Deep reinforcement learning for computation offloading in mobile edge computing environment

Yousefpour, 2019, All one needs to know about fog computing and related edge computing paradigms: A complete survey, J. Syst. Archit., 98, 289, 10.1016/j.sysarc.2019.02.009

Yu, 2020, An intelligent game based offloading scheme for maximizing benefits of IoT-edge-cloud ecosystems, IEEE Internet Things J.

Liu, 2020, Unmanned aerial vehicle trajectory optimization for improved data collection in social networks, IEEE Trans. Netw. Sci. Eng.

Huang, 2020, BD-VTE: A novel baseline data based verifiable trust evaluation scheme for smart network systems, IEEE Trans. Netw. Sci. Eng.

Liu, 2020, Objective-variable tour planning for mobile data collection in partitioned sensor networks, IEEE Trans. Mob. Comput., 10.1109/TMC.2020.3003004

Liu, 2020, Artificial intelligence aware and security-enhanced trace-back technique in mobile edge computing, Comput. Commun., 161, 375, 10.1016/j.comcom.2020.08.006

Zhu, 2020, Multi-agent deep reinforcement learning for vehicular computation offloading in IoT, IEEE Internet Things J.

Huang, 2020, An trust interconnections system for mobile information control in ubiquitous 5g networks, IEEE Trans. Netw. Sci. Eng.

Li, 2020, Fast multicast with adjusting transmission power and active slots in software define IoT, IEEE Acceess

Luo, 2019, Qoe-driven computation offloading for edge computing, J. Syst. Archit., 97, 34, 10.1016/j.sysarc.2019.01.019

Huang, 2020, Result return aware offloading scheme in vehicular edge networks for 6g driving application, Comput. Commun., 164, 201, 10.1016/j.comcom.2020.10.019

Garcia Lopez, 2015, Edge-centric computing: Vision and challenges, ACM SIGCOMM Comput. Commun. Rev., 45, 2015

B. Varghese, N. Wang, S. Barbhuiya, P. Kilpatrick, D.S. Nikolopoulos, Challenges and opportunities in edge computing, in: Proceedings of the IEEE International Conference on Smart Cloud, (2016) pp. 20–26.

Ouyang, 2020, An effective early message ahead join adaptive data aggregation scheme for sustainable IoT, IEEE Trans. Netw. Sci. Eng.

Chen, 2020, Intelligent resource allocation management for vehicles network: An A3c learning approach, Comput. Commun., 151, 485, 10.1016/j.comcom.2019.12.054

Wang, 2020, Mobility based trust evaluation for heterogeneous electric vehicles network in smart cities, IEEE Trans. Intell. Transp. Syst.

Satyanarayanan, 2013, The role of cloudlets in hostile environments, IEEE Pervas. Comput., 12, 40, 10.1109/MPRV.2013.77

Sanaei, 2014, Heterogeneity in mobile cloud computing: Taxonomy and open challenges, IEEE Commun. Surv. Tutor., 16, 369, 10.1109/SURV.2013.050113.00090

Dai, 2019, A scheduling algorithm for autonomous driving tasks on mobile edge computing servers, J. Syst. Archit., 94, 14, 10.1016/j.sysarc.2019.02.004

Wan, 2020, Efficient computation offloading for internet of vehicles in edge computing-assisted 5g networks, J. Supercomput., 76, 2518, 10.1007/s11227-019-03011-4

Zhu, 2020, A deep learning-based mobile crowdsensing scheme by predicting vehicle mobility, IEEE Trans. Intell. Transp. Syst.

F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the Internet of Things, in: Proc. ACM MCC, Helsinki, Finland, (2012) pp. 13–16.

Huang, 2020, Mobile vehicles joint UAVs for secure data collection in smart city, Ann. Telecommun.

Zhang, 2020, When deep reinforcement learning meets 5g-enabled vehicular networks: A distributed offloading framework for traffic big data, IEEE Trans. Ind. Inf., 16, 1352, 10.1109/TII.2019.2937079

Zhang, 2018, Synergy of big data and 5g wireless networks: Opportunities, approaches, and challenges, IEEE Wirel. Commun., 25, 12, 10.1109/MWC.2018.1700193

Li, 2021, A trustworthiness-based vehicular recruitment scheme for information collections in distributed networked systems, Inf. Sci., 545, 65, 10.1016/j.ins.2020.07.052

Fernando, 2013, Mobile cloud computing: A survey, Future Gener. Comput. Syst., 29, 84, 10.1016/j.future.2012.05.023

Amini, 2019, Sustainable smart cities through the lens of complex interdependent infrastructures: Panorama and state-of-the-art, 45

C.F. Liu, M. Bennis, H.V. Poor, Latency and Reliability-Aware Task Offloading and Resource Allocation for Mobile Edge Computing, in: Proc. 2017 IEEE Globecom Workshops (GC Wkshps), (2017) pp. 1-7.

Xiong, 2019, Deep reinforcement learning for mobile 5g and beyond: Fundamentals, applications, and chal-lenges, IEEE Veh. Technol. Mag., 14, 44, 10.1109/MVT.2019.2903655

Wan, 2020, Deep learning models for real-time human activity recognition with smartphones, Mob. Netw. Appl., 25, 743, 10.1007/s11036-019-01445-x

Zhou, 2020, LMM: latency-aware micro-service mashup in mobile edge computing environment, Neural Comput. Appl.

Liu, 2020, Energy-aware MAC protocol for data differentiated services in sensor-cloud computing, J. Cloud Comput., 10.1186/s13677-020-00196-5

Kosta, 2012, Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading, 945

Benedetto, 2019, Towards a practical framework for code offloading in the internet of things, Future Gener. Comput. Syst., 92, 424, 10.1016/j.future.2018.09.056

Sutton, 1998

Mnih, 2015, Human-level control through deep reinforcement learning, Nature, 518, 529, 10.1038/nature14236

Lillicrap, 2016, Continuous control with deep reinforcement learning

Sutton, 2000, Policy gradient methods for reinforcement learning with functionapproximation

D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, M. Riedmiller, Deterministic Policy Gradient Algorithms, in: Proceedings of the 31 st International Conference on Machine Learning, (2014).

Yu, 2013, Coordinated resource provisioning and maintenance scheduling in cloud data centers, 345

Weiz, 1995, Distributed reinforcement learning, Biol. Technol. Intell. Auton. Agents, 144, 415

G. Weiz, Action selection and learning in multi-agent environments, in: Proceedings of the second international conference on from animals to animats 2: simulation of adaptive behavior: simulation of adaptive behavior, (1993) pp. 502-510.

Sztrik, 2011

Kwon, 2016, Precise execution offloading for applications with dynamic behavior in mobile cloud computing, Pervasive Mob. Comput., 27, 58, 10.1016/j.pmcj.2015.10.001

Sun, 2018, Deep reinforcement learning-based mode selection and resource management for green fog radio access networks, IEEE Internet Things J., 6, 1960, 10.1109/JIOT.2018.2871020

O. Skarlat, S. Schulte, M. Borkowski, P. Leitner, Resource provisioning for IoT services in the fog, in: Proc. IEEE 9th Int. Conf. Service Oriented Comput. Appl. (SOCA), (2016) pp. 32–39.