Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks

Kadota, Igor; Sinha, Abhishek; Uysal-Biyikoğlu, Elif; Singh, Rahul; Modiano, Eytan

doi:10.1109/tnet.2018.2873606

Cited by 524 publications

(447 citation statements)

References 50 publications

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“…When there is no packet-loss in the network, this greedy policy is equivalent to the round robin strategy, which is shown to be order optimal when update packets can not be generated at will and arrive randomly [24]. In [18], it is revealed that users with relatively bad channel states are updated less frequently. Scheduling in networks with timevarying channels are studied in [20], [21], where channels with two states is considered, and centralized and decentralized policies to minimize AoI are proposed respectively.…”

Section: Related Workmentioning

confidence: 99%

“…The constraint Eq. (19b)-(19d) can be obtained by substituting ξ x,q with y x,q and µ x,q with relationship (18). Notice that ξ x,q ≤ 1, the inequality constraint (19e) can be obtained.…”

Section: Probabilistic Scheduling Policy For Single Sensor Casementioning

confidence: 99%

“…Notice that from [18], the optimal policy to minimize AoI performance when all the sensors are identical is a greedy policy that selects the sensor with the largest AoI. If there is no packet-loss in the network, the greedy policy is equivalent to round robin, which requires a minimum power consumption of E RR = M N Q q=1 η q ω(q) for each sensor.…”

Section: Simulationsmentioning

confidence: 99%

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Minimizing Age of Information With Power Constraints: Multi-User Opportunistic Scheduling in Multi-State Time-Varying Channels

Tang

Wang

Song

et al. 2020

IEEE J. Select. Areas Commun.

137

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This work is motivated by the need of collecting fresh data from power-constrained sensors in the industrial Internet of Things (IIoT) network. A recently proposed metric, the Age of Information (AoI) is adopted to measure data freshness from the perspective of the central controller in the IIoT network. We wonder what is the minimum average AoI the network can achieve and how to design scheduling algorithms to approach it. To answer these questions when the channel states of the network are Markov time-varying and scheduling decisions are restricted to bandwidth constraint, we first decouple the multi-sensor scheduling problem into a single-sensor constrained Markov decision process (CMDP) by relaxing the hard bandwidth constraint. Next we exploit the threshold structure of the optimal policy for the decoupled single sensor CMDP and obtain the optimum solution through linear programming (LP). Finally, an asymptotically optimal truncated policy that can satisfy the hard bandwidth constraint is built upon the optimal solution to each of the decoupled single-sensor. Our investigation shows that to obtain a small AoI performance: (1) The scheduler exploits good channels to schedule sensors supported by limited power; (2) Sensors equipped with enough transmission power are updated in a timely manner such that the bandwidth constraint can be satisfied.

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Section: Related Workmentioning

confidence: 99%

Section: Probabilistic Scheduling Policy For Single Sensor Casementioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

Minimizing Age of Information With Power Constraints: Multi-User Opportunistic Scheduling in Multi-State Time-Varying Channels

Tang

Wang

Song

et al. 2020

IEEE J. Select. Areas Commun.

137

View full text Add to dashboard Cite

show abstract

“…Next, we optimize parameter θ to reduce the right-hand side of (17). Note that scheme (18) always minimizes the right-hand side of (17), and the stationary randomized scheme that independently updates with probability ρ at each time slot is also a feasible scheme. Substituting the stationary randomized scheme into the right-hand side of (17) yields…”

Section: B Status Updating Schemementioning

confidence: 99%

Context-Aware Information Lapse for Timely Status Updates in Remote Control Systems

Zheng

Zhou

Niu

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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As 5G and Internet-of-Things (IoT) are deeply integrated into vertical industries such as autonomous driving and industrial robotics, timely status update is crucial for remote monitoring and control. In this regard, Age of Information (AoI) has been proposed to measure the freshness of status updates.However, it is just a metric changing linearly with time and irrelevant of context-awareness. We propose a context-based metric, named as Urgency of Information (UoI), to measure the nonlinear time-varying importance and the non-uniform context-dependence of the status information. This paper first establishes a theoretical framework for UoI characterization and then provides UoI-optimal status updating and user scheduling schemes in both single-terminal and multi-terminal cases. Specifically, an update-indexbased scheme is proposed for a single-terminal system, where the terminal always updates and transmits when its update index is larger than a threshold. For the multi-terminal case, the UoI of the proposed scheduling scheme is proven to be upper-bounded and its decentralized implementation by Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) is also provided. In the simulations, the proposed updating and scheduling schemes notably outperform the existing ones such as round robin and AoI-optimal schemes in terms of UoI, error-bound violation and control system stability. Index TermsRemote control systems, status update, transmission scheduling, Age-of-Information, 5G, Internet of Things.

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“…Aside from the conventional throughput QoS considera-arXiv:1903.02242v1 [cs.IT] 6 Mar 2019 tion [4], various types of latency metrics are among the main concerns. For example, the recently proposed AoI [5] can be regarded as a metric for information delay; a line of work [2], [3], [6], [7] has been focused on optimizing AoI in wireless multi-access (or broadcast) networks wherein Whittle's index was used to capture the transmission urgency of terminals. Conventional packet delay metric was widely investigated in the literature and various scheduling policies for multi-access networks have been proposed, most evidently in [8].…”

Section: A Related Workmentioning

confidence: 99%

Distributed Policy Learning Based Random Access for Diversified QoS Requirements

Jiang

Zhou

Niu

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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Future wireless access networks need to support diversified quality of service (QoS) metrics required by various types of Internet-of-Things (IoT) devices, e.g., age of information (AoI) for status generating sources and ultra low latency for safety information in vehicular networks. In this paper, a novel inner-state driven random access (ISDA) framework is proposed based on distributed policy learning, in particular a cross-entropy method. Conventional random access schemes, e.g., p-CSMA, assume state-less terminals, and thus assigning equal priorities to all. In ISDA, the inner-states of terminals are described by a time-varying state vector, and the transmission probabilities of terminals in the contention period are determined by their respective inner-states. Neural networks are leveraged to approximate the function mappings from inner-states to transmission probabilities, and an iterative approach is adopted to improve these mappings in a distributed manner. Experiment results show that ISDA can improve the QoS of heterogeneous terminals simultaneously compared to conventional CSMA schemes.

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Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks

Cited by 524 publications

References 50 publications

Minimizing Age of Information With Power Constraints: Multi-User Opportunistic Scheduling in Multi-State Time-Varying Channels

Minimizing Age of Information With Power Constraints: Multi-User Opportunistic Scheduling in Multi-State Time-Varying Channels

Context-Aware Information Lapse for Timely Status Updates in Remote Control Systems

Distributed Policy Learning Based Random Access for Diversified QoS Requirements

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