7,825 research outputs found
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Semi-Trusted Mixer Based Privacy Preserving Distributed Data Mining for Resource Constrained Devices
In this paper a homomorphic privacy preserving association rule mining
algorithm is proposed which can be deployed in resource constrained devices
(RCD). Privacy preserved exchange of counts of itemsets among distributed
mining sites is a vital part in association rule mining process. Existing
cryptography based privacy preserving solutions consume lot of computation due
to complex mathematical equations involved. Therefore less computation involved
privacy solutions are extremely necessary to deploy mining applications in RCD.
In this algorithm, a semi-trusted mixer is used to unify the counts of itemsets
encrypted by all mining sites without revealing individual values. The proposed
algorithm is built on with a well known communication efficient association
rule mining algorithm named count distribution (CD). Security proofs along with
performance analysis and comparison show the well acceptability and
effectiveness of the proposed algorithm. Efficient and straightforward privacy
model and satisfactory performance of the protocol promote itself among one of
the initiatives in deploying data mining application in RCD.Comment: IEEE Publication format, International Journal of Computer Science
and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
SenMinCom: Pervasive Distributed Dynamic Sensor Data Mining for Effective Commerce
In last few years, the use of wireless sensor networks and cell phones has become ubiquitous; fusing these technologies in the field of business will open up new possibilities. To fill this lacuna, I propose a novel idea where the combination of these will facilitate companies to receive feedback on their products and services. System\u27s unobtrusive sensors will not only collect shopping, mobile usage data from consumers but will also make effective use of this information to increase revenue, cut costs, etc.; the use of mobile agent based data mining allows analyzing the data from different dimensions, categorizing it on factors such as product positioning, promotion of goods, etc. as in the case of a shopping store. Additionally, because of the dynamic mining system the companies get on-the-scene recommendation of products rather than off-the-scene. In this thesis, a novel distributed pervasive mining system is proposed to get dynamic shopping information and mobile device usage of the customers
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