363","volume":"37","author":"C Zhang","year":"2019","unstructured":"Zhang, C., Zhang, H., Qiao, J., Yuan, D., Zhang, M.: Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data. IEEE J. Sel. Areas Commun. 37(6), 1389\u20131401 (2019)","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"7","key":"13_CR56","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1109\/TMC.2018.2864155","volume":"18","author":"L Zhang","year":"2018","unstructured":"Zhang, L., Restuccia, F., Melodia, T., Pudlewski, S.M.: Taming cross-layer attacks in wireless networks: a Bayesian learning approach. IEEE Trans. Mob. Comput. 18(7), 1688\u20131702 (2018)","journal-title":"IEEE Trans. Mob. Comput."},{"key":"13_CR57","doi-asserted-by":"publisher","first-page":"118898","DOI":"10.1109\/ACCESS.2019.2937108","volume":"7","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Cai, P., Pan, C., Zhang, S.: Multi-agent deep reinforcement learning-based cooperative spectrum sensing with upper confidence bound exploration. IEEE Access 7, 118898\u2013118906 (2019)","journal-title":"IEEE Access"},{"key":"13_CR58","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1109\/TCOMM.2019.2952580","volume":"68","author":"P Zhao","year":"2019","unstructured":"Zhao, P., Tian, H., Cheny, K.C., Fan, S., Nie, G.: Context-aware TDD configuration and resource allocation for mobile edge computing. IEEE Trans. Commun. 68, 1118\u20131131 (2019)","journal-title":"IEEE Trans. Commun."},{"issue":"2","key":"13_CR59","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s11465-018-0499-5","volume":"13","author":"P Zheng","year":"2018","unstructured":"Zheng, P., et al.: Smart manufacturing systems for Industry 4.0: conceptual framework, scenarios, and future perspectives. Front. Mech. Eng. 13(2), 137\u2013150 (2018). https:\/\/doi.org\/10.1007\/s11465-018-0499-5","journal-title":"Front. Mech. Eng."},{"issue":"5","key":"13_CR60","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1109\/TCYB.2016.2543238","volume":"47","author":"L Zhou","year":"2016","unstructured":"Zhou, L., Yang, P., Chen, C., Gao, Y.: Multiagent reinforcement learning with sparse interactions by negotiation and knowledge transfer. IEEE Trans. Cybern. 47(5), 1238\u20131250 (2016)","journal-title":"IEEE Trans. Cybern."},{"issue":"2","key":"13_CR61","doi-asserted-by":"publisher","first-page":"2074","DOI":"10.1109\/JIOT.2018.2882583","volume":"6","author":"H Zhu","year":"2019","unstructured":"Zhu, H., Cao, Y., Wei, X., Wang, W., Jiang, T., Jin, S.: Caching transient data for Internet of Things: a deep reinforcement learning approach. IEEE Internet Things J. 6(2), 2074\u20132083 (2019)","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"13_CR62","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1109\/JIOT.2017.2759728","volume":"5","author":"J Zhu","year":"2018","unstructured":"Zhu, J., Song, Y., Jiang, D., Song, H.: A new deep-Q-learning-based transmission scheduling mechanism for the cognitive Internet of Things. IEEE Internet Things J. 5(4), 2375\u20132385 (2018)","journal-title":"IEEE Internet Things J."}],"container-title":["IFIP Advances in Information and Communication Technology","Internet of Things. A Confluence of Many Disciplines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-43605-6_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T01:06:32Z","timestamp":1710723992000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-43605-6_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030436049","9783030436056"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-43605-6_13","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"18 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IFIPIoT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Internet of Things Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tampa, FL","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ifipiot2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ifip-iotconference.org\/2019","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8 invited papers are included.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}