Energy-Constrained Data Freshness Optimization in Self-Powered Networked Embedded Systems

Zhou, Zimeng; Fu, Chenchen; Han, Song

doi:10.1109/tcad.2019.2948905

Cited by 13 publications

(7 citation statements)

References 34 publications

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“…Because there are many different sensor nodes in the cluster and the position of each node changes continuously, it can be assumed that the process of sending packets from each source node to the destination node follows a Poisson distribution with parameter λ (λ represents the packet generation rate, 1/λ is the time interval for packet generation, and n i λ is the packet generation rate on source node n for cluster i). Because the time-varying availability of energy harvesting at the transmitter limits the sampling rate at the source, it can be assumed that the process of energy harvesting energy supply to the source node obeys the Poisson distribution with parameter l (Zhou et al, 2020). Here l represents the energy harvested rate, also called the energy supply rate of the source node, T represents the total time of energy supply, and N is the total number of node samples.…”

Section: Measurement Methods For Information Freshnessmentioning

confidence: 99%

“…where i=1, 2, …, m and q=1, 2, …, n. Definition 1 (Zhou et al, 2020) The AoI is the time elapsed since the generation of the last sample packet. At sampling time t, if the latest packet generation has a timestamp U(t), the AoI is Δ(t)=t−U(t).…”

Section: Measurement Methods For Information Freshnessmentioning

confidence: 99%

“…To minimize the average AoI, the sum of Y w is equal to the total energy supply time T (Zhou et al, 2020). Because T=N/l, E[Y]=1/l.…”

Section: Threshold Selection For Et-kcf By Minimizing the Average Aoimentioning

confidence: 99%

“…Talak et al (2018) presented the minimum value calculation of the network average AoI under double-interference constraints for time-varying channels and pointed out that the network average AoI is most efficient in providing information updates when it is the smallest. In an embedded system with energy constraints, Zhou et al (2020) obtained the minimum average AoI of the whole network by determining whether to transmit, discard, or save the real-time data update sequences. With regard to designing a sampler, Sun Y and Cyr (2019) developed a method for extracting samples from data sources to remotely improve the freshness of received data samples; i.e., the AoI of sampled data is small.…”

Section: Introductionmentioning

confidence: 99%

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Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network

Wang

Sun

et al. 2021

Front Inform Technol Electron Eng

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This paper presents the design of a new event-triggered Kalman consensus filter (ET-KCF) algorithm for use over a wireless sensor network (WSN). This algorithm is based on information freshness, which is calculated as the age of information (AoI) of the sampled data. The proposed algorithm integrates the traditional event-triggered mechanism, information freshness calculation method, and Kalman consensus filter (KCF) algorithm to estimate the concentrations of pollutants in the aircraft more efficiently. The proposed method also considers the influence of data packet loss and the aircraft's loss of communication path over the WSN, and presents an AoI-freshness-based threshold selection method for the ET-KCF algorithm, which compares the packet AoI to the minimum average AoI of the system. This method can obviously reduce the energy consumption because the transmission of expired information is reduced. Finally, the convergence of the algorithm is proved using the Lyapunov stability theory and matrix theory. Simulation results show that this algorithm has better fault tolerance compared to the existing KCF and lower power consumption than other ET-KCFs.

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Section: Measurement Methods For Information Freshnessmentioning

confidence: 99%

Section: Measurement Methods For Information Freshnessmentioning

confidence: 99%

“…To minimize the average AoI, the sum of Y w is equal to the total energy supply time T (Zhou et al, 2020). Because T=N/l, E[Y]=1/l.…”

Section: Threshold Selection For Et-kcf By Minimizing the Average Aoimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network

Wang

Sun

et al. 2021

Front Inform Technol Electron Eng

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“…The work [19] introduced a deep reinforcement learning-based approach to minimize the AoI in the networks whose topology was without prior assumptions. A recent work [20] designed an optimal offline solution and an effective online solution to minimize the average AoI in energy harvesting-based networked embedded systems with energy constraints.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things: The Optimal Generation Rates under Preemption Strategy in a Multi-Source Queuing System

Zhang

Chen

2021

Entropy

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With the rapid development and wide application of the Internet of Things (IoT), how to provide timely and fresh information for strategic analysis and decision-making has become a key issue. Recent studies have shown that preemption strategies are of great importance to the improvement of information freshness. In view of this, we focus on the multi-source preemptive queuing model and investigate how to control the generation rate of each source to achieve the optimal overall information freshness. Specifically, we consider two typical preemption strategies: self-preemption strategy and global-preemption strategy. Noting that the urgency requirements of the systems on the data of each source are different, we propose the weighted average age of information (AoI) to characterize the overall information freshness of the system. For the self-preemption strategy, we prove that the optimal generation rate allocation is a convex problem and present an efficient algorithm to find the optimal solution. Additionally, we also derive a closed-form approximate optimal solution under light load cases to meet the demands for rapid deployment. For the global-preemption strategy, we directly derive the closed-form optimal solution of the corresponding problem. By comparing the optimized weighted average AoIs, the performance achieved by the global-preemption system was better than that achieved by the self-preemption system in terms of the overall timeliness. The numerical analysis verified the correctness of the theoretical analysis and that the proposed approximate solution had high accuracy not only under light load cases but also under other cases.

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Joint Optimization of PAoI and Queue Backlog with Energy Constraints in LoRa Gateway Systems

Shi,

Ji,

Wei

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Energy-Constrained Data Freshness Optimization in Self-Powered Networked Embedded Systems

Cited by 13 publications

References 34 publications

Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network

Freshness constraints of an age of information based event-triggered Kalman consensus filter algorithm over a wireless sensor network

Internet of Things: The Optimal Generation Rates under Preemption Strategy in a Multi-Source Queuing System

Joint Optimization of PAoI and Queue Backlog with Energy Constraints in LoRa Gateway Systems

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