Scalable Mobile Crowdsensing via Peer-to-Peer Data Sharing

Jiang, Changkun; Gao, Lin; Duan, Lingjie; Huang, Jianwei

doi:10.1109/tmc.2017.2743718

Cited by 64 publications

(36 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lemma 1. At the optimality of problem (7), for any two users i, j ∈ N with θ i > θ j , if user j is included in the platform (i.e., π j = 1), then user i should also be included (π i = 1).…”

Section: Pricing Under Complete Informationmentioning

confidence: 99%

See 1 more Smart Citation

Optimal pricing for a peer-to-peer sharing platform under network externalities

Liu

Courcoubetis

Duan

et al. 2018

Proceedings of the 13th Workshop on Economics of Networks, Systems and Computation

Self Cite

View full text Add to dashboard Cite

In this paper, we analyse how a peer-to-peer sharing platform should price its service (when imagined as an excludable public good) to maximize profit, when each user's participation adds value to the platform service by creating a positive externality to other participants. To characterize network externalities as a function of the number of participants, we consider different bounded and unbounded user utility models. The bounded utility model fits many infrastructure sharing applications with bounded network value, in which complete coverage has a finite user valuation (e.g., WiFi or hotspot). The unbounded utility model fits the large scale data sharing and explosion in social media, where it is expected that the network value follows Metcalfe's or Zipf's law. For both models, we analyze the optimal pricing schemes to select heterogeneous users in the platform under complete and incomplete information of users' service valuations. We propose the concept of price of information (PoI) to characterize the profit loss due to lack of information, and present provable PoI bounds for different utility models. We show that the PoI = 2 for the bounded utility model, meaning that just half of profit is lost, whereas the PoI ≥ 2 for the unbounded utility model and increases as for a less concave utility function. We also show that the complicated differentiated pricing scheme which is optimal under incomplete user information, can be replaced by a single uniform price scheme that is asymptotic optimal. Finally, we extend our pricing schemes to a two-sided market by including a new group of 'pure' service users contributing no externalities, and show that the platform may charge zero price to the original group of users in order to attract the pure user group.

show abstract

Section: Pricing Under Complete Informationmentioning

confidence: 99%

“…It follows from Lemma 1 that the platform will select m users with the largest service valuations and problem (7) reduces to…”

Section: Pricing Under Complete Informationmentioning

confidence: 99%

Optimal pricing for a peer-to-peer sharing platform under network externalities

Liu

Courcoubetis

Duan

et al. 2018

Proceedings of the 13th Workshop on Economics of Networks, Systems and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Depending on who (i.e., the server or each user) will make the task scheduling decision, there are two types of different PS models: Server-centric Participatory Sensing (SPS) [8]- [13] and User-centric Participatory Sensing (UPS) [14]- [18]. In the SPS model, the server will make the task scheduling decision and determine the joint scheduling of all tasks among all users, often in a centralized manner with complete information (as in [8]- [13]).…”

Section: A Background and Motivationsmentioning

confidence: 99%

“…In the SPS model, the server will make the task scheduling decision and determine the joint scheduling of all tasks among all users, often in a centralized manner with complete information (as in [8]- [13]). In the UPS model, each participating user will make his individual task scheduling decision and determine the tasks he is going to execute, often in a distributed manner with local information (as in [14]- [18]). Clearly, the SPS model assigns more control to the server to make the (centralized) joint scheduling decision, hence can better satisfy the requirements of various tasks.…”

Section: A Background and Motivationsmentioning

confidence: 99%

User-Centric Participatory Sensing: A Game Theoretic Analysis

Li²,

Gao

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

Self Cite

View full text Add to dashboard Cite

Participatory sensing (PS) is a novel and promising sensing network paradigm for achieving a flexible and scalable sensing coverage with a low deploying cost, by encouraging mobile users to participate and contribute their smartphones as sensors. In this work, we consider a general PS system model with location-dependent and time-sensitive tasks, which generalizes the existing models in the literature. We focus on the task scheduling in the user-centric PS system, where each participating user will make his individual task scheduling decision (including both the task selection and the task execution order) distributively. Specifically, we formulate the interaction of users as a strategic game called Task Scheduling Game (TSG) and perform a comprehensive game-theoretic analysis. First, we prove that the proposed TSG game is a potential game, which guarantees the existence of Nash equilibrium (NE). Then, we analyze the efficiency loss and the fairness index at the NE. Our analysis shows the efficiency at NE may increase or decrease with the number of users, depending on the level of competition. This implies that it is not always better to employ more users in the user-centric PS system, which is important for the system designer to determine the optimal number of users to be employed in a practical system.

show abstract

“…For example, online platform CrowdSpark follows this crowdsourcing approach and maintains a large pool of professional and citizen journalists who are paid to submit reports, news and videos. Another example is Waze platform who asks and rewards millions of drivers to report location-based observations (e.g., of road visibility, congestion, and "black-ice" segments) when travelling in different routes of the city ( [17], [18]). We wonder how much crowdsourced data a platform should buy, expecting a balance between its AoI performance and the incurred sampling cost.…”

Section: Introductionmentioning

confidence: 99%

Regulating Competition in Age of Information Under Network Externalities

Hao

Duan

2020

IEEE J. Select. Areas Commun.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Scalable Mobile Crowdsensing via Peer-to-Peer Data Sharing

Cited by 64 publications

References 39 publications

Optimal pricing for a peer-to-peer sharing platform under network externalities

Optimal pricing for a peer-to-peer sharing platform under network externalities

User-Centric Participatory Sensing: A Game Theoretic Analysis

Regulating Competition in Age of Information Under Network Externalities

Contact Info

Product

Resources

About