Closed-Form Analysis of Non-Linear Age of Information in Status Updates With an Energy Harvesting Transmitter

Zheng, Xinqian; Zhou, Sheng; Jiang, Zhiyuan; Niu, Zhisheng

doi:10.1109/twc.2019.2921372

Cited by 102 publications

(38 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [6], a matrix geometric method is introduced to minimize the average peak AoI of the IoT devices. The authors in [7] optimized the AoI of each user under a sampling cost constraint.…”

Section: A Related Workmentioning

confidence: 99%

Reinforcement Learning for Minimizing Age of Information under Realistic Physical Dynamics

Wang

Chen

Saad

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

View full text Add to dashboard Cite

In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied. In the considered model, each IoT device monitors a physical process that follows nonlinear dynamics. As the dynamics of the physical process vary over time, each device must find an optimal sampling frequency to sample the real-time dynamics of the physical system and send sampled information to a base station (BS). Due to limited wireless resources, the BS can only select a subset of devices to transmit their sampled information.Meanwhile, changing the sampling frequency will also impact the energy used by each device for sampling and information transmission. Thus, it is necessary to jointly optimize the sampling policy of each device and the device selection scheme of the BS so as to accurately monitor the dynamics of the physical process using minimum energy. This problem is formulated as an optimization problem whose goal is to minimize the weighted sum of AoI cost and energy consumption. To solve this problem, a distributed reinforcement learning approach is proposed to optimize the sampling policy.The proposed learning method enables the IoT devices to find the optimal sampling policy using their local observations. Given the sampling policy, the device selection scheme can be optimized so as to minimize the weighted sum of AoI and energy consumption of all devices. Simulations with real data of PM 2.5 pollution show that the proposed algorithm can reduce the sum of AoI by up to 17.8% and

show abstract

“…In [6], a matrix geometric method is introduced to minimize the average peak AoI of the IoT devices. The authors in [7] optimized the AoI of each user under a sampling cost constraint.…”

Section: A Related Workmentioning

confidence: 99%

Reinforcement Learning for Minimizing Age of Information under Realistic Physical Dynamics

Wang

Chen

Saad

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

View full text Add to dashboard Cite

show abstract

“…Keeping the average AoI small corresponds to having fresh information, which is critical for time-sensitive applications in the Internet of Things (IoT) scenarios and future wireless systems [7], [8]. This notion has been extended to other metrics such as the value of information, cost of update delay, and non-linear AoI [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Information Freshness in a Multiple Access Channel With Heterogeneous Devices

Chen

Παππάς

Björnson

et al. 2021

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

In this work, we study age-optimal scheduling with stability constraints in a multiple access channel with two heterogeneous source nodes transmitting to a common destination. The first node is connected to a power grid and it has randomly arriving data packets. Another energy harvesting (EH) sensor monitors a stochastic process and sends status updates to the destination. We formulate an optimization problem that aims at minimizing the average age of information (AoI) of the EH node subject to the queue stability condition of the grid-connected node. First, we consider a Probabilistic Random Access (PRA) policy where both nodes make independent transmission decisions based on some fixed probability distributions. We show that with this policy, the average AoI is equal to the average peak AoI, if the EH node only sends freshly generated samples. In addition, we derive the optimal solution in closed form, which reveals some interesting properties of the considered system. Furthermore, we consider a Drift-Plus-Penalty (DPP) policy and develop AoI-optimal and peak-AoI-optimal scheduling algorithms using the Lyapunov optimization theory. Simulation results show that the DPP policy outperforms the PRA policy in various scenarios, especially when the destination node has low multi-packet reception capabilities.

show abstract

“…The concept of the "age penalty" was proposed in [17], where it was assumed to be a non-decreasing function of the AoI and provided a general way to measure the dissatisfaction of the staleness of information. Closed-form expressions of the general penalty functions were studied in energy harvesting networks in [18]. In [19][20][21], three specific penalty functions (exponential, linear and logarithmic functions) and their statistical characterisations were further investigated.…”

Section: Introductionmentioning

confidence: 99%

Relationship between Age and Value of Information for a Noisy Ornstein–Uhlenbeck Process

Wang

Badiu

Coon

2021

Entropy

View full text Add to dashboard Cite

The age of information (AoI) has been widely used to quantify the information freshness in real-time status update systems. As the AoI is independent of the inherent property of the source data and the context, we introduce a mutual information-based value of information (VoI) framework for hidden Markov models. In this paper, we investigate the VoI and its relationship to the AoI for a noisy Ornstein–Uhlenbeck (OU) process. We explore the effects of correlation and noise on their relationship, and find logarithmic, exponential and linear dependencies between the two in three different regimes. This gives the formal justification for the selection of non-linear AoI functions previously reported in other works. Moreover, we study the statistical properties of the VoI in the example of a queue model, deriving its distribution functions and moments. The lower and upper bounds of the average VoI are also analysed, which can be used for the design and optimisation of freshness-aware networks. Numerical results are presented and further show that, compared with the traditional linear age and some basic non-linear age functions, the proposed VoI framework is more general and suitable for various contexts.

show abstract

Closed-Form Analysis of Non-Linear Age of Information in Status Updates With an Energy Harvesting Transmitter

Cited by 102 publications

References 38 publications

Reinforcement Learning for Minimizing Age of Information under Realistic Physical Dynamics

Reinforcement Learning for Minimizing Age of Information under Realistic Physical Dynamics

Optimizing Information Freshness in a Multiple Access Channel With Heterogeneous Devices

Relationship between Age and Value of Information for a Noisy Ornstein–Uhlenbeck Process

Contact Info

Product

Resources

About