Economics of Age of Information Management under Network Externalities

Hao, Shugang; Duan, Lingjie

doi:10.1145/3323679.3326511

Cited by 13 publications

(14 citation statements)

References 12 publications

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“…Then, for any t ≤ T − t 0 , we have A(t) = A ∞ (t). Moreover, according to (9) and (20), we have p(t) = p ∞ (t) = 1 K for any t ≤ T −t 0 . Therefore, the expected discounted cost can be rewrite as…”

Section: A ε-Optimality For Expected Discounted Costmentioning

confidence: 96%

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Dynamic Pricing for Controlling Age of Information

Wang

Duan

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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Fueled by the rapid development of communication networks and sensors in portable devices, today many mobile users are invited by content providers to sense and send back real-time useful information (e.g., traffic observations and sensor data) to keep the freshness of the providers' content updates. However, due to the sampling cost in sensing and transmission, an individual may not have the incentive to contribute the realtime information to help a content provider reduce the age of information (AoI). Accordingly, we propose dynamic pricing for the provider to offer age-dependent monetary returns and encourage users to sample information at different rates over time. This dynamic pricing design problem needs to balance the monetary payments to users and the AoI evolution over time, and is challenging to solve especially under the incomplete information about users' arrivals and their private sampling costs. For analysis tractability, we linearize the nonlinear AoI evolution in the constrained dynamic programming problem, by approximating the dynamic AoI reduction as a time-average term and solving the approximate dynamic pricing in closed-form. Then, we estimate this approximate term based on Brouwer's fixed-point theorem. Finally, we provide the steady-state analysis of the optimized approximate dynamic pricing scheme for an infinite time horizon, and show that the pricing scheme can be further simplified to an ε-optimal version without recursive computing over time.

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Section: A ε-Optimality For Expected Discounted Costmentioning

confidence: 96%

“…Insert (4) = 0, we have the optimal price p(t) as given in (9). Then, insert p(t) in (9) into V (A(t), t) in (14), we have V (A(t), t) as a function of Q t+1 , M t+1 , S t+1 and A(t).…”

Section: A Analysis Of Approximate Dynamic Pricingmentioning

confidence: 99%

Dynamic Pricing for Controlling Age of Information

Wang

Duan

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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“…Based on the aforementioned analysis, we obtain the average AoI and the average peak AoI of the multicast transmission with exponentially distributed deadlines by substituting (24), (25), (31), (32), (36), and (38) into (22) and (23), respectively.…”

Section: E Average (Peak) Aoi For Random Deadline Casementioning

confidence: 99%

Age of Information for Multicast Transmission With Fixed and Random Deadlines in IoT Systems

Zhou

Chen

2020

IEEE Internet Things J.

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In this paper, we consider the multicast transmission of a real-time Internet of Things (IoT) system, where an access point (AP) transmits time-stamped status updates to multiple IoT devices. Different from the existing studies that only considered multicast transmission without deadlines, we enforce a deadline for the service time of each multicast status update, taking into account both the fixed and randomly distributed deadlines. In particular, a status update is dropped when either its deadline expires or it is successfully received by a certain number of IoT devices. Considering deadlines is important for many emerging IoT applications, where the outdated status updates are of no use to IoT devices. We evaluate the timeliness of the status update delivery by applying a recently proposed metric, named the age of information (AoI), which is defined as the time elapsed since the generation of the most recently received status update. After deriving the distributions of the service time for all possible reception outcomes at IoT devices, we manage to obtain the closed-form expressions of both the average AoI and the average peak AoI. Simulations validate the performance analysis, which reveals that the multicast transmission with deadlines achieves a lower average AoI than that without deadlines and there exists an optimal value of the deadline that can minimize the average (peak) AoI. Results also show that the fixed and random deadlines have respective advantages in different deadline regimes.Index Terms-Age of information, fixed deadline, randomly distributed deadline, multicast transmission, information freshness.J. Li is with the

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“…Without much loss of generality, we assume that the content delivery network applies a LCFS M/M/1 queue with preemption policy for processing the N platforms' updates as in [11], where the latest content arrival can preempt any platform's ongoing update in the network. 1 According to [11], the time-average AoI at platform i is given by…”

Section: As Shown Inmentioning

confidence: 99%

Regulating Competition in Age of Information Under Network Externalities

Hao

Duan

2020

IEEE J. Select. Areas Commun.

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Online content platforms are concerned about the freshness of their content updates to their end customers, and increasingly more platforms now invite and pay the crowd to sample real-time information (e.g., traffic observations and sensor data) to help reduce their ages of information (AoI). How much crowdsourced data to sample and buy over time is a critical question for a platform's AoI management, requiring a good balance between its AoI and the incurred sampling cost. This question becomes more interesting by considering the stage after sampling, where multiple platforms coexist in sharing the content delivery network of limited bandwidth, and one platform's update may jam or preempt the others' under negative network externalities. When these selfish platforms know each other's sampling cost, we formulate their competition as a non-cooperative game and show they want to over-sample to reduce their own AoIs, causing the price of anarchy (PoA) to be infinity. To remedy this huge efficiency loss, we propose a trigger mechanism of non-monetary punishment in a repeated game to enforce the platforms' cooperation to approach the social optimum. We also study the more challenging scenario of incomplete information that some new platform hides its private sampling cost information from the other incumbent platforms in the Bayesian game. Perhaps surprisingly, we show that even the platform with more information may get hurt. We successfully redesign the trigger-and-punishment mechanism to negate the platform's information advantage and ensure no cheating. Our extensive simulations show that the mechanisms can remedy the huge efficiency loss due to platform competition, and the performance improves as we have more incumbent platforms with known cost information. Index TermsPart of this work has appeared in ACM MobiHoc 2019 Symposium [1]. S. Hao and L. Duan are with the Pillar 2 Age of information, Mobile crowdsourcing, Network externalities, Repeated games, Trigger mechanism of non-monetary punishment.

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Economics of Age of Information Management under Network Externalities

Cited by 13 publications

References 12 publications

Dynamic Pricing for Controlling Age of Information

Dynamic Pricing for Controlling Age of Information

Age of Information for Multicast Transmission With Fixed and Random Deadlines in IoT Systems

Regulating Competition in Age of Information Under Network Externalities

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