Improving Location Reliability in Crowd Sensed Data with Minimal Efforts Authors: Manoop Talasila, Reza Curtmola, and Cristian Borcea Presenter: Hillol Debnath Department of Computer Science New Jersey Institute of Technology •This work is focused on Mobile crowd sensing •Static sensors were used Previously to sense data from the physical world (e.g. traffic monitoring, environment/weather monitoring etc.). Which was much more expensive and not scalable. - Everyone is using smartphones these days. So, people centric sensing can be used for large scale sensing of the physical world by leveraging smartphone sensors such as camera, microphone, GPS, accelerometer, etc. This type of sensing is less costly and more scalable than the static sensing. - However, one major problem with these sensed data is - how to validate them whether they are fake or real. •Why we need to validate the data? … Complex problem….And we have alleviated the validation problem. (professor’s mail)

Background – Crowd Sensing Crowd Sensing: Scalable and cost effective coverage of large regions Crowd Sensing Crowd Sensing Source: IDC Sensed Data Camera Accelerometer GPS Bluetooth Microphone Let me start with an overview of crowd-sensing. (talk about the stat) Talk about sensors on smartphones. How we can use sensors for collecting task data Using these two features we can achieve crowd-sensing Map – to show that we can collect data from a very wide region from which collecting data manually would be burdensome. (Regular people would submit sensor data, the central system would collect data. The 3rd party would be road transportation agency, news orgs, govt orgs like municipal corporations, etc) Motivation Problem Design Evaluation Conclusion

Examples of Mobile Crowd Sensing Traffic jam alerts Citizen-journalism Environment Traffic data – Citizen journalism – people take photos (and then news org can bid for their data) individual app – no centralized sys Environment sensor – (pollution sensors) Motivation Problem Design Evaluation Conclusion

How Reliable is Crowd Sensed Data? Submit “false” traffic jam Submit “fake” pollution Need methods to validate sensed data! (from paper) Fake traffic data to get benefit by diverting all the traffic to another route Fake photo can be submitted to earn money easily Environment – even though it’s clean and green. A malicious person (rival party) can report fake pollution data to hurt the org Submit photo from “fake” location Motivation Problem Design Evaluation Conclusion

Challenges Validating every sensed data point of each participant is difficult and not scalable This research: mitigate the problem by validating the location associated with the sensed data points to improve reliability We cannot manually validate huge amount data. Not scalable, and data with high sampling rate (acc) cannot be validated manually We alleviate the problem by validating the location associated with sensed data points. This improves the reliability of sensed data and provides guarantees to clients that collected data is valid. However, Validating locations without support from the wireless carriers is difficult. Location validation without support from the wireless carriers is difficult Motivation Problem Design Evaluation Conclusion

Contributions Proposed ILR scheme for location validation of crowd sensed data Developed McSense system & Android application for crowd sensing Ran two-months user study of McSense and evaluated ILR scheme on the collected data - To solve this problem we propose ILR scheme. We developed McSense – a crowd sensing system and it’s sensing application in android (note: how to increase reliability, how to schedule tasks efficiently, how increase the penetration rate by motivating people) We evaluated the ILR scheme on the real time data collected from the user study during a two months interval We also performed simulations to quantify ILR performance at large scale Evaluated ILR performance at larger scale through simulations Motivation Problem Design Evaluation Conclusion

ILR: Improve Location Reliability ILR starts from a small set of validated photos Implicitly, their location is validated User co-location is used to extend the set of validated sensed data points starting from validated photos Co-location: users have Bluetooth ON and perform periodic scans to detect neighbors Detect False Claim Transitive Trust Photo Validation ILR has 3 different phases. Photo selection phase – we start with a small set of validated photos. How we select those photo is discussed in later slides Extending the trust – we call it transitive trust. Using BT discoveries, we detect neighbors from user co-location After that we execute validation phase to detect fake location claims A B C Motivation Problem Design Evaluation Conclusion

Photo Selection & Validation Using collected data from Bluetooth scans, construct connected graph of co-located data points for a given location and a time interval From these graphs, select the photo data points that have node degree greater than a threshold Reduce the cost of photo validation by selecting only the most connected photo data points Selected photos are validated by Humans (Crowdsourced to Amazon MTurk) Computer vision techniques Validators: Locations of the validated photos which are considered reliable Don’t talk much about vision techniques Motivation Problem Design Evaluation Conclusion

Extend Trust Transitive Trust Trusted Trusted Trusted Unknown Example of how ILR works One person doing the accelerometer sensing does a BT scan periodically A second person performs a photo task in te same location and time of the 1st person. After validating the photo task of 2nd person manually, we consider his location as trusted Because they see each other in their BT scans, trust can be extended from the person performing the photo task to the person performing the acc sensing Transitive trust: Mark co-located data points as trusted Extend the trust from Validators to other co-located data points found in Bluetooth scans Trust is extended until all co-located data points are trusted or no other data point is found Alternately, ILR can set a TTL on extended trust Trusted Trusted Unknown Validator Motivation Problem Design Evaluation Conclusion

Detect False Claims Chicago NJIT Student Center Trusted Proven Fake B C Chicago NJIT Student Center M1 P3 P2 P3 P4 P1 P2 P4 Proven Fake Trusted Claims Location: “NJIT student center @ 10AM” P2 P3 P1 P1 ILR also applies a basic spatio-temporal correlation check Trusted Trusted P5 P1 P2 P4 Trusted Unknown  Students found around 10AM Motivation Problem Design Evaluation Conclusion

Prototype Implementation McSense: a platform for mobile crowd sensing McSense application is implemented in Android and deployed to Google Play 4 Tabs provided in the mobile application: For our research, we developed McSense system Back end server (glassfish, java, MVC…) The android application developed and deployed in Google Play. (v 2.2 +) Explain each tab and relate that to the life cycle of a task If a task is posted by the admin, it would be shown in the available task tab Completed: User completed tasks are listed Available: Available tasks on server are listed daily until they expire Earnings: User’s earnings for all successfully completed tasks Accepted: User accepted tasks are listed until they expire Motivation Problem Design Evaluation Conclusion

Tasks Developed for McSense Manual Photo Sensing Task Automated Sensing Task using Accelerometer and GPS Sensors Automated Sensing Task using Bluetooth radio Automated Sensing Task for Application/Network/Battery Usage Automated Sensing Task for WiFi Access Points Recording Motivation Problem Design Evaluation Conclusion

User Study Demographics: Deployed participatory sensing tasks in real time to campus students and other participants Participants downloaded and installed the application on their Android phones 2 months user study at NJIT campus to collect crowd sensed data Users received monetary compensation based on the number/type of completed tasks Demographics: Total participants: 58 Motivation Problem Design Evaluation Conclusion

Real-world Evaluation Total Photos: 1784 Manually Validated: 204 Extend Trust: 148 25% Data points involving false claims are detected 40% Cheating participants are detected 40% is out of total fake participants (not out if all 58 participants.) How will ILR perform at larger scale if users are required to have Bluetooth on? The results are promising given the user study conditions: Only users collecting co-location data had Bluetooth ON Users didn’t meet often due to campus size & limited number of users in the study Motivation Problem Design Evaluation Conclusion

Simulation Results Say on the first graph what density you used (density = 5) Say on the second one what’s the percentage false location claims from the total number of tasks (10% fake claims) (This shows that our system works perfectly at large scale. ) We proved that sys would be befitted by minimizing the number of photo tasks required for manual validation. even with small number of manually validated photos, we can efficiently detect false claims We also prove that ILR works at low densities Significant percentage of false location claims detected with few validations ILR works well at varying node densities Motivation Problem Design Evaluation Conclusion

Summary ILR Scheme Prototype Simulation Improves location reliability of mobile crowd sensed data Detects false location claims Prototype Developed McSense platform for crowd sensing Evaluated ILR on real-world data Simulation ILR works well at various node densities Low % of photo tasks needed to bootstrap ILR Motivation Problem Design Evaluation Conclusion

Thank You Acknowledgment: This work was supported by NSF Grant CNS 0831753 and CNS 1054754 http://web.njit.edu/~mt57/mcsense/

Related Work MetroSense, Participatory Sensing, Urbanets, Medusa framework Trusted Platform Module (TPM) YouProve LINK protocol Motivation Problem Design Evaluation Conclusion