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Self-Adaptive Bandwidth Control for Balanced QoS and Energy Aware Optimization in Wireless Sensor Network

Abstract : In the Wireless Multimedia Sensor Networks (WMSNs) field, highly saturated flow increases the probability of collision and congestion in data transmission which dramatically degrade the performance of Quality of Service (QoS). Multi-channels deployment technique is often applied to parallel transmission for QoS guarantee. However, how to make trade-off between QoS requirement and energy efficiency is a challenges to energy-constrained WMSNs. Theoretical analysis of MAC layer and PHY layer structure based on IEEE 802.15.4 standard, aim to study on the cross-layer analytical model in order to provide stronger understanding on the relationship between sensor network parameters and performance, pave the way for new enhancements in succedent multi-channel optimization research. Find effective performance indicator and design efficient performance collection or estimation approach based on the corresponding metrics, which could be used as the parameter input of multi-channel assignment mechanism. Comprehensive dynamically control system is designed for multi-channel assignment task based on light weight and high efficient computation intelligence techniques. We present a fuzzy-based dynamic bandwidth multi-channel assignment mechanism (MCDB_FLS). Cross-layer proactive available bandwidth is estimated as parameters for multi-channel deployment admission control. Reinforcement learning-based approach is proposed for more wisely decision-making in multi- channel allocation mission. Furthermore, fuzzy logic-based bandwidth threshold model provides dynamic optimization on system admission control. Simulations show the MCDB_FLS performs better than benchmark on the metrics of QoS and energy efficiency, achieves the trade-off between energy efficiency and QoS improvement. Finally, we introduce the integration of incremental machine learning approach into multi-channel assignment mechanism with Deep Q Network reinforcement learning method (DQMC). Besides, fully action weight initialization is implemented based on multi-class supervised learning classifier with stacking ensemble approach. DQMC improve the ability of self-adaptive and smart control to learn pattern from different environment of multi-tasks WMSNs.
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Submitted on : Tuesday, April 9, 2019 - 4:44:07 PM
Last modification on : Monday, July 4, 2022 - 9:32:49 AM


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  • HAL Id : tel-02094515, version 1


Zongyi Liu. Self-Adaptive Bandwidth Control for Balanced QoS and Energy Aware Optimization in Wireless Sensor Network. Networking and Internet Architecture [cs.NI]. INSA de Toulouse, 2017. English. ⟨NNT : 2017ISAT0034⟩. ⟨tel-02094515⟩



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