Radhakrishnan Maivizhi

@nitpy.ac.in

Faculty (On Contract), CSE
National Institute of Technology Puducherry, Karaikal, Puducherry, India

Radhakrishnan Maivizhi


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https://researchid.co/maivizhi16

RESEARCH INTERESTS

Wireless Sensor Networks
Internet of Things
Machine Learning
Network Security
15

Scopus Publications

117

Scholar Citations

6

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques
    Radhakrishnan Maivizhi, P. Yogesh
    Cybersecurity Unlocked Cryptography Network Security Data Protection, 2025
  • Energy efficient in-network data aggregation in Internet-of-Things
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    Intelligent Wireless Sensor Networks and the Internet of Things Algorithms Methodologies and Applications, 2024
    The widespread deployment of IoT technologies promotes the human lifestyle more intelligently by offering flexibility and facility in a range of day-to-day applications. In-network data aggregation in IoT has received a lot of interest from both industry and academia because of its potential for conserving a significant amount of energy of resource-constrained smart devices. This chapter describes how in-network data aggregation approaches minimize the amount of data transmissions among the smart devices in order to enhance the lifetime of IoT–based systems. This chapter explains several techniques describing how to perform data aggregation and how it impacts data traffic, latency, energy, scalability, and security.
  • A Lightweight Privacy-Preserving Hop-by-Hop Data Aggregation in Wireless Sensor Networks
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    10th International Conference on Advanced Computing and Communication Systems Icaccs 2024, 2024
    Data aggregation is a vital technique in resource-limited wireless sensor networks (WSNs), improving energy efficiency as sensor nodes combine data on its way towards the base station, minimizing the number of data packets that needs to be transmitted. In order to preserve the privacy of aggregated data from compromises, many secure data aggregation (SDA) protocols have been developed. However, the implementation of these protocols increase the computational cost of data aggregation. Performing, thus data aggregation in a secure manner in resource-limited WSNs is a challenging task. To preserve privacy and reduce the computational cost of data aggregation, this paper proposes a novel and lightweight privacy-preserving hop-by-hop data aggregation (PHDA) scheme for WSNs. Unlike other SDA schemes, our scheme employs only hash operations which is computationally inexpensive. In addition to preserving privacy, our scheme protects data integrity and achieves message authentication. Security analysis shows that our scheme complies with the security requirements of WSNs and performance evaluation demonstrates that, PHDA is computationally more efficient than state-of-the-art schemes.
  • A Lightweight Signcryption Protocol for Multihop Data Aggregation in Wireless Sensor Networks
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    2024 5th International Conference on Innovative Trends in Information Technology Icitiit 2024, 2024
    Data aggregation is a fundamental and inherent technique for increasing the lifespan of wireless sensor networks. By combining multiple data into a single one, it conserves the bandwidth and energy of sensor nodes. To preserve the privacy of aggregated data, security features are added during the aggregation process. Nevertheless, the addition of security features increases the computational cost of data aggregation algorithms. To overcome this issue, this paper proposes a lightweight Signcryption protocol for Multihop Data Aggregation (SMDA) in wireless sensor networks. The proposed protocol, due to its homomorphic feature, preserves privacy and significantly reduces the computational overhead of sensor nodes. Also, the proposed SMDA protocol is oracle free. Without executing signing algorithms, it can generate signatures on aggregated ciphertext during transmissions. In addition, the proposed scheme validates the signatures in batches and the signature verification cost does not depend on the number of messages. Extensive security analysis demonstrates that the proposed protocol meets the security requirements of WSNs. Furthermore, the results of performance evaluation proves that the computational efficiency of the proposed protocol outperforms other existing secure aggregation schemes.
  • Fuzzy routing for in-network aggregation in wireless sensor networks
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    Peer to Peer Networking and Applications, 2022
  • Identity-based secure data aggregation in big data wireless sensor networks
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    International Journal of Ad Hoc and Ubiquitous Computing, 2022
    Secure data aggregation (SDA) is an inherent paradigm in big data wireless sensor networks (WSNs) to reduce data transmissions, maximise the network lifetime and provide security. However, SDA protocols that use elliptic curve suffer from mapping and reverse mapping functions. Currently, there is no known mapping function which is both homomorphic and effective in reverse mapping. In addition, SDA protocols that use public key infrastructure incurs high computation and communication cost. To overcome these challenges, this paper proposes a novel identity-based secure data aggregation (ISDA) protocol for big data wireless sensor networks. This protocol is based on bilinear pairing and combines identity-based homomorphic encryption scheme with identity-based signature to achieve end-to-end security. In WSNs, this is the first SDA protocol that employs both identity-based encryption and identity-based signature scheme. ISDA shares the same public/private keys during encryption and signature generation, which significantly reduces the complexity of the protocol. Security analysis reveals that ISDA is secure against various internal and external attacks and proves the correctness of the proposed protocol. Performance evaluation shows that ISDA incurs less overhead than state-of-the-art bilinear pairing-based SDA schemes, thereby it minimises the energy consumption and increases the lifetime of wireless sensor networks.
  • Q-learning based routing for in-network aggregation in wireless sensor networks
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    Wireless Networks, 2021
  • Spatial Correlation based Data Redundancy Elimination for Data Aggregation in Wireless Sensor Networks
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    2020 International Conference on Innovative Trends in Information Technology Icitiit 2020, 2020
    The dense distributed deployment of Wireless Sensor Networks (WSNs) causes the sensors to generate big amount of data which are highly correlated and redundant. Transmitting such spatially correlated and redundant data consumes more sensor energy and consequently decreases the lifetime of network. Eliminating redundancy is thus necessary in the sensed data and also while processing the sensed data. By employing appropriate data aggregation techniques, the data redundancy can be minimized. We leverage statistical techniques in sensor networks and propose a novel Spatial Correlation based Data Redundancy Elimination for Data Aggregation (SCDRE) protocol that eliminates redundancy at two levels: at source level, it uses simple data similarity function and at aggregator level, it uses correlation coefficient to eliminate redundancy and aggregate the data. We have evaluated the proposed protocol in terms of aggregation ratio, data accuracy and energy consumption and the results show that SCDRE outperforms other existing techniques. In addition, SCDRE is more robust in the presence of noise and outliers. By eliminating data redundancy to greater extent, our protocol experiences less communication overhead and significantly enhances the lifetime of sensor networks.
  • Concealed multidimensional data aggregation in big data wireless sensor networks
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    ACM International Conference Proceeding Series, 2020
    Wireless sensor networks (WSNs) deployed in a plethora of applications produce a significant portion of big data. Handling these huge volume of data is a critical challenge in a resource constrained wireless sensor networks. Data aggregation is the most practical and important paradigm in big data wireless sensor networks. It reduces the huge volume of data by combining the similar data and eliminating data redundancy and reduces thereby the resource consumption. However preserving data confidentiality and integrity along with en-route aggregation is a great challenge. In this paper, we propose a novel Concealed Multidimensional Data Aggregation (CMDA) protocol for big data wireless sensor networks. CMDA integrates super-increasing sequence and homomorphic encryption to structure the multidimensional data and protect the data privacy and a homomorphic signature to check the integrity of data. In addition, the proposed protocol filters false data packets and achieves data freshness. Security analysis reveals that the proposed protocol achieves end-to-end security and performance evaluation shows that CMDA incurs less communication overhead and consequently reduces energy consumption which enhances the lifetime of sensor networks. To the best of our knowledge, this is the first work that achieves end-to-end security in multidimensional data aggregation.
  • Linear Discriminant Analysis for Data Aggregation in Big Data Wireless Sensor Networks
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    ACM International Conference Proceeding Series, 2020
    1 EXTENDED ABSTRACT Data aggregation or in-network aggregation plays a fundamental and vital role in maximizing the lifetime of wireless sensor networks (WSNs). However, in big data applications, existing data aggregation (DA) techniques based on compressed sensing, discrete cosine tranform and principal component analysis [2] suffer from problems such as high energy consumption and complex data analysis. To overcome these problems, we propose a novel linear discriminant analysis based data aggregation protocol for multidimensional data in big data WSNs. The proposed protocol employs Fisher Linear Discriminant (FLD) [1], a machine learning technique for aggregating multidimensional data. Researchers defined FLD as a classifier and a dimensionality reduction technique. Classifier: Data aggregation is performed after projecting the multidimensional data down to one dimension as follows. y =WT x ≥ −w0 (1)
  • Secure in-network aggregation in wireless sensor networks
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    International Journal of Intelligent Information Technologies, 2020
  • Intrusion Resilient Concealed Data Aggregation in Wireless Sensor Networks
    Maivizhi Radhakrishnan, Yogesh Palanichamy
    2017 9th International Conference on Advanced Computing Icoac 2017, 2018
  • Efficient and robust secure in-network aggregation in wireless sensor networks
    Radhakrishnan Maivizhi, Palanichamy Yogesh
    Communications in Computer and Information Science, 2018
  • A survey of tools for community detection and mining in social networks
    R. Maivizhi, S. Sendhilkumar, G. S. Mahalakshmi
    ACM International Conference Proceeding Series, 2016
  • Distance based detection and localization of multiple spoofing attackers for wireless networks
    Maivizhi R., Matilda S.
    2014 International Conference on Computation of Power Energy Information and Communication Iccpeic 2014, 2014

RECENT SCHOLAR PUBLICATIONS

  • Energy efficient in-network data aggregation in Internet-of-Things
    R Maivizhi, P Yogesh
    Intelligent Wireless Sensor Networks and the Internet of Things, 66-85 , 2024
    2024
    Citations: 2
  • A Lightweight Signcryption Protocol for Multihop Data Aggregation in Wireless Sensor Networks
    R Maivizhi, P Yogesh
    2024 5th International Conference on Innovative Trends in Information … , 2024
    2024
    Citations: 1
  • A Lightweight Privacy-Preserving Hop-by-Hop Data Aggregation in Wireless Sensor Networks
    R Maivizhi, P Yogesh
    2024 10th International Conference on Advanced Computing and Communication … , 2024
    2024
    Citations: 1
  • Identity-based secure data aggregation in big data wireless sensor networks
    R Maivizhi, P Yogesh
    International Journal of Ad Hoc and Ubiquitous Computing 41 (1), 16-28 , 2022
    2022
    Citations: 1
  • Fuzzy routing for in-network aggregation in wireless sensor networks
    R Maivizhi, P Yogesh
    Peer-to-Peer Networking and Applications 15 (1), 592-611 , 2022
    2022
    Citations: 9
  • Q-learning based routing for in-network aggregation in wireless sensor networks
    R Maivizhi, P Yogesh
    Wireless Networks 27 (3), 2231-2250 , 2021
    2021
    Citations: 40
  • Linear Discriminant Analysis for Data Aggregation in Big Data Wireless Sensor Networks
    R Maivizhi, P Yogesh
    Proceedings of the 3rd ACM India Joint International Conference on Data … , 2021
    2021
  • Spatial correlation based data redundancy elimination for data aggregation in wireless sensor networks
    R Maivizhi, P Yogesh
    2020 international conference on innovative trends in information technology … , 2020
    2020
    Citations: 24
  • Concealed multidimensional data aggregation in big data wireless sensor networks
    R Maivizhi, P Yogesh
    Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, 19-27 , 2020
    2020
    Citations: 6
  • Secure in-network aggregation in wireless sensor networks
    R Maivizhi, P Yogesh
    International Journal of Intelligent Information Technologies (IJIIT) 16 (1 … , 2020
    2020
    Citations: 3
  • Smart Secure Systems–Iot and Analytics Perspective
    GP Venkataramani, K Sankaranarayanan, S Mukherjee, K Arputharaj, ...
    Comminications in Computer and Information Science ed.). Springer , 2018
    2018
    Citations: 7
  • Efficient and Robust Secure In-Network Aggregation in Wireless Sensor Networks
    R Maivizhi, P Yogesh
    International Conference on Intelligent Information Technologies, 139-152 , 2017
    2017
  • Intrusion Resilient Concealed Data Aggregation in Wireless Sensor Networks
    M Radhakrishnan, Y Palanichamy
    2017 Ninth International Conference on Advanced Computing (ICoAC), 227-234 , 2017
    2017
  • A survey of tools for community detection and mining in social networks
    R Maivizhi, S Sendhilkumar, GS Mahalakshmi
    Proceedings of the International Conference on Informatics and Analytics, 1-8 , 2016
    2016
    Citations: 15
  • Detection and Localization of Multiple Spoofing Attackers for Mobile Wireless Networks
    R Maivizhi, S Matilda
    ICTACT Journal on Communication Technology 6 (2) , 2015
    2015
    Citations: 1
  • Distance based Detection and Localization of multiple spoofing attackers for wireless networks
    R Maivizhi, S Matilda
    2014 International Conference on Computation of Power, Energy, Information … , 2014
    2014
    Citations: 7

MOST CITED SCHOLAR PUBLICATIONS

  • Q-learning based routing for in-network aggregation in wireless sensor networks
    R Maivizhi, P Yogesh
    Wireless Networks 27 (3), 2231-2250 , 2021
    2021
    Citations: 40
  • Spatial correlation based data redundancy elimination for data aggregation in wireless sensor networks
    R Maivizhi, P Yogesh
    2020 international conference on innovative trends in information technology … , 2020
    2020
    Citations: 24
  • A survey of tools for community detection and mining in social networks
    R Maivizhi, S Sendhilkumar, GS Mahalakshmi
    Proceedings of the International Conference on Informatics and Analytics, 1-8 , 2016
    2016
    Citations: 15
  • Fuzzy routing for in-network aggregation in wireless sensor networks
    R Maivizhi, P Yogesh
    Peer-to-Peer Networking and Applications 15 (1), 592-611 , 2022
    2022
    Citations: 9
  • Smart Secure Systems–Iot and Analytics Perspective
    GP Venkataramani, K Sankaranarayanan, S Mukherjee, K Arputharaj, ...
    Comminications in Computer and Information Science ed.). Springer , 2018
    2018
    Citations: 7
  • Distance based Detection and Localization of multiple spoofing attackers for wireless networks
    R Maivizhi, S Matilda
    2014 International Conference on Computation of Power, Energy, Information … , 2014
    2014
    Citations: 7
  • Concealed multidimensional data aggregation in big data wireless sensor networks
    R Maivizhi, P Yogesh
    Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, 19-27 , 2020
    2020
    Citations: 6
  • Secure in-network aggregation in wireless sensor networks
    R Maivizhi, P Yogesh
    International Journal of Intelligent Information Technologies (IJIIT) 16 (1 … , 2020
    2020
    Citations: 3
  • Energy efficient in-network data aggregation in Internet-of-Things
    R Maivizhi, P Yogesh
    Intelligent Wireless Sensor Networks and the Internet of Things, 66-85 , 2024
    2024
    Citations: 2
  • A Lightweight Signcryption Protocol for Multihop Data Aggregation in Wireless Sensor Networks
    R Maivizhi, P Yogesh
    2024 5th International Conference on Innovative Trends in Information … , 2024
    2024
    Citations: 1
  • A Lightweight Privacy-Preserving Hop-by-Hop Data Aggregation in Wireless Sensor Networks
    R Maivizhi, P Yogesh
    2024 10th International Conference on Advanced Computing and Communication … , 2024
    2024
    Citations: 1
  • Identity-based secure data aggregation in big data wireless sensor networks
    R Maivizhi, P Yogesh
    International Journal of Ad Hoc and Ubiquitous Computing 41 (1), 16-28 , 2022
    2022
    Citations: 1
  • Detection and Localization of Multiple Spoofing Attackers for Mobile Wireless Networks
    R Maivizhi, S Matilda
    ICTACT Journal on Communication Technology 6 (2) , 2015
    2015
    Citations: 1
  • Linear Discriminant Analysis for Data Aggregation in Big Data Wireless Sensor Networks
    R Maivizhi, P Yogesh
    Proceedings of the 3rd ACM India Joint International Conference on Data … , 2021
    2021
  • Efficient and Robust Secure In-Network Aggregation in Wireless Sensor Networks
    R Maivizhi, P Yogesh
    International Conference on Intelligent Information Technologies, 139-152 , 2017
    2017
  • Intrusion Resilient Concealed Data Aggregation in Wireless Sensor Networks
    M Radhakrishnan, Y Palanichamy
    2017 Ninth International Conference on Advanced Computing (ICoAC), 227-234 , 2017
    2017