461 research outputs found
Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning
Mobile crowdsourcing has become easier thanks to the widespread of
smartphones capable of seamlessly collecting and pushing the desired data to
cloud services. However, the success of mobile crowdsourcing relies on
balancing the supply and demand by first accurately forecasting spatially and
temporally the supply-demand gap, and then providing efficient incentives to
encourage participant movements to maintain the desired balance. In this paper,
we propose Deep-Gap, a deep learning approach based on residual learning to
predict the gap between mobile crowdsourced service supply and demand at a
given time and space. The prediction can drive the incentive model to achieve a
geographically balanced service coverage in order to avoid the case where some
areas are over-supplied while other areas are under-supplied. This allows
anticipating the supply-demand gap and redirecting crowdsourced service
providers towards target areas. Deep-Gap relies on historical supply-demand
time series data as well as available external data such as weather conditions
and day type (e.g., weekday, weekend, holiday). First, we roll and encode the
time series of supply-demand as images using the Gramian Angular Summation
Field (GASF), Gramian Angular Difference Field (GADF) and the Recurrence Plot
(REC). These images are then used to train deep Convolutional Neural Networks
(CNN) to extract the low and high-level features and forecast the crowdsourced
services gap. We conduct comprehensive comparative study by establishing two
supply-demand gap forecasting scenarios: with and without external data.
Compared to state-of-art approaches, Deep-Gap achieves the lowest forecasting
errors in both scenarios.Comment: Accepted at CloudCom 2019 Conferenc
Incentive Mechanism Design in Mobile Crowdsensing Systems
In the past few years, the popularity of Mobile Crowdsensing Systems (MCSs) has been greatly prompted, in which sensory data can be ubiquitously collected and shared by mobile devices in a distributed fashion. Typically, a MCS consists of a cloud platform, sensing tasks, and mobile users equipped with mobile devices, in which the mobile users carry out sensing tasks and receive monetary rewards as compensation for resource consumption ( e.g., energy, bandwidth, and computation) and risk of privacy leakage ( e.g., location exposure). Compared with traditional mote-class sensor networks, MCSs can reduce the cost of deploying specialized sensing infrastructures and enable many applications that require resources and sensing modalities beyond the current mote-class sensor processes as today’s mobile devices (smartphones (iPhones, Sumsung Galaxy), tablets (iPad) and vehicle-embedded sensing devices (GPS)) integrate more computing, communication, and storage resources than traditional mote-class sensors. The current applications of MCSs include traffic congestion detection, wireless indoor localization, pollution monitoring, etc . There is no doubt that one of the most significant characteristics of MCSs is the active involvement of mobile users to collect and share sensory data.
In this dissertation, we study the incentive mechanism design in mobile crowdsensing system with consideration of economic properties.
Firstly, we investigate the problem of joining sensing task assignment and scheduling in MCSs with the following three considerations: i) partial fulfillment, ii) attribute diversity, and iii) price diversity. Then, we design a distributed auction framework to allow each task owner to independently process its local auction without collecting global information in a MCS, reducing communication cost. Next, we propose a cost-preferred auction scheme (CPAS) to assign each winning mobile user one or more sub- working time durations and a time schedule-preferred auction scheme (TPAS) to allocate each winning mobile user a continuous working time duration.
Secondly, we focus on the design of an incentive mechanism for an MCS to minimize the social cost. The social cost represents the total cost of mobile devices when all tasks published by the MCS are finished. We first present the working process of a MCS, and then build an auction market for the MCS where the MCS platform acts as an auctioneer and users with mobile devices act as bidders. Depending on the different requirements of the MCS platform, we design a Vickrey-Clarke-Groves (VCG)-based auction mechanism for the continuous working pattern and a suboptimal auction mechanism for the discontinuous working pattern. Both of them can ensure that the bidding of users are processed in a truthful way and the utilities of users are maximized. Through rigorous theoretical analysis and comprehensive simulations, we can prove that these incentive mechanisms satisfy economic properties and can be implemented in reasonable time complexcity.
Next, we discuss the importance of fairness and unconsciousness of MCS surveillance applications. Then, we propose offline and online incentive mechanisms with fair task scheduling based on the proportional share allocation rules. Furthermore, to have more sensing tasks done over time dimension, we relax the truthfulness and unconsciousness property requirements and design a (ε, μ)-unconsciousness online incentive mechanism. Real map data are used to validate these proposed incentive mechanisms through extensive simulations.
Finally, future research topics are proposed to complete the dissertation
Game-theoretic Resource Allocation Methods for Device-to-Device (D2D) Communication
Device-to-device (D2D) communication underlaying cellular networks allows
mobile devices such as smartphones and tablets to use the licensed spectrum
allocated to cellular services for direct peer-to-peer transmission. D2D
communication can use either one-hop transmission (i.e., in D2D direct
communication) or multi-hop cluster-based transmission (i.e., in D2D local area
networks). The D2D devices can compete or cooperate with each other to reuse
the radio resources in D2D networks. Therefore, resource allocation and access
for D2D communication can be treated as games. The theories behind these games
provide a variety of mathematical tools to effectively model and analyze the
individual or group behaviors of D2D users. In addition, game models can
provide distributed solutions to the resource allocation problems for D2D
communication. The aim of this article is to demonstrate the applications of
game-theoretic models to study the radio resource allocation issues in D2D
communication. The article also outlines several key open research directions.Comment: Accepted. IEEE Wireless Comms Mag. 201
Service-Based Wireless Energy Crowdsourcing
We propose a novel service-based ecosystem to crowdsource wireless energy to
charge IoT devices. We leverage the service paradigm to abstract wireless
energy crowdsourcing from nearby IoT devices as energy services. The proposed
energy services ecosystem offers convenient, ubiquitous, and cost-effective
power access to charge IoT devices. We discuss the impact of a crowdsourced
wireless energy services ecosystem, the building components of the ecosystem,
the energy services composition framework, the challenges, and proposed
solutions.Comment: 15 pages, 7 figures, This is an invited paper and it will appear in
the proceedings of the 20th International Conference on Service Oriented
Computing (ICSOC
Incentive Design and Market Evolution of Mobile User-Provided Networks
An operator-assisted user-provided network (UPN) has the potential to achieve
a low cost ubiquitous Internet connectivity, without significantly increasing
the network infrastructure investment. In this paper, we consider such a
network where the network operator encourages some of her subscribers to
operate as mobile Wi-Fi hotspots (hosts), providing Internet connectivity for
other subscribers (clients). We formulate the interaction between the operator
and mobile users as a two-stage game. In Stage I, the operator determines the
usage-based pricing and quota-based incentive mechanism for the data usage. In
Stage II, the mobile users make their decisions about whether to be a host, or
a client, or not a subscriber at all. We characterize how the users' membership
choices will affect each other's payoffs in Stage II, and how the operator
optimizes her decision in Stage I to maximize her profit. Our theoretical and
numerical results show that the operator's maximum profit increases with the
user density under the proposed hybrid pricing mechanism, and the profit gain
can be up to 50\% in a dense network comparing with a pricing-only approach
with no incentives.Comment: This manuscript serves as the online technical report of the article
published in IEEE Workshop on Smart Data Pricing (SDP), 201
- …