Abstract
Nowadays, the growth of the Internet of things makes necessary to improve systems in terms of reliability, autonomy, and adaptation. Some research lines are focused on these issues to be part of new necessities. The main idea of this chapter is to go further than a wide extended communication among devices or remote control focusing on decision making of cooperative systems. Voting algorithms are widespread in this kind of applications since they allow to combine multiple outputs to generate the final solution. The simplest voting methodology is majority but, in this case, past classifications do not affect the following ones. However, weighted majority can make the system adaptive since weights are calculated according to the previous behavior of each device. Two different weighted methods are analyzed in this chapter. The first one establishes the definition of how weights have to evolve depending on the matches between the solution of each device and the final cooperative solution. In contrast, the second weighted approach estimates weights using a stochastic-based method which gives weight assignments after analyzing multiple combinations. Rewards and penalties will be different every time. Final weights are not given by a specific combination; they are calculated according to all the valid combination distribution (i.e., geometric center of all of them). The way to define the valid combinations will determine the system reliability. Additionally, in order to verify the performance of each method, a case of use is also presented. Results demonstrate the adaptation of both methods and how the system reliability is also improved comparing to the simple majority solution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
B. Guo, D. Zhang, Z. Wang, Living with Internet of Things: The emergence of embedded Intelligence. 4th International Conference of Cyber Physical and Social Computing, pp. 297–304, October 2011
A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Commun. Surv. Tutorials 17(4), 2347–2376, Fourthquarter (2015). https://doi.org/10.1109/comst.2015.2444095
T. Xu, J.B. Wendt, M. Potkonjak, in Proceedings of the 2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD ‘14). Security of IoT Systems: Design Challenges and Opportunities (IEEE Press, Piscataway, NJ, USA, 2014), pp. 417–423
Z.K. Zhang, M.C.Y. Cho, C.W. Wang, C.W. Hsu, C.K. Chen, S. Shieh, in 2014 IEEE 7th International Conference on Service-Oriented Computing and Applications. IoT Security: Ongoing Challenges and Research Opportunities (Matsue, 2014), pp. 230–234. https://doi.org/10.1109/soca.2014.58
R.H. Weber, Internet of things—new security and privacy challenges. Comput. Law Secur. Rev. 26(1), 23–30 (2010), ISSN 0267-3649. https://doi.org/10.1016/j.clsr.2009.11.008
W.L. Chin, W. Li, H.H. Chen, Energy big data security threats in IoT-based smart grid communications. IEEE Commun. Mag. 55(10), 70–75 (2017). https://doi.org/10.1109/mcom.2017.170015
A.F. Skarmeta, J.L. Hernández-Ramos, M.V. Moreno, in 2014 IEEE World Forum on Internet of Things (WF-IoT), A Decentralized Approach for Security and Privacy Challenges in the Internet of Things (Seoul, 2014), pp. 67–72. https://doi.org/10.1109/wf-iot.2014.6803122
X. Wang, J. Zhang, E.M. Schooler, M. Ion, in 2014 IEEE International Conference on Communications (ICC). Performance Evaluation of Attribute-Based Encryption: Toward Data Privacy in the IoT (Sydney, NSW, 2014), pp. 725–730. https://doi.org/10.1109/icc.2014.6883405
D. Macedo, L.A. Guedes, I. Silva, in Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control. A Dependability Evaluation for Internet of Things Incorporating Redundancy Aspects (Miami, FL, 2014), pp. 417–422. https://doi.org/10.1109/icnsc.2014.6819662
R. Venkatakrishnan, M.A. Vouk, Using Redundancy to Detect Security Anomalies: Towards IoT security attack detectors: The Internet of Things (Ubiquity symposium). Ubiquity, Article 1 (2016), p. 19. https://doi.org/10.1145/2822881
H. Chourabi et al., in 2012 45th Hawaii International Conference on System Sciences, Understanding Smart Cities: An Integrative Framework (Maui, HI, 2012), pp. 2289–2297. https://doi.org/10.1109/hicss.2012.615
F. Li, R. Zhang, F. You, Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment. IET Image Process. 11(10), 833–840 (2017). https://doi.org/10.1049/iet-ipr.2016.0931
B. Xu, X.J. Ban, Y. Bian, J. Wang, K. Li, in 2017 IEEE Intelligent Vehicles Symposium (IV), V2I Based Cooperation Between Traffic Signal and Approaching Automated Vehicles (Los Angeles, CA, 2017), pp. 1658–1664. https://doi.org/10.1109/ivs.2017.7995947
P. Kamalinejad, C. Mahapatra, Z. Sheng, S. Mirabbasi, V.C.M. Leung, Y.L. Guan, Wireless energy harvesting for the internet of things. IEEE Commun. Mag. 53(6), 102–108 (2015). https://doi.org/10.1109/MCOM.2015.7120024
R.M. Ferdous, A.W. Reza, M.F. Siddiqui, Renewable energy harvesting for wireless sensors using passive RFID tag technology: A review. Renew. Sustain. Energy Rev. 58, 1114–1128, ISSN 1364-0321 (2016) https://doi.org/10.1016/j.rser.2015.12.332
B. Nordman, K. Christensen, in 2013 International Green Computing Conference Proceedings, Local Power Distribution with Nanogrids (Arlington, VA, 2013), pp. 1–8. https://doi.org/10.1109/igcc.2013.6604464
J.M. Guerrero, J.C. Vasquez, J. Matas, L.G. de Vicuna, M. Castilla, Hierarchical control of droop-controlled AC and DC microgrids—a general approach toward standardization. IEEE Trans. Industr. Electron. 58(1), 158–172 (2011). https://doi.org/10.1109/TIE.2010.2066534
D. Boroyevich, I. Cvetković, D. Dong, R. Burgos, F. Wang, F. Lee, in 2010 12th International Conference on Optimization of Electrical and Electronic Equipment, Future Electronic Power Distribution Systems a Contemplative View (Basov, 2010), pp. 1369–1380. https://doi.org/10.1109/optim.2010.5510477
S. Massoud Amin, B.F. Wollenberg, Toward a smart grid: power delivery for the 21st century. IEEE Power Energy Mag. 3(5), 34–41 (2005). https://doi.org/10.1109/mpae.2005.1507024
Q. Ou, Y. Zhen, X. Li, Y. Zhang, L. Zeng, in 2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing, Application of Internet of Things in Smart Grid Power Transmission (Vancouver, BC, 2012), pp. 96–100. https://doi.org/10.1109/music.2012.24
S.E. Collier, in 2015 IEEE Rural Electric Power Conference, The Emerging Enernet: Convergence of the Smart Grid with the Internet of Things (Asheville, NC, 2015), pp. 65–68. https://doi.org/10.1109/repc.2015.24
A. Ferreira, P. Leitão, P. Vrba, in 2014 IEEE International Workshop on Intelligent Energy Systems (IWIES), Challenges of ICT and Artificial Intelligence in Smart Grids (San Diego, CA, 2014), pp. 6–11. https://doi.org/10.1109/iwies.2014.6957039
Z. Li, P. Liu, C. Xu, H. Duan, W. Wang, Reinforcement learning-based variable speed limit control strategy to reduce traffic congestion at freeway recurrent bottlenecks. IEEE Trans. Intell. Transp. Syst. PP(99),1–14. https://doi.org/10.1109/tits.2017.2687620
D. Pérez, M. Villaverde, F. Moreno, N. Nogar, F. Ezcurra, E. Aznar, in , 2014 12th IEEE International Conference on Industrial Informatics (INDIN), Low-cost radar-Based Target Identification Prototype Using an Expert System (Porto Alegre, 2014), pp. 54–59. https://doi.org/10.1109/indin.2014.6945483
M. Villaverde, D. Perez, F. Moreno, Self-learning embedded system for object identification in intelligent infrastructure sensors. Sensors 15, 29056–29078 (2015)
E. Ordoni, A. Moeini, K. Badie, in 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), A New Approach to Recognize Activities in Smart Environments Based on Cooperative Game Theory (Gdynia, 2017), pp. 334–338. https://doi.org/10.1109/inista.2017.8001181
S. Srivastava, A. Bisht, N. Narayan, in 2017 7th International Conference on Cloud Computing, Data Science & Engineering—Confluence, Safety and Security in Smart Cities Using Artificial Intelligence—A Review (Noida, 2017), pp. 130–133. https://doi.org/10.1109/confluence.2017.7943136
L. Li, K. Ota, M. Dong, When weather matters: IoT-Based electrical load forecasting for smart grid. IEEE Commun. Mag. 55(10), 46–51 (2017). https://doi.org/10.1109/mcom.2017.1700168
W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bulletin Math. Biophys. 5, 115–133 (1943)
D.O. Hebb, The organisation of behaviour: a neuropsychological theory (John Wiley, New York, 1949)
F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958)
M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, 2nd edn. (Pearson Education, 2005), ISBN: 0321204662
S.J. Russell, P. Norvig, Artificial intelligence: a modern approach, 3rd edn. (Prentice Hall, Pearson Education, 2009). ISBN 0-13-604259-7
A.M. Turing, Computing machinery and intelligence. Mind JSTOR 59(236), 433–460, (1950). www.jstor.org/stable/2251299
J. McCarthy, M.L. Minsky, N. Rochestery, C.E. Shannon, A proposal for the Dartmouth summer research project on artificial intelligence. Hanover (New Hampshire, United States, 1955)
A.S. Fraser, Simulation of genetic systems by automatic digital computers. I. Introduction. Aust. J. Biol. Sci. 10, 484–491 (1957)
A.S. Fraser, Simulation of genetic systems by automatic digital computers. II. Effects of linkage or rates of advance under selection. Aust. J. Biol. Sci. 10, 492–499 (1957)
G.E.P. Box, Evolutionary operation: a method for increasing industrial productivity. J. R. Stat. Soc. Ser. C (Applied Statistics) 6(2), 81–101. (1957). www.jstor.org/stable/2985505
H.J. Bremermann, The evolution of intelligence: The nervous system as a model of its environment, Technical report, no. 1, contract no. 477(17) (Dept. Mathematics, University of Washington, Seattle, July, 1958)
J.H. Holland, Adaptation in Natural and Artificial Systems (MIT Press, Cambrige, 1975)
S. Frankliny, A. Graesser, in Proceedings of the Third International Workshop on Agent Theories, Architectures, and Languages, Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents (Springer-Verlag, 1996), pp. 21–35
P. Maes, Artificial life meets entertainment: life like autonomous agents. Commun. ACM 38(11), 108–114 (1995)
B. Hayes-Roth, An architecture for adaptive intelligent systems. Artif. Intell. Elsevier 72, 329–365 (1995)
M. Wooldridgey, N.R. Jennings, Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), (1995)
D.D. Corkill, in Proceedings of the International Lisp Conference, Colaborating Software. Blackboard and Multi-Agent Systems & the Future (New York, 2003)
L. Breiman, J.H. Friedman, R.A. Olsheny, C.J. Stone, Classification and Regression Trees (Nueva York: Chapman & Hall/CRC, 1984)
Z.H. Zho, Ensemble Methods: Foundations and Algorithms (CRC-Press. Taylor & Francis Group, an Informa business, 2012)
T.G. Dietterich, in Proceedings of the First International Workshop on Multiple Classifier Systems, Ensemble Methods in Machine Learning (Springer, 2000), pp. 1–15. https://doi.org/10.1007/3-540-45014-9 1
M.P. Ponti, in 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials, Combining Classifiers: From the Creation of Ensembles to the Decision Fusion (Alagoas, 2011), pp. 1–10. https://doi.org/10.1109/sibgrapi-t.2011.9
D. Opitz, R. Maclin, Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)
Breiman, L. Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997). https://doi.org/10.1006/jcss.1997.1504
D.H. Wolpert, Stacked generalization. Neural Networks 5, 241–259 (1992)
L.I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms (Wiley‐Interscience, 2004)
M.P. Ponti, in 2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials, Combining Classifiers: From the Creation of Ensembles to the Decision Fusion ( Alagoas, 2011), pp. 1–10. https://doi.org/10.1109/sibgrapi-t.2011.9
Acknowledgements
This work was partially supported by the Spanish Ministry of Education, Culture and Sports under the FPU grant program (FPU13/04424).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Villaverde, M., Pérez, D., Moreno, F. (2019). Beyond IoT: Adaptive Approaches to Collaborative Smart Environments. In: Kabalci, E., Kabalci, Y. (eds) Smart Grids and Their Communication Systems. Energy Systems in Electrical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-1768-2_14
Download citation
DOI: https://doi.org/10.1007/978-981-13-1768-2_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1767-5
Online ISBN: 978-981-13-1768-2
eBook Packages: EnergyEnergy (R0)