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Dynamic data aggregation protocol based on multiple objective tree in Wireless Sensor Networks.

, , , and . ISSNIP, page 1-7. IEEE, (2015)

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Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?, , and . IEEE Commun. Surv. Tutorials, 20 (2): 945-977 (2018)Scheduling policies based on dynamic throughput and fairness tradeoff control in LTE-A networks., , , , , and . LCN, page 418-421. IEEE Computer Society, (2014)A novel dynamic Q-learning-based scheduler technique for LTE-advanced technologies using neural networks., , , , and . LCN, page 332-335. IEEE Computer Society, (2012)Backup Path with Energy Prediction Based on Energy-Aware Spanning Tree in Wireless Sensor Networks., , , and . CyberC, page 392-397. IEEE Computer Society, (2013)Reinforcement learning based radio resource scheduling in LTE-advanced., , , , and . ICAC, page 219-224. IEEE, (2011)Dynamic data aggregation protocol based on multiple objective tree in Wireless Sensor Networks., , , and . ISSNIP, page 1-7. IEEE, (2015)QoS-Driven Scheduling in 5G Radio Access Networks - A Reinforcement Learning Approach., , and . GLOBECOM, page 1-7. IEEE, (2017)Construction of Data Aggregation Tree for Multi-objectives in Wireless Sensor Networks through Jump Particle Swarm Optimization., , , , and . KES, volume 35 of Procedia Computer Science, page 73-82. Elsevier, (2014)Work-in-Progress-Motion Tracking Data as a Proxy for Cognitive Load in Immersive Learning., , , and . iLRN, page 1-3. IEEE, (2022)360° Mulsemedia: A Way to Improve Subjective QoE in 360° Videos., , , , , , and . ACM Multimedia, page 2378-2386. ACM, (2019)