An Intelligent Architectural Framework for Fog Computing Supported IoT Applications.

To detect emergencies and inform medical personnel about the status of the patient. Physician has to frequently visit the patient and asses the parameter like Temperature ,Blood pressure , Pulse and Heart rate to know the current status of the patient.

1. An Intelligent Architectural Framework for Fog Computing Supported IoT Applications F. Mohamed shakir Department of Information Technology Thiagarajar College of Engineering

2. MOTIVATION In healthcare, the patient monitoring is one of the pivotal application as it deals with human life. The patient physiological parameters like Heart rate, Pulse rate, Temperature, Blood pressure are monitored to know the condition of the patient. The criticality are occurred only if these parameters are not known to the physician.

3. PROBLEM STATMENT To detect emergencies and inform medical personnel about the status of the patient. Physician has to frequently visit the patient and asses the parameter like Temperature ,Blood pressure , Pulse and Heart rate to know the current status of the patient. To avoid this problem, data are gathered from patient and transmitted to fog layer for quick processing rather than sending huge data to the cloud.

4. FOG COMPUTING Fog computing is the computing paradigm, which extends cloud nearer to the devices. The primary aim of fog is to solve the problems faced by the cloud computing during the data processing. The characteristics of fog computing are Edge location Mobility support Real time interaction Large number of nodes The advantage of fog computing are Low latency Quick decision making Store confidential data on local servers

5. LITERATURE SURVEY Title of the paper Fog Assisted- IoT Enabled Patient Health Monitoring in Smart Homes Year of Publication 2018 Author Names Prabal Verma and Sandeep K. Sood Source IEEE Internet of Things Volume:5 Extract of the paper In this paper they used fog computing at the gateway. Event triggering based data transmission methodology is used to process real time patient’s data. For the patient classification they used Bayesian Belief Network. Findings The issue in this paper is, Information that is to be delivered to the responder from the cloud layer is challenging. Issues in sensor can make difficulty in capturing the data

6. CONT.. Title of the paper Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues Year of Publication 2018 Author Names Tasneem s. j. Darwish and Kamalrulnizam Abu bakar Source IEEE Access, Volume:6 Extract of the paper In this paper they merged three dimensions including intelligent computing (i.e. cloud and fog computing) dimension, real-time big data analytics dimension, and IoV dimension. Fog computing complements the cloud computing by providing distributed, intelligent, and fast data processing at the network edge Findings The ITS concept was introduced to increase road safety, improve transportation systems efficiency, and preserve our environment. ITS applications are delay-sensitive and processing the data at the cloud centers creates long delays.

7. CONT .. Title of the paper IFCIoT : Integrated Fog Cloud IoT : A novel architectural paradigm for the future Internet of Things Year of Publication 2017 Author Names Munir , Arslan , Prasanna Kansakar , and Samee U. Khan Source IEEE Consumer Electronics Magazine , Volume 3. Extract of the paper This work presents a IFCIoT architecture by which fog act as an intermediate layer between cloud and IoT . Fog comprises of fog nodes like base station, gateway, smart routers. The entire fog deployment is located locally. A fog node in the IFCIoT architecture manages all IoT devices within its radio network. The IoT devices typically leverage radio-access networks to communicate with the fog. Findings The proposed architecture increased the performance, energy efficiency, reduced latency and scalability. To adapt the workload reconfigurable fog-node architecture is proposed.

8. CONT .. Title of the paper Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach Year of Publication 2018 Author Names Amir M. Rahmani , Tuan Nguyen Gia , Behailu Negash , Arman Anzanpour , Iman Azimi , Mingzhe Jiang , Pasi Liljeberg Source Elsevier Future generation Computer Systems, Vol 80 Extract of the paper This paper proposed a placement of Smart gateway by which it needs a bridging between the sensor and the internet. The bridging is the gateway which is at the edge of the network to perform functionalities. This gateway will have some control over the sensor data that are transmitted through the internet. Findings One more tier adds complexity in terms of integration. High level services is offered by Smart gateway to sensors and end users at the edge of the network.

9. CONT .. Title of the paper Edge cognitive computing based smart healthcare system Year of Publication 2018 Author Names Chen, Min, et al Source Elsevier Future generation Journal , Volume:86 Extract of the paper This paper proposed a Edge-Cognitive-Computing-based (ECC-based) smart-healthcare system. This system is used to monitor and analyse the physical health of users using cognitive computing . It optimizes the computing resources by resource allocation of the whole edge computing network comprehensively according to the health-risk grade of each user. Findings This ECC based system improves the survival rate of patients in emergency situation. This system solved the problem of inflexible network resource deployment.

10. FINDINGS Damage in sensors leads to difficulty in capturing the data Any damage in fog node leads to interruption of communication between the layers The data communication between Application layer and cloud takes more response time The framework is very useful for senior citizens and disabled people

11. PROPOSED SYSTEM The objective is to capturing the data from Application layer and send to the fog layer which consists of fog nodes. The fog nodes can be Wi-Fi routers, gateway devices, base stations etc. The Machine learning algorithms and decision-making system are deployed on those fog nodes based on its capability. Based on the results of processing, the alert or notification kind of output is delivered through actuators to physician or care taker and data for long term and periodical analysis is sent to the cloud layer which is the top most layer

12. LAYERED FRAMEWORK FOR PROPOSED SYSTEM

13. APPLICATION LAYER The role of application layer is to capture all the real time data for various applications. This can be done by placing various kinds of sensors. Then the collected data are transmitted through Bluetooth or W i-Fi to the intermediate layer called fog layer.

14. FOG LAYER The fog layer consists of various fog nodes. Each fog node takes responsibility to process the data. In our proposed framework, AI methodologies and decision making systems are deployed on fog nodes. Based on the results, alert or notification kind of output are delivered through the actuators to the cloud layer.

15. CLOUD LAYER This is the top most layer in our proposed framework. The cloud layer comprises of centralized data centers. The data of various applications are stored in the servers. The authority person can fetch the data from the cloud for further processing.

16. USE CASE

17. OBJECTIVES To have assessment of important physiological variables of elderly patients, disabled patients or patient with chronic disease during critical periods of their biological functions. It is necessary to know their actual value or trend of change. In critical cases, Artificial Intelligence (AI) techniques can be applied to manage the patient data.

18. CONCLUSION W e proposed an intelligent architectural framework for fog computing that can adapt according to the application requirements . The main motive is to overcome the drawback of cloud which takes more response time. The health monitoring is the critical application when compared to other application, so fog layer is added as the intermediate layer for the quick processing.

19. FUTURE WORK T he proposed work can be extended by incorporating machine learning algorithm in the fog layer for decision making during emergency situation and further notification to the care takers as well as the physicians.

20. REFERENCES [ 1 ]. Munir , Arslan , Prasanna Kansakar , and Samee U. Khan. " IFCIoT : Integrated Fog Cloud IoT : A novel architectural paradigm for the future Internet of Things." IEEE Consumer Electronics Magazine 6, no. 3 (2017): 74-82 . [ 2]. OpenFog Consortium. (2017, Apr.). OpenFog . [Online]. Available: http://www.openfogconsortium.org / [ 3].S . Yi, Z. Hao , Z. Qin, and Q. Li, “Fog computing: Platform and applications,” in Proc. IEEE Workshop on Hot Topics in Web Systems and Technologies ( HotWeb ), Nov. 2015, pp. 73–78 [ 4].M . Aazam and E.-N. Huh, “Fog computing: The cloud- IoT /IoE middleware paradigm,” IEEE Potentials, vol. 35, no. 3, pp. 40–44, May 2016 . [ 5].Chen , Min, et al. "Edge cognitive computing based smart healthcare system." Future Generation Computer Systems (2018).

21. REFERENCES [6] .Lu , Jingyang , Lun Li, Genshe Chen, Dan Shen, Khanh Pham, and Erik Blasch . "Machine learning based Intelligent cognitive network using fog computing." In Sensors and Systems for Space Applications X, vol. 10196, p. 101960G. International Society for Optics and Photonics, 2017 [7].Shi , Weisong , Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu. "Edge computing : Vision and challenges." IEEE Internet of Things Journal 3, no. 5 (2016): 637-646 . [8].Wen , Zhenyu , Renyu Yang, Peter Garraghan , Tao Lin, Jie Xu, and Michael Rovatsos . "Fog orchestration for internet of things services." IEEE Internet Computing 21, no. 2 (2017): 16-24 . [9]. Darwish , Tasneem SJ, and Kamalrulnizam Abu Bakar. "Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues." IEEE Access 6 (2018): 15679-15701 . [10]. Verma , Prabal, and Sandeep K. Sood . "Fog Assisted- IoT Enabled Patient Health Monitoring in Smart Homes." IEEE Internet of Things Journal (2018 ). [11 ].G . Eysenbach . What is e-health? J. Med. Internet Res. Vol.3 no. 2, 2001.doi:10.2196/jmir.3.2.e20

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