Energy Efficiency Maximization Strategy for Sink Node in SWIPT-Enabled Sensor-Cloud Based on Optimal Stopping Rules

Leveraging energy harvesting abilities in wireless network devices has emerged as an effective way to prolong the lifetime of energy constrained systems.The system gains are usually optimized by designing resource allocation algorithm appropriately.However,few works focus on the interaction that channel’s time-vary characters make the energy transfer inefficiently.To address this,we propose a novel system operation sequence for sensor-cloud system where the Sinks provide SWIPT for sensor nodes opportunistically during downlink phase and collect the data transmitted from sensor nodes in uplink phase.Then,the energy-efficiency maximization problem of the Sinks is presented by considering the time costs and energy consumption of channel detection.It is proved that the formulated problem is an optimal stopping process with optimal stopping rules.An optimal energy-efficiency(OEE)algorithm is designed to obtain the optimal stopping rules for SWIPT.Finally,the simulations are performed based on the OEE algorithm compared with the other two strategies to verify the effectiveness and gains in improving the system efficiency.

Channel List Selection Based on Quality Prediction in WirelessHART Networks

WirelessHART is one of the most widely used technologies in industrial wireless networks.However,its performance is highly influenced by the quality of wireless channels.To improve the reliability of wireless communications,WirelessHART employs channel blacklisting and channel hopping mechanisms,which highlights the importance of channel assessment.Traditional methods generally resort to packet reception ratio(PRR)of the previous time slot to assess and allocate channels,but this is not accurate.In this paper,we propose a learning-based framework for predicting the PRR,and on the basis of the predicted PRR,we develop a heuristic channel selection algorithm to confirm the channel list,which takes into account the balance of channel diversity and route diversity.Simulation results demonstrate that our algorithm outperforms existing ones in terms of achieved reliability.

Addressing the CQI feedback delay in 5G/6G networks via machine learning and evolutionary computing

5G networks apply adaptive modulation and coding according to the channel condition reported by the user in order to keep the mobile communication quality.However,the delay incurred by the feedback may make the channel quality indicator(CQI)obsolete.This paper addresses this issue by proposing two approaches,one based on machine learning and another on evolutionary computing,which considers the user context and signal-to-interference-plus-noise ratio(SINR)besides the delay length to estimate the updated SINR to be mapped into a CQI value.Our proposals are designed to run at the user equipment(UE)side,neither requiring any change in the signalling between the base station(gNB)and UE nor overloading the gNB.They are evaluated in terms of mean squared error by adopting 5G network simulation data and the results show their high accuracy and feasibility to be employed in 5G/6G systems.