Mobile Computing, IoT and Big Data for Urban Informatics: Challenges and Opportunities

Over the past few decades, the population in the urban areas has been increasing in a dramatic manner. Currently, about 80% of the U.S. population and about 50% of the world’s population live in urban areas and the population growth rate for urban areas is estimated to be over one million people per week [1, 2]. By 2050, it has been predicted that 64% of people in the developing nations and 85% of people in the developed world would be living in urban areas [1, 2]. Such a dramatic population growth in urban areas has been placing demands on urban infrastructure like never before [1].

[1]  Paul Fergus,et al.  SCCIR: Smart Cities Critical Infrastructure Response Framework , 2011, 2011 Developments in E-systems Engineering.

[2]  Arkady B. Zaslavsky,et al.  Waste Management as an IoT-Enabled Service in Smart Cities , 2015, NEW2AN.

[3]  Chenglin Miao,et al.  Cloud-Enabled Privacy-Preserving Truth Discovery in Crowd Sensing Systems , 2015, SenSys.

[4]  Xi Fang,et al.  Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing , 2012, Mobicom '12.

[5]  Jens Lehmann,et al.  DBpedia - A crystallization point for the Web of Data , 2009, J. Web Semant..

[6]  Rajesh Vargheese,et al.  An IoT/IoE enabled architecture framework for precision on shelf availability: Enhancing proactive shopper experience , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[7]  Deborah Estrin,et al.  PEIR, the personal environmental impact report, as a platform for participatory sensing systems research , 2009, MobiSys '09.

[8]  Marin Litoiu,et al.  Sipresk: A Big Data Analytic Platform for Smart Transportation , 2016 .

[9]  James A. Hendler,et al.  Matrix "Bit" loaded: a scalable lightweight join query processor for RDF data , 2010, WWW '10.

[10]  Dimitrios Gunopulos,et al.  Intelligent Urban Data Monitoring for Smart Cities , 2016, ECML/PKDD.

[11]  Byron J. Gao,et al.  SmartMart: IoT-based In-store mapping for mobile devices , 2013, 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing.

[12]  Karl Aberer,et al.  Crowdsourcing Behavioral Incentives for Pervasive Demand Response , 2014 .

[13]  Arpita Ghosh,et al.  Behavioral Mechanism Design: Optimal Crowdsourcing Contracts and Prospect Theory , 2015, EC.

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

[15]  Sotiris Zygiaris Smart City Reference Model: Assisting Planners to Conceptualize the Building of Smart City Innovation Ecosystems , 2012, Journal of the Knowledge Economy.

[16]  Daniel J. Abadi,et al.  Scalable SPARQL querying of large RDF graphs , 2011, Proc. VLDB Endow..

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

[18]  Qinghua Li,et al.  Providing Privacy-Aware Incentives in Mobile Sensing Systems , 2016, IEEE Transactions on Mobile Computing.

[19]  Hai Jin,et al.  TripleBit: a Fast and Compact System for Large Scale RDF Data , 2013, Proc. VLDB Endow..

[20]  Sherif Sakr,et al.  DREAM: Distributed RDF Engine with Adaptive Query Planner and Minimal Communication , 2015, Proc. VLDB Endow..

[21]  Martin Theobald,et al.  TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing , 2014, SIGMOD Conference.

[22]  Abraham Bernstein,et al.  Hexastore: sextuple indexing for semantic web data management , 2008, Proc. VLDB Endow..

[23]  Prabal Mahanta,et al.  Impact of Internet of Things in the Retail Industry , 2015, OTM Workshops.

[24]  Hassan Basri,et al.  Wireless Sensor Network Prototype for Solid Waste Bin Monitoring with Energy Efficient Sensing Algorithm , 2013, 2013 IEEE 16th International Conference on Computational Science and Engineering.

[25]  Dimitrios Gunopulos,et al.  Insights on a Scalable and Dynamic Traffic Management System , 2015, EDBT.

[26]  Artemios G. Voyiatzis,et al.  A versatile scalable smart waste-bin system based on resource-limited embedded devices , 2015, 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA).

[27]  Anirban Mondal,et al.  CityZen: A Cost-Effective City Management System with Incentive-Driven Resident Engagement , 2014, 2014 IEEE 15th International Conference on Mobile Data Management.

[28]  Mohit Jain,et al.  Speed-Breaker Early Warning System , 2012, NSDR.

[29]  Vasil Slavov,et al.  Fast Processing of SPARQL Queries on RDF Quadruples , 2016, J. Web Semant..

[30]  B. J. Fogg,et al.  A behavior model for persuasive design , 2009, Persuasive '09.

[31]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[32]  Hisashi Kashima,et al.  Preserving worker privacy in crowdsourcing , 2014, Data Mining and Knowledge Discovery.

[33]  Sihem Amer-Yahia,et al.  Task assignment optimization in knowledge-intensive crowdsourcing , 2015, The VLDB Journal.

[34]  A. Giusti,et al.  Sensorized waste collection container for content estimation and collection optimization. , 2009, Waste management.

[35]  Anirban Mondal,et al.  E-ARL: An Economic incentive scheme for Adaptive Revenue-Load-based dynamic replication of data in Mobile-P2P networks , 2010, Distributed and Parallel Databases.

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

[37]  Cláudio T. Silva,et al.  Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips , 2013, IEEE Transactions on Visualization and Computer Graphics.

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

[39]  Stathes Hadjiefthymiades,et al.  Challenges and Opportunities of Waste Management in IoT-Enabled Smart Cities: A Survey , 2017, IEEE Transactions on Sustainable Computing.

[40]  Nalini Venkatasubramanian,et al.  CrowdMAC: A Crowdsourcing System for Mobile Access , 2012, Middleware.

[41]  Haixun Wang,et al.  A Distributed Graph Engine for Web Scale RDF Data , 2013, Proc. VLDB Endow..

[42]  Marcus Foth,et al.  Urban informatics , 2011, CSCW.

[43]  Ioannis Konstantinou,et al.  H2RDF+: an efficient data management system for big RDF graphs , 2014, SIGMOD Conference.

[44]  Walid G. Aref,et al.  The Palm-tree Index: Indexing with the crowd , 2013, DBCrowd.

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

[46]  Heng Ji,et al.  FaitCrowd: Fine Grained Truth Discovery for Crowdsourced Data Aggregation , 2015, KDD.

[47]  Stavros A. Koubias,et al.  An integrated node for Smart-City applications based on active RFID tags; Use case on waste-bins , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[48]  Daniel J. Abadi,et al.  SW-Store: a vertically partitioned DBMS for Semantic Web data management , 2009, The VLDB Journal.

[49]  Anirban Mondal,et al.  Mobile Computing, Internet of Things, and Big Data for Urban Informatics , 2016, 2016 17th IEEE International Conference on Mobile Data Management (MDM).

[50]  Andreas Butz,et al.  Location-Aware Shopping Assistance: Evaluation of a Decision-Theoretic Approach , 2002, Mobile HCI.

[51]  Klara Nahrstedt,et al.  Enabling Privacy-Preserving Incentives for Mobile Crowd Sensing Systems , 2016, 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS).

[52]  Jean C. Walrand,et al.  Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing , 2012, 2012 Proceedings IEEE INFOCOM.

[53]  Christoph Lofi,et al.  Towards Mobile Sensor-Aware Crowdsourcing: Architecture, Opportunities and Challenges , 2014, DASFAA Workshops.

[54]  Eugene Inseok Chong,et al.  An Efficient SQL-based RDF Querying Scheme , 2005, VLDB.

[55]  Sun Yu,et al.  Mobile Crowd Sensing for Internet of Things: A Credible Crowdsourcing Model in Mobile-Sense Service , 2015 .

[56]  Markus Krötzsch,et al.  Wikidata , 2014, Commun. ACM.

[57]  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.

[58]  Freddy Lécué,et al.  Westland row why so slow?: fusing social media and linked data sources for understanding real-time traffic conditions , 2013, IUI '13.

[59]  Georg Lausen,et al.  S2RDF: RDF Querying with SPARQL on Spark , 2015, Proc. VLDB Endow..

[60]  George Suciu,et al.  Smart Cities Built on Resilient Cloud Computing and Secure Internet of Things , 2013, 2013 19th International Conference on Control Systems and Computer Science.

[61]  Sanjay Kumar Madria,et al.  M-Grid: a distributed framework for multidimensional indexing and querying of location based data , 2017, Distributed and Parallel Databases.

[62]  Vana Kalogeraki,et al.  Reliable crowdsourced event detection in smartcities , 2016, 2016 1st International Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE) in partnership with Global City Teams Challenge (GCTC) (SCOPE - GCTC).

[63]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[64]  Sanjay Kumar Madria,et al.  Incentive based approach to find selfish nodes in Mobile P2P networks , 2012, 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC).

[65]  Alessandro Giusti,et al.  Early detection and evaluation of waste through sensorized containers for a collection monitoring application. , 2009, Waste management.

[66]  Merkourios Karaliopoulos,et al.  Mobile crowdsensing incentives under participation uncertainty , 2016, MSCC '16.

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

[68]  Julian Dolby,et al.  Building an efficient RDF store over a relational database , 2013, SIGMOD '13.

[69]  Tassos Dimitriou,et al.  Privacy-Respecting Auctions as Incentive Mechanisms in Mobile Crowd Sensing , 2015, WISTP.

[70]  Alain Biem,et al.  IBM infosphere streams for scalable, real-time, intelligent transportation services , 2010, SIGMOD Conference.

[71]  Ygal Bendavid,et al.  Exploring the impact of RFID technology and the EPC network on mobile B2B eCommerce: A case study in the retail industry , 2008 .

[72]  Sanjay Kumar Madria Security and Risk Assessment in the Cloud , 2016, Computer.

[73]  Dimitrios Gunopulos,et al.  Towards Detection of Faulty Traffic Sensors in Real-Time , 2015, MUD@ICML.

[74]  Merkourios Karaliopoulos,et al.  First learn then earn: optimizing mobile crowdsensing campaigns through data-driven user profiling , 2016, MobiHoc.

[75]  María Bermúdez-Edo,et al.  IoT-Lite: A Lightweight Semantic Model for the Internet of Things , 2016, UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld.

[76]  Derek Powell,et al.  Marginally Significant Effects as Evidence for Hypotheses , 2016, Psychological science.

[77]  Anirban Mondal,et al.  RoadEye: A System for Personalized Retrieval of Dynamic Road Conditions , 2014, 2014 IEEE 15th International Conference on Mobile Data Management.

[78]  Gerhard Weikum,et al.  The RDF-3X engine for scalable management of RDF data , 2010, The VLDB Journal.