scholarly journals A Wearable Patient Healthcare Monitoring System Using IoT and Cloud Computing Based Security

Author(s):  
Chitra Selvi S

Now-a-days, a developing number of individuals at some point of a developing international locations like India forces to seem for brand spanking new answers for the persistent tracking of fitness check-up for stable data. It’s emerge as a need to go to hospitals frequently for doctor’s consultation, which has Growth to be financially associated and a time ingesting process. To beat this situation, we endorse a design to observe the patient’s fitness situations like heartbeat, temperature, ECG and BP and ship the message to guardian the use of GSM. Within the recent improvement of internet of factors (IoT) makes all objects interconnected and cloud been diagnosed due to the fact the subsequent technical revolution and now not secure for patient data. Patient monitoring is one a few of the IoT application to watch the affected person fitness status to collect facts to security for both medical doctor and patients Internet of things makes clinical equipment greater efficient with the aid of allowing actual time tracking of health in security privateness of the patient. Using IoT doctor can continuously monitor the patient’s cloud the usage of protection on his smartphone and also the affected person history could be stored on the web server and health practitioner can get entry to the statistics on every occasion wished from anywhere.

2018 ◽  
Vol 3 (2) ◽  
pp. 74
Author(s):  
Helina Apriyani ◽  
Sismadi Sismadi ◽  
Sefrika Sefrika

AbstrakInternet of things (IoT) adalah sebuah konsep yang menghubungkan komputer dan perangkat elektronik melalui internet dan dapat dikendalikan dari jarak jauh. Konsep terpenting dalam Internet of things (Iot) adalah modul sistem informasi, koneksi internet dan penyimpanan datanya dalam cloud computing. Konsep ini memiliki manfaat besar dalam perkembangan usaha dan kelangsungan bisnis perusahaan dimana hampir semua bidang menggunakan IoT untuk dapat bersaing di pasaran. Indonesia merupakan sebuah negara yang dianugerahi kekayaan alam yang melimpah ruah. Salah satu komoditi unggulan adalah produk pertanian. Penelitian ini bertujuan untuk membantu para petani dalam memasarkan produknya melalui e-commerce dengan menggunakan konsep Internet of things IoT. Metode penelitian dengan menggunakan metode incremental.  Incremental digunakan untuk mendesai produk, kemudian  diimplementasikan, dan diuji secara bertahap (setiap modul akan ditambahkan bertahap) hingga produk selesai. Hasil penelitian ini digunakan untuk membantu petani di Kabupaten Bogor untuk mendistribusikan penjualannya secara luas, meningkatkan revenue dan memutus rantai panjang proses penjualan. Kata kunci— sistem penjualan, incremental, Internet of Things (IoT), produk pertanian, Kabupaten Bogor Abstract Internet of things (IoT) is a concept that connects computers and electronic devices via the internet and can be controlled remotely. The main concept in Internet of things (IoT) is information systems, internet connections and data storage in cloud computing. This concept has great benefits in the efforts and efforts used to use IoT to be able to compete in the market. Indonesia is a country that is blessed with abundant natural resources. One of the leading commodities is agricultural products. This study aims to help farmers market their products through e-commerce using the IoT Internet of things concept. Research method using incremental method. Incremental to design the product, then implemented, and gradually delay (each module will be added gradually) until the product is finished. The results of this study are to help farmers in Bogor Regency to distribute sales widely, increase revenue and break the sales process. Keywords—sales system, incremental, Internet of Things (IoT), agricultural products, Kabupaten Bogor


Author(s):  
Ramandeep Kaur ◽  
Navpreet Kaur

The cloud computing can be essentially expressed as aconveyance of computing condition where distinctive assets are conveyed as a support of the client or different occupants over the web. The task scheduling basically concentrates on improving the productive use of assets and henceforth decrease in task fruition time. Task scheduling is utilized to allot certain tasks to specific assets at a specific time occurrence. A wide range of systems has been exhibited to take care of the issues of scheduling of various tasks. Task scheduling enhances the productive use of asset and yields less reaction time with the goal that the execution of submitted tasks happens inside a conceivable least time. This paper talks about the investigation of need, length and due date based task scheduling calculations utilized as a part of cloud computing.


Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).


Author(s):  
Marlon C. Domenech ◽  
Leonardo P. Rauta ◽  
Marcelo Dornbusch Lopes ◽  
Paulo H. Da Silva ◽  
Rodrigo C. Da Silva ◽  
...  

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