Social opportunistic sensing and social centric networking: enabling technology for smart cities

In recent years, with tremendous advances in areas like mobile devices, algorithms for distributed systems, communication technology or protocols, all basic technological pieces to realise a Smart City are at hand. Missing, however, is a mechanism that bridges these pieces to ease the creation of Smart Cities at a larger scale. In this visionary paper, we discuss challenges of Smart Cities and propose enabling technologies to bridge the above mentioned pieces for their actual realisation. In particular, we introduce the concepts of Social Opportunistic Sensing (SOS) and Social Centric Networking (SCN). While the former is an enabling technology to interconnect all parties in a Smart City, the latter has the potential to enhance offline social networks in Internet of Things (IoT) enhanced Smart Cities by connecting individuals based on their automatically updated profile via context-based routing.

[1]  Ramachandran Ramjee,et al.  Nericell: rich monitoring of road and traffic conditions using mobile smartphones , 2008, SenSys '08.

[2]  Marco Gruteser,et al.  ParkNet: drive-by sensing of road-side parking statistics , 2010, MobiSys '10.

[3]  Stephan Sigg,et al.  Secure Communication Based on Ambient Audio , 2013, IEEE Transactions on Mobile Computing.

[4]  Tim Kraska,et al.  CrowdDB: answering queries with crowdsourcing , 2011, SIGMOD '11.

[5]  M. Hansen,et al.  Participatory Sensing , 2019, Internet of Things.

[6]  Yusheng Ji,et al.  RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals , 2014, IEEE Transactions on Mobile Computing.

[7]  Prem Prakash Jayaraman,et al.  Using On-the-Move Mining for Mobile Crowdsensing , 2012, 2012 IEEE 13th International Conference on Mobile Data Management.

[8]  Loïc Kessous,et al.  Emotion Recognition through Multiple Modalities: Face, Body Gesture, Speech , 2008, Affect and Emotion in Human-Computer Interaction.

[9]  José Ramón Gil-García,et al.  Understanding Smart Cities: An Integrative Framework , 2012, HICSS.

[10]  Theresa A. Pardo,et al.  Conceptualizing smart city with dimensions of technology, people, and institutions , 2011, dg.o '11.

[11]  Emiliano Miluzzo,et al.  BikeNet: A mobile sensing system for cyclist experience mapping , 2009, TOSN.

[12]  David Kotz,et al.  AnonySense: Opportunistic and Privacy-Preserving Context Collection , 2009, Pervasive.

[13]  Jiafu Wan,et al.  M2M Communications for Smart City: An Event-Based Architecture , 2012, 2012 IEEE 12th International Conference on Computer and Information Technology.

[14]  Nathan Eagle,et al.  txteagle: Mobile Crowdsourcing , 2009, HCI.

[15]  Mirco Musolesi,et al.  Urban sensing systems: opportunistic or participatory? , 2008, HotMobile '08.

[16]  Cornel Klein,et al.  From Smart Homes to Smart Cities: Opportunities and Challenges from an Industrial Perspective , 2008, NEW2AN.

[17]  Yusheng Ji,et al.  Monitoring Attention Using Ambient FM Radio Signals , 2014, IEEE Pervasive Computing.

[18]  Vinicius Cardoso Garcia,et al.  Smart cities software architectures: a survey , 2013, SAC '13.

[19]  Luis A. Hernández Gómez,et al.  Smart Cities at the Forefront of the Future Internet , 2011, Future Internet Assembly.

[20]  Noordin Ahmad,et al.  Smart City Components Architicture , 2009, 2009 International Conference on Computational Intelligence, Modelling and Simulation.

[21]  Deborah Estrin,et al.  Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype , 2007, EmNets '07.

[22]  Dirk Trossen,et al.  NORS: An Open Source Platform to Facilitate Participatory Sensing with Mobile Phones , 2007, 2007 Fourth Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services (MobiQuitous).

[23]  Van Jacobson,et al.  Networking named content , 2009, CoNEXT '09.

[24]  Yang Zhang,et al.  CarTel: a distributed mobile sensor computing system , 2006, SenSys '06.

[25]  George Kozmetsky,et al.  The Technopolis Phenomenon: Smart Cities, Fast Systems, Global Networks , 1992 .

[26]  Jun Li,et al.  Towards robust device-free passive localization through automatic camera-assisted recalibration , 2012, SenSys '12.

[27]  Tara S. Behrend,et al.  The viability of crowdsourcing for survey research , 2011, Behavior research methods.

[28]  Ingolf Krüger,et al.  A Rich Services Approach to CoCoME , 2007, CoCoME.

[29]  B. de Gelder,et al.  Rapid detection of fear in body expressions, an ERP study , 2007, Brain Research.

[30]  Stephan Sigg,et al.  Algorithms for closed-loop feedback based distributed adaptive beamforming in wireless sensor networks , 2009, 2009 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[31]  Richard Howard,et al.  SCPL: Indoor device-free multi-subject counting and localization using radio signal strength , 2013, 2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[32]  Deepak Ganesan,et al.  mCrowd: a platform for mobile crowdsourcing , 2009, SenSys '09.

[33]  H. Wallbott Bodily expression of emotion , 1998 .

[34]  J. Millard,et al.  Mapping Smart Cities in the EU , 2014 .

[35]  Paul Lukowicz,et al.  OPPORTUNITY: Towards opportunistic activity and context recognition systems , 2009, 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops.

[36]  Gerhard Tröster,et al.  The telepathic phone: Frictionless activity recognition from WiFi-RSSI , 2014, 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[37]  Jayant Kalagnanam,et al.  Foundations for Smarter Cities , 2010, IBM J. Res. Dev..

[38]  Björn Hartmann,et al.  MobileWorks: A Mobile Crowdsourcing Platform for Workers at the Bottom of the Pyramid , 2011, Human Computation.

[39]  Fan Ye,et al.  Mobile crowdsensing: current state and future challenges , 2011, IEEE Communications Magazine.

[40]  Nik Bessis,et al.  Buildings and Crowds: Forming Smart Cities for More Effective Disaster Management , 2011, 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[41]  Wolf-Tilo Balke,et al.  Information Extraction Meets Crowdsourcing: A Promising Couple , 2012, Datenbank-Spektrum.

[42]  Edward Cutrell,et al.  mClerk: enabling mobile crowdsourcing in developing regions , 2012, CHI.

[43]  Wolf-Tilo Balke,et al.  Pushing the Boundaries of Crowd-enabled Databases with Query-driven Schema Expansion , 2012, Proc. VLDB Endow..

[44]  Jacco van Ossenbruggen,et al.  Do you need experts in the crowd?: a case study in image annotation for marine biology , 2013, OAIR.

[45]  G. Jouret Inside Cisco's Search for the Next Big Idea , 2009 .

[46]  Aniket Kittur,et al.  Crowdsourcing user studies with Mechanical Turk , 2008, CHI.

[47]  Kai Kunze,et al.  Towards inferring language expertise using eye tracking , 2013, CHI Extended Abstracts.

[48]  Hojung Cha,et al.  Automatically characterizing places with opportunistic crowdsensing using smartphones , 2012, UbiComp.

[49]  Hans Schaffers,et al.  Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation , 2011, Future Internet Assembly.

[50]  Jun Li,et al.  Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods , 2012, 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN).

[51]  Andrea Vitaletti,et al.  Smart City: An Event Driven Architecture for Monitoring Public Spaces with Heterogeneous Sensors , 2010, 2010 Fourth International Conference on Sensor Technologies and Applications.

[52]  Minho Shin,et al.  AnonySense: A system for anonymous opportunistic sensing , 2011, Pervasive Mob. Comput..

[53]  Bengt Ahlgren,et al.  A survey of information-centric networking , 2012, IEEE Communications Magazine.

[54]  P. Nijkamp,et al.  Smart Cities in Europe , 2011 .

[55]  R. E. Hall,et al.  VISION OF A SMART CITY , 2000 .

[56]  A. Atkinson,et al.  Evidence for distinct contributions of form and motion information to the recognition of emotions from body gestures , 2007, Cognition.

[57]  Michael Beigl,et al.  Feedback-Based Closed-Loop Carrier Synchronization: A Sharp Asymptotic Bound, an Asymptotically Optimal Approach, Simulations, and Experiments , 2011, IEEE Transactions on Mobile Computing.

[58]  Yusheng Ji,et al.  PINtext: A Framework for Secure Communication Based on Context , 2011, MobiQuitous.

[59]  Mirco Musolesi,et al.  The Rise of People-Centric Sensing , 2008, IEEE Internet Comput..

[60]  Paul Lukowicz,et al.  The OPPORTUNITY Framework and Data Processing Ecosystem for Opportunistic Activity and Context Recognition , 2012 .

[61]  Artemis Moroni,et al.  Vision and Challenges for Realising the Internet of Things , 2010 .

[62]  Michael Beigl,et al.  Investigation of Context Prediction Accuracy for Different Context Abstraction Levels , 2012, IEEE Transactions on Mobile Computing.

[63]  Allison Woodruff,et al.  Common Sense: participatory urban sensing using a network of handheld air quality monitors , 2009, SenSys '09.

[64]  Ramesh Govindan,et al.  Medusa: a programming framework for crowd-sensing applications , 2012, MobiSys '12.