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Title FOG-ORIENTED SECURE AND LIGHTWEIGHT HEALTHCARE DATA AGGREGATION IN INTERNET OF THINGS

Author(s) Muhammad Azeem

Abstract Internet of things (IoT) is becoming an essential research concern because of its broad applicability in the real world. IoT-enabled wireless sensors are deployed to collect the information of patient health. In a healthcare scenario, sensor devices are placed on the body of the patient. Smart healthcare devices securely aggregate healthcare information and forward healthcare data to the base station. Sensor nodes have limited energy, computational, and storage capabilities for communication. Although, several aggregation techniques are utilized to reduce communication costs in healthcare data transmission. However, secure and lightweight data transmission of healthcare data is the main concern. This thesis explored the existing secure data aggregation schemes and presents an appropriate solution for challenging issues of existing schemes. This thesis presents an Efficient and Secure Data Transmission and Aggregation (ESDTA) scheme that provides secure and lightweight data transmission. A secure message aggregation (SMA) algorithm is employed to aggregate data at Mobile nodes (MN). Healthcare parameter values are aggregated by using the colon as a delimiter. Moreover, a secure message decryption (SMD) algorithm is employed at the fog node (FN). The proposed algorithm provides lightweight and secure data transmission by applying symmetric key-based data encryption and removing redundant healthcare parameter values from transmitted data. The simulation scenario for ESDTA proposed scheme is implemented through simulation tool NS2.35. We have compared ESDTA with existing related studies EHDA, SPPDA, APPA, and ASAS. The proposed scheme is compared with the related research studies and provide 23% better communication cost in terms of bytes exchange, 19% better computational cost, 15% better energy utilization, 57% better storage, and 32% less number of compromised bytes. Hence, results prove the sovereignty of the proposed work

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2021-07-06

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