Emerging Ubiquitous Knowledge Services: From Mobile Sensing to Ubiquitous Crowdsourcing and Beyond

1. Emerging Ubiquitous Knowledge Services: From Mobile Sensing to Ubiquitous Crowdsourcing and Beyond Uichin Lee, HowonLee*, Bang ChulJung**, JunehwaSong*** KAIST Knowledge Service Engineering *KAIST Institute ICT **Kyungsang University ***KAIST Computer Science Oct. 11, 2011

2. Knowledge Service Innovation • Traditional knowledge services: mainly delivered by experts with domain specific knowledge • Dramatic change of knowledge services due to recent advances of ICTs (information and communication technologies) and networked collaboration among people Smart Devices Web 2.0 Social Networking

3. Major Contributors • Human: crowdsourcing, human computation • Device: processing, sensing, networking • Network: in-network services • Application: Web 2.0, service mashup, data mining Content Fixed access Content provider Internet Smart home/office Human Device Radioaccess Network Applications On the move Crowdsourcing Human computation Networking Application Our focus: ubiquitous knowledge services with mobile sensing and crowdsourcing

4. Mobile Sensing • Sensors in smart devices (smartphones, pads): accelerometer, magnetometer, gyroscope, light, proximity, camera, voice, GPS • Wireless communications: Bluetooth, Wi-Fi, 3/4G • Sensor applications: • User experience enhancement • Resizing screen/tilt, • Gaming, augmented reality • Mobile sensing apps: • Traffic information systems • Location-based services

5. Crowdsourcing • “Crowd" + “Outsourcing" • “distributed labor networks over the Internet to exploit the spare processing power of millions of human brains” – best example, Wikipedia • Related concepts: collaborative system, wisdom of crowd, human computation, collective intelligence, wikinomics 인소싱(insourcing) 비용 감소 스마트폰을 이용한 모바일센싱 가치실현시간(Time-to-Value) 단축 모바일 소셜네트워크 아웃소싱(Outsourcing) 크라우드소싱(Crowdsourcing) 해결책의 폭 노동 마켓플레이스 위키피디아소셜 Q&A (지식인) 인간계산 Ubiquitous Knowledge Services

6. Ubiquitous Knowledge Services • Goals: • Seamlessly integrate content from various sources at large scales • Content: data, information, knowledge • Sources: databases, grassroots (sensors, humans) • Infrastructure dependence is minimal (compared to existing approaches) • Also derive new values for end users in ways that the contributor of the content did not plan or imagine Examples of Ubiquitous Knowledge Services 범죄수사 교통정보수집 공동체의식제고 도시계획 현장조사 모바일 UX 테스트 연구지원

7. Contents • UKS Applications • Vehicular apps • Traffic engineering, ride quality monitoring (cracks, potholes) • Community-awareness apps (e.g., health and wellness) • Social sensing with Twitter • UKS Platform Design • Unique features • UKS platform Potholes Social networks Air pollution (CO2)

8. Pothole Patrol • Acceleration data gathering from vehicles (geo-tagged) • Simple data processing to detect a pothole, and statistical processing (clustering) for accurate detection Smooth Road Pothole The Pothole Patrol: Using a Mobile Sensor Network for Road Surface Monitoring, Eriksson et al, MobiSys, 2008

9. Community Awareness: Health and Wellness • Personal environmental impact report (PIER) on “health and wellness” • Participants use mobile phones to gather location data and web services to aggregate and interpret the assembled information (e.g., air pollution, CO2 emission, fast food exposure)             "Sensing Pollution without Pollution-Sensors” Annotation /Inferences Scientific  Models Existing  Infrastructure Activity Classificatione.g., staying, walking, driving GIS Data Annotation e.g. weather, traffic Fast food exposure Weather, traffic data Air pollutionexposure(PM 2.5) CO2 emissions Tracklog format School,hospital,fast food restaurant locations Impact and Exposure  Calculation User profile Data Aggregation PEIR, the Personal Environmental Impact Report as a Platform for Participatory Sensing Systems Research, Mun et al., Mobisys 2009

10. Social Sensing with Twitter Object detection in ubiquitous environments Event detection from twitter detect an earthquake detect an earthquake some earthquake sensors responses positive value search and classify them into positive class Probabilistic model Probabilistic model values Classifier tweets ・・・ ・・・ ・・・ ・・・ ・・・ some users posts “earthquake right now!!” observation by sensors observation by twitter users earthquake occurrence target object target event Earthquake shakes Twitter users: real-time event detection by social sensors, Takeshi et al, WWW 2010

11. Building Ubiquitous Knowledge Service (UKS) Platform • Research community: • ArchRock, SensorBase (UCLA), SensorMap (Microsoft), IrisNet (CMU), and many others.. • Standardization efforts: • Semantic Web Enablement (SWE) • Semantic Sensor Web (SSW) • Machine-to-Machine (M2M) • Internet of Things (IoT), e.g, EPCglobal Network • Essential features of UKS: • Humans are part of the systems (e.g., crowdsourcing, human computation) • Exploiting social networks is also important • Due to resource constraints (e.g., battery), mobile operations must be optimized, which may require service-device/-network interactions • System should perceive the intention of a user and provide the customized knowledge services at any place and any time

12. Building Ubiquitous Knowledge Service (UKS) Platform • Key components of UKS: • Smart device, knowledge gateway, knowledge server • User agents, service-device/network interactions and optimization, content integration/negotiation

13. Conclusion • Knowledge service innovation with recent advances of ICT (i.e., device, network, and application intelligences) and networked collaboration among people • Ubiquitous knowledge services aim at (1) seamlessly integrating content from various sources at large scales, and (2) deriving new values for end users in ways that the contributor of the content did not plan or imagine • Example services: vehicular applications (pothole detection), community awareness (health and wellness), SoundSense, event detection with Tweets • Building a ubiquitous knowledge service (UKS) platform that can integrate grassroots knowledge (and also existing content) and dynamically generate new values to end users