2018
Journal article
Open Access
Computational topology to monitor human occupancy
Barsocchi P., Cassará P., Giorgi D., Moroni D., Pascali M. A.
WSN Persistent homology Surveillance Occupancy persistent homology computational topology surveillance Human behavior Computational topology [MATH.MATH-AT]Mathematics [math]/Algebraic Topology [math.AT]
The recent advances in sensing technologies, embedded systems, and wireless communication technologies, make it possible to develop smart systems to monitor human activities continuously. The occupancy of specific areas or rooms in a smart building is an important piece of information, to infer the behavior of people, or to trigger an advanced surveillancemodule. We propose a method based on computational topology to infer the occupancy of a room monitored for a week by a system of low-cost sensors.
Source: Proceedings (MDPI) 2 (2018). doi:10.3390/proceedings2020099
Publisher: MDPI, Basel, Svizzera
Barsocchi, P.; Ferro, E.; Fortunati, L.; Mavilia, F.; Palumbo, F. EMS@ CNR: An energy monitoring sensor network infrastructure for in-building location-based services. In Proceedings of the 2014 International Conference on High Performance Computing & Simulation (HPCS), Bologna, Italy, 21-25 July 2014; pp. 857-862.
Barsocchi, P.; Cimino, M.G.; Ferro, E.; Lazzeri, A.; Palumbo, F.; Vaglini, G. Monitoring elderly behavior via indoor position-based stigmergy. Pervasive Mob. Comput. 2015, 23, 26-42.
Barsocchi, P.; Crivello, A.; Girolami, M.; Mavilia, F.; Ferro, E. Are you in or out? Monitoring the human behavior through an occupancy strategy. In Proceedings of the 2016 IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, 27-30 June 2016; pp. 159-162.
In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, Roma, Italy, 11-15 November 2013; pp. 101-108.
Chen, D.; Barker, S.; Subbaswamy, A.; Irwin, D.; Shenoy, P. Nonintrusive occupancy monitoring using smart meters. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, Roma, Italy, 11-15 November 2013; pp. 91-98.
Patel, S.N.; Robertson, T.; Kientz, J.A.; Reynolds, M.S.; Abowd, G.D. At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. In UbiComp 2007: Ubiquitous Computing; Springer-Verlag: Berlin, Germany, 2007; pp. 271-288.
7. Ruzzelli, A.G.; Nicolas, C.; Schoofs, A.; O'Hare, G.M.P. Realtime recognition and profiling of appliances through a single electricity sensor. In Proceedings of the 2010 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks (SECON), Boston, MA, USA, 21-25 June 2010; pp. 1-9.
8. Rosdi, N.A.M.; Nordin, F.H.; Ramasamy, A.K. Identification of electrical appliances using non-intrusive magnetic field and probabilistic neural network (pnn). In Proceedings of the 2014 IEEE International Conference on Power and Energy (PECon), Kuching, Malaysia, 1-3 December 2014; pp. 47-52.
9. Klingensmith, N.; Willis, D.; Banerjee, S. Extracting events from spatial time series. In Proceedings of the 11th ACM Conference on Embedded Network Sensor Systems Roma, Italy, 11-15 November 2013; pp. 5-8.
10. Ghrist, R. Barcodes: The Persistent Topology of Data. Bull. Am. Math. Soc. 2008, 45, 61-75.
11. Hatcher, A. Algebraic Topology; Cornell University: New York, NY, USA, 2001.
12. Edelsbrunner, H.; Harer, J. Persistent homology-A survey. Contemp. Math. 2008, 453, 257-282.
13. Zomorodian, A.; Carlsson, G. Computing Persistent Homology. Discret. Comput. Geom. 2005, 33, 249-274.
14. Otter, N.; Porter, M.A.; Tillmann, U.; Grindrod, P.; Harrington, H.A. A roadmap for the computation of persistent homology. arXiv 2017, arxiv:1506.08903.
15. Biasotti, S.; Falcidieno, B.; Giorgi, D.; Spagnuolo, M. Mathematical Tools for Shape Analysis and Description; Morgan & Claypool: Williston, VT, USA, 2014; Volume 6.
16. Jonoska, N.; Saito, M. Discrete and Topological Models in Molecular Biology; Natural Computing Series; Springer: Berlin/Heidelberg, Germany, 2014.
17. Silva, V.D.; Ghrist, R. Coverage in sensor networks via persistent homology. Algebr. Geom. Topol. 2007, 7, 339-358.
18. Carlsson, G.; Ishkhanov, T.; de Silva, V.; Zomorodian, A. On the local behaviour of spaces of natural images. Int. J. Comput. Vis. 2008, 76, 1-12.
19. Perea, J.A.; Harer, J. Sliding Windows and Persistence: An Application of Topological Methods to Signal Analysis. Found. Comput. Math. 2015, 15, 799-838.
20. Pereira, C.M.; de Mello, R.F. Persistent Homology for Time Series and Spatial Data Clustering. Expert Syst. Appl. 2015, 42, 6026-6038.
21. Biasotti, S.; De Floriani, L.; Falcidieno, B.; Frosini, P.; Giorgi, D.; Landi, C.; Papaleo, L.; Spagnuolo, M. Describing Shapes by Geometrical-topological Properties of Real Functions. ACM Comput. Surv. 2008, 40, 12:1-12:87.
22. Tausz, A.; Vejdemo-Johansson, M.; Adams, H. JavaPlex: A research software package for persistent (co)homology. In Mathematical Software-ICMS 2014; Hong, H., Yap, C., Eds.; Springer: Berlin/Heidelberg, Germany, 2014; pp. 129-136. Available online: http://appliedtopology.github.io/javaplex/ (accessed on March 2017).
Barsocchi, P.; Cimino, M.G.; Ferro, E.; Lazzeri, A.; Palumbo, F.; Vaglini, G. Monitoring elderly behavior via indoor position-based stigmergy. Pervasive Mob. Comput. 2015, 23, 26-42.
Barsocchi, P.; Crivello, A.; Girolami, M.; Mavilia, F.; Ferro, E. Are you in or out? Monitoring the human behavior through an occupancy strategy. In Proceedings of the 2016 IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, 27-30 June 2016; pp. 159-162.
In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, Roma, Italy, 11-15 November 2013; pp. 101-108.
Chen, D.; Barker, S.; Subbaswamy, A.; Irwin, D.; Shenoy, P. Nonintrusive occupancy monitoring using smart meters. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, Roma, Italy, 11-15 November 2013; pp. 91-98.
Patel, S.N.; Robertson, T.; Kientz, J.A.; Reynolds, M.S.; Abowd, G.D. At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. In UbiComp 2007: Ubiquitous Computing; Springer-Verlag: Berlin, Germany, 2007; pp. 271-288.
7. Ruzzelli, A.G.; Nicolas, C.; Schoofs, A.; O'Hare, G.M.P. Realtime recognition and profiling of appliances through a single electricity sensor. In Proceedings of the 2010 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks (SECON), Boston, MA, USA, 21-25 June 2010; pp. 1-9.
8. Rosdi, N.A.M.; Nordin, F.H.; Ramasamy, A.K. Identification of electrical appliances using non-intrusive magnetic field and probabilistic neural network (pnn). In Proceedings of the 2014 IEEE International Conference on Power and Energy (PECon), Kuching, Malaysia, 1-3 December 2014; pp. 47-52.
9. Klingensmith, N.; Willis, D.; Banerjee, S. Extracting events from spatial time series. In Proceedings of the 11th ACM Conference on Embedded Network Sensor Systems Roma, Italy, 11-15 November 2013; pp. 5-8.
10. Ghrist, R. Barcodes: The Persistent Topology of Data. Bull. Am. Math. Soc. 2008, 45, 61-75.
11. Hatcher, A. Algebraic Topology; Cornell University: New York, NY, USA, 2001.
12. Edelsbrunner, H.; Harer, J. Persistent homology-A survey. Contemp. Math. 2008, 453, 257-282.
13. Zomorodian, A.; Carlsson, G. Computing Persistent Homology. Discret. Comput. Geom. 2005, 33, 249-274.
14. Otter, N.; Porter, M.A.; Tillmann, U.; Grindrod, P.; Harrington, H.A. A roadmap for the computation of persistent homology. arXiv 2017, arxiv:1506.08903.
15. Biasotti, S.; Falcidieno, B.; Giorgi, D.; Spagnuolo, M. Mathematical Tools for Shape Analysis and Description; Morgan & Claypool: Williston, VT, USA, 2014; Volume 6.
16. Jonoska, N.; Saito, M. Discrete and Topological Models in Molecular Biology; Natural Computing Series; Springer: Berlin/Heidelberg, Germany, 2014.
17. Silva, V.D.; Ghrist, R. Coverage in sensor networks via persistent homology. Algebr. Geom. Topol. 2007, 7, 339-358.
18. Carlsson, G.; Ishkhanov, T.; de Silva, V.; Zomorodian, A. On the local behaviour of spaces of natural images. Int. J. Comput. Vis. 2008, 76, 1-12.
19. Perea, J.A.; Harer, J. Sliding Windows and Persistence: An Application of Topological Methods to Signal Analysis. Found. Comput. Math. 2015, 15, 799-838.
20. Pereira, C.M.; de Mello, R.F. Persistent Homology for Time Series and Spatial Data Clustering. Expert Syst. Appl. 2015, 42, 6026-6038.
21. Biasotti, S.; De Floriani, L.; Falcidieno, B.; Frosini, P.; Giorgi, D.; Landi, C.; Papaleo, L.; Spagnuolo, M. Describing Shapes by Geometrical-topological Properties of Real Functions. ACM Comput. Surv. 2008, 40, 12:1-12:87.
22. Tausz, A.; Vejdemo-Johansson, M.; Adams, H. JavaPlex: A research software package for persistent (co)homology. In Mathematical Software-ICMS 2014; Hong, H., Yap, C., Eds.; Springer: Berlin/Heidelberg, Germany, 2014; pp. 129-136. Available online: http://appliedtopology.github.io/javaplex/ (accessed on March 2017).
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BibTeX entry
@article{oai:it.cnr:prodotti:382739,
title = {Computational topology to monitor human occupancy},
author = {Barsocchi P. and Cassará P. and Giorgi D. and Moroni D. and Pascali M. A.},
publisher = {MDPI, Basel, Svizzera},
doi = {10.3390/proceedings2020099 and 10.5281/zenodo.1159169 and 10.5281/zenodo.1159170},
journal = {Proceedings (MDPI)},
volume = {2},
year = {2018}
}
0000-0002-6862-7593
