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Compressive sensing based secure data aggregation scheme for IoT based WSN applications

Ahmed SalimDept. of Computer Science, College of Science and Arts, Al-methnab, Qassim University, Buridah, Al-mithnab, KSAAhmed IsmailComputer Science Dept., Faculty of Computers and Artificial Intelligence, Benha University, Benha, EgyptWalid OsamyComputer Science Dept., Faculty of Computers and Artificial Intelligence, Benha University, Benha, EgyptAhmed M. KhedrComputer Science Dept., University of Sharjah, Sharjah, UAE
PLoS ONEjournal2021en
ABI

Аннотация

Compressive Sensing (CS) based data collection schemes are found to be effective in enhancing the data collection performance and lifetime of IoT based WSNs. However, they face major challenges related to key distribution and adversary attacks in hostile and complex network deployments. As a result, such schemes cannot effectively ensure the security of data. Towards the goal of providing high security and efficiency in data collection performance of IoT based WSNs, we propose a new security scheme that amalgamates the advantages of CS and Elliptic Curve Cryptography (ECC). We present an efficient algorithms to enhance the security and efficiency of CS based data collection in IoT-based WSNs. The proposed scheme operates in five main phases, namely Key Generation, CS-Key Exchange, Data Compression with CS Encryption, Data Aggregation and Encryption with ECC algorithm, and CS Key Re-generation. It considers the benefits of ECC as public key algorithm and CS as encryption and compression method to provide security as well as energy efficiency for cluster based WSNs. Also, it solves the CS- Encryption key distribution problem by introducing a new key sharing method that enables secure exchange of pseudo-random key between the BS and the nodes in a simple way. In addition, a new method is introduced to safeguard the CS scheme from potential security attacks. The efficiency of our proposed technique in terms of security, energy consumption and network lifetime is proved through simulation analysis.

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