References

Anguita, D., A. Ghio, L. Oneto, X. Parra and J.L. Reyes-Ortiz, 2012. Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. Proceedings of the 4th International Workshop on Ambient Assisted Living, December 3-5, 2012, Vitoria, Gasteiz, Spain, pp: 216-223.

Bhattacharya, S., P. Nurmi, N. Hammerla and T. Plotz, 2014. Using unlabeled data in a sparse-coding framework for human activity recognition. Pervasive Mobile Comput., 15: 242-262.
CrossRef  |  Direct Link  |  

Bisht, N. and S. Singh, 2015. Analytical study of different network topologies. Int. Res. J. Eng. Technol., 2: 88-90.
Direct Link  |  

Del Rosario, M.B., S.J. Redmond and N.H. Lovell, 2015. Tracking the evolution of smartphone sensing for monitoring human movement. Sensors, 15: 18901-18933.
CrossRef  |  Direct Link  |  

Farej, Z.K. and A.M. Abdul-Hameed, 2015. Performance comparison among (star, tree and mesh) topologies for large scale WSN based IEEE 802.15.4 standard. Int. J. Comput. Applic., 124: 41-44.
CrossRef  |  Direct Link  |  

Harle, R., 2013. A survey of indoor inertial positioning systems for pedestrians. IEEE Commun. Surv. Tutorials, 15: 1281-1293.
CrossRef  |  Direct Link  |  

Incel, O.D., 2015. Analysis of movement, orientation and rotation-based sensing for phone placement recognition. Sensors, 15: 25474-25506.
CrossRef  |  Direct Link  |  

Jimenez, A.R., F. Zampella and F. Seco, 2014. Improving inertial pedestrian dead-reckoning by detecting unmodified switched-on lamps in buildings. Sensors, 14: 731-769.
CrossRef  |  Direct Link  |  

Khan, A.M., M.H. Siddiqi and S.W. Lee, 2013. Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones. Sensors, 13: 13099-13122.
CrossRef  |  Direct Link  |  

Li, Y., Y. Zhuang, H. Lan, Q. Zhou, X. Niu and N. El-Sheimy, 2016. A hybrid WiFi/magnetic matching/PDR approach for indoor navigation with smartphone sensors. IEEE Commun. Lett., 20: 169-172.
CrossRef  |  

Liu, W., Y. Zhang, S. Tang, J. Tang, R. Hong and J. Li, 2013. Accurate estimation of human body orientation from RGB-D sensors. IEEE Trans. Cybern., 43: 1442-1452.
CrossRef  |  

Miao, F., Y. He, J. Liu, Y. Li and I. Ayoola, 2015. Identifying typical physical activity on smartphone with varying positions and orientations. Biomed. Eng. OnLine, Vol. 14. 10.1186/s12938-015-0026-4

Reiter, G., 2014. Wireless connectivity for the internet of things. White Paper. Texas Instruments Inc., USA., June 2014.

Shala, U. and A. Rodriguez, 2011. Indoor positioning using sensor-fusion in android devices. School of Health and Society, Department Computer Science, Kristianstad University, Kristianstad Sweden.

Susi, M., V. Renaudin and G. Lachapelle, 2013. Motion mode recognition and step detection algorithms for mobile phone users. Sensors, 13: 1539-1562.
CrossRef  |  Direct Link  |  

Tian, Z., Y. Zhang, M. Zhou and Y. Liu, 2014. Pedestrian dead reckoning for MARG navigation using a smartphone. EURASIP J. Adv. Signal Process. 10.1186/1687-6180-2014-65

Varjonen, S., 2015. Internet of things. DIGILE, Espoo, Finland. http://www.internetofthings.fi/extras/IoTMagazine2015.pdf.

Witten, I.H., E. Frank and M.A. Hall, 2011. Data Mining: Practical Machine Learning Tools and Techniques. 3rd Edn., Elsevier, New York, ISBN-13: 9780080890364, Pages: 664.

Yun, J. and S.S. Lee, 2014. Human movement detection and identification using pyroelectric infrared sensors. Sensors, 14: 8057-8081.
CrossRef  |  Direct Link  |  

Zachariah, T., N. Klugman, B. Campbell, J. Adkins, N. Jackson and P. Dutta, 2015. The internet of things has a gateway problem. Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, February 12-13, 2015, Santa Fe, NM., USA., pp: 27-32.