Advertisement

A review of mobile sensing systems, applications, and opportunities

  • Francisco Laport-López
  • Emilio SerranoEmail author
  • Javier Bajo
  • Andrew T. Campbell
Regular Paper
  • 253 Downloads

Abstract

Mobile phones, vehicles, appliances, and other types of devices have sensors in the last few years. On the good side, this makes the world increasingly interconnected every day. However, this interconnection generates Big Data that cannot be processed using traditional tools because of its volume, variety, and speed. This paper contributes with a review of mobile sensing systems, including their applications, shortcomings, and opportunities. A taxonomy covering the different systems revised is proposed. Moreover, the main characteristics of mobile sensing architectures are explained and research-related works are studied into the context of these characteristics. Multi-agent systems (MASs) are considered as a perfect match to create large-scale, multi-device, and multi-purpose mobile sensing systems with the potential of obtaining information from heterogeneous devices, open sources, and social networks. Finally, the paper also contributes with the overview of a MAS architecture that aims to leverage these features while the studied dimensions observed in the reviewed literature are covered.

Keywords

Mobile sensing Multi-agent systems Human agent societies Social computing 

Notes

Acknowledgements

This research work is supported by a contract granted by the Xunta de Galicia and the European Social Fund of the European Union (Francisco Laport, code ED481A-2018/156); and by the Spanish Ministry of Economy, Industry and Competitiveness under the R&D project Datos 4.0: Retos y soluciones (TIN2016-78011-C4-4-R, AEI/FEDER, UE).

References

  1. 1.
    Almehmadi A (2017) The Spy in your pocket: what the smartphones and social networks are collecting that we do not know about! CreateSpace Independent Publishing Platform, ISBN-10: 1542729866, ISBN-13: 978-1542729864Google Scholar
  2. 2.
    Baek S-H, Choi E-C, Huh J-D, Park K-R (2007) Sensor information management mechanism for context-aware service in ubiquitous home. IEEE Trans Consum Electron 53(4):1393–1400CrossRefGoogle Scholar
  3. 3.
    Bajo J, Campbell AT, Zhou X (2016) Mobile sensing agents for social computing environments. In: PAAMS (Special Sessions), Advances in Intelligent Systems and Computing, vol 473. Springer, pp 157–167Google Scholar
  4. 4.
    Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: International conference on pervasive computing. Springer, pp 1–17Google Scholar
  5. 5.
    Beach A, Gartrell M, Akkala S, Elston J, Kelley J, Nishimoto K, Ray B, Razgulin S, Sundaresan K, Surendar B et al (2008) Whozthat? Evolving an ecosystem for context-aware mobile social networks. IEEE Netw 22(4):50–55CrossRefGoogle Scholar
  6. 6.
    Bordini RH, Braubach L, Dastani M, Fallah-Seghrouchni AE, Gómez-Sanz JJ, Leite J, O’Hare GMP, Pokahr A, Ricci A (2006) A survey of programming languages and platforms for multi-agent systems. Informatica 30(1):33–44zbMATHGoogle Scholar
  7. 7.
    Cabri G, Ferrari L, Leonardi L, Zambonelli F (2005) The laica project: supporting ambient intelligence via agents and ad-hoc middleware. In: 14th IEEE international workshops on enabling technologies: infrastructure for collaborative enterprise, 2005. IEEE, pp 39–44Google Scholar
  8. 8.
    Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, Klasnja P, LaMarca A, LeGrand L, Libby R, et al (2008) Activity sensing in the wild: a field trial of ubifit garden. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1797–1806Google Scholar
  9. 9.
    da Cruz MA, Rodrigues JJ, Sangaiah AK, Al-Muhtadi J, Korotaev V (2018) Performance evaluation of iot middleware. J Netw Comput Appl 109:53–65CrossRefGoogle Scholar
  10. 10.
    Dai J, Bai X, Yang Z, Shen Z, Xuan D (2010) Perfalld: A pervasive fall detection system using mobile phones. In: 2010 8th IEEE international conference on pervasive computing and communications workshops (PERCOM workshops). IEEE, pp 292–297Google Scholar
  11. 11.
    Dong YF, Kanhere S, Chou CT, Bulusu N (2008) Automatic collection of fuel prices from a network of mobile cameras. In: International conference on distributed computing in sensor systems. Springer, pp 140–156Google Scholar
  12. 12.
    Dutta P, Aoki PM, Kumar N, Mainwaring A, Myers C, Willett W, Woodruff A (2009) Common sense: participatory urban sensing using a network of handheld air quality monitors. In: Proceedings of the 7th ACM conference on embedded networked sensor systems. ACM, pp 349–350Google Scholar
  13. 13.
    Eisenman SB, Miluzzo E, Lane ND, Peterson RA, Ahn G-S, Campbell AT (2009) Bikenet: a mobile sensing system for cyclist experience mapping. ACM Trans Sens Netw: TOSN 6(1):6CrossRefGoogle Scholar
  14. 14.
    Fernandez A, Insfran E, Abrahão S (2011) Usability evaluation methods for the web: a systematic mapping study. Inf Softw Technol 53(8):789–817. Advances in functional size measurement and effort estimation—extended best papersGoogle Scholar
  15. 15.
    Feuz KD, Cook DJ (2017) Collegial activity learning between heterogeneous sensors. Knowl Inf Syst 53(2):337–364CrossRefGoogle Scholar
  16. 16.
    Ganti RK, Pham N, Ahmadi H, Nangia S, Abdelzaher TF (2010) Greengps: a participatory sensing fuel-efficient maps application. In: Proceedings of the 8th international conference on mobile systems, applications, and services. ACM, pp 151–164Google Scholar
  17. 17.
    Gao C, Kong F, Tan J (2009) Healthaware: tackling obesity with health aware smart phone systems. In: 2009 IEEE international conference on robotics and biomimetics (ROBIO). IEEE, pp 1549–1554Google Scholar
  18. 18.
    García-Valverde T, Campuzano F, Serrano E, Villa A, Botía JA (2012) Simulation of human behaviours for the validation of ambient intelligence services: a methodological approach. JAISE 4(3):163–181Google Scholar
  19. 19.
    Gilbert P, Cox LP, Jung J, Wetherall D (2010) Toward trustworthy mobile sensing. In: Proceedings of the eleventh workshop on mobile computing systems and applications, HotMobile ’10, ACM, New York, pp 31–36Google Scholar
  20. 20.
    Goel D, Jain AK (2017) Mobile phishing attacks and defence mechanisms: state of art and open research challenges. Comput Secur 73:519–544CrossRefGoogle Scholar
  21. 21.
    Gokul S (2011) Location dependent query processing. PhD thesis, Cochin University of Science and TechnologyGoogle Scholar
  22. 22.
    Hashmi M, Governatori G, Lam H-P, Wynn MT (2017) Are we done with business process compliance: state of the art and challenges ahead. Knowl Inf Syst (in press)Google Scholar
  23. 23.
    Honicky R, Brewer EA, Paulos E, White R (2008) N-smarts: networked suite of mobile atmospheric real-time sensors. In: Proceedings of the second ACM SIGCOMM workshop on Networked systems for developing regions. ACM, pp 25–30Google Scholar
  24. 24.
    Hu S, Wei H, Chen Y, Tan J (2012) A real-time cardiac arrhythmia classification system with wearable sensor networks. Sensors 12(9):12844–12869CrossRefGoogle Scholar
  25. 25.
    Hull B, Bychkovsky V, Zhang Y, Chen K, Goraczko M, Miu A, Shih E, Balakrishnan H, Madden S (2006) Cartel: a distributed mobile sensor computing system. In: Proceedings of the 4th international conference on embedded networked sensor systems. ACM, pp 125–138Google Scholar
  26. 26.
    Hunter A (2015) Modelling the persuadee in asymmetric argumentation dialogues for persuasion. In: Proceedings of the 24th international conference on artificial intelligence, IJCAI’15. AAAI Press, pp 3055–3061Google Scholar
  27. 27.
    Itria ML, Kocsis-Magyar M, Ceccarelli A, Lollini P, Giunta G, Bondavalli A (2017) Identification of critical situations via event processing and event trust analysis. Knowl Inf Syst 52(1):147–178CrossRefGoogle Scholar
  28. 28.
    Jin Z, Oresko J, Huang S, Cheng AC (2009) Hearttogo: a personalized medicine technology for cardiovascular disease prevention and detection. In: Life science systems and applications workshop, 2009. LiSSA 2009. IEEE/NIH. IEEE, pp 80–83Google Scholar
  29. 29.
    Kanhere SS (2011) Participatory sensing: crowdsourcing data from mobile smartphones in urban spaces. In: 2011 12th IEEE international conference on mobile data management (MDM), vol 2. IEEE, pp 3–6Google Scholar
  30. 30.
    Kapadia A, Kotz D, Triandopoulos N (2009) Opportunistic sensing: security challenges for the new paradigm. In: First international communication systems and networks and workshops, 2009. COMSNETS 2009. IEEE, pp 1–10Google Scholar
  31. 31.
    Karthick Anand Babu KA, Sivakumar R (2015) Multi agents for context awareness in ambient intelligence: a survey. Int J Eng Res Technol 4:983–991Google Scholar
  32. 32.
    Karim A, Siddiqa A, Safdar Z, Razzaq M, Gillani SA, Tahir H, Kiran S, Ahmed E, Imran M (2017) Big data management in participatory sensing: issues, trends and future directions. Future Gener Comput Syst.  https://doi.org/10.1016/j.future.2017.10.007
  33. 33.
    Khan WZ, Xiang Y, Aalsalem MY, Arshad Q (2013) Mobile phone sensing systems: a survey. IEEE Commun Surv Tutor 15(1):402–427.  https://doi.org/10.1109/SURV.2012.031412.00077 CrossRefGoogle Scholar
  34. 34.
    Kitchenham B (2004) Procedures for performing systematic reviews 33. https://www.bibsonomy.org/bibtex/2e48137ec01b6308876e05ab1ecdf4bc4/wiljami74
  35. 35.
    Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering. Technical report EBSE 2007-001, Keele University and Durham University Joint ReportGoogle Scholar
  36. 36.
    Konecný J, McMahan HB, Yu FX, Richtárik P, Suresh AT, Bacon D (2016) Federated learning: strategies for improving communication efficiency. CoRR arXiv:1610.05492
  37. 37.
    Lane ND, Georgiev P (2015) Can deep learning revolutionize mobile sensing? In: Proceedings of the 16th international workshop on mobile computing systems and applications. ACM, pp 117–122Google Scholar
  38. 38.
    Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150CrossRefGoogle Scholar
  39. 39.
    Lara OD, Labrador MA (2012) A mobile platform for real-time human activity recognition. In: Consumer communications and networking conference (CCNC), 2012 IEEE. IEEE, pp 667–671Google Scholar
  40. 40.
    Lara OD, Pérez AJ, Labrador MA, Posada JD (2012) Centinela: a human activity recognition system based on acceleration and vital sign data. Pervasive Mobile Comput 8(5):717–729CrossRefGoogle Scholar
  41. 41.
    Lee U, Gerla M (2010) A survey of urban vehicular sensing platforms. Comput Netw 54(4):527–544zbMATHCrossRefGoogle Scholar
  42. 42.
    Leibiusky J, Eisbruch G, Simonassi D (2012) Getting started with storm. O’Reilly Media Inc., SebastopolGoogle Scholar
  43. 43.
    Lin C-W, Yang Y-TC, Wang J-S, Yang Y-C (2012) A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation. IEEE Trans Inf Technol Biomed 16(5):991–998CrossRefGoogle Scholar
  44. 44.
    Lingaraj K, Biradar RV, Patil V (2017) Eagilla: an enhanced mobile agent middleware for wireless sensor networks. Alex Eng J 57:1197–1204CrossRefGoogle Scholar
  45. 45.
    Liu B, Jiang Y, Sha F, Govindan R (2012) Cloud-enabled privacy-preserving collaborative learning for mobile sensing. In: Proceedings of the 10th ACM conference on embedded network sensor systems. ACM, pp 57–70Google Scholar
  46. 46.
    Lu H, Pan W, Lane ND, Choudhury T, Campbell AT (2009) Soundsense: scalable sound sensing for people-centric applications on mobile phones. In: Proceedings of the 7th international conference on mobile systems, applications, and services. ACM, pp 165–178Google Scholar
  47. 47.
    Lu H, Brush AB, Priyantha B, Karlson AK, Liu J (2011) Speakersense: energy efficient unobtrusive speaker identification on mobile phones. In: International conference on pervasive computing. Springer, pp 188–205Google Scholar
  48. 48.
    Lu H, Frauendorfer D, Rabbi M, Mast MS, Chittaranjan GT, Campbell AT, Gatica-Perez D, Choudhury T (2012) Stresssense: detecting stress in unconstrained acoustic environments using smartphones. In: Proceedings of the 2012 ACM conference on ubiquitous computing. ACM, pp 351–360Google Scholar
  49. 49.
    Lu H, Yang J, Liu Z, Lane ND, Choudhury T, Campbell AT (2010) The jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th ACM conference on embedded networked sensor systems. ACM, pp 71–84Google Scholar
  50. 50.
    Maisonneuve N, Stevens M, Niessen ME, Hanappe P, Steels L (2009) Citizen noise pollution monitoring. In: Proceedings of the 10th annual international conference on digital government research: social networks: making connections between citizens, data and government. Digital Government Society of North America, pp 96–103Google Scholar
  51. 51.
    Maisonneuve N, Stevens M, Niessen ME, Steels L (2009) Noisetube: measuring and mapping noise pollution with mobile phones. In: Information technologies in environmental engineering. Springer, pp 215–228Google Scholar
  52. 52.
    Marz N, Warren J (2015) Big data: principles and best practices of scalable realtime data systems. Manning Publications Co., Shelter IslandGoogle Scholar
  53. 53.
    Miluzzo E, Lane ND, Fodor K, Peterson R, Lu H, Musolesi M, Eisenman SB, Zheng X, Campbell AT (2008) Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In: Proceedings of the 6th ACM conference on embedded network sensor systems. ACM, pp 337–350Google Scholar
  54. 54.
    Miluzzo E, Cornelius CT, Ramaswamy A, Choudhury T, Liu Z, Campbell AT (2010) Darwin phones: the evolution of sensing and inference on mobile phones. In: Proceedings of the 8th international conference on mobile systems, applications, and services. ACM, pp 5–20Google Scholar
  55. 55.
    Mohan P, Padmanabhan VN, Ramjee R (2008) Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM conference on embedded network sensor systems. ACM, pp 323–336Google Scholar
  56. 56.
    Montagud S, Abrahão S, Insfran E (2012) A systematic review of quality attributes and measures for software product lines. Softw Qual J 20(3):425–486CrossRefGoogle Scholar
  57. 57.
    MQTT 101 - How to Get Started with the lightweight IoT Protocol. https://www.hivemq.com/blog/how-to-get-started-with-mqtt. Accessed March 2018
  58. 58.
    MQTT Version 3.1.1 . http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/mqtt-v3.1.1.html. Accessed March 2018
  59. 59.
    Mueen A, Chavoshi N, Abu-El-Rub N, Hamooni H, Minnich A, MacCarthy J (2018) Speeding up dynamic time warping distance for sparse time series data. Knowl Inf Syst 54(1):237–263CrossRefGoogle Scholar
  60. 60.
    Mun M, Reddy S, Shilton K, Yau N, Burke J, Estrin D, Hansen M, Howard E, West R, Boda P (2009) Peir, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the 7th international conference on Mobile systems, applications, and services. ACM, pp 55–68Google Scholar
  61. 61.
    Myrhaug H, Whitehead N, Goker A, Faegri TE, Lech TC (2004) Ambiesense—a system and reference architecture for personalised context-sensitive information services for mobile users. In: European symposium on ambient intelligence. Springer, pp 327–338Google Scholar
  62. 62.
    Predić B, Yan Z, Eberle J, Stojanovic D, Aberer K (2013) Exposuresense: integrating daily activities with air quality using mobile participatory sensing. In: 2013 IEEE international conference on pervasive computing and communications workshops (PERCOM Workshops). IEEE, pp 303–305Google Scholar
  63. 63.
    Quwaider M, Biswas S (2008) Body posture identification using hidden markov model with a wearable sensor network. In: Proceedings of the ICST 3rd international conference on Body area networks. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), p 19Google Scholar
  64. 64.
    Rachuri KK, Musolesi M, Mascolo C, Rentfrow PJ, Longworth C, Aucinas A (2010) Emotionsense: a mobile phones based adaptive platform for experimental social psychology research. In: Proceedings of the 12th ACM international conference on Ubiquitous computing. ACM, pp 281–290Google Scholar
  65. 65.
    Reddy S, Mun M, Burke J, Estrin D, Hansen M, Srivastava M (2010) Using mobile phones to determine transportation modes. ACM Trans Sens Netw: TOSN 6(2):13CrossRefGoogle Scholar
  66. 66.
    Russell S, Norvig P, Intelligence A (1995) A modern approach. Artificial intelligence, vol 25. Prentice-Hall, Egnlewood Cliffs, p 27zbMATHGoogle Scholar
  67. 67.
    Serrano E, Botóa JA (2013) Validating ambient intelligence based ubiquitous computing systems by means of artificial societies. Inf Sci 222:3–24CrossRefGoogle Scholar
  68. 68.
    Serrano E, Iglesias CA (2016) Validating viral marketing strategies in twitter via agent-based social simulation. Expert Syst Appl 50:140–150CrossRefGoogle Scholar
  69. 69.
    Serrano E, Rovatsos M, Botía JA (2012) A qualitative reputation system for multiagent systems with protocol-based communication. In: van der Hoek W, Padgham L, Conitzer V, Winikoff M (eds) International conference on autonomous agents and multiagent systems, AAMAS 2012, Valencia, Spain, June 4–8, 2012 (3 Volumes). IFAAMAS, pp 307–314Google Scholar
  70. 70.
    Serrano E, Rovatsos M, Botía JA (2013) Data mining agent conversations: a qualitative approach to multiagent systems analysis. Inf Sci 230:132–146MathSciNetCrossRefGoogle Scholar
  71. 71.
    Shvachko K, Kuang H, Radia S, Chansler R (2010) The hadoop distributed file system. In: 2010 IEEE 26th symposium on mass storage systems and technologies (MSST). IEEE, pp 1–10Google Scholar
  72. 72.
    Siewiorek DP, Smailagic A, Furukawa J, Krause A, Moraveji N, Reiger K, Shaffer J, Wong FL (2003) Sensay: a context-aware mobile phone. In: ISWC, vol 3, p 248Google Scholar
  73. 73.
    Singh S, Chana I (2016) Cloud resource provisioning: survey, status and future research directions. Knowl Inf Syst 49(3):1005–1069CrossRefGoogle Scholar
  74. 74.
    Tan X, Kim D, Usher N, Laboy D, Jackson J, Kapetanovic A, Rapai J, Sabadus B, Zhou X (2006) An autonomous robotic fish for mobile sensing. In: 2006 IEEE/RSJ international conference on intelligent robots and systems. IEEE, pp 5424–5429Google Scholar
  75. 75.
    Twardowski B, Ryzko D (2014) Multi-agent architecture for real-time big data processing. In: 2014 IEEE/WIC/ACM international joint conferences on Web intelligence (WI) and intelligent agent technologies (IAT), vol 3. IEEE, pp 333–337Google Scholar
  76. 76.
    Van T, Vo B, Le B (2018) Mining sequential patterns with itemset constraints. Knowl Inf Syst 57:311–330CrossRefGoogle Scholar
  77. 77.
    Vavilapalli VK, Murthy AC, Douglas C, Agarwal S, Konar M, Evans R, Graves T, Lowe J, Shah H, Seth S, et al (2013) Apache hadoop yarn: yet another resource negotiator. In: Proceedings of the 4th annual symposium on cloud computing. ACM, p 5Google Scholar
  78. 78.
    Wang T, Cardone G, Corradi A, Torresani L, Campbell AT (2012) Walksafe: a pedestrian safety app for mobile phone users who walk and talk while crossing roads. In: Proceedings of the twelfth workshop on mobile computing systems & applications. ACM, p 5Google Scholar
  79. 79.
    Wang Y, Lin J, Annavaram M, Jacobson QA, Hong J, Krishnamachari B, Sadeh N (2009) A framework of energy efficient mobile sensing for automatic user state recognition. In: Proceedings of the 7th international conference on mobile systems, applications, and services. ACM, pp 179–192Google Scholar
  80. 80.
    Wooldridge M (2009) An introduction to multiagent systems. Wiley, HobokenGoogle Scholar
  81. 81.
    You C-W, Lane ND, Chen F, Wang R, Chen Z, Bao TJ, Montes-de Oca M, Cheng Y, Lin M, Torresani L, et al (2013) Carsafe app: alerting drowsy and distracted drivers using dual cameras on smartphones. In: Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM, pp 13–26Google Scholar
  82. 82.
    Yürür Ö, Liu CH, Sheng Z, Leung VC, Moreno W, Leung KK (2016) Context-awareness for mobile sensing: a survey and future directions. IEEE Commun Surv Tutor 18(1):68–93CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Group of Electronic Technology and Communications, Department of Computer EngineeringUniversity of A CoruñaA CoruñaSpain
  2. 2.Ontology Engineering Group, Artificial Intelligence DepartmentUniversidad Politécnica de MadridMadridSpain
  3. 3.Dartmouth CollegeHanoverUSA

Personalised recommendations