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Commun. 7(1), 186–197 (2015)
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18(1), 2–20 (2018)
16. Serrano, M., Barnaghi, P., Carrez, F., Cousin, P., Vermesan, O., Friess, P.: Internet of things
IoT semantic interoperability: research challenges, best practices, recommendations and next
steps. In: IERC: European Research Cluster on the Internet of Things, Tech. Rep (2015)
17. Gyrard, A., Serrano, M.: Connected smart cities: interoperability with SEG 3.0 for the internet
of things. In: Proceedings of 30th IEEE International Conference on Advanced Information
Networking and Applications Workshops, pp. 796–802 (2016)
18. Hahm, O., Baccelli, E., Petersen, H., Tsiftes, N.: Operating systems for low-end devices in the
internet of things: a survey. IEEE Internet Things J. 3(5), 720–734 (2016)
19. Bello, O., Zeadally, S., Badra, M.: Network Layer Inter-Operation of Device-to-Device
communication technologies in Internet of Things (IoT). Ad Hoc Networks, pp. 1–11 (2016)
20. Noura, M., Atiquzzaman, M., Gaedke, M.: Interoperability in internet of things: taxonomies
and open challenges. Mob. Netw. Appl. 24, 796809 (2019)
21. W3C: Semantic Integration and Interoperability Using RDF and OWL. www.w3.org/2001/sw/
BestPractices/OEP/SemInt (2018)
22. Jabbar, S., Ullah, F., Khalid, S., Khan, M., Han, K.: Semantic interoperability in heterogeneous
IoT infrastructure for healthcare. Wirel. Commun. Mob. Comput. 9731806, 1–10 (2017)
23. Serrano, M., Gyrard, A.: A review of tools for IoT semantics and data streaming analytics.
Build. Blocks IoT Anal. 6, 139–163 (2015)
24. Swetina, J., Lu, G., Jacobs, P., Ennesser, F., Song, J.: Toward a standardized common M2M
service layer platform: Introduction to oneM2M. IEEE Wirel. Commun. 21(3), 20–26 (2014)
25. Mohanty, M.N., Palo, H.K.: Segment based emotion recognition using combined reduced
features. Int. J. Speech Tech. 22(4), 865–884 (2019)
26. Palo, H.K., Mohanty, M.N., Chandra, M.: Efficient feature combination techniques for
emotional speech classification. Int. J .Speech Tech. 19(1), 135–150 (2016)
27. Palo, H.K., Sagar, S.: Comparison of neural network models for speech emotion recognition.
In: 2nd IEEE International Conference on Data Science and Business Analytics (ICDSBA),
pp. 127–131 (2018)
28. Khan, A.M., Lee, Y.K., Lee, S.Y., Kim, T.S.: A triaxial accelerometer-based physical-activity
recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans. Inf.
Technol. B 14(5), 1166–1172 (2010)
29. Altun, K., Barshan, B.: Human activity recognition using inertial/magnetic sensor units. In:
International Workshop on Human Behavior Understanding. Springer, Berlin, Heidelberg,
pp. 38–51 (2010)
30. Lane, N.D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Kawsar, F.: An early resource characterization of deep learning on wearables, smartphones and internet of things devices. In:
International Workshop on Internet of Things towards Applications. ACM, pp. 7–12 (2015)
31. Chen, Y., Zhou, J., Guo, M.: A context-aware search system for internet of things based on
hierarchical context model. Telecommun. Syst. 62(1), 77–91 (2016)
32. Bhide, V.H., Wagh, S.: I-learning IoT: an intelligent self learning system for home automation
using IoT. Int. Conf. Commun. Sig. Process. 1763–1767 (2015)
33. https://www.accenture.com/_acnmedia/pdf-77/accenture-pulse-survey.pdf (2018)
34. Ruta, M., Scioscia, F., Loseto, G., Pinto, A., Di Sciascio, E.: Machine Learning in the Internet
of Things: a Semantic-enhanced Approach. Semantic Web, IOS Press, pp. 1–22 (2018)
35. Sezer, O.B., Dogdu, E., Ozbayoglu, M., Onal, A.: An extended IOT framework with semantics,
big data, and analytics. In: IEEE International Conference on Big Data (Big Data), pp. 849–1856
(2016)
36. Koru, A.G., Tian, J.: An empirical comparison and characterization of high defect and high
complexity modules. J. Syst. Softw. 67(3), 153–163 (2003)
37. Weyuker, E.J.: Evaluating software complexity measures. IEEE Trans. Softw. Eng. 14(9),
1357–1365 (1988)
101
14. Vyas, D.A., Bhatt, D., Jha, D.: IoT: trends, challenges and future scope. Int. J. Comput.
Commun. 7(1), 186–197 (2015)
15. Shi, F., Li, Q., Zhu, T., Ning, H.: A survey of data somatization in internet of things. Sensors
18(1), 2–20 (2018)
16. Serrano, M., Barnaghi, P., Carrez, F., Cousin, P., Vermesan, O., Friess, P.: Internet of things
IoT semantic interoperability: research challenges, best practices, recommendations and next
steps. In: IERC: European Research Cluster on the Internet of Things, Tech. Rep (2015)
17. Gyrard, A., Serrano, M.: Connected smart cities: interoperability with SEG 3.0 for the internet
of things. In: Proceedings of 30th IEEE International Conference on Advanced Information
Networking and Applications Workshops, pp. 796–802 (2016)
18. Hahm, O., Baccelli, E., Petersen, H., Tsiftes, N.: Operating systems for low-end devices in the
internet of things: a survey. IEEE Internet Things J. 3(5), 720–734 (2016)
19. Bello, O., Zeadally, S., Badra, M.: Network Layer Inter-Operation of Device-to-Device
communication technologies in Internet of Things (IoT). Ad Hoc Networks, pp. 1–11 (2016)
20. Noura, M., Atiquzzaman, M., Gaedke, M.: Interoperability in internet of things: taxonomies
and open challenges. Mob. Netw. Appl. 24, 796809 (2019)
21. W3C: Semantic Integration and Interoperability Using RDF and OWL. www.w3.org/2001/sw/
BestPractices/OEP/SemInt (2018)
22. Jabbar, S., Ullah, F., Khalid, S., Khan, M., Han, K.: Semantic interoperability in heterogeneous
IoT infrastructure for healthcare. Wirel. Commun. Mob. Comput. 9731806, 1–10 (2017)
23. Serrano, M., Gyrard, A.: A review of tools for IoT semantics and data streaming analytics.
Build. Blocks IoT Anal. 6, 139–163 (2015)
24. Swetina, J., Lu, G., Jacobs, P., Ennesser, F., Song, J.: Toward a standardized common M2M
service layer platform: Introduction to oneM2M. IEEE Wirel. Commun. 21(3), 20–26 (2014)
25. Mohanty, M.N., Palo, H.K.: Segment based emotion recognition using combined reduced
features. Int. J. Speech Tech. 22(4), 865–884 (2019)
26. Palo, H.K., Mohanty, M.N., Chandra, M.: Efficient feature combination techniques for
emotional speech classification. Int. J .Speech Tech. 19(1), 135–150 (2016)
27. Palo, H.K., Sagar, S.: Comparison of neural network models for speech emotion recognition.
In: 2nd IEEE International Conference on Data Science and Business Analytics (ICDSBA),
pp. 127–131 (2018)
28. Khan, A.M., Lee, Y.K., Lee, S.Y., Kim, T.S.: A triaxial accelerometer-based physical-activity
recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans. Inf.
Technol. B 14(5), 1166–1172 (2010)
29. Altun, K., Barshan, B.: Human activity recognition using inertial/magnetic sensor units. In:
International Workshop on Human Behavior Understanding. Springer, Berlin, Heidelberg,
pp. 38–51 (2010)
30. Lane, N.D., Bhattacharya, S., Georgiev, P., Forlivesi, C., Kawsar, F.: An early resource characterization of deep learning on wearables, smartphones and internet of things devices. In:
International Workshop on Internet of Things towards Applications. ACM, pp. 7–12 (2015)
31. Chen, Y., Zhou, J., Guo, M.: A context-aware search system for internet of things based on
hierarchical context model. Telecommun. Syst. 62(1), 77–91 (2016)
32. Bhide, V.H., Wagh, S.: I-learning IoT: an intelligent self learning system for home automation
using IoT. Int. Conf. Commun. Sig. Process. 1763–1767 (2015)
33. https://www.accenture.com/_acnmedia/pdf-77/accenture-pulse-survey.pdf (2018)
34. Ruta, M., Scioscia, F., Loseto, G., Pinto, A., Di Sciascio, E.: Machine Learning in the Internet
of Things: a Semantic-enhanced Approach. Semantic Web, IOS Press, pp. 1–22 (2018)
35. Sezer, O.B., Dogdu, E., Ozbayoglu, M., Onal, A.: An extended IOT framework with semantics,
big data, and analytics. In: IEEE International Conference on Big Data (Big Data), pp. 849–1856
(2016)
36. Koru, A.G., Tian, J.: An empirical comparison and characterization of high defect and high
complexity modules. J. Syst. Softw. 67(3), 153–163 (2003)
37. Weyuker, E.J.: Evaluating software complexity measures. IEEE Trans. Softw. Eng. 14(9),
1357–1365 (1988)