Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

Zhou, Bo; Saad, Walid

doi:10.1109/twc.2019.2959777

Cited by 96 publications

(37 citation statements)

References 42 publications

(115 reference statements)

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“…The algorithm uses the packet combination to minimize the instant AoI at the edge server as the optimization objective. Zhou et al [21] considered a real-time IoT monitoring system, in which IoT devices sample the underlying physical processes and send status updates to a common destination. Since the dynamics of each physical process may differ, the sizes of the status updates for different devices vary.…”

Section: B Related Workmentioning

confidence: 99%

Link Resource Allocation Strategy Based on Age of Information and Sample Extrusion Awareness in Dynamic Channels

Wei

Huang

2021

IEEE Access

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As an emerging measure of data freshness, the age of information (AoI) is receiving extensive attention. Many methods using AoI have been proposed for IoT communication scheduling. However, most of them are aimed at constant channel conditions in the ideal state, and the utilization of link resources is not sufficient. In addition, only the optimization of AoI is considered, without considering whether the sample is extruded or not. The occurrence of sample extrusion means that the transmission of the remaining untransmitted sample of the source node cannot be completed within the transmission time interval (TTI) before the arrival of the new sampling period of the source node, resulting in a phenomenon in which the new sample arrives while the old sample has not yet been completely transmitted. This scenario has a serious impact on delay-sensitive IoT applications. Therefore, under dynamic channel conditions and limited link resources, this paper establishes a mathematical model for AoI and sample extrusion. The influence of the scheduling algorithm on these two target values is analyzed and proved. Based on a greedy strategy, we propose an online allocation algorithm of preemptive link resources that considers two objectives: to give full play to the value of link resources and to minimize sample extrusion. The simulation results show that the proposed strategy can achieve better comprehensive performance in two scenarios where the sample variance between each source node is small and large.INDEX TERMS Age of information, sample extrusion, dynamic channel, preemptive.

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Section: B Related Workmentioning

confidence: 99%

Link Resource Allocation Strategy Based on Age of Information and Sample Extrusion Awareness in Dynamic Channels

Wei

Huang

2021

IEEE Access

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“…At the beginning of each time slot, the status update (if any) of the underlying process arrives at the IoT device randomly. Similar to [5] and [6], the process of the status update arrivals is modeled by an independent and identically distributed (i.i.d.) Bernoulli process with mean rate λ ∈ [0, 1].…”

Section: System Modelmentioning

confidence: 99%

“…We consider a wireless packet erasure channel between the IoT device and the receiver, and, upon transmission, each status update will be successfully delivered to the receiver with probability p. As in [6]- [8], we further assume that the IoT device will be notified immediately upon a successful transmission, through a perfect feedback channel between the device and the receiver.…”

Section: System Modelmentioning

confidence: 99%

“…In [4], the authors study the optimal status sampling and updating policy to minimize the average AoI for an IoT monitoring system under device energy constraints. The problem of AoI minimization for IoT monitoring systems with non-uniform status packet sizes is studied in [5] and [6]. The works in [7], [8] investigate the problem of AoI minimization for wireless status updating systems with noisy channels.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Risk-Aware Optimization of Age of Information in the Internet of Things

Zhou

Saad

Bennis

et al. 2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

Self Cite

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Minimization of the expected value of age of information (AoI) is a risk-neutral approach, and it thus cannot capture rare, yet critical, events with potentially large AoI. In order to capture the effect of these events, in this paper, the notion of conditional value-at-risk (CVaR) is proposed as an effective coherent risk measure that is suitable for minimization of AoI for real-time IoT status updates. In the considered monitoring system, an IoT device monitors a physical process and sends the status updates to a remote receiver with an updating cost. The optimal status update process is designed to jointly minimize the AoI at the receiver, the CVaR of the AoI at the receiver, and the energy cost. This stochastic optimization problem is formulated as an infinite horizon discounted riskaware Markov decision process (MDP), which is computationally intractable due to the time inconsistency of the CVaR. By exploiting the special properties of coherent risk measures, the riskaware MDP is reduced to a standard MDP with an augmented state space, for which we derive the optimal stationary policy using dynamic programming. In particular, the optimal historydependent policy of the risk-aware MDP is shown to depend on the history only through the augmented system states and can be readily constructed using the optimal stationary policy of the augmented MDP. The proposed solution is shown to be computationally tractable and able to minimize the AoI in realtime IoT monitoring systems in a risk-aware manner.

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“…At the same time, the corresponding image and data processing equipment loss are also large [8][9][10][11][12]; in view of this problem, the relevant researchers in the United States proposed to use a large number of data sensors for action recognition and motion monitoring. e intelligent wearable device based on this design needs a large number of sensors, and its estimation of the amount of motion still has a relatively large error, but this kind of method is in recognition on the level of human motion; the corresponding accuracy rate can reach more than 90% [13][14][15]; in terms of the corresponding monitoring technology of human motion energy consumption, the mainstream methods include human body thermal test, indirect human body thermal test, double mark water heat measurement method, and corresponding accelerometer heat measurement method. In essence, only direct thermal measurement method has relatively accurate accuracy, and other methods exist.…”

Section: Introductionmentioning

confidence: 99%

Wearable Device Monitoring Exercise Energy Consumption Based on Internet of Things

Shi

Huang

2021

Complexity

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Computer technology and related Internet of things technology have penetrated into people’s daily life and industrial production; even in competitive sports training and competition, the Internet of things technology has also been a large number of applications. Traditional intelligent wearable devices are mainly used to calculate the steps of athletes or sports enthusiasts, corresponding physical data, and corresponding body indicators. The energy consumption calculated by these indexes is rough and the corresponding error is large. Based on this, this paper will design a wearable device which can accurately calculate and monitor sports energy consumption based on relevant sensors and Internet of things technology. The corresponding core algorithm is the step counting algorithm, which can accurately calculate the relationship between human motion and the corresponding energy consumption and feed back to the intelligent device. In the experiment, the wearable device designed in this paper is compared with the traditional intelligent device. The experiment shows that the wearable device proposed in this paper is more accurate in energy consumption estimation than the traditional device, and its corresponding energy consumption is relatively small.

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Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

Cited by 96 publications

References 42 publications

Link Resource Allocation Strategy Based on Age of Information and Sample Extrusion Awareness in Dynamic Channels

Link Resource Allocation Strategy Based on Age of Information and Sample Extrusion Awareness in Dynamic Channels

Risk-Aware Optimization of Age of Information in the Internet of Things

Wearable Device Monitoring Exercise Energy Consumption Based on Internet of Things

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