Abstract
Mobile sensing systems based on smartphones, connected vehicles and integrated sensors on new mobile devices have become an important alternative for the development of intelligent services in large urban environments. Massive data collection and its real-time analysis are essential for big cities to move towards energy efficiency, sustainable mobility, protection of the environment and economic sustainability. Current research and applications are mainly focused on the use of individual devices and the analysis of information on a single domain (e.g. activity recognition). However, it is still necessary to provide solutions for social problems based on smart mobile devices connected to the city. In this paper, we present an architecture for mobile sensing systems in large cities based on the intelligent agent paradigm and multi-agent systems. The presented platform provides support for multi-purpose machine learning services, implementing expert learning agents in each domain where the system collects data. Furthermore, the main challenges in mobile sensing systems such as scalability in crowded environments, handling of a large amount of data and the increasing appearance of sensing devices are addressed by the architecture due to the agent paradigm and multi-agent systems suit these demands naturally.
Similar content being viewed by others
References
Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In: Ricci F, Rokach L, Shapira B, Kantor P (eds) Recommender systems handbook. Springer, Boston MA, pp 217–253
Alsheikh MA, Niyato D, Lin S, Tan H-P, Han Z (2016) Mobile big data analytics using deep learning and apache spark. IEEE Netw 30(3):22–29
Bajo J, Campbell AT, Zhou X (2016) Mobile sensing agents for social computing environments. In: de la Prieta F, et al (eds) Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection. PAAMS 2016. Advances in Intelligent Systems and Computing, vol 473. Springer, Cham, pp 157–167
Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Ferscha A, Mattern F (eds) Pervasive computing. Pervasive 2004. Lecture Notes in Computer Science, vol 3001. Springer, Berlin, Heidelberg, pp 1–17
Bergmann R (2007) Ambient intelligence for decision making in fire service organizations. In: Schiele B et al (eds) Ambient intelligence. AmI 2007. Lecture notes in Computer Sciences, vol 4794. Springer, Berlin, Heidelberg, pp 73–90
Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132
Celik T, Demirel H, Ozkaramanli H (2006) Automatic fire detection in video sequences. In: 14th European Signal Processing Conference (EUSIPCO 2006), Florence, Italy, September 4–8, 2006. pp 1–5
Çelik T, Özkaramanlı H, Demirel H (2007) Fire and smoke detection without sensors: image processing based approach. In: 15th European Signal Processing Conference (EUSIPCO 2007), Poznan, Poland, September 3–7, 2007. pp 1794–1798
Collier RW, O’Neill E, Lillis D, O’Hare G (2019) Mams: multi-agent microservices. In: Companion proceedings of the 2019 world wide web conference, San Francisco, USA, May 13–17, 2019. pp 655–662
Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B 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, Florence, Italy, April 05–10, 2008. pp 1797–1806
Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems, Boston, Massachusetts, USA, September 15–19, 2016. pp 191–198
Das J, Mukherjee P, Majumder S, Gupta P (2014) Clustering-based recommender system using principles of voting theory. In: 2014 international conference on contemporary computing and informatics (IC3I), Mysore, India, 27–29 November 2014. pp 230–235
Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113
Delen D, Sharda R (2008) Artificial neural networks in decision support systems. In: Handbook on Decision Support Systems 1. International Handbooks Information System. Springer, Berlin, Heidelberg, pp 557–580
Diaconita V, Bologa A-R, Bologa R (2018) Hadoop oriented smart cities architecture. Sensors 18(4):1181
Dong YF, Kanhere S, Chou CT, Bulusu N (2008) Automatic collection of fuel prices from a network of mobile cameras. In: Nikoletseas SE, Chlebus BS, Johnson DB, Krishnamachari B (eds) Distributed computing in sensor systems. DCOSS 2008. Lecture Notes in Computer Science, vol 5067. Springer, Berlin, Heidelberg, pp 140–156
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):6
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), Guilin, China, 19–23 December 2009. pp 1549–1554
Harper PR (2005) A review and comparison of classification algorithms for medical decision making. Health Policy 71(3):315–331
Horng W-B, Peng J-W, Chen C-Y (2005) A new image-based realtime flame detection method using color analysis. In: Proceedings of IEEE networking, sensing and control. pp 100–105
Hull B, Bychkovsky V, Zhang Y, Chen K, Goraczko M, Miu A, Madden S (2006) Cartel: a distributed mobile sensor computing system. In: Proceedings of the 4th international conference on embedded networked sensor systems, SenSys 2006, Boulder, Colorado, USA, October 31–November 3, 2006. pp 125–138
Jayaputera GT, Zaslavsky A, Loke SW (2007) Enabling run-time composition and support for heterogeneous pervasive multi-agent systems. J Syst Softw 80(12):2039–2062
Karim A, Siddiqa A, Safdar Z, Razzaq M, Gillani SA, Tahir H, 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
Kohl N, Miikkulainen R (2009) Evolving neural networks for strategic decision-making problems. Neural Netw 22(3):326–337
Konečnỳ J, McMahan HB, Yu FX, Richtárik P, Suresh AT, Bacon D (2016) Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492
Krivic P, Skocir P, Kusek M, Jezic G (2018) Microservices as agents in IoT Systems. In: Jezic G, Kusek M, Chen-Burger YH, Howlett R, Jain L (eds) Agent and multi-agent systems: technology and applications. KES-AMSTA 2017. Smart innovation, systems and technologies, vol 74. Springer, Cham, pp 22–31
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. https://doi.org/10.1145/3065386
Lane ND, Georgiev P, Qendro L (2015) Deepear: robust smartphone audio sensing in unconstrained acoustic environments using deep learning. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. pp 283–294. https://doi.org/10.1145/2750858.2804262
Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150
Leibiusky J, Eisbruch G, Simonassi D (2012) Getting started with storm. O’Reilly Media Inc., Sebastopol
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):991–998
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 (MobiSys 2009), Kraków, Poland, June 22–25, 2009. pp 165–178. https://doi.org/10.1145/1555816.1555834
Lu H, Bernheim Brush AJ, Priyantha B, Karlson AK, Liu J (2011) SpeakerSense: energy efficient unobtrusive speaker identification on mobile phones. In: Lyons K, Hightower J, Huang EM (eds) Pervasive computing. Pervasive 2011. Lecture notes in Computer Science, vol 6696. Springer, Berlin, Heidelberg, pp 188–205
Lu H, Frauendorfer D, Rabbi M, Mast MS, Chittaranjan GT, Campbell AT, Choudhury T (2012) Stresssense: detecting stress in unconstrained acoustic environments using smartphones. In: Proceedings of the 2012 ACM conference on ubiquitous computing. pp 351–360. https://doi.org/10.1145/2370216.2370270
Marz N, Warren J (2015) Big data: principles and best practices of scalable real-time data systems. Manning Publications Co, New York
Matthies M, Giupponi C, Ostendorf B (2007) Environmental decision support systems: current issues, methods and tools. Elsevier, New York
Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D (2016) Mllib: machine learning in apache spark. J Mach Learn Res 17(1):1235–1241
MQTT 101-How to Get Started with the lightweight IoT Protocol (2015) https://goo.gl/Lm6n9m. Accessed May 2018
MQTT Version 3.1.1 (2015) https://goo.gl/7jxqNU. Accessed May 2018
Mun M, Reddy S, Shilton K, Yau N, Burke J, Estrin D, 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 (MobiSys 2009), Kraków, Poland, June 22–25, 2009. pp 55–68. https://doi.org/10.1145/1555816.1555823
Niska H, Hiltunen T, Karppinen A, Ruuskanen J, Kolehmainen M (2004) Evolving the neural network model for forecasting air pollution time series. Eng Appl Artif Intell 17(2):159–167
Osman AMS (2019) A novel big data analytics framework for smart cities. Future Gener Comput Syst 91:620–633
Pecora F, Cesta A (2007) Dcop for smart homes: a case study. Comput Intell 23(4):395–419
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, Tempe, Arizona, March 13–17, 2008. p 19
Ramirez AJ, Knoester DB, Cheng BH, McKinley PK (2009) Applying genetic algorithms to decision making in autonomic computing systems. In: Proceedings of the 6th international conference on autonomic computing, ICAC 2009, June 15–19, 2009, Barcelona, Spain. pp 97–106. https://doi.org/10.1145/1555228.1555258
Romero C, Olmo JL, Ventura S (2013) A meta-learning approach for recommending a subset of white-box classification algorithms for Moodle datasets. In: Educational data mining
Sánchez D, López A, Mendoza F, Cabarcos PA (2018) Dns-based dynamic authentication for microservices in IOT. Multidiscip Digit Publ Inst Proc 2:1233
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), Incline Village, NV, USA, 3–7 May 2010. pp 1–10. https://doi.org/10.1109/MSST.2010.5496972
Tapia DI, Rodríguez S, Bajo J, Corchado JM (2009) FUSION@, a SOA-based multi-agent architecture. In: Corchado JM, Rodríguez S, Llinas J, Molina JM (eds) International symposium on distributed computing and artificial intelligence 2008 (DCAI 2008) Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg, pp 99–107
Ta-Shma P, Akbar A, Gerson-Golan G, Hadash G, Carrez F, Moess-ner K (2017) An ingestion and analytics architecture for iot applied to smart city use cases. IEEE Internet Things J 5(2):765–774
Unger M, Bar A, Shapira B, Rokach L (2016) Towards latent context-aware recommendation systems. Knowl Based Syst 104:165–178
Vavilapalli VK, Murthy AC, Douglas C, Agarwal S, Konar M, Evans R (2013) Apache hadoop yarn: yet another resource negotiator. In: Proceedings of the 4th annual symposium on cloud computing. https://doi.org/10.1145/2523616.2523633
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 12th workshop on mobile computing systems and applications. https://doi.org/10.1145/2162081.2162089
Wang H, Wang N, Yeung D-Y (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM sigkdd international conference on knowledge discovery and data mining, Sydney, NSW, Australia, August 10–13, 2015. pp 1235–1244
Wang J, Chen Y, Hao S, Peng X, Hu L (2019) Deep learning for sensor-based activity recognition: a survey. Pattern Recognit Lett 119:3–11
Yao S, Hu S, Zhao Y, Zhang A, Abdelzaher T (2017) Deepsense: A unified deep learning framework for time-series mobile sensing data processing. In: Proceedings of the 26th international conference on world wide web, Perth, Australia, April 03–07, 2017. pp 351–360
Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark: cluster computing with working sets. HotCloud 10(10–10):95
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) 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).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Laport, F., Serrano, E. & Bajo, J. A multi-agent architecture for mobile sensing systems. J Ambient Intell Human Comput 11, 4439–4451 (2020). https://doi.org/10.1007/s12652-019-01608-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-019-01608-4