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IRJET- A Smart Medical Monitoring Systems using Cloud Computing and Internet of Things
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1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2682 A Smart Medical Monitoring Systems using Cloud Computing and Internet of Things Parth Vaishnavi, Ananya Bansal, Ritik Mittal, Parv Rustagi, Shubham Upadhyaya ---------------------------------------------------------------***-------------------------------------------------------------- Abstract - With the ever-growing development of cloud computingandcomputersciencetechnology, theamalgamationofIOT and cloud computing within the medical domain is urgently needed. The prior studies point to a greater on person development of the method, less research on the sector of medical monitoring and dealing with service software have been conducted. Therefore, on this paper, we look and examine the application of cloud computing and the Internet of Thingsinthe field of medicine. Remote monitoring cloud platform architecture version (RMCPHI) setupscientificdata withinthe firstplace and the RMCPHI architecture was analyzed. In the end, the effective PSOSAA algorithm proposed the hospital medical records service cloud system monitoring and management application. Experimental simulation shows that the proposed algorithm outperforms the alternative modern-day algorithms. Similarly, potential research areas are also discussed. Introduction Computing capability extends to objects, sensors and everyday itemsnotnormallyconsideredcomputers, Theterm Internetof Things generally refers to scenarios where network connectivity and allowing these devices to generate, exchange and consume data with minimal human intervention. There is, however, no single, universal definition. IOT implementations use different technical communicationsmodels,eachwithitsowncharacteristics.Fourcommoncommunicationsmodelsdescribed by the Internet Architecture Board include: Device-to-Device, Device-to-Cloud, Device-to-Gateway, and Back-EndData-Sharing. These models highlight the flexibility in the ways that IoT devices can connect and provide value to the user. IOT implementations use different technical communications models, each with its own characteristics. Four common communications models described by the Internet Architecture Board include: Device-to-Device, Device-to-Cloud, Device-to- Gateway, and Back-End Data-Sharing. These models highlight the flexibility in the ways that IoT devices can connect and provide value to the user.The concept of combining computers, sensors, and networks to monitor and control devices has existed for decades. The recent confluence of several technology market trends, however, is bringing the Internet of Things closer to widespread reality. These include Ubiquitous Connectivity, Widespread Adoption of IP-based Networking, Computing Economics, Miniaturization, Advances in Data Analytics, and the Rise of Cloud Computing. The Internet of Things (IoT) paradigm is based on intelligent and self-configuring nodes (things) interconnected in a dynamic and global network infrastructure. It speaks to a standout amongst the most troublesome advances which make the omnipresent and unavoidable registering scene. Web of things is typically alludes to this present reality and seemingly insignificant details restricted capacity and preparing capacity, and the critical issues about dependability,execution,security and protection. Then again, distributed computing has the practicallyboundlesslimitofcapacityandpreparingpowerwhichis a progressively develop innovation in any event to a limited degree to tackletheissue ofa largeportionoftheInternetofthings [1-3]. Subsequently, a novel IT worldview in which Cloud and IoT are two integral advances combined is relied upon to upset both present and future world. We call it Cloud-IoTnewworldview.Thispapersurveys thewritingreconciliationofdistributed computing and Internet of things promising subject research and industry. We have led a survey work to propose an incorporated utilization of Cloud and IoT. We delineate the two points picked up prevalence over the most recent couple of years and the aggregate sum of papers taking care of with these two subjects, individually. The Internet of things is the age of data innovation. This is a noteworthy advancement in the field of data and transformation opportunity [4]. The commission believes that development of the Internet of things application will solve the problems of modern society in the future, [5] make a big contribution. Modern logistics use modern information technology in modernlogisticsmanagement instruction. The three basic requirements are: good service, low cost, speed faster. Health information technology has a wide range of function in modern applications [6].
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2683 The Necessaries of the Integration Both cloud computing and IOT have seen an independent evolution.However,plentyofcommonadvantageisthe resultoftheir integration have been identified in literature, predict the future Fromoneperspective, theInternetofthingscanprofitbycloud practically boundless limit and assets to compensate for the specialized imperatives. In particular, distributed computing can give a viable answer for acknowledge the board of Internet administrations and structure and utilization of things or information applications. Cloud computing can benefit from the Internet of things, on the other hand, byextendingitsscopeto deal with things in the real world more distributed and dynamic way, and to providenewservicesona largenumber ofreal life scenarios. The complementary characteristics of cloud computing and Internet of things is attractive because of the different proposals reported in literature and encouraging Cloud-IoT paradigm shown in Table. Essentially, the Cloud acts as intermediate layer between the things and the applications, where it hides all the complexity andthefunctionalitiesnecessary to implement the latter. Below, we summarize the problem and gain the advantage when using Cloud-IoT paradigm. Internet of Things Cloud Computing Pervasive Ubiquitous Real world Virtual resources Limited computational Unlimited computational Limited storage Unlimited storage Point of convergence Service delivery Big data source Means to manage big data The Applications of the Integration IOT includes by definition a lot of data sources. It creates a lotofunstructuredorsemi-organizedinformationofthe threemajor qualities of the information: volume, speed and assortment. Subsequently this implies the gathering, securing, preparing and representation, chronicle, share, seek a lot of information. Give practically boundlessandon-requeststockpilinglimit,minimal effort, cloud is the most advantageous and cost-efficient answers for arrangementwiththeinformationcreated by theInternet of things [15]. This integration realizes a new convergence scenario, where new opportunities arise for data aggregation, integration, and sharing with third parties [16]. IoT and multimedia technologies have made their entrance in the healthcare field thanks to ambient-assisted living and telemedicine [17-20]. The adoptionofCloudinthisscenarioleadstothe abstraction of technical details, eliminating the need for expertise in, or control over, the technology infrastructure. In addition, it makes the implementation of security (cloud) multimedia health services, to overcome the problem, on the device running a large number of multimedia & security algorithm with limited computing power and small batteries.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2684 RMCPHI Architecture The Remote Monitoring solution accelerator implements an end-to-end monitoring solution for multiple machines in remote locations. The solution combines key Cloud Computing services to provide a generic implementation of the business scenario. You can use the solution as a starting point for your own implementation and customize it to meet your own specific business requirements. Remote monitoring cloud platform of medical information includes body sensors, sensor networks, communication module, family gateway, medical information analysis and processing platform, the medical staff and so on. The figure indicates the information flow chart from sensor information to statistics transferred by means of gateway. A few important research issues include real time location, routing, medium access scheduling, and so forth. Communication device can be soft real-time or difficult real-time. Real-time verbal exchangemoduleistocreatea real-timeabstractionlayerwishesto be disbursed real-time computing and real-time communication strategies inside the dynamic network topology. Cloud assistance offers user applications, such as social networking, environment data analysis and monitoring the patient's health etc. Cloud computing plays an important role in support of different types of operating system platforms and provides high-performance computing technique. The analysis of huge data sets produced by these sensors (Big Data) could allow quick and accurate decision making. For example, productivity improvements can be achievedby analyzingdevice performanceanddegradationforreal-timefeedback on configuration and optimization. This work proposes a Cloud-based architecture for Internet of Things(IoT)applicationsto improve the deployment of smart industrial systems based on remote monitoring and control. By using RMCPHI architecture we demonstrate the usage of the proposed subsystem. This approach includes layers for sensor network data gathering, data transformation between standard protocols, message queuing, real-time data analysis,reportingforfurtheranalysis,andreal-
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2685 time control. Particularly, by using the proposedarchitecture, weremotelymonitored, controlledandprocesseddataproduced by the sensors. Proposed Algorithm for monitoring In the PSO algorithm, a particle is an independent agent which can search the problem space according to the experienceofits own and its companions. As mentioned above, the former is the cognitive part of the particles update formula,andthelatteris the social part. Both parts play keyrole in guiding the particles’search.Therefore,choosingtheappropriatesocial andcognitive guide (g B e s t and p B e s t ) is the key problems of the MOPSO algorithm. Selection of thecognitiveguideconforms thesame rule as that of the traditional PSO algorithm, and the only difference is that the guide should be determined inaccordance with Pareto dominance. In the PSO (particle swarm optimization) algorithm, a particle is an independent agent which can search the problem space according to the experience of its own and its companions. As mentioned above, theformeristhecognitivepartoftheparticles update formula, and the latter is the social part. Both parts play keyroleinguidingtheparticles’search.Therefore,choosingthe appropriate social and cognitive guide is the key problems of the MOPSO algorithm. Selection of the cognitive guide conforms the same rule as that of the traditional PSO algorithm, and the only difference is that the guide should be determined in accordance with Pareto dominance. It is based on the idea of distributing the search procedure among a large number of “agents”, which act independently of each other. Each agent moves through the search space with a simple dynamics, reacting to fictitious forces drawing it towards its own current best solution and the global best solution for the whole swarm. In this way, when an agent finds a better solution than the current global best, it becomes the new global best and all theotheragents react instantly, the swarm is directed towards the new solution. For a set of n particles represented by their positions the velocity for the particle and the step is denoted as the following, and the position is denoted in In the initial speed and position of each particle set random search space. In the process of evolution, the particle evaluationis according to its present location. If the present be replaced by the current solution that includes the positionandfitness. Then, each particle's speed and position using the following two equations will be updated: Parameter q is weight which improves the overall performance. Small weight value tends to promote local exploration and encourage global exploration. Suitable generally provides a balance weight selection asked local and global exploration and reduces the iterative search for the optimal solution of average number. Inordertoachievegoodperformance,weaskedlinear increase the value of the weight from about 0.5 to 0.9 during the operation. From the point of evolutionary process, particle swarm optimization algorithm (PSO) has fast convergence speed in initial phase, but through several iterations,particlestend to the same and the convergence speed becomes slow. Simulatedannealing(SA)algorithmforfastrandomglobal searchability and is easy to implement.
5.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2686 Result The execution time of each task is shown in the Figure. As a whole, the ant colony optimization algorithm and the simulated annealing algorithm spend more time as the number of tasks increases. Ant colony optimization algorithm to perform a task slowly at first, but later when the increase is less than the improved simulated annealing algorithm, because the positive feedback. The experimental results show that the PSOSAA algorithm execution time than other two algorithms. The main reason is that PSOSAA algorithm combines the fast searching ability of simulated annealing technique, which not only can improve the convergence speed, but also avoid falling into local optimum conditions. Thus PSOSAA algorithm shortens the average operation time of tasks.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2687 Average Execution Time Comparison of service applications oT-based healthcare systems can be applied to a diverse array of fields, including care for pediatric and elderly patients Conclusion In this paper we focused on the the model of medical information remote monitoring cloud platform architecture (RMCPHI) was founded in the first place. Then RMCPHI architecture is analyzed. Finally an efficient PSOSAA algorithm was proposedfor the medical monitoring application in the hospital information system. The simulation results show that the proposed algorithm is superior to the analog anneanling algorithm and ant colony optimization algorithmandourproposedschemecan improve the efficiency of about 50%. Some further modifications can be made such as(1)Applyourmethodtonon-coordinate system; (2) Find out more solutions to the scattering and optimization. (3) Conduct more in-depth research on mathematical analysis of our method, is our potential research basis. References [1] F. Tao, “CCIoT-CMfg: cloud computing and Internet of Things based cloud manufacturing service system”, (2014), pp. 1-1. [2] Y. Chen, S. Zhao and Y. Zhai, “Construction of intelligent logistics system by RFID of Internet of things based on cloud computing”, Journal of Chemical and Pharmaceutical Research, vol. 6, no. 7, (2014), pp. 1676-1679. [3] H. Z. Wang, “Management of Big Data in the Internet of Things in Agriculture Based on Cloud Computing”, Applied Mechanics and Materials, vol. 548, (2014). [4] J. Kang, S. Yin and W. Meng, “An Intelligent Storage Management SystemBasedonCloudComputingandInternet ofThings”, Proceedings of International Conference on Computer Science and Information Technology, Springer India, (2014). [5] M. Aazam, “Cloud of Things: Integrating Internet of Things and cloud computing and the issues involved”, AppliedSciences and Technology (IBCAST), 2014 11th International Bhurban Conference on IEEE, (2014). [6] J. H. Li, S. Zhao and Y. R. Wang, “Using Cloud Computing and the Internet of Things to Construct Vehicle Condition Fault Diagnosis Platform”, Applied Mechanics and Materials, vol. 574, (2014). [7] Y. Mao, “Applied Mathematics and Algorithms for Cloud Computing and IoT”, (2014). [8] Y. Wang and X. Wang, “The novel analysis model of cloud computing based on RFID internet of things”, Journal of Chemical and Pharmaceutical Research, vol. 6, no. 6, (2014), pp. 661-668.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2688 [9] D. Talia, “Towards Internet Intelligent Services Based onCloudComputingandMulti-Agents”,AdvancesontotheInternetof Things. Springer International Publishing, (2014), pp. 271-283. [10] F. Bonomi, “Fog Computing: A Platform for Internet of Things and Analytics”, Big Data and InternetofThings: ARoadmap for Smart Environments. Springer International Publishing, (2014), pp. 169-186. 11] J. Soldatos, “Design principles for utility–driven services and cloud–based computingmodellingfortheInternet ofThings”, International Journal of Web and Grid Services, vol. 10, no. 2, (2014), pp. 139-167. [12] F. Xie and C. Z. Liang, “Research of Internet of Things Based on Cloud Computing”, Applied Mechanics and Materials, vol. 443, (2014), pp. 589-593. [13] S. Fang, “An Integrated System for Regional Environmental Monitoring and Management Based on Internet of Things”, IEEE Trans. Industrial Informatics, vol. 10, no. 2, (2014), pp. 1596-1605. [14] H. J. Ding, “Traffic Flow Data Collection and Signal Control System Based on Internet of Things and Cloud Computing”, Advanced Materials Research, vol. 846, (2014), pp. 1608-1611. [15] A. R. Biswas and R. Giaffreda, “IoT and cloud convergence: Opportunities and challenges”, Internet of Things (WF-IoT), IEEE World Forum on IEEE, (2014). [16] G. B. Gebremeskel, Y. Chai and Z. Yang, “The Paradigm of Big Data for Augmenting Internet of Vehicle into the Intelligent Cloud Computing Systems”,InternetofVehicles–TechnologiesandServices.SpringerInternational Publishing,(2014),pp.247- 261. [17] S. Kiranyaz, T. Ince and M. Gabbouj, “Particle Swarm Optimization”, Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition, Springer Berlin Heidelberg, (2014), pp. 45-82. [18] S. U. Kolte, “A Particle Swarm Optimization Tool for Decoupling Automotive Power train Torque Roll Axis”, No. 2014-01- 1687. SAE Technical Paper, (2014). [19] M. R. Tanweer and S. Sundaram, “Human cognition inspired particle swarm optimization algorithm”, Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), IEEE Ninth International Conference on IEEE, (2014). [20] H.-D. He, W.-Z. Lu and Y. Xue, “Prediction of particulate matter at street level using artificial neural networkscouplingwith chaotic particle swarm optimization algorithm”, Building and Environment, vol. 78, (2014), pp. 111-117.
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