ILR: Improving Location Reliability in Mobile Crowd Sensing

ILR: Improving Location Reliability in Mobile Crowd Sensing

Manoop Talasila (Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA), Reza Curtmola (Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA) and Cristian Borcea (Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA)
DOI: 10.4018/ijbdcn.2013100104
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Abstract

People-centric sensing with smart phones can be used for large scale sensing of the physical world at low cost by leveraging the available sensors on the phones. However, the sensed data submitted by participants is not always reliable as they can submit false data to earn money without executing the actual task at the desired location. To address this problem, the authors propose ILR, a scheme which Improves the Location Reliability of mobile crowd sensed data with minimal human efforts. In this scheme, the authors bootstrap the trust in the system by first manually validating a small number of photos submitted by participants. Based on these validations, the location of these photos is assumed to be trusted. Second, the authors extend this location trust to co-located sensed data points found in the Bluetooth range of the devices that provided the validated photos. In addition, the scheme also helps to detect false location claims associated with sensed data. The authors applied ILR on data collected from their McSense prototype deployed on Android phones used by students on their campus and detected a significant percentage of the malicious users.
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Introduction

Mobile sensors such as smart phones and vehicular systems represent a new type of geographically distributed sensing infrastructure that enables mobile people-centric sensing (Riva, 2007; Sensor Lab at Dartmouth, 2013; Urban Sensing at UCLA, 2013). This new type of sensing can be a scalable and cost-effective alternative to deploying static wireless sensor networks for dense sensing coverage across large areas. Many clients can use this mobile people-centric sensing on demand and pay just for the actual usage (i.e., collected data). mCrowd (Yan, 2009) and Medusa (Ra, 2012) are some of the recent mobile crowd sensing platforms proposed to provide a common platform to perform any type of sensing tasks supported by the smart phone sensors.

Mobile crowd sensing can be used to enable a broad spectrum of applications, ranging from monitoring pollution or traffic in cities to epidemic disease monitoring or real-time reporting from disaster situations. While all of us could directly take advantage of such applications (e.g., real-time traffic monitoring), we believe that researchers in many fields of science and engineering as well as local, state, and federal agencies could greatly benefit from this new sensing infrastructure as they will have access to valuable data from the physical world. Additionally, commercial organizations may be very interested in collecting mobile sensing data to learn more about customer behavior.

A major challenge for broader adoption of these sensing systems is that the sensed data submitted by the participants is not always reliable (Downs, 2010) as they can submit false data to earn money without executing the actual task. Clients need guarantees from the mobile crowd sensing system that the collected data is valid. Hence, it is very important to validate the sensed data. However, it is challenging to validate each and every sensed data point of each participant because sensing measurements are highly dependent on context. One approach to handle the issue is to validate the location associated with the sensed data point in order to achieve a certain degree of reliability on the sensed data.

Therefore, in this article we focus on validating the location data submitted by the participants. Still, we need to overcome a major challenge: How to validate the location of data points in a scalable and cost-effective way without help from the wireless carrier? Wireless carriers may not help with location validation for legal reasons related to user privacy or even commercial interests.

To achieve reliability on participants’ location data, there are a few traditional solutions such as using Trusted Platform Modules (TPM) (Trusted Platform Module, 2013) on smart phones or duplicating the tasks among multiple participants. However, these solutions cannot be used directly for a variety of reasons. For example, it is not cost-effective to have TPM modules on every smart phone, while task replication may not be feasible at some locations due to a lack of additional users there. Another solution is to verify location through the use of secure location verification mechanisms (Capkun, 2005; Capkun, 2006; Sastry, 2003; Talasila, 2010) in real time when the participant is trying to submit the sensing data location. Unfortunately, this solution requires infrastructure support or adds significant overhead on user phones if it is applied for each sensed data point.

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