2 research outputs found
Technical Report for "User-Centric Participatory Sensing: A Game Theoretic Analysis"
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.Comment: This manuscript serves as the online technical report for the paper
published in IEEE GLOBECOM 201
A Double Auction Mechanism for Mobile Crowd Sensing with Data Reuse
Mobile Crowd Sensing (MCS) is a new paradigm of sensing, which can achieve a
flexible and scalable sensing coverage with a low deployment cost, by employing
mobile users/devices to perform sensing tasks. In this work, we propose a novel
MCS framework with data reuse, where multiple tasks with common data
requirement can share (reuse) the common data with each other through an MCS
platform. We study the optimal assignment of mobile users and tasks (with data
reuse) systematically, under both information symmetry and asymmetry, depending
on whether the user cost and the task valuation are public information. In the
former case, we formulate the assignment problem as a generalized Knapsack
problem and solve the problem by using classic algorithms. In the latter case,
we propose a truthful and optimal double auction mechanism, built upon the
above Knapsack assignment problem, to elicit the private information of both
users and tasks and meanwhile achieve the same optimal assignment as under
information symmetry. Simulation results show by allowing data reuse among
tasks, the social welfare can be increased up to 100~380%, comparing with those
without data reuse. We further show that the proposed double auction is not
budget balance for the auctioneer, mainly due to the data reuse among tasks. To
this end, we further introduce a reserve price into the double auction (for
each data item) to achieve a desired tradeoff between the budget balance and
the social efficiency.Comment: This manuscript serves as the online technical report for the paper
published in IEEE GLOBECOM 201