Journal ArticleDOI
Incentive mechanisms for crowdsensing: crowdsourcing with smartphones
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TLDR
This work designs an auction-based incentive mechanism for crowdsensing, which is computationally efficient, individually rational, profitable, and truthful, and shows how to compute the unique Stackelberg Equilibrium, at which the utility of the crowdsourcer is maximized.Abstract:
Smartphones are programmable and equipped with a set of cheap but powerful embedded sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and camera. These sensors can collectively monitor a diverse range of human activities and the surrounding environment. Crowdsensing is a new paradigm which takes advantage of the pervasive smartphones to sense, collect, and analyze data beyond the scale of what was previously possible. With the crowdsensing system, a crowdsourcer can recruit smartphone users to provide sensing service. Existing crowdsensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for crowdsensing. We consider two system models: the crowdsourcer-centric model where the crowdsourcer provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the crowdsourcer-centric model, we design an incentive mechanism using a Stackelberg game, where the crowdsourcer is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the crowdsourcer is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.read more
Citations
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A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Andrea Capponi,Claudio Fiandrino,Burak Kantarci,Luca Foschini,Dzmitry Kliazovich,Pascal Bouvry +5 more
TL;DR: A survey on existing works in the MCS domain is presented and a detailed taxonomy is proposed to shed light on the current landscape and classify applications, methodologies, and architectures to outline potential future research directions and synergies with other research areas.
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Blockchain-Enabled Data Collection and Sharing for Industrial IoT With Deep Reinforcement Learning
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When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning
TL;DR: This paper considers the fixed-wing UAV-aided MCS system and investigates the corresponding joint route planning and task assignment problem from an energy efficiency perspective and provides a comprehensive theoretical analysis, and elaborate the procedures of practical implementation.
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Auction Mechanisms in Cloud/Fog Computing Resource Allocation for Public Blockchain Networks
TL;DR: This work focuses on the trading between the cloud/fog computing service provider and miners, and proposes an auction-based market model for efficient computing resource allocation, and designs an approximate algorithm which guarantees the truthfulness, individual rationality and computational efficiency.
References
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Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Journal ArticleDOI
Optimal Auction Design
TL;DR: Optimal auctions are derived for a wide class of auction design problems when the seller has imperfect information about how much the buyers might be willing to pay for the object.
Journal ArticleDOI
An analysis of approximations for maximizing submodular set functions--I
TL;DR: It is shown that a “greedy” heuristic always produces a solution whose value is at least 1 −[(K − 1/K]K times the optimal value, which can be achieved for eachK and has a limiting value of (e − 1)/e, where e is the base of the natural logarithm.
Posted Content
An analysis of approximations for maximizing submodular set functions II
TL;DR: In this article, the authors considered the problem of finding a maximum weight independent set in a matroid, where the elements of the matroid are colored and the items of the independent set can have no more than K colors.
Proceedings ArticleDOI
MAUI: making smartphones last longer with code offload
Eduardo Cuervo,Aruna Balasubramanian,Dae-Ki Cho,Alec Wolman,Stefan Saroiu,Ranveer Chandra,Paramvir Bahl +6 more
TL;DR: MAUI supports fine-grained code offload to maximize energy savings with minimal burden on the programmer, and decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains.