87 research outputs found
Seamless Service Provisioning for Mobile Crowdsensing: Towards Integrating Forward and Spot Trading Markets
The challenge of exchanging and processing of big data over Mobile
Crowdsensing (MCS) networks calls for the new design of responsive and seamless
service provisioning as well as proper incentive mechanisms. Although
conventional onsite spot trading of resources based on real-time network
conditions and decisions can facilitate the data sharing over MCS networks, it
often suffers from prohibitively long service provisioning delays and
unavoidable trading failures due to its reliance on timely analysis of complex
and dynamic MCS environments. These limitations motivate us to investigate an
integrated forward and spot trading mechanism (iFAST), which entails a new
hybrid service trading protocol over the MCS network architecture. In iFAST,
the sellers (i.e., mobile users with sensing resources) can provide long-term
or temporary sensing services to the buyers (i.e., sensing task owners). iFast
enables signing long-term contracts in advance of future transactions through a
forward trading mode, via analyzing historical statistics of the market, for
which the notion of overbooking is introduced and promoted. iFAST further
enables the buyers with unsatisfying service quality to recruit temporary
sellers through a spot trading mode, upon considering the current
market/network conditions. We analyze the fundamental blocks of iFAST, and
provide a case study to demonstrate its superior performance as compared to
existing methods. Finally, future research directions on reliable service
provisioning for next-generation MCS networks are summarized
Matching-based Hybrid Service Trading for Task Assignment over Dynamic Mobile Crowdsensing Networks
By opportunistically engaging mobile users (workers), mobile crowdsensing
(MCS) networks have emerged as important approach to facilitate sharing of
sensed/gathered data of heterogeneous mobile devices. To assign tasks among
workers and ensure low overheads, a series of stable matching mechanisms is
introduced in this paper, which are integrated into a novel hybrid service
trading paradigm consisting of futures trading mode and spot trading mode to
ensure seamless MCS service provisioning. In the futures trading mode, we
determine a set of long-term workers for each task through an
overbooking-enabled in-advance many-to-many matching (OIA3M) mechanism, while
characterizing the associated risks under statistical analysis. In the spot
trading mode, we investigate the impact of fluctuations in long-term workers'
resources on the violation of service quality requirements of tasks, and
formalize a spot trading mode for tasks with violated service quality
requirements under practical budget constraints, where the task-worker mapping
is carried out via onsite many-to-many matching (O3M) and onsite many-to-one
matching (OMOM). We theoretically show that our proposed matching mechanisms
satisfy stability, individual rationality, fairness and computational
efficiency. Comprehensive evaluations also verify the satisfaction of these
properties under practical network settings, while revealing commendable
performance on running time, participators' interactions, and service quality
Understanding the current trends in mobile crowdsensing - a business model perspective: case MyGeo Trust
Crowdsensing and personal data markets that have emerged around it have rapidly gained momentum in parallel with the appearance of mobile devices. Collecting information via mobile sensors and the applications relying on these, the privacy of mobile users can be threatened, especially in the case of location-related data. In 2015, a research project called MyGeoTrust was initiated to investigate this issue. One aim of the project was to study the potential business models for a trusted, open-source crowdsourcing platform. This study, carried within the MyGeoTrust project, reviews existing literature about business models, location-based services, and open-source software development. It then investigates the relationship between these topics and mobile crowdsensing. As a whole, this thesis provides an overview on the development of location-based services, as well as the current trends and business models in crowdsensing.
The empirical part of the thesis employs embedded case study methodology, acquiring empirical data from several sources. The analyzed case is the MyGeoTrust project itself, and other empirical data is collected via market analysis, interim reports, a user survey, and semi-structured interviews. This material forms the baseline for the empirical study and project-specific recommendations.
The findings suggest that creating a two- or multisided platform is the most robust business model for mobile crowdsensing. The identified benefits of platform-based business models include facilitating the value exchange between self-governing groups and possibilities to build positive network effects. This is especially the case with open-source software and open data since the key value for users - or “the crowd” in other terms - is created through network effects. In the context of open business models, strategic planning, principally licensing, plays a central role. Also, for a differentiated platform like MyGeoTrust finding the critical mass of users is crucial, in order to create an appealing alternative to current market leaders. Lastly, this study examines how transformational political or legal factors may shape the scene and create requirements for novel, privacy-perceiving solutions. In the present case study, the upcoming European Union (EU) General Data Protection Regulation (GDPR) legislation is a central example of such a factor
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