Timely reliability modeling and evaluation of wireless sensor networks with adaptive N-policy sleep scheduling

Zhang, Changzhen; Yang, Jingwen; Wang, Ning

doi:10.1016/j.ress.2023.109270

Cited by 8 publications

(2 citation statements)

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“…In the transmission of information, in addition to information timeliness, energy consumption is also an important issue that cannot be ignored and is a challenge to extend the working life of edge nodes [35]. In order to reduce the number of redundant nodes, ensure the efficiency of the nodes and thus reduce the overall energy consumption of the work, the sleep/wake mechanism is therefore widely used.…”

Section: Related Workmentioning

confidence: 99%

“…In order to reduce the number of redundant nodes, ensure the efficiency of the nodes and thus reduce the overall energy consumption of the work, the sleep/wake mechanism is therefore widely used. Reference [35] proposes an M /M /1/C queuing model based on the N policy, where nodes in the dormant state are switched to the wake-up state for data transmission work when the packet volume reaches a threshold N , effectively extending the working life of the nodes. Reference [36] then models a dynamic N policy based on the different arrival rates of the packets.…”

Section: Related Workmentioning

confidence: 99%

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AoI-Aware Resource Management for Smart Health Via Deep Reinforcement Learning

Cai

et al. 2023

IEEE Access

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The freshness of information is critical for patient vital signs and physiological parameters in the healthcare system because changes in these parameters can indicate a patient's overall health status and guide treatment decisions. In this paper, we consider an edge device-aided smart healthcare system that relies on a resource management scheme. The medical center requires patient information, and edge nodes process the latest measurements received by each wearable device. Our goal is to find the optimal strategy to minimize the worst case of information freshness, i.e., the peak AoI age of information (PAoI). Firstly, we model the problem as a Markov Decision Process (MDP). Then, we design two separate Reinforcement Learning (RL)-based algorithms to find the optimal strategy that minimizes energy consumption and the average PAoI. To minimize energy consumption, we propose a pair of sleep mechanisms, including the N policy and p wake-up policy, to improve the energy efficiency of each wearable device. Simulation results show that the proposed wake-up strategy and the proposed RL algorithm make a better trade-off between the average PAoI and power dissipation compared to the baseline schemes.INDEX TERMS Smart health, age of information (AoI), sleep-scheduling, deep reinforcement learning (DRL), deep deterministic policy gradient (DDPG).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%