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E. Umamaheswari

Bio: E. Umamaheswari is an academic researcher from VIT University. The author has contributed to research in topics: Cloud computing & Data security. The author has an hindex of 3, co-authored 17 publications receiving 46 citations.

Papers
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Proceedings ArticleDOI
01 Aug 2018
TL;DR: The security of the health care data is enhanced using Fog computing, where a digital human temperature sensor device is built using DS18B20 temperature sensor and data collected is being encrypted in fog node using Advance Encryption Standard(AES) algorithm and it is send to cloud.
Abstract: Internet of Things (IoT) is the interconnection of physical objects or devices that can transmit and receive data through the internet without human involvement. With the advancement in IoT devices particularly in healthcare sector, huge amount of data is collected from different sensors and all this data are transferred and stored in cloud. It becomes difficult to handle such huge amount of data in cloud specially the healthcare data where it requires real time data computation and storage. Security of the data is also major challenge in cloud. Fog computing is the answer to overcome the challenges. Fog nodes works at the edge side and enhances data security, accuracy, consistency and reduces the latency rate which is an important factor for application like medical data. Implementation work is also described in the paper where a digital human temperature sensor device is built using DS18B20 temperature sensor. The data collected from it is being encrypted in fog node using Advance Encryption Standard(AES) algorithm and it is send to cloud. Therefore, the security of the health care data is enhanced using Fog computing.

20 citations

Journal ArticleDOI
TL;DR: To achieve efficient resource scaling, proposed approach lease advantages of fuzzy time series and machine learning algorithms and is able to reach effective resource scaling mechanism with better results.
Abstract: Resource Scaling is one of the important job in cloud environment while adapting resource configurations due to elasticity mechanism. In the view of cloud computing, resource scaling mechanism hold the assurance of QoS (Quality of Service), So, one of the key challenging task in cloud environment is, resource scaling. Effective scaling mechanism gives an optimal solutions for computational problems while achieving QoS and avoiding SLA (Service Level Agreement) violations. To enhance resource scaling mechanism in cloud environment, predicting future workload to the each application in different manners like number of physical machines, number of virtual machines, number of requests and resource utilization etc., is an essential step. According to the prediction results, resource scaling can be done in the right time, while preventing QoS dropping and SLA violations. To achieve efficient resource scaling, proposed approach lease advantages of fuzzy time series and machine learning algorithms. The proposed approach is able to reach effective resource scaling mechanism with better results.

6 citations

Book ChapterDOI
O. V. Gnana Swathika1, G. Kanimozhi1, E. Umamaheswari1, Soj Rujay1, Soudeep Saha1 
TL;DR: An IoT-based energy management system with Web server-type manual control, serial communication between microcontrollers such as Arduino Uno and NodeMCUs, sensors and computational intelligence is demonstrated.
Abstract: With increasing energy demand and the necessity to fulfill the energy requirement, it is mandatory to increase the energy generation. However, shortage of supply resources stands as a blockade in this present scenario. Hence, an efficient energy management system is required. This paper demonstrates a working prototype of intelligent energy management system. It is an IoT-based energy management system with Web server-type manual control. It involves serial communication between microcontrollers such as Arduino Uno and NodeMCUs, sensors and computational intelligence. The sensors used in this case are LDR, thermistor, and PIR motion sensor. The system includes LED lights and fans as load. The raw data from the sensors is read by the Arduino and it serially communicates the raw data to both the NodeMCUs. The data is then converted to standard formats and uploaded to the ThingSpeak cloud server for data logging and analysis by one of the NodeMCU. Using the second NodeMCU, a Web server is created and used for manual control of the loads. This Wi-Fi server sends data to the NodeMCU as per the user input from the browser and this NodeMCU controls the load accordingly. A current sensor is also connected along the supply line for power measurement. The current sensor is an ACS712 20 A sensor which is connected to the NodeMCU and is responsible for uploading data to the ThingSpeak cloud server. The working model has three rooms for demonstration purpose but can be increased accordingly as per need with certain changes in system hardware model.

4 citations

Journal ArticleDOI
TL;DR: The proposed authentication protocol is named as, Adaptive XOR, hashing and Encryption Key Exchange (AXHE) protocol, which is the combination of the functions, such as encryption function, hashing function, and adaptive XOR function.
Abstract: Internet of Things (IoT) plays a prominent role in health-care of patients, which assist the physicians and patients through the assistance in effective decision-making and additionally, in the medical field, IoT plays a significant role in real-time monitoring of the patients. Even though the data provided by the IoT devices ensure the effective decision-making, the data is susceptible to the network attacks. Thus, the paper proposes an authentication protocol for enabling the secure data transmission in IoT based on three functions, such as encryption function, hashing function, and adaptive XOR function. The proposed authentication protocol is named as, Adaptive XOR, hashing and Encryption Key Exchange (AXHE) protocol, which is the combination of the functions, such as encryption function, hashing function, and adaptive XOR function. The protocol ensures the security in the communication through two successive phases, such as registration and authentication of the user, where the user name, password, public keys, private keys, and security factor are employed. The authentication is progressed as seven levels and whenever the security factor matches, the user is authenticated and the communication continues. The analysis of the proposed AXHE is performed using 50 and 100 nodes in the presence of DOS and black hole attacks, which acquires the detection rate, throughput, and detection delay of 0.3859, 0.32, and 6.535 s, respectively.

4 citations


Cited by
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Journal Article
TL;DR: In this paper, a new time-series forecasting model based on the flexible neural tree (FNT) is introduced. But the model is not suitable for time series forecasting and it is difficult to select the proper input variables or time-lags for constructing a time series model.
Abstract: Time-series forecasting is an important research and application area. Much effort has been devoted over the past several decades to develop and improve the time-series forecasting models. This paper introduces a new time-series forecasting model based on the flexible neural tree (FNT). The FNT model is generated initially as a flexible multi-layer feed-forward neural network and evolved using an evolutionary procedure. Very often it is a difficult task to select the proper input variables or time-lags for constructing a time-series model. Our research demonstrates that the FNT model is capable of handing the task automatically. The performance and effectiveness of the proposed method are evaluated using time series prediction problems and compared with those of related methods.

272 citations

Journal ArticleDOI
TL;DR: A survey of different data collection and secure transmission schemes where fog computing based architectures are considered is presented in this article, where fog assisted smart city, smart vehicle and smart grids are also considered that achieve secure, efficient and reliable data collection with low computational cost and compression ratio.
Abstract: Internet of medical things (IoMT) is getting researchers’ attention due to its wide applicability in healthcare Smart healthcare sensors and IoT enabled medical devices exchange data and collaborate with other smart devices without human interaction to securely transmit collected sensitive healthcare data towards the server nodes Alongside data communications, security and privacy is also quite challenging to securely aggregate and transmit healthcare data towards Fog and cloud servers We explored the existing surveys to identify a gap in literature that a survey of fog-assisted secure healthcare data collection schemes is yet contributed in literature This paper presents a survey of different data collection and secure transmission schemes where Fog computing based architectures are considered A taxonomy is presented to categorize the schemes Fog assisted smart city, smart vehicle and smart grids are also considered that achieve secure, efficient and reliable data collection with low computational cost and compression ratio We present a summary of these scheme along with analytical discussion Finally, a number of open research challenges are identified Moreover, the schemes are explored to identify the challenges that are addressed in each scheme

104 citations

Journal ArticleDOI
TL;DR: In this article , the authors identify the role of Internet of Medical Things (IoMT) applications in improving healthcare system and analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system.
Abstract: Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new 'Smart' healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system. An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered. The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently.

50 citations

Journal ArticleDOI
TL;DR: IoT-based sensors upon certain fixation in terms of categorization and proper orientation can be very useful to make a smarter human society, this work can conclude.

49 citations

Journal ArticleDOI
TL;DR: A public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer is proposed.
Abstract: The recent developments in fog computing architecture and cloud of things (CoT) technology includes data mining management and artificial intelligence operations. However, one of the major challenges of this model is vulnerability to security threats and cyber-attacks against the fog computing layers. In such a scenario, each of the layers are susceptible to different intimidations, including the sensed data (edge layer), computing and processing of data (fog (layer), and storage and management for public users (cloud). The conventional data storage and security mechanisms that are currently in use appear to not be suitable for such a huge amount of generated data in the fog computing architecture. Thus, the major focus of this research is to provide security countermeasures against medical data mining threats, which are generated from the sensing layer (a human wearable device) and storage of data in the cloud database of internet of things (IoT). Therefore, we propose a public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer. The blockchain technology approach also helps to mitigate these issues of latency, centralization, and scalability in the fog model.

34 citations