Power transmission and workload balancing policies in eHealth mobile cloud computing scenarios

Future Generation Computer Systems

Volumn 78, Issue , 2018, Pages 587-601

  Pagán, Josué ^a,b   Zapater, Marina ^a,b   Ayala, José L ^a  

^a UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

^b University of Madrid   (Spain)

Author keywords

Balancing workload; Data Center; Economic savings; Energy optimization; Migraine prediction; WBSN

Indexed keywords

BODY SENSOR NETWORKS; EHEALTH; ENERGY POLICY; ENERGY UTILIZATION; FORECASTING; GREEN COMPUTING; INTERNET OF THINGS; MOBILE CLOUD COMPUTING; MONITORING; SIGNAL PROCESSING;

DATA CENTERS; ECONOMIC SAVINGS; ENERGY OPTIMIZATION; INTELLIGENT MANAGEMENT; INTERNET OF THING (IOT); NEUROLOGICAL DISORDERS; WBSN; WIRELESS BODY SENSOR NETWORKS;

INFORMATION MANAGEMENT;

EID: 85013944489     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2017.02.015     Document Type: Article

References (64)

1. Zheng, Y.-L., Ding, X.-R., Poon, C.C.Y., Lo, B.P.L., Zhang, H., Zhou, X.-L., Yang, G.-Z., Zhao, N., Zhang, Y.-T., Unobtrusive sensing and wearable devices for health informatics. IEEE Trans. Biomed. Eng. 61:5 (2014), 1538–1554.
2. Dorschky, E., Schuldhaus, D., Koerger, H., Eskofier, B., A framework for early event detection for wearable systems. Proceedings of the 2015 ACM International Symposium on Wearable Computers, 2015, ACM, 109–112.
3. Heldberg, B.E., Kautz, T., Leutheuser, H., Hopfengartner, R., Kasper, B.S., Eskofier, B.M., Using wearable sensors for semiology-independent seizure detection-towards ambulatory monitoring of epilepsy. Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, 2015, IEEE, 5593–5596.
4. Stovner, L.J., Andree, C., Prevalence of headache in europe: a review for the eurolight project. J. Headache Pain 11:4 (2010), 289–299.
5. Linde, M., Gustavsson, A., Stovner, L., Steiner, T., Barré, J., Katsarava, Z., Lainez, J., Lampl, C., Lantéri-Minet, M., Rastenyte, D., et al. The cost of headache disorders in europe: the eurolight project. Eur. J. Neurol. 19:5 (2012), 703–711.
6. Hu, X.H., Raskin, N.H., Cowan, R., Markson, L.E., Berger, M.L., Group, U.S.M.S.P.U., et al. Treatment of migraine with rizatriptan: when to take the medication. J. Head Face Pain 42:1 (2002), 16–20.
7. Ordás, C.M., Cuadrado, M.L., Rodríguez-Cambrón, A.B., Casas-Limón, J., del Prado, N., Porta-Etessam, J., Increase in body temperature during migraine attacks. Pain Med. 14:8 (2013), 1260–1264.
8. Porta-Etessam, J., Cuadrado, M.L., Rodríguez-Gómez, O., Valencia, C., García-Ptacek, S., Hypothermia during migraine attacks. Cephalalgia 30:11 (2010), 1406–1407.
9. Pagn, J., De Orbe, M.I., Gago, A., Sobrado, M., Risco-Martn, J.L., Mora, J.V., Moya, J.M., Ayala, J.L., Robust and accurate modeling approaches for migraine per-patient prediction from ambulatory data. Sensors 1424-8220, 15(7), 2015, 15419, 10.3390/s150715419.
10. Pagn, J., Risco-Martn, J.L., Moya, J.M., Ayala, J.L., Grammatical evolutionary techniques for prompt migraine prediction. Genetic and Evolutionary Computation Conference (GECCO), 2016, 2016, ACM, 10.1145/2908812.2908897.
11. Khan, A.N., Kiah, M.M., Khan, S.U., Madani, S.A., Towards secure mobile cloud computing: A survey. Future Gener. Comput. Syst. 29:5 (2013), 1278–1299.
12. Zapater, M., Arroba, P., Ayala, J.L., Moya, J.M., Olcoz, K., A novel energy-driven computing paradigm for e-health scenarios. Future Gener. Comput. Syst. 0167-739X, 34, 2014, 138–154.
13. Van Overschee, P., De Moor, B., N4sid: subspace algorithms for the identification of combined deterministic-stochastic systems. Automatica 30:1 (1994), 75–93.
14. Pagán, J., Risco-Martín, J.L., Moya, J.M., Ayala, J.L., Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases. J. Biomed. Inform., 2016.
15. U. Housing, M.S. OTB, Smart cities ranking of european medium-sized cities.
16. Deakin, M., Al Waer, H., From intelligent to smart cities. Intell. Build. Int. 3:3 (2011), 140–152.
17. Groh, B., Reinfelder, S., Streicher, M., Taraben, A., Eskofier, B., Movement prediction in rowing using a Dynamic Time Warping based stroke detection. IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2014), 2014, IEEE, 1–6.
18. Kugler, P., Schuldhaus, D., Jensen, U., Eskofier, B., Mobile recording system for sport applications. Proceedings of the 8th International Symposium on Computer Science in Sport (IACSS 2011), Liverpool, 2011, 67–70.
19. Alemdar, H., Ersoy, C., Wireless sensor networks for healthcare: A survey. Comput. Netw. 54:15 (2010), 2688–2710.
20. Milosevic, J., Dittrich, A., Ferrante, A., Malek, M., Quiros, C.R., Braojos, R., Ansaloni, G., Atienza, D., Risk assessment of atrial fibrillation: a failure prediction approach. Computing in Cardiology Conference, CinC, 2014, 2014, IEEE, 801–804.
21. Leutheuser, H., Gradl, S., Kugler, P., Anneken, L., Arnold, M., Achenbach, S., Eskofier, B.M., Comparison of real-time classification systems for arrhythmia detection on android-based mobile devices. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 2014, IEEE, 2690–2693.
22. Burns, A., Greene, B.R., McGrath, M.J., O'Shea, T.J., Kuris, B., Ayer, S.M., Stroiescu, F., Cionca, V., Shimmer–a wireless sensor platform for noninvasive biomedical research. IEEE Sens. J. 10:9 (2010), 1527–1534.
23. Casino, F., Lopez-Iturri, P., Aguirre, E., Azpilicueta, L., Solanas, A., Falcone, F., Dense wireless sensor network design for the implementation of smart health environments. Electromagnetics in Advanced Applications (ICEAA), 2015 International Conference on, 2015, IEEE, 752–754.
24. Dhamdhere, A., Sivaraman, V., Mathur, V., Xiao, S., Algorithms for transmission power control in biomedical wireless sensor networks. Asia-Pacific Services Computing Conference, 2008. APSCC’08. IEEE, 2008, IEEE, 1114–1119.
25. Xiao, S., Dhamdhere, A., Sivaraman, V., Burdett, A., Transmission power control in body area sensor networks for healthcare monitoring. IEEE J. Sel. Areas Commun. 27:1 (2009), 37–48.
26. Augustine, R., Electromagnetic Modelling of Human Tissues and its Application on the Interaction Between Antenna and Human Body in the Ban Context. 2009, Université Paris-Est (Ph.D. thesis).
27. Smith, D.B., Miniutti, D., Hanlen, L.W., Characterization of the body-area propagation channel for monitoring a subject sleeping. IEEE Trans. Antennas Propag. 59:11 (2011), 4388–4392.
28. Roberts, N.E., Oh, S., Wentzloff, D.D., Exploiting channel periodicity in body sensor networks. IEEE J. Emerging Sel. Top. Circ. Syst. 2:1 (2012), 4–13.
29. Tang, Q., Tummala, N., Gupta, S.K.S., Schwiebert, L., Communication scheduling to minimize thermal effects of implanted biosensor networks in homogeneous tissue. IEEE Trans. Biomed. Eng. 52:7 (2005), 1285–1294.
30. Aulery, A., Diguet, J.-P., Roland, C., Sentieys, O., Low-complexity energy proportional posture/gesture recognition based on wbsn. Wearable and Implantable Body Sensor Networks (BSN), 2015 IEEE 12th International Conference on, 2015, IEEE, 1–6.
31. Vallejo, M., Recas, J., del Valle, P.G., Ayala, J.L., Accurate human tissue characterization for energy-efficient wireless on-body communications. Sensors 13:6 (2013), 7546–7569.
32. Mamaghanian, H., Khaled, N., Atienza, D., Vandergheynst, P., Compressed sensing for real-time energy-efficient ecg compression on wireless body sensor nodes. IEEE Trans. Biomed. Eng. 58:9 (2011), 2456–2466.
33. Lee, S.Y., Hong, J.H., Hsieh, C.H., Liang, M.C., Chien, S.Y.C., Lin, K.H., Low-power wireless ecg acquisition and classification system for body sensor networks. IEEE J. Biomed. Health Inform. 2168-2194, 19(1), 2015, 236–246, 10.1109/JBHI.2014.2310354.
34. Tobola, A., Korpok, O., Leutheuser, H., Schmitz, B., Hofmann, C., Struck, M., Weigand, C., Eskofier, B., Heuberger, A., Fischer, G., System design impacts on battery runtime of wearable medical sensors. Novk, D., (eds.) Proceedings of International Conference on Mobile and Information Technologies in Medicine and Health, 2014, 1–4.
35. Tobola, A., Streit, F.J., Espig, C., Korpok, O., Sauter, C., Lang, N., Schmitz, B., Hofmann, C., Struck, M., Weigand, C., et al. Sampling rate impact on energy consumption of biomedical signal processing systems. Wearable and Implantable Body Sensor Networks (BSN), 2015 IEEE 12th International Conference on, 2015, IEEE, 1–6.
36. Rincón, F., Recas, J., Khaled, N., Atienza, D., Development and evaluation of multilead wavelet-based ecg delineation algorithms for embedded wireless sensor nodes. IEEE Trans. Inf. Technol. Biomed. 15:6 (2011), 854–863.
37. Koomey, J., Growth in data center electricity use 2005 to 2010., 2011, Analytics Press, Oakland, CA Tech. rep.
38. A. Venkatraman, Global census shows datacentre power demand grew 63% in 2012.
39. Matt Stansberry, J.K., Uptime institute 2013 data center industry survey., 2013.
40. Vallejo, M., Recas Piorno, J., Ayala Rodrigo, J.L., A link quality estimator for power-efficient communication over on-body channels. Embedded and Ubiquitous Computing (EUC), 2014 12th IEEE International Conference on, 2014, IEEE, 250–257.
41. Vallejo, M., Recas, J., Ayala, J.L., Proactive and reactive transmission power control for energy-efficient on-body communications. Sensors 15:3 (2015), 5914–5934.
42. Rasmussen, C.E., Williams, C.K.I., Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), 2005, The MIT Press.
43. MATLAB, version 8.5.0.197613 (R2015a), 2015, The MathWorks Inc., Natick, Massachusetts.
44. Mason, J.W., Ramseth, D.J., Chanter, D.O., Moon, T.E., Goodman, D.B., Mendzelevski, B., Electrocardiographic reference ranges derived from 79,743 ambulatory subjects. J. Electrocardiol. 40:3 (2007), 228–234.
45. A. Lucero, Simulation of batch scheduling using real production-ready software tools, http://www.bsc.es/media/4856.pdf.
46. EuroStat, Electricity price statisticcs, http://ec.europa.eu/eurostat/statistics-explained/index.php/Electricity_price_statistics.
47. A. of Swiss electricity companies VSE-AES, Swiss electricity prices-development 1991–2015. http://www.strom.ch/de/metanavigation/download.html (Accessed 1 June 2016).
48. INOGATE, A review of energy tariffs in INOGATE partner countries, 2015. http://www.inogate.org/ (Accessed 1 June 2016).
49. J.M.P. Monteiro, Cefaleias: estudo epidemiológico e clínico de uma população urbana.
50. Merikangas, K.R., Whitaker, A.E., Isler, H., Angst, J., The Zurich study: XXIII. Epidemiology of headache syndromes in the Zurich cohort study of young adults. Eur. Arch. Psychiatry Clin. Neurosci. 244:3 (1994), 145–152.
51. EuroStat, Population on 1 January by age and sex. http://ec.europa.eu/eurostat/en/web/products-datasets/-/DEMO_PJAN (Accessed 1 June 2016).
52. Celik, Y., Ekuklu, G., Tokuç, B., Utku, U., Migraine prevalence and some related factors in Turkey. Headache: J. Head Face Pain 45:1 (2005), 32–36.
53. Steiner, T., Scher, A., Stewart, W., Kolodner, K., Liberman, J., Lipton, R., The prevalence and disability burden of adult migraine in England and their relationships to age, gender and ethnicity. Cephalalgia 23:7 (2003), 519–527.
54. Pfaffenrath, V., Fendrich, K., Vennemann, M., Meisinger, C., Ladwig, K.-H., Evers, S., Straube, A., Hoffmann, W., Berger, K., Regional variations in the prevalence of migraine and tension-type headache applying the new IHS criteria: the German DMKG Headache Study. Cephalalgia 29:1 (2009), 48–57.
55. Lantéri-Minet, M., Valade, D., Geraud, G., Chautard, M., Lucas, C., Migraine and probable migraine–results of FRAMIG 3, a French nationwide survey carried out according to the 2004 IHS classification. Cephalalgia 25:12 (2005), 1146–1158.
56. Matías-Guiu, J., Porta-Etessam, J., Mateos, V., Díaz-Insa, S., Lopez-Gil, A., Fernández, C., One-year prevalence of migraine in Spain: a nationwide population-based survey. Cephalalgia 31:4 (2011), 463–470.
57. Launer, L.J., Terwindt, G.M., Ferrari, M.D., The prevalence and characteristics of migraine in a population-based cohort the GEM study. Neurology, 53(3), 1999, 537.
58. Dahlöf, C., Linde, M., One-year prevalence of migraine in Sweden: a population-based study in adults. Cephalalgia 21:6 (2001), 664–671.
59. Sjaastad, O., Bakketeig, L., Migraine without aura: comparison with cervicogenic headache. Vågå study of headache epidemiology. Acta Neurol. Scand. 117:6 (2008), 377–383.
60. Russell, M.B., Levi, N., Šaltytė-Benth, J., Fenger, K., Tension-type headache in adolescents and adults: a population based study of 33,764 twins. Eur. J. Epidemiol. 21:2 (2006), 153–160.
61. Bank, J., Marton, S., Hungarian migraine epidemiology. Headache: The Journal of Head and Face Pain 40:2 (2000), 164–169.
62. Lampl, C., Buzath, A., Baumhackl, U., Klingler, D., One-year prevalence of migraine in Austria: a nation-wide survey. Cephalalgia 23:4 (2003), 280–286.
63. Zivadinov, R., Willheim, K., Jurjevic, A., Sepic-Grahovac, D., Bucuk, M., Zorzon, M., Prevalence of migraine in Croatia: A population-based survey. Headache: J. Head Face Pain 41:8 (2001), 805–812.
64. Katsarava, Z., Dzagnidze, A., Kukava, M., Mirvelashvili, E., Djibuti, M., Janelidze, M., Jensen, R., Stovner, L., Steiner, T., et al. Primary headache disorders in the Republic of Georgia Prevalence and risk factors. Neurology 73:21 (2009), 1796–1803.