3 research outputs found
Scalable Mobile Crowdsensing via Peer-to-Peer Data Sharing
Mobile crowdsensing (MCS) is a new paradigm of sensing by taking advantage of
the rich embedded sensors of mobile user devices. However, the traditional
server-client MCS architecture often suffers from the high operational cost on
the centralized server (e.g., for storing and processing massive data), hence
the poor scalability. Peer-to-peer (P2P) data sharing can effectively reduce
the server's cost by leveraging the user devices' computation and storage
resources. In this work, we propose a novel P2P-based MCS architecture, where
the sensing data is saved and processed in user devices locally and shared
among users in a P2P manner. To provide necessary incentives for users in such
a system, we propose a quality-aware data sharing market, where the users who
sense data can sell data to others who request data but not want to sense the
data by themselves. We analyze the user behavior dynamics from the
game-theoretic perspective, and characterize the existence and uniqueness of
the game equilibrium. We further propose best response iterative algorithms to
reach the equilibrium with provable convergence. Our simulations show that the
P2P data sharing can greatly improve the social welfare, especially in the
model with a high transmission cost and a low trading price
Optimal pricing for peer-to-peer sharing with network externalities
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
Regulating Competition in Age of Information under Network Externalities
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