Crptography based Lifi for Patient Privacy and Emergency Health Service Using IOT
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Abstract
Medical care is one such region, where WIFI is as yet not utilized as the electromagnetic waves influences patients with sicknesses like neurological problems, diseases and so forth. Accordingly, LIFI can be respected the following large thing, as it represents no gamble to patients and offers more advantages than WIFI, such as faster speeds and a larger spectrum. The only issue that hospitals have while exchanging data through it is ensuring confidentiality. The methodology proposed here leverages Secure Hash Algorithms to give maximum security as a solution to this challenge. The Secure Hash Algorithm is a bonus feature that is mostly utilised for authentication. IoT connects physical devices such as sensors and actuators to networks. The programming routines can be visualised from any location thanks to cloud storage. These algorithms can be employed in a variety of applications, including smart homes, digital technologies, and banking systems. This research presents a model that takes into account a human's heart rate, glucose level, and temperature. In the even to fan emergency, adjacent hospitals are alerted to the patient's condition, allowing them to provide timely and correct care. This will save you from having to go to the hospital. Temperature, blood pressure, heart rate, gas sensor, and fall detection are among the vital signs monitored by the system. An Arduino controller and a GSM900Amodule make up the system design. The monitored values can be supplied via mobile phones, and if an abnormal state is detected, the buzzer is activated, and the information is communicated to the concerned members via the mobile app.
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Kearns,W.D.; Fozard, J.L.; Becker, M.; Jasiewicz, J.M.; Craighead, J.D.; Holtsclaw, L.; Dion, C. Path Tortuosity in Everyday Movements of Elderly Persons Increases Fall Prediction beyond Knowledge of Fall History, Medication Use, and Standardized Gait and Balance Assessments. J. Am. Med. Dir. Assoc. 2012, 13, 665.e7–665.e13.
Taylor, J.; Coates, E.; Brewster, L.; Mountain, G.;Wessels, B.; Hawley, M.S. Examining the use of telehealth in community nursing: Identifying the factors affecting frontline staff acceptance and telehealth adoption. J. Adv. Nurs. 2015, 71, 326–337.
Kang, Y.; McHugh, M.D.; Chittams, J.; Bowles, K.H. Utilizing Home Healthcare Electronic Health Records for Telehomecare Patients with Heart Failure: A Decision Tree Approach to Detect Associations with Rehospitalizations. CIN Comput. Inform. Nurs. 2016, 34, 175–182.
Jarrett, E.; Amy, P.; Tsien, S.S.; Neil, C.; Loretta, S.; Cindy, C.; Michele, M.; Beth,E.C. Remote Health Monitoring for Older Adults and Those with Heart Failure: Adherence and System Usability. Telemed.e-Health 2016, 2, 480–488.
Liu, L.; Stroulia, E.; Nikolaidis, I.; Miguel-Cruz, A.; Rincon, A.R. Smart homes and home health monitoring technologies for older adults: A systematic review. Int. J. Med. Inform. 2016, 91, 44–59.
Institute of Electrical and Electronics Engineers. Standard for Medical device communication–Part 00000: Framework and overview. IEEE Std. 2008, 1073.Available online:https://standards.ieee.org/ findstds/ standard/1073-1996.html (accessed on 7 March 2017).
Institute of Electrical and Electronics Engineers. Health informatics-Personal health device communication. IEEE Std. 2008, 11073–20601. Available online: https://standards.ieee.org/findstds/standard/11073-20601-2008.html (accessed on 3 March 2017).
Pak, J.; Park, K. A smart personal activity monitoring system based on wireless device management methods. Commun.Comput. Inf. Sci. 2011, 184, 335–342.
Pak, J.; Park, K. UbiMMS: An ubiquitous medication monitoring system based on remote device management methods. Healthc. Inf. Manag. J. 2012, 41, 26–30. [CrossRef]
Pak, J.; Park, K. Advanced pulse oximetry system for remote monitoring and management. Biomed. Res. Int. 2002.
Park, K.; Lim, S. A multipurpose smart activity monitoring system for personalized health services. Inf. Sci. 2015, 314, 240–254.
oneM2M. Functional Architecture (TS-0001-V1.6.1). Available online: http://www.onem2m.org (accessed on 10 December 2015).
oneM2M. Service Layer Core Protocol Specification (TS-0004-V1.0.1). Available online: http:www.inem2m. org (accessed on 10 December 2015).
Grieco, L.A.; Alaya, M.; Montei, T.; Drira, K. Architecting information centric ETSIM2M systems. In Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMWorkshops), Budapest, Hungary, 24–28 March 2014; pp. 211–214.
Suo,H.;Wan,J.;Zou,C.;Liu,J.Securityintheinternetofthings:Areview.InProceedingsofthe2012InternationalConferenceonComputerScienceandElectronicsEngineering,ICCSEE2012,Hangzhou,China,23–25March2012;Volume3,pp.648–651.
Glombitza, N.; Mietz, R.; Römer, K.; Fischer, S.; Pfisterer, D. SelfDescription and Protocol Conversion for a Web of Things. In Proceedings of the 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC), Newport Beach, CA, USA, 7–9 June 2010; pp. 229–236.
Classical Energy Detection Method For Spectrum Detecting in Cognitive Radio Networks by using robust augmented threshold techniques, B.Sarala, D.Rukmani Devi, D.S. Bargava, cluster computing,Springer - the journal of networks software Tools and applications, Springer, 22,11109-11118 ( Sep 2019) Impact factor -3.458, science citation index, expanded [scvisearh], Scopus, Google scholar, WOS, pp. 1-10. https://doi.org/ 10.1007/s10586-017-1311-8, volume 22, issue -5, 1.9.2019., page 11109-11118, cited by 7, ISSN no : 1386-7857
Spectrum energy detection in cognitive radio networks based on a novel adaptive threshold energy detection method, B.Sarala , S.Rukmani Devi &J.JoselinJeyaSheela , computer communications, Elsevier, volume 152, 9 Jan 2020, page 1-7, Science direct, Scopus ,SCI ISSN- 0140 – 3664 https://doi.org/10.1016/j.comcom. 2019.12.058, Impact Factor: 4.08, cited by 28.
Simulation and comparison of single and differential ended CG-CS LNA for CognitiveRadio, B.Sarala, S.Rukmani Devi, Jasmine Hepzibah, P.Gunasekhar , J. JoselineJeyaSheela- International journal of wavelets multiresolution and Information Processing, April 2021, Page no: 2150013, SCIE,Scopus.https://doi.org/10.1142/ S0219691321500132, Vol. 19, No. 05, 2150013 (2021),impact factor: 1.04, cited by 1.
Lab view based non-invasive single channel field electrocardiogram extraction IEEE, Suganthy M, Immaculate Joy S, B.Sarala, International conference on energy system & Information Processing (ICESIP), June 2019, cited by 4
Vehicle Seat Vacancy Identification Using Image Processing Technique, Darwin Nesakumar A, Suresh T , Kanimozhi P, Lokeshwari A , Manjuparkavi T , B.sarala, P.Mugila - AIP Publishing,2519,050023(2022) – Scopus,WoS, https://doi.org/10.1063/5.0109641
Automated Seed Sowing and Watering Robot using Wireless Sensor Network,Dr.M.Somasundaram , A.Naveen Kumar, B.NikhilVamsi, B.VishalChowdary,S.P.Karthikeyan, B.Sarala-AIP Publishing – Scopus, WOS,2519,050027(2022), https://doi.org/10.1063/5.0109648
R.Sujatha, Mahaboob Basha.S, B.Sarala, J.Jasmine Hepzhipah, N.G..Praveena, IoT Enabled Smart Logistics Vehicle using Semantic Communication, International journal of Intelligent Systems & Applications in Engineering,vol10,issue4https://ijisae.org/index.php/IJISAE/article/view/2317, (accessed on 24 December 2022)
J.Jasmine Hephzipah,P.Thirumurugan”Performance Analysis of Meningioma Brain Tumor Detection System Using Feature Learning Optimization and ANFIS Classification Method”IETE Journal of Research,volume no.68,Issue No.2.
J.Jasmine Hepzhipah, Johnpeter,Thirumurugan PonnuchamyComputer aided automated detection andclassification of brain tumors using CANFIS classification method, 28 March 2019, 29(4)