Context-Awareness for Mobile Sensing: A Survey and Future Directions

The evolution of smartphones together with increasing computational power has empowered developers to create innovative context-aware applications for recognizing user-related social and cognitive activities in any situation and at any location. The existence and awareness of the context provide the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze, and share local sensory knowledge in the purpose for a large-scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects and also assist individuals. However, many open challenges remain, which are mostly arisen because the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved and, at the same time, better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlightens them by proposing possible solutions.

[1]  Victor C. M. Leung,et al.  Context-aware dissemination of information and services in heterogeneous network environments , 2013, J. Ambient Intell. Humaniz. Comput..

[2]  Shivakant Mishra,et al.  Fusing mobile, sensor, and social data to fully enable context-aware computing , 2010, HotMobile '10.

[3]  David S. Rosenblum,et al.  Model-based fault detection in context-aware adaptive applications , 2008, SIGSOFT '08/FSE-16.

[4]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[5]  Yi Wang,et al.  A framework of energy efficient mobile sensing for automatic user state recognition , 2009, MobiSys '09.

[6]  Lin Sun,et al.  The architecture design of a cross-domain context management system , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[7]  Gernot Heiser,et al.  An Analysis of Power Consumption in a Smartphone , 2010, USENIX Annual Technical Conference.

[8]  C. S. Gehrke,et al.  A Hybrid Network Architecture Applied to Smart Grid , 2013 .

[9]  Henry A. Kautz,et al.  Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields , 2007, Int. J. Robotics Res..

[10]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .

[11]  Artem Katasonov,et al.  Smart Semantic Middleware for the Internet of Things , 2008, ICINCO-ICSO.

[12]  Matthias Budde,et al.  ActiServ: Activity Recognition Service for mobile phones , 2010, International Symposium on Wearable Computers (ISWC) 2010.

[13]  Lian-Wen Jin,et al.  Activity recognition from acceleration data using AR model representation and SVM , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[14]  Claudio Bettini,et al.  COSAR: hybrid reasoning for context-aware activity recognition , 2011, Personal and Ubiquitous Computing.

[15]  Xing Xie,et al.  Social itinerary recommendation from user-generated digital trails , 2012, Personal and Ubiquitous Computing.

[16]  Jeen-Shing Wang,et al.  A Wearable Sensor Module With a Neural-Network-Based Activity Classification Algorithm for Daily Energy Expenditure Estimation , 2012, IEEE Transactions on Information Technology in Biomedicine.

[17]  Gregory D. Abowd,et al.  The context toolkit: aiding the development of context-enabled applications , 1999, CHI '99.

[18]  Charalabos Skianis,et al.  A Survey on Context-Aware Mobile and Wireless Networking: On Networking and Computing Environments' Integration , 2013, IEEE Communications Surveys & Tutorials.

[19]  Jadwiga Indulska,et al.  A survey of context modelling and reasoning techniques , 2010, Pervasive Mob. Comput..

[20]  Marjorie Skubic.  A ubiquitous sensing environment to detect functional changes in assisted living apartments: The Tiger Place experience , 2010, Alzheimer's & Dementia.

[21]  Young-Koo Lee,et al.  Semi-Markov conditional random fields for accelerometer-based activity recognition , 2010, Applied Intelligence.

[22]  Erik Duval,et al.  Context-Aware Recommender Systems for Learning: A Survey and Future Challenges , 2012, IEEE Transactions on Learning Technologies.

[23]  Chi Harold Liu,et al.  Unsupervised posture detection by smartphone accelerometer , 2013 .

[24]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[25]  Paolo Bellavista,et al.  COSMOS: A Context-Centric Access Control Middleware for Mobile Environments , 2003, MATA.

[26]  Claudia Linnhoff-Popien,et al.  A Context Modeling Survey , 2004 .

[27]  S. Eddy.  Hidden Markov models. , 1996, Current opinion in structural biology.

[28]  Tao Gu,et al.  A service-oriented middleware for building context-aware services , 2005, J. Netw. Comput. Appl..

[29]  Sasu Tarkoma,et al.  Collaborative Energy Debugging for Mobile Devices , 2012, HotDep.

[30]  Katie Shilton,et al.  Four billion little brothers? , 2009, Commun. ACM.

[31]  Sergio Ilarri,et al.  Towards a Context-Aware Mobile Recommendation Architecture , 2014, MobiWIS.

[32]  M. Shinozuka,et al.  Auto‐Regressive Model for Nonstationary Stochastic Processes , 1988 .

[33]  Pei Zhang,et al.  SensorFly: a controlled-mobile aerial sensor network , 2009, SenSys '09.

[34]  Subir Biswas,et al.  Body posture identification using hidden Markov model with a wearable sensor network , 2008, BODYNETS.

[35]  Miguel A. Labrador,et al.  A mobile platform for real-time human activity recognition , 2012, 2012 IEEE Consumer Communications and Networking Conference (CCNC).

[36]  C. Randell,et al.  Context awareness by analysing accelerometer data , 2000, Digest of Papers. Fourth International Symposium on Wearable Computers.

[37]  Juha Röning,et al.  Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data , 2012, Int. J. Interact. Multim. Artif. Intell..

[38]  Deborah Estrin,et al.  Biketastic: sensing and mapping for better biking , 2010, CHI.

[39]  Mashfiqui Rabbi,et al.  Objective Measurement of Sociability and Activity: Mobile Sensing in the Community , 2011, The Annals of Family Medicine.

[40]  Jeffrey G. Andrews,et al.  Femtocell networks: a survey , 2008, IEEE Communications Magazine.

[41]  Andrew T. Campbell,et al.  Community-Guided Learning: Exploiting Mobile Sensor Users to Model Human Behavior , 2010, AAAI.

[42]  A. Rehman,et al.  Survey of Wearable Sensors with Comparative Study of Noise Reduction ECG Filters , 2012 .

[43]  Weihua Sheng,et al.  Human daily activity recognition in robot-assisted living using multi-sensor fusion , 2009, 2009 IEEE International Conference on Robotics and Automation.

[44]  Nigel Baker,et al.  ContextML: A light-weight context representation and context management schema , 2010, IEEE 5th International Symposium on Wireless Pervasive Computing 2010.

[45]  Surapa Thiemjarus,et al.  Accurate Activity Recognition Using a Mobile Phone Regardless of Device Orientation and Location , 2011, 2011 International Conference on Body Sensor Networks.

[46]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[47]  Eric A. Brewer,et al.  N-smarts: networked suite of mobile atmospheric real-time sensors , 2008, NSDR '08.

[48]  Lei Yang,et al.  Accurate online power estimation and automatic battery behavior based power model generation for smartphones , 2010, 2010 IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[49]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[50]  Mirco Musolesi,et al.  MetroTrack: Predictive Tracking of Mobile Events Using Mobile Phones , 2010, DCOSS.

[51]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[52]  Fehmi Ben Abdesslem,et al.  Less is more: energy-efficient mobile sensing with senseless , 2009, MobiHeld '09.

[53]  Zhigang Liu,et al.  Darwin phones: the evolution of sensing and inference on mobile phones , 2010, MobiSys '10.

[54]  J. Friedman.  Regularized Discriminant Analysis , 1989 .

[55]  Zhigang Liu,et al.  The Jigsaw continuous sensing engine for mobile phone applications , 2010, SenSys '10.

[56]  Mark Burnett,et al.  Activities, context and ubiquitous computing , 2002, Comput. Commun..

[57]  Jukka Riekki,et al.  Context-aware middleware for mobile multimedia applications , 2004, MUM '04.

[58]  Ozgur Yurur,et al.  Adaptive and Energy Efficient Context Representation Framework in Mobile Sensing , 2014, IEEE Transactions on Mobile Computing.

[59]  Atta Badii,et al.  A Context-Awareness Framework for Intelligent Networked Embedded Systems , 2010, 2010 Third International Conference on Advances in Human-Oriented and Personalized Mechanisms, Technologies and Services.

[60]  David W. McDonald,et al.  Activity sensing in the wild: a field trial of ubifit garden , 2008, CHI.

[61]  Cecilia Mascolo,et al.  Energy-Accuracy Trade-offs in Querying Sensor Data for Continuous Sensing Mobile Systems , 2010 .

[62]  David Garlan,et al.  Project Aura: Toward Distraction-Free Pervasive Computing , 2002, IEEE Pervasive Comput..

[63]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[64]  Kevin G. Stanley,et al.  The Potential of Sensor-Based Monitoring as a Tool for Health Care, Health Promotion, and Research , 2011, The Annals of Family Medicine.

[65]  H. T. Mouftah,et al.  The internet of things [Guest Editorial] , 2011, IEEE Commun. Mag..

[66]  Steven Myers,et al.  Mobile location tracking in metro areas: malnets and others , 2010, CCS '10.

[67]  Alexandre M. Bayen,et al.  Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment , 2009 .

[68]  Emiliano Miluzzo,et al.  CenceMe - Injecting Sensing Presence into Social Networking Applications , 2007, EuroSSC.

[69]  Oscar Mayora,et al.  Mobile Habits: Inferring and Predicting User Activities with a Location-Aware Smartphone , 2009 .

[70]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[71]  Siobhán Clarke,et al.  CASS - Middleware for Mobile Context-Aware Applications , 1990 .

[72]  Tom Rodden,et al.  Exploiting Context in HCI Design for Mobile Systems , 1998 .

[73]  Xue Liu,et al.  Adaptive Sampling and Duty Cycling for Smartphone Accelerometer , 2013, 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems.

[74]  Mike Y. Chen,et al.  Tracking Free-Weight Exercises , 2007, UbiComp.

[75]  Cecilia Mascolo,et al.  EmotionSense: a mobile phones based adaptive platform for experimental social psychology research , 2010, UbiComp.

[76]  Guan Le,et al.  Survey on NoSQL database , 2011, 2011 6th International Conference on Pervasive Computing and Applications.

[77]  J. Blum,et al.  M-Psychiatry: Sensor Networks for Psychiatric Health Monitoring , 2008 .

[78]  John A. Stankovic,et al.  Context-aware wireless sensor networks for assisted living and residential monitoring , 2008, IEEE Network.

[79]  Matt Welsh,et al.  CitySense: A Vision for an Urban-Scale Wireless Networking Testbed , 2007 .

[80]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[81]  Katie Shilton,et al.  Four Billion Little Brothers? Privacy, mobile phones, and ubiquitous data collection , 2009, ACM Queue.

[82]  Bernt Schiele,et al.  Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[83]  Chin-Hui Lee,et al.  Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..

[84]  Reto Krummenacher,et al.  Ontology-Based Context Modeling , 2007 .

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

[86]  Bernt Schiele,et al.  Discovery of activity patterns using topic models , 2008 .

[87]  Patrick P. Tsang,et al.  DEAMON: Energy-efficient Sensor Monitoring , 2009, 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[88]  Marco Bessi.  A survey about context-aware middleware , 2009 .

[89]  Aleksandar Milenkovic,et al.  Body Area Networks for Ubiquitous Healthcare Applications: Opportunities and Challenges , 2011, Journal of Medical Systems.

[90]  David Kotz,et al.  Extracting a Mobility Model from Real User Traces , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[91]  Sivan Toledo,et al.  VTrack: accurate, energy-aware road traffic delay estimation using mobile phones , 2009, SenSys '09.

[92]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[93]  Romit Roy Choudhury,et al.  SurroundSense: mobile phone localization via ambience fingerprinting , 2009, MobiCom '09.

[94]  Dean Allemang,et al.  Semantic Web for the Working Ontologist - Effective Modeling in RDFS and OWL, Second Edition , 2011 .

[95]  Zhenyu He,et al.  Activity recognition from acceleration data based on discrete consine transform and SVM , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[96]  T. Luckenbach,et al.  TinyREST – a Protocol for Integrating Sensor Networks into the Internet , 2005 .

[97]  B. Scrosati,et al.  Lithium batteries: Status, prospects and future , 2010 .

[98]  P.H.L. Notten,et al.  From battery modeling to Battery Management , 2011, 2011 IEEE 33rd International Telecommunications Energy Conference (INTELEC).

[99]  Ilias Maglogiannis,et al.  An overview of body sensor networks in enabling pervasive healthcare and assistive environments , 2010, PETRA '10.

[100]  Jaydip Sen,et al.  Internet of Things - Applications and Challenges in Technology and Standardization , 2011 .

[101]  Xiaojin Zhu,et al.  --1 CONTENTS , 2006 .

[102]  Andreas Krause,et al.  SenSay: a context-aware mobile phone , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[103]  Harry Chen,et al.  Intelligent Agents Meet the Semantic Web in Smart Spaces , 2004, IEEE Internet Comput..

[104]  Arkady B. Zaslavsky,et al.  Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[105]  Xing Xie,et al.  Towards mobile intelligence: Learning from GPS history data for collaborative recommendation , 2012, Artif. Intell..

[106]  Yen-Ping Chen,et al.  Online classifier construction algorithm for human activity detection using a tri-axial accelerometer , 2008, Appl. Math. Comput..

[107]  Jie Liu,et al.  LittleRock: Enabling Energy-Efficient Continuous Sensing on Mobile Phones , 2011, IEEE Pervasive Computing.

[108]  James H. Aylor,et al.  Computer for the 21st Century , 1999, Computer.

[109]  Guanling Chen,et al.  A Survey of Context-Aware Mobile Computing Research , 2000 .

[110]  Rajkumar Buyya,et al.  Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges , 2013, IEEE Communications Surveys & Tutorials.

[111]  Tadahiro Kuroda,et al.  Haar-Like Filtering for Human Activity Recognition Using 3D Accelerometer , 2009, 2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop.

[112]  Miguel A. Labrador,et al.  Centinela: A human activity recognition system based on acceleration and vital sign data , 2012, Pervasive Mob. Comput..

[113]  C. Gomez-Otero,et al.  ClimApp: A novel approach of an intelligent HVAC control system , 2012, 7th Iberian Conference on Information Systems and Technologies (CISTI 2012).

[114]  Kent Larson,et al.  Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[115]  Gregory D. Abowd,et al.  The Aware Home: A Living Laboratory for Ubiquitous Computing Research , 1999, CoBuild.

[116]  Marcos Dipinto,et al.  Discriminant analysis , 2020, Predictive Analytics.

[117]  Cecilia Mascolo,et al.  CARISMA: Context-Aware Reflective mIddleware System for Mobile Applications , 2003, IEEE Trans. Software Eng..

[118]  Jadwiga Indulska,et al.  Generating context management infrastructure from high level context models , 2003 .

[119]  Victor C. M. Leung,et al.  Request-Adaptive Packet Dissemination for Context-Aware Services in Vehicular Networks , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[120]  Min Chen,et al.  Energy-Efficient and Context-Aware Smartphone Sensor Employment , 2015, IEEE Transactions on Vehicular Technology.

[121]  Kwang-Rok Han,et al.  Implementation of Ubiquitous Health Care System for Active Measure of Emergencies , 2007, Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007).

[122]  Rinke Hoekstra.  Representing Social Reality in OWL 2 , 2010, OWLED.

[123]  Carlo Curino,et al.  A data-oriented survey of context models , 2007, SGMD.

[124]  Young-Koo Lee,et al.  Daily life activity tracking application for smart homes using android smartphone , 2012, 2012 14th International Conference on Advanced Communication Technology (ICACT).

[125]  Tapio Seppänen,et al.  RDF-based model for context-aware reasoning in rich service environment , 2005, Third IEEE International Conference on Pervasive Computing and Communications Workshops.

[126]  Klara Nahrstedt,et al.  A Middleware Infrastructure for Active Spaces , 2002, IEEE Pervasive Comput..

[127]  Junzhong Gu,et al.  Metadata Management of Context Resources in Context-Aware Middleware System , 2012, WISM.

[128]  Sea Ling,et al.  CoMiHoC: A Middleware Framework for Context Management in MANET Environment , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[129]  Matthias Baldauf,et al.  A survey on context-aware systems , 2007, Int. J. Ad Hoc Ubiquitous Comput..

[130]  Kirk Martinez,et al.  Environmental Sensor Networks: A revolution in the earth system science? , 2006 .

[131]  Gaetano Borriello,et al.  A Practical Approach to Recognizing Physical Activities , 2006, Pervasive.

[132]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[133]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[134]  Luc Cluitmans,et al.  Advancing from offline to online activity recognition with wearable sensors , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[135]  Bill N. Schilit,et al.  Disseminating active map information to mobile hosts , 1994, IEEE Network.

[136]  Jatinder Pal Singh,et al.  Improving energy efficiency of location sensing on smartphones , 2010, MobiSys '10.

[137]  Boudewijn R. Haverkort,et al.  Computing Battery Lifetime Distributions , 2007, 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN'07).

[138]  Hojung Cha,et al.  LifeMap: A Smartphone-Based Context Provider for Location-Based Services , 2011, IEEE Pervasive Computing.

[139]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[140]  Vasile-Marian Scuturici,et al.  An Ontology-Based Approach to Context Modeling and Reasoning in Pervasive Computing , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07).

[141]  Terry A. Halpin.  Object-Role Modeling: Principles and Benefits , 2010, Int. J. Inf. Syst. Model. Des..

[142]  Qing Guo,et al.  Balancing energy, latency and accuracy for mobile sensor data classification , 2011, SenSys.

[143]  Marco Gruteser,et al.  Towards fine-grained urban traffic knowledge extraction using mobile sensing , 2012, UrbComp '12.

[144]  Archan Misra,et al.  SAMMPLE: Detecting Semantic Indoor Activities in Practical Settings Using Locomotive Signatures , 2012, 2012 16th International Symposium on Wearable Computers.

[145]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

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

[147]  Ramesh Govindan,et al.  Energy-delay tradeoffs in smartphone applications , 2010, MobiSys '10.

[148]  Emiliano Miluzzo,et al.  Research in the App Store Era : Experiences from the CenceMe App Deployment on the iPhone , 2010 .

[149]  Emiliano Miluzzo,et al.  The BikeNet mobile sensing system for cyclist experience mapping , 2007, SenSys '07.

[150]  Lin Sun,et al.  Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations , 2010, UIC.

[151]  Pei Zhang,et al.  SensorFly: Controlled-mobile sensing platform for indoor emergency response applications , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[152]  David Minnen,et al.  Recognizing Soldier Activities in the Field , 2007, BSN.

[153]  Gernot Heiser,et al.  The systems hacker's guide to the galaxy energy usage in a modern smartphone , 2013, APSys.

[154]  Ricardo Gutierrez-Osuna,et al.  Using Heart Rate Monitors to Detect Mental Stress , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[155]  D. Craig.  Cognitive prosthetics in Alzheimer's disease: A trial of a novel cell phoned- based reminding system , 2010, Alzheimer's & Dementia.

[156]  Upkar Varshney,et al.  Challenges and business models for mobile location-based services and advertising , 2011, Commun. ACM.

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

[158]  Tarek F. Abdelzaher,et al.  GreenGPS: a participatory sensing fuel-efficient maps application , 2010, MobiSys '10.

[159]  Eric Horvitz,et al.  Predestination: Inferring Destinations from Partial Trajectories , 2006, UbiComp.

[160]  I.A. Essa,et al.  Ubiquitous sensing for smart and aware environments , 2000, IEEE Wirel. Commun..

[161]  Wilhelm Stork,et al.  Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[162]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[163]  Jadwiga Indulska,et al.  Modeling Context Information in Pervasive Computing Systems , 2002, Pervasive.

[164]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[165]  Maria E. Niessen,et al.  Citizen noise pollution monitoring , 2009, D.GO.

[166]  Emil Jovanov,et al.  Stress monitoring using a distributed wireless intelligent sensor system. , 2003, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[167]  Felix Wortmann,et al.  Internet of Things , 2015, Business & Information Systems Engineering.

[168]  Demetrios Zeinalipour-Yazti,et al.  Crowdsourcing with Smartphones , 2012, IEEE Internet Computing.

[169]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[170]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[171]  Octavian Postolache,et al.  Pervasive and Mobile Sensing and Computing for Healthcare , 2013 .

[172]  Antonio Corradi,et al.  Implementing a scalable context-aware middleware , 2009, 2009 IEEE Symposium on Computers and Communications.

[173]  Peter J. Brown,et al.  Context-aware applications: from the laboratory to the marketplace , 1997, IEEE Wirel. Commun..

[174]  Luca Benini,et al.  Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection , 2008, EWSN.

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

[176]  Tao Gu,et al.  Ontology based context modeling and reasoning using OWL , 2004, IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second.

[177]  James A. Landay,et al.  UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits , 2009, CHI.

[178]  Anshul Kumar,et al.  Battery model for embedded systems , 2005, 18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design.

[179]  Jeen-Shing Wang,et al.  Development of a portable activity detector for daily activity recognition , 2009, 2009 IEEE International Symposium on Industrial Electronics.

[180]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[181]  Jindong Tan,et al.  A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks , 2012, Sensors.

[182]  Schahram Dustdar,et al.  A survey on context-aware web service systems , 2009, Int. J. Web Inf. Syst..

[183]  Youngki Lee,et al.  SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments , 2008, MobiSys '08.