Manoop Talasila

Fostering participaction in smart cities: a geo-social crowdsensing platform

This article investigates how and to what extent the power of collective although imprecise intelligence can be employed in smart cities. The main visionary goal is to automate the organization of spontaneous and impromptu collaborations of large groups of people participating in collective actions (i.e., participAct), such as in the notable case of urban crowdsensing. In a crowdsensing environment, people or their mobile devices act as both sensors that collect urban data and actuators that take actions in the city, possibly upon request. Managing the crowdsensing process is a challenging task spanning several socio-technical issues: from the characterization of the regions under control to the quantification of the sensing density needed to obtain a certain accuracy; from the evaluation of a good balance between sensing accuracy and resource usage (number of people involved, network bandwidth, battery usage, etc.) to the selection of good incentives for people to participAct (monetary, social, etc.). To tackle these problems, this article proposes a crowdsensing platform with three main original technical aspects: an innovative geo-social model to profile users along different variables, such as time, location, social interaction, service usage, and human activities; a matching algorithm to autonomously choose people to involve in participActions and to quantify the performance of their sensing; and a new Android-based platform to collect sensing data from smart phones, automatically or with user help, and to deliver sensing/actuation tasks to users.

Improving location reliability in crowd sensed data with minimal efforts

People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the cameras, microphones, GPSs, accelerometers, and other sensors on the phones. Ranging from manual photo tasks to automated sensing tasks for activity monitoring, any task can be crowd sourced to smart phones to sense data from different locations at reduced cost. 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. Therefore, it is important to validate the sensed data. Validating the context of every sensed data point of each participant is not a scalable solution. One alternative is to first validate the location associated with the sensed data points in order to achieve a certain degree of reliability about the sensed data. However, location validation without support from the wireless carriers is difficult. To address this problem, we propose ILR, a scheme in which we Improve the Location Reliability of mobile crowd sensed data with minimal human efforts. In this scheme, we bootstrap the trust in the system by first manually or automatically using image processing techniques validating a small number of photos submitted by participants. Based on these validations, the location of these photos is assumed to be trusted. Second, we extend this location trust to co-located sensed data points found in the Bluetooth range of the devices that provided the validated photos. This transitive trust is extended until all the co-located tasks are trusted or no new data points are found. In addition, the scheme also helps to detect false location claims associated with sensed data. We applied ILR on data collected from our McSense prototype deployed on Android phones used by students on our campus and detected a significant percentage of the malicious users. Simulation results demonstrate that ILR works well at various densities and helps detect the false location claims based on a minimal numher of validations.

LINK: Location Verification through Immediate Neighbors Knowledge

In many location-based services, the user location is determined on the mobile device and then shared with the service. For this type of interaction, a major problem is how to prevent service abuse by malicious users who lie about their location. This paper proposes LINK (Location verification through Immediate Neighbors Knowledge), a location authentication protocol in which users help verify each other’s location claims. This protocol is independent of the wireless network carrier, and thus works for any third-party service. For each user’s location claim, a centralized Location Certification Authority (LCA) receives a number of verification messages from neighbors contacted by the claimer using short-range wireless networking such as Bluetooth. The LCA decides whether the claim is authentic or not based on spatio-temporal correlation between the users, trust scores associated with each user, and historical trends of the trust scores. LINK thwarts attacks from individual malicious claimers or malicious verifiers. Over time, it also detects attacks involving groups of colluding users.

Crowdsensing in the Wild with Aliens and Micropayments

This article presents results and lessons learned from two user studies on crowdsensing incentives-specifically, on mobile gaming and micropayments. The analysis of the results suggests that gaming is a cost-effective solution for uniform area coverage, whereas micropayments work well for sensing tasks with tight time constraints or for long-term tasks for personal analytics.

Collaborative Bluetooth-based location authentication on smart phones

Third-party location-based services are independent of wireless carriers and receive the user location from mobile devices GPS. A major problem in this context is how to prevent service abuse by malicious users who submit false locations by tampering with their phones. This paper presents LINK (Location authentication through Immediate Neighbors Knowledge), a location authentication protocol working independent of wireless carriers, in which nearby users help authenticate each others location claims using Bluetooth communication. Simulation results demonstrate that LINK thwarts individual user attacks and a number of colluding users attacks. Experimental results over Android phones show that LINK works well at walking speeds and phone battery is not impacted significantly even for relatively high usage.

ILR: Improving Location Reliability in Mobile Crowd Sensing

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.