Combining fog computing with sensor mote machine learning for industrial IoT

Sensors (Switzerland)

Volumn 18, Issue 5, 2018, Pages

  Lavassani, Mehrzad ^a   Forsström, Stefan ^a   Jennehag, Ulf ^a   Zhang, Tingting ^a  

^a MID SWEDEN UNIVERSITY   (Sweden)

Author keywords

Data mining; Fog computing; IoT; Monitoring; Online learning

Indexed keywords

DATA MINING; FOG; INTERNET OF THINGS; LEARNING SYSTEMS; MONITORING; SENSOR NODES;

DISTRIBUTED DATA; DISTRIBUTED LEARNING; END TO END DELAY; INDUSTRIAL INTERNETS; ONLINE LEARNING; QUANTITATIVE MEASUREMENT; SUSTAINABLE SOCIETY; THREE LAYER FRAMEWORKS;

FOG COMPUTING;

EID: 85047063861     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s18051532     Document Type: Article

References (28)

1. Atzori, L.; Iera, A.; Morabito, G. The internet of things: A survey. Comput. Netw. 2010, 54, 2787–2805. [CrossRef]
2. Da Xu, L.; He, W.; Li, S. Internet of things in industries: A survey. IEEE Trans. Ind. Inform. 2014, 10, 2233–2243.
3. Bonomi, F.; Milito, R.; Zhu, J.; Addepalli, S. Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland, 13–17 August 2012; pp. 13–16.
4. Han, G.; Dong, Y.; Guo, H.; Shu, L.; Wu, D. Cross-layer optimized routing in wireless sensor networks with duty cycle and energy harvesting. Wirel. Commun. Mob. Comput. 2015, 15, 1957–1981. [CrossRef]
5. Rasouli, H.; Kavian, Y.S.; Rashvand, H.F. ADCA: Adaptive duty cycle algorithm for energy efficient IEEE 802.15.4 beacon-enabled wireless sensor networks. IEEE Sens. J. 2014, 14, 3893–3902. [CrossRef]
6. Xie, R.; Liu, A.; Gao, J. A residual energy aware schedule scheme for WSNs employing adjustable awake/sleep duty cycle. Wirel. Pers. Commun. 2016, 90, 1859–1887. [CrossRef]
7. Choudhury, N.; Matam, R.; Mukherjee, M.; Shu, L. Adaptive Duty Cycling in IEEE 802.15. 4 Cluster Tree Networks Using MAC Parameters. In Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Chennai, India, 10–14 July 2017; p. 37.
8. Huang, M.; Liu, A.; Wang, T.; Huang, C. Green data gathering under delay differentiated services constraint for internet of things. Wirel. Commun. Mob. Comput. 2018, 2018. [CrossRef]
9. Azharuddin, M.; Kuila, P.; Jana, P.K. Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks. Comput. Electr. Eng. 2015, 41, 177–190. [CrossRef]
10. Naranjo, P.G.V.; Shojafar, M.; Mostafaei, H.; Pooranian, Z.; Baccarelli, E. P-SEP: A prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks. J. Supercomput. 2017, 73, 733–755. [CrossRef]
11. Oller, J.; Demirkol, I.; Casademont, J.; Paradells, J.; Gamm, G.U.; Reindl, L. Has time come to switch from duty-cycled MAC protocols to wake-up radio for wireless sensor networks? IEEE/ACM Trans. Netw. 2016, 24, 674–687. [CrossRef]
12. Mahmood, A.; Shi, K.; Khatoon, S.; Xiao, M. Data mining techniques for wireless sensor networks: A survey. Int. J. Distrib. Sens. Netw. 2013, 9, 406316. [CrossRef]
13. Alsheikh, M.A.; Lin, S.; Niyato, D.; Tan, H.P. Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Commun. Surv. Tutor. 2014, 16, 1996–2018. [CrossRef]
14. Guestrin, C.; Bodik, P.; Thibaux, R.; Paskin, M.; Madden, S. Distributed regression: an efficient framework for modeling sensor network data. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks, Berkeley, CA, USA, 26–27 April 2004; pp. 1–10.
15. Gelb, A. Applied Optimal Estimation; MIT Press: Cambridge, MA, USA, 1974.
16. Abbeel, P.; Coates, A.; Montemerlo, M.; Ng, A.Y.; Thrun, S. Discriminative Training of Kalman Filters. In Proceedings of the Robotics: Science and Systems, Cambridge, MA, USA, 8–11 June 2005; Volume 2, p. 1.
17. Liu, Q.; Wang, Z.; He, X.; Zhou, D. Event-based recursive distributed filtering over wireless sensor networks. IEEE Trans. Autom. Control 2015, 60, 2470–2475. [CrossRef]
18. Leskovec, J.; Rajaraman, A.; Ullman, J.D. Mining of Massive Datasets; Cambridge University Press: New York, NY, USA, 2014.
19. Schütz, N.; Holschneider, M. Detection of trend changes in time series using Bayesian inference. Phys. Rev. E 2011, 84, 021120. [CrossRef] [PubMed]
20. Chung, F.L.; Fu, T.C.; Ng, V.; Luk, R.W. An evolutionary approach to pattern-based time series segmentation. IEEE Trans. Evolut. Comput. 2004, 8, 471–489. [CrossRef]
21. Fuchs, E.; Gruber, T.; Nitschke, J.; Sick, B. Online segmentation of time series based on polynomial least-squares approximations. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32, 2232–2245. [CrossRef] [PubMed]
22. Wang, K.; Wang, Y.; Sun, Y.; Guo, S.; Wu, J. Green industrial internet of things architecture: An energy-efficient perspective. IEEE Commun. Mag. 2016, 54, 48–54. [CrossRef]
23. Li, H.; Ota, K.; Dong, M. Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing. IEEE Netw. 2018, 32, 96–101. [CrossRef]
24. Dautov, R.; Distefano, S.; Bruneo, D.; Longo, F.; Merlino, G.; Puliafito, A. Pushing Intelligence to the Edge with a Stream Processing Architecture. In Proceedings of the 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Devon, UK, 21–23 June 2017; pp. 792–799.
25. Dunkels, A.; Gronvall, B.; Voigt, T. Contiki-a lightweight and flexible operating system for tiny networked sensors. In Proceedings of the 2004 29th Annual IEEE International Conference on Local Computer Networks, Tampa, FL, USA, 16–18 November 2004; pp. 455–462.
26. Crossbow TelosB Mote Plattform, Datasheet. Available online: http://www.willow.co.uk/TelosB_Datasheet.pdf (accessed on 4 March 2018).
27. Single-Chip 2.4 GHz IEEE 802.15.4 Compliant and ZigBee Ready RF Transceiver. Available online: http://www.ti.com/product/CC2420 (accessed on 4 March 2018).
28. Osterlind, F.; Dunkels, A.; Eriksson, J.; Finne, N.; Voigt, T. Cross-level sensor network simulation with Cooja. In Proceedings of the 2006 31st IEEE Conference on Local Computer Networks, Tampa, FL, USA, 14–16 November 2006; pp. 641–648.