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Self-Organizing Control Mechanism Based on Collective Decision-Making for Information Uncertainty.

, , , , and . ACM Trans. Auton. Adapt. Syst., 13 (1): 7:1-7:21 (2018)

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Performance evaluation of intermittent receiver-driven data transmission on wireless sensor networks., , , , and . ISWCS, page 141-145. IEEE, (2009)Enhancing Convergence with Optimal Feedback for Controlled Self-Organizing Networks., , , , and . VTC Fall, page 1-7. IEEE, (2014)Lifetime extension based on residual energy for receiver-driven multi-hop wireless network., , , , and . HPCS, page 442-448. IEEE, (2011)Multimodal Object Recognition Using Bayesian Attractor Model for 2D and 3D data., , , , and . ICIN, page 272-278. IEEE, (2024)Multi-Object Recognition Method Inspired by Multimodal Information Processing in the Human Brain., , , , and . GLOBECOM (Workshops), page 569-574. IEEE, (2022)Rate adaptation with Bayesian attractor model for MPEG-DASH., , , and . CCWC, page 859-865. IEEE, (2019)Constructing virtual IoT network topologies with a brain-inspired connectivity model., , , , and . IMCOM, page 78. ACM, (2017)Impact of Fluctuating Goals on Adaptability of Evolvable VNF Placement Method., , , , , and . CANDAR, page 304-310. IEEE Computer Society, (2016)Controlled and self-organized routing for large-scale wireless sensor networks., , , and . ACM Trans. Sens. Networks, 10 (1): 13:1-13:27 (2013)Self-organizing wireless sensor networks based on biological collective decision making for treating information uncertainty., , , , and . WiMob, page 167-174. IEEE Computer Society, (2017)