Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Gopinathan, B.
- Scalable Access Limitation for Privacy-Aware Media Sharing
Abstract Views :162 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Adhiyamaan College of Engineering, Hosur, IN
1 Department of Computer Science and Engineering, Adhiyamaan College of Engineering, Hosur, IN
Source
Software Engineering, Vol 11, No 2 (2019), Pagination: 34-39Abstract
The social networks has made it easier than ever for users to share their text file, document and other media content with anybody from anywhere. It’s easy to access the user-generated media content which brings about the privacy concerns. Traditional access control mechanisms were implemented for a single access policy is made for a specific piece of the content, cannot satisfy the user privacy requirements in large-scale media data sharing systems. Instead, configuring many levels of access privileges for the shared media data content is needed. On one hand, it determines the principle of social networks in information circulation. On the other hand, it accords with the diverse social relationship among social network users. In this paper, we propose the Scalable Media Access Control (SMAC) system to enable such a configuration in a secure and in an efficient manner. The proposed system SMAC system is permitted by the Scalable Cipher Text Policy Attribute-Based Encryption (SCP-ABE) algorithm as well as a comprehensive key management schema. Also we provide formal security proof to prove the security of the proposed SMAC system. Additionally, we conduct the intensive experiments on mobile devices to demonstrate its efficiency.
Keywords
Social Media Sharing, Privacy, Access Control, SCP-ABE, Scalable Media Format.
References
- R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic,“Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Gener. Comp. Sy., vol. 25, no. 6, pp. 599 – 616, 2009.
- H. Qian, J. Li, Y. Zhang and J. Han, “Privacy preserving personal health record using multi-authority attribute-based encryption with revocation,” Int. J. Inf. Secur., vol. 14, no. 6, pp. 487-497, 2015.
- J. Li, W. Yao, Y. Zhang, H. Qian and J. Han, “Flexible and fine-grained attribute-based data storage in cloud computing,” IEEE Trans. Service Comput., DOI: 10.1109/TSC.2016.2520932.
- J. Li, X. Lin, Y. Zhang and J. Han, “KSF-OABE: outsourced attribute-based encryption with keyword search function for cloud storage,” IEEE Trans. Service Comput., DOI: 10.1109/TSC.2016. 2542813.
- J. Li, Y. Shi and Y. Zhang, “Searchable ciphertext-policy attribute-based encryption with revocation in cloud storage,” Int. J.Commun. Syst., DOI: 10.1002/dac.2942.
- J.G. Han, W. Susilo, Y. Mu and J. Yan, “Privacy-Preserving Decentralized Key-Policy Attribute-Based Encryption,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no.11, pp. 2150-2162, 2012
- Z. J. Fu, X. M. Sun, Q. Liu, L. Zhou, and J. G. Shu, “Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing,” IEICE Transactions on Communications, vol. E98-B, no. 1, pp.190-200, 2015.
- Z. J. Fu, K. Ren, J. G. Shu, X. M. Sun, and F. X. Huang, “Enabling personalized search over encrypted outsourced data with efficiency improvement,” IEEE Transactions on Parallel and Distributed Systems, DOI: 10.1109/TPDS.2015.2506573, 2015.
- Z. H. Xia, X. H. Wang, X. M. Sun, and Q. Wang, “A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 2, pp. 340-352, 2015.
- Y. J. Ren, J. Shen, J. Wang, J. Han and S. Y. Lee, “Mutual verifiable provable data auditing in public cloud storage,” Journal of Internet Technology, vol. 16, no. 2, pp. 317-323, 2015.
- Y. Deswarte, J. J. Quisquater, and A. Saïdane, “Remote integrity checking,” in Proc. 6th Working Conf. Integr. Internal Control Inf. Syst. (IICIS), 2003, pp. 1–11.
- Z. Hao, S. Zhong, and N. Yu, “A privacy-preserving remote data integrity checking protocol with data dynamics and public verifiability,” IEEE Trans. Knowl. Data Eng., vol. 23, no. 9, pp. 1432–1437, Sep. 2011.
- G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song, ‘‘Provable Data Possession at Untrusted Stores,’’ in Proc. 14th ACM Conf. on Comput. and Commun. Security (CCS), 2007, pp. 598-609.
- G. Ateniese, R. D. Pietro, L. V. Mancini, and G. Tsudik, ‘‘Scalable and Efficient Provable Data Possession,’’ in Proc. 4th Int’l Conf. Security and Privacy in Commun. Netw. (SecureComm), 2008, pp. 1-10.
- F. Sebé, J. Domingo-Ferrer, A. Martinez-balleste, Y. Deswarte, and J. Quisquater, “Efficient Remote Data Possession Checking in Critical Information Infrastructures,” IEEE Trans. Knowledge and Data Eng., vol. 20, no. 8, pp. 1034-1038, Aug. 2008.
- C. Erway, A. Küpçü, C. Papamanthou, and R. Tamassia, “Dynamic Provable Data Possession,’’ in Proc. 16th ACM Conf. on Comput. And Commun. Security (CCS), 2009, pp. 213-222.
- Q. Wang, C. Wang, K. Ren, W. Lou, and J. Li, ‘‘Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing,’’ IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 5, pp. 847-859, May, 2011.
- K. Yang and X. Jia, ‘‘An efficient and secure dynamic auditing protocol for data storage in cloud computing,’’ IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 9, pp. 1717-1726, 2013.
- L. Chen, S. Zhou, X. Huang and L. Xu, ‘‘Data dynamics for remote data possession checking in cloud storage, ’’ Comput. Electr. Eng., vol. 39, no. 7, pp. 2413-2424, 2013.
- M. N. Krohn, M. J. Freedman and D. Mazieres, ‘‘On-the-fly verification of rateless erasure codes for efficient content distribution,’’ in Proc. 2004 IEEE Symp. on Security and Privacy (S&P), 2004, pp. 226–240.
- Y. Yu, J. Ni, M. H. Au, H. Liu, H. Wang and C. Xu, ‘‘Improved security of a dynamic remote data possession checking protocol for cloud storage,’’ Expert Syst. Appl., vol. 41, no. 7, pp. 7789-7796, 2014.
- R. Curtmola, O. Khan, R. Burns, and G. Ateniese, ‘‘MR-PDP: Multiple-replica provable data possession,’’ in Proc. 28th IEEE Conf. on Distrib. Comput. Syst. (ICDCS), 2008, pp. 411-420.
- Z. Hao and N. Yu, ‘‘A multiple-replica remote data possession checking protocol with public verifiability,’’ in Proc. 2th Int’l Symp. Data, Privacy, E-Comm. (ISDPE), 2010, pp. 84-89.
- R. Mukundan, S. Madria and M. Linderman, ‘‘Efficient integrity verification of replicated data in cloud using homomorphic encryption,’’ Distrib. Parallel Dat., vol. 32, no. 4, pp. 507-534, 2014.
- A. F. Barsoum and M. A. Hasan, ‘‘Provable multicopy dynamic data possession in cloud computing systems,’’ IEEE Trans. Inf. Foren. Sec., vol. 10, no. 3, pp. 485-497, 2015.
- Y. Zhu, H. Hu, G. J. Ahn and M. Yu, ‘‘Cooperative provable data possession for integrity verification in multicloud storage,’’ IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 12, pp. 2231-2244, 2012.
- H. Wang and Y. Zhang, ‘‘On the knowledge soundness of a cooperative provable data possession scheme in multicloud storage,’’ IEEE Trans. Parallel Distrib. Syst., vol. 25, no. 1, pp. 264-267, 2014.
- H. Wang, ‘‘Identity-Based distributed provable data possession in Multicloud storage,’’ IEEE Trans. Service Comput., vol. 8, no. 2, pp. 328-340, 2015.
- L. Chen, ‘‘Using algebraic signatures to check data possession in cloud storage,’’ Future Gener. Comp. Sy., vol. 29, no. 7, pp. 1709-1715, 2013.
- W. Litwin and T. Schwarz, ‘‘Algebraic signatures for scalable distributed data structures,’’ in Proc. 20th Int'l Conf. on Data Eng. (ICDE), 2004, pp. 412-423.
- Y. Yu, J. Ni, J. Ren, W. Wu, L. Chen and Q. Xia, ‘‘Improvement of a remote data possession checking protocol from algebraic signatures,’’ in Proc. 9th Int'l Conf. on Information Security Practice and Experience (ISPEC), 2014, pp. 359-372.
- E. Zhou and Z. Li, ‘‘An improved remote data possession checking protocol in cloud storage,’’ in Proc. 14th Int'l Conf. on Algs. and Archs. for Parall Proc. (ICA3PP), 2014, pp. 611-617.
- H. Wang and J. Li, ‘‘Private certificate-based remote data integrity checking in public clouds,’’ in Proc. 21th Int'l Computing and Combinatorics. (COCOON), 2015, pp. 575–586.
- A. Juels and B.S. Kaliski Jr., ‘‘PORs: Proofs of Retrievability for Large Files,’’ in Proc. 14th ACM Conf. on Comput. and Commun. Security (CCS), 2007, pp. 584-597.
- H. Shacham and B. Waters, ‘‘Compact Proofs of Retrievability,’’ in Proc. 14th Int’l Conf. on Theory and Appl. of Cryptol. and Inf. Security (ASIACRYPT), 2008, pp. 90-107.
- D. Boneh, H. Shacham, and B. Lynn, ‘‘Short Signatures From the Weil Pairing,’’ J. Cryptol., vol. 17, no. 4, pp. 297-319, Sept. 2004.
- K. D. Bowers, A. Juels, and A. Oprea, “Hail: A high-availability and integrity layer for cloud storage,” in Proc. 16th ACM Conf. on Comput. and Commun. Security (CCS), 2009, pp. 187–198.
- K. D. Bowers, A. Juels, and A. Oprea, “Proofs of retrievability: Theory and implementation,” in Proc. 1th ACM Cloud Comput. Secur. Workshop (CCSW), 2009, pp. 43–54.
- Y. Dodis, S. Vadhan, and D.Wichs, “Proofs of retrievability via hardness amplification,” in Proc. 6th Theory Cryptograph. Conf. (TCC), 2009, pp. 109–127.
- Multiprecision Integer and Rational Arithmetic C/C++ library (MIRACL). [Online]. Available: http://info.certivox.com/miracl.
- The Pairing-based Cryptography Library (PBC). [Online]. Available: https://crpto.stanford.edu/pbc/download.html.
- The GNU Multiple Precision Arithmetic Library (GMP). [Online]. Available: http://gmplib.org/
- M. Sookhak, A. Gani, M. K. Khan and R. Buyya. “Dynamic remote data auditing for securing big data storage in cloud computing,” Inform. Sciences, DOI: 10.1016/j.ins.2015.09.004.
- C. Zhang, J. Sun, X. Zhu and Y. Fang, “Privacy and security for online social networks: challenges and opportunities," IEEE Network, vol. 24, no. 4, pp. 13-18, 2010.
- M. Fire, R. Goldschmidt and Y. Elovici, “Online Social Networks: Threats and Solutions," IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 2019-2036, 2014.
- L. Wei, H. Zhu, Z. Cao, X. Dong, W. Jia, Y. Chen, A. V. Vasilakos, “Security and privacy for storage and computation in cloud computing," Information Sciences: an International Journal, 258, p.371-386, 2014.
- R. Shokri, V. Shmatikov, “Privacy-Preserving Deep Learning," Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310-1321, 2015.
- M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep Learning with Differential Privacy," CCS, pp. 308-318, 2016.
- L. Yuan, P. Korshunov, T. Ebrahimi, “Privacy-preserving photo sharing Based on a secure JPEG," IEEE Conf. Computer Communications Workshops (INFOCOM WKSHPS), pp. 185-190, 2015.
- F. Dufaux and T. Ebrahimi, “Scrambling for Privacy Protection in Video Surveillance Systems," IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 8, pp. 1168-1174, 2008.
- Analyzing The Software Quality In Image Processing Software In Industry Using Machine Learning
Abstract Views :154 |
PDF Views:0
Authors
Affiliations
1 Jaya Sakthi Engineering, IN
2 Department of Computer Science and Engineering, Jaya Engineering College, IN
3 Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering, IN
1 Jaya Sakthi Engineering, IN
2 Department of Computer Science and Engineering, Jaya Engineering College, IN
3 Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 3 (2022), Pagination: 2674-2678Abstract
The ability of manufacturing organizations to generate defect-free, high-quality products is critical to their long-term success in the marketplace. Despite increased product diversity and complexity, as well as the necessity for cost-effective manufacturing, it is frequently important to conduct a thorough and reliable quality examination. There are bottlenecks in the manufacturing process because there are so many checks done. In this paper, we aim to automate the process of quality control in industries using a machine learning classifier that monitors the manufactured product namely the central processing unit via imaging technique. Development of a model with high quality control improves the productivity and efficacy of production that rejects the malignant and defect pieces from the supply chain. The use of imaging systems or high-speed camera enables the improvement of software quality, where the analysis is built using high clarity input images. The data processed by these imaging systems are transferred to the cyber-physical system for secured access within an organization. The results of classification of input images and process via machine learning improves the efficacy of the model over various machine learning models.Keywords
Software quality, Image Processing, Machine Learning, CyberReferences
- M.H. Zawawi, F.C. Ng and N.H. Hassan, “Particle Image Velocimetry Analysis on the Liquid-Sediment Model”, Proceedings of International Conference on Climate Change and Water Security, pp. 201-206, 2022.
- B. Guan, W. Kang and H. Lin, “Freshness Identification of Oysters Based on Colorimetric Sensor Array Combined with Image Processing and Visible Near-Infrared Spectroscopy”, Sensors, Vol. 22, No. 2, pp. 683-694, 2022.
- M. Moganti, F. Ercal and C.H. Dagli, “Automatic PCB Inspection Algorithms: A Survey”, Computer Vision and Image Understanding, Vol. 63, No. 2, pp. 287-313, 1996.
- K.H. Kim, Y.W. Kim and S.W. Suh, “Automatic Visual Inspections System to Detect Wrongly Attached Components”, Proceedings of International Conference on Signal Processing Applications and Technology, pp. 13-16, 1998.
- S. Sledz and M.W. Ewertowski, “Evaluation of the Influence of Processing Parameters in Structure-from-Motion Software on the Quality of Digital Elevation Models and Orthomosaics in the Context of Studies on Earth Surface Dynamics”, Remote Sensing, Vol. 14, No. 6, pp. 1312-1324, 2022.
- B. Vineetha and R.B. Madhumala, “Providing Security and Managing Quality Through Machine Learning Techniques for an Image Processing Model in the Industrial Internet of Things”, Proceedings of International Conference on Smart IoT for Research and Industry, pp. 161-177, 2022.
- G.U. Nneji, J. Cai, J. Deng and C.C. Ukwuoma, “MultiChannel Based Image Processing Scheme for Pneumonia Identification”, Diagnostics, Vol. 12, No. 2, pp. 325-334, 2022.
- J.L. Salgueiro, L. Perez and C. Miguez, “Microalgal Biomass Quantification from the Non-Invasive Technique of Image Processing through Red–Green–Blue (RGB) Analysis”. Journal of Applied Phycology, Vol. 32, pp. 1-11, 2022.
- X. Tian, Y. Li, D. Ma and L. Xia, “Strand width Uniformly Control for Silicone Extrusion Additive Manufacturing based on Image Processing”, The International Journal of Advanced Manufacturing Technology, Vol. 11, No. 2, pp. 114, 2022.
- J. Wang, Y. Yang and Y. Hua, “Image Quality Enhancement using Hybrid Attention Networks”, IET Image Processing, Vol. 16, No. 2, pp. 521-534, 2022.
- B. An and Y. Kim, “Image Link Through Adaptive Encoding Data Base and Optimized GPU Algorithm for Real-time Image Processing of Artificial Intelligence”, Journal of Web Engineering, Vol. 65, No. 1, pp. 459-496, 2022.
- V. Laghi, V. Ricci, F. De Santa and A. Torcinaro, “A UserFriendly Approach for Routine Histopathological and Morphometric Analysis of Skeletal Muscle using Cell Profiler Software”, Diagnostics, Vol. 12, No. 3, pp. 561-568, 2022.
- A Novel Efficient Security Veerification Technique Based on Service Package Identifier in Wireless Mobile Ad-Hoc Networks
Abstract Views :118 |
PDF Views:0
Authors
Affiliations
1 Department of Artificial Intelligence and Machine Learning, Vemana Institute of Technology, IN
2 Department of Computer Science, Acharya Bangalore B School, IN
3 Department of Computer Science and Engineering, Adhiyamaan College of Engineering, IN
4 Department of Electronics and Communication Engineering, Roever Engineering College, IN
1 Department of Artificial Intelligence and Machine Learning, Vemana Institute of Technology, IN
2 Department of Computer Science, Acharya Bangalore B School, IN
3 Department of Computer Science and Engineering, Adhiyamaan College of Engineering, IN
4 Department of Electronics and Communication Engineering, Roever Engineering College, IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 2 (2023), Pagination: 2907-2912Abstract
This paper presents a novel and efficient security verification technique for Wireless Mobile Ad-Hoc Networks (MANETs) using The Service Package Identifier (SPI). The dynamic and self-organizing nature of MANETs makes them susceptible to a variety of security threats. Using the SPI, the proposed technique authenticates and verifies the integrity of service bundles exchanged between network nodes. By employing the SPI as a unique identifier for each service packet, the technique protects against unauthorized access and data tampering and ensures secure communication. The experimental results demonstrate the efficacy and efficiency of the proposed technique, which offers improved MANET security and resiliency.Keywords
Security Verification, Service Package Identifier, Authentication, Integrity, Secure Communication, Unauthorized Access, Data Tampering, Resilience.References
- M. Garofalakis, J.M. Hellerstein and P. Maniatis, “Proof Sketches: Verifiable in Network Aggregation”, Proceedings of IEEE International Conference on Data Engineering, pp. 132-136, 2007.
- H. Chan, A. Perrig and D. Song, “Secure Hierarchical inNetwork Aggregation in Sensor Networks”, Proceedings of ACM Conference on Computer and Communications Security, pp. 278-287, 2006.
- L. Buttyan, P. Schaffer and I. Vajda, “Resilient Aggregation with Attack Detection in Sensor Networks”, Proceedings of ACM Conference on Sensor Networks and Systems for Pervasive Computing, pp. 331-336, 2006.
- Y. Yang, X. Wang, S. Zhu and G. Cao, “SDAP: A Secure Hop-by-Hop Data Aggregation Protocol for Sensor Networks”, Proceedings of ACM Symposium on Mobile Ad Hoc Networking and Computing, pp. 889-893, 2006.
- S. Devaraju and S. Ramakrishnan, “Performance Analysis of Intrusion Detection System using Various Neural Network Classifiers”, Proceedings of International Conference on International Conference on Recent Trends in Information Technology, pp. 1033-1038, 2011.
- P. Vivekanandan and A. Sunitha Nadhini, “A Survey on Efficient Routing Protocol using Mobile Networks”, International Journal of Advances in Engineering and Technology, Vol. 6, No. 1, pp. 370-382, 2013.
- U. Meena and A. Sharma, “Secure Key Agreement with Rekeying using FLSO Routing Protocol in Wireless Sensor Network”, Wireless Personal Communications, Vol. 101, pp. 1177-1199, 2018.
- S. Kaur and R. Mahajan, “Hybrid Meta-Heuristic Optimization based Energy Efficient Protocol for Wireless Sensor Networks”, Egyptian Informatics Journal, Vol. 19, No. 3, pp. 145-150, 2018.
- B. Gobinathan, M.A. Mukunthan, S. Surendran, and V.P. Sundramurthy, “A Novel Method to Solve Real Time Security Issues in Software Industry using Advanced Cryptographic Techniques”, Scientific Programming, Vol. 2021, pp. 1-7, 2021.
- M. Ramkumar and T. Husna, “CEA: Certification based Encryption Algorithm for Enhanced Data Protection in Social Networks”, Fundamentals of Applied Mathematics and Soft Computing, Vol. 1, pp. 161-170, 2022.
- J. Singh and S. Sakthivel, “Energy-Efficient Clustering and Routing Algorithm Using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks. Security and Communication Networks, Vol. 2022, pp. 1-14, 2022.
- S. Rajeshwari and P. Jayashree, “Security Issues in Protecting Computers and Maintenance”, Journal of Global Research in Computer Science, Vol. 4, No. 1, pp. 55-58, 2013.
- Energy-Efficient Cluster Head Selection in Manets Using Firefly Algorithm
Abstract Views :112 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, St. Joseph’s Institute of Technology, IN
2 Department of Artificial Intelligence and Machine Learning, Vemana Institute of Technology, IN
3 Department of Computer Science and Engineering, Adhiyamaan College of Engineering, IN
4 Department of Mathematics and Computer Science, University of Africa, NG
1 Department of Electronics and Communication Engineering, St. Joseph’s Institute of Technology, IN
2 Department of Artificial Intelligence and Machine Learning, Vemana Institute of Technology, IN
3 Department of Computer Science and Engineering, Adhiyamaan College of Engineering, IN
4 Department of Mathematics and Computer Science, University of Africa, NG
Source
ICTACT Journal on Communication Technology, Vol 14, No 2 (2023), Pagination: 2933-2938Abstract
In mobile ad hoc networks (MANETs), the efficient energy-efficient cluster head selection (EECHS) is a significant issue. When an appropriate cluster head (CH) is chosen, MANETs' energy efficiency improves. Clusters are built in MANETs to facilitate communication between nodes. The EECHS problem has been addressed using a variety of clustering strategies, including distributed clustering and cluster-based routing. However, these approaches require high overhead and complexity, leading to suboptimal performance. Therefore, it is necessary to develop an algorithm that can select the best CH to facilitate efficient energy-efficient communication in MANETs. In this regard, the Firefly Algorithm (FA) has been proposed as an effective approach to solving the EECHS problem. FA is a type of swarm intelligence-based meta-heuristics algorithm inspired by the behavior of fireflies. FA is a simple and efficient optimization method that can be used to select the best CH in MANETs. The method has a number of benefits, including enhanced convergence rate and local search functionality. FA is also computationally effective and can adapt to shifting network conditions. This essay offers a thorough evaluation of the literature on the use of FA in MANETs to address the EECHS. Along with comparisons of their performances, a thorough examination of the suggested methodologies and algorithms is offered. Last but not least, various difficulties and potential research paths pertaining to the usage of the FA are explored.Keywords
Energy, Cluster Head, Selection, MANET, Firefly Algorithm.References
- B. Pitchaimanickam and G. Murugaboopathi, “A Hybrid Firefly Algorithm with Particle Swarm Optimization for Energy Efficient Optimal Cluster Head Selection in Wireless Sensor Networks”, Neural Computing and Applications, Vol. 32, pp. 7709-7723, 2020.
- Z. Wang and Q. Liu, “Energy Efficient Cluster based Routing Protocol for WSN using Firefly algorithm and Ant Colony Optimization”, Wireless Personal Communications, Vol. 125, No. 3, pp. 2167-2200, 2022.
- M. Baskaran and C. Sadagopan, “Synchronous Firefly Algorithm for Cluster Head Selection in WSN”, The Scientific World Journal, Vol. 2015, pp. 1-14, 2015.
- A. Sarkar and T. Senthil Murugan, “Cluster Head Selection for Energy Efficient and Delay-Less Routing in Wireless Sensor Network”, Wireless Networks, Vol. 25, pp. 303-320, 2019.
- T. Karthikeyan and K. Praghash, “Improved Authentication in Secured Multicast Wireless Sensor Network (MWSN) using Opposition Frog Leaping Algorithm to Resist Man-in-Middle Attack”, Wireless Personal Communications, Vol. 123, No. 2, pp. 1715-1731, 2022.
- K. Praghash and A.A. Stonier, “An Artificial Intelligence Based Sustainable Approaches-IoT Systems for Smart Cities”, Springer, 2023.
- N. Shanmugasundaram and J. Lloret, “Energy‐Efficient Resource Allocation Model for Device‐to‐Device Communication in 5G Wireless Personal Area Networks”, International Journal of Communication Systems, Vol. 43, pp. 5524-5534, 2023.
- J. Deepika and J. Jegathesh Amalraj, “Energy-Efficient Clustering and Routing Algorithm using Hybrid Fuzzy with Grey Wolf Optimization in Wireless Sensor Networks”, Security and Communication Networks, Vol. 2022, pp. 1-15, 2022.
- Y.H. Robinson, V. Saravanan and P.E. Darney, “Enhanced Energy Proficient Encoding Algorithm for Reducing Medium Time in Wireless Networks”, Wireless Personal Communications, Vol. 119, pp. 3569-3588, 2021.
- L. Nagarajan and S. Thangavelu, “Hybrid Grey Wolf Sunflower Optimisation Algorithm for Energy‐Efficient Cluster Head Selection in Wireless Sensor Networks for Lifetime Enhancement”, IET Communications, Vol. 15, No. 3, pp. 384-396, 2021.
- R. Sabitha and V. Saravanan, “Network Based Detection of IoT Attack using AIS-IDS Model”, Wireless Personal Communications, Vol. 128, No. 3, pp. 1543-1566, 2023.
- R.S. Latha and G. Murugesan, “Multi-Metric Clustering in Mobile Ad-Hoc Networks using Firefly Optimization Algorithm”, Asian Journal of Research in Social Sciences and Humanities, Vol. 6, No. 11, pp. 630-641, 2016.
- V. Saravanan and R. Rajkumar, “Secure Source-Based Loose RSA Encryption for Synchronization (SSOBRSAS) and Evolutionary Clustering Based Energy Estimation for Wireless Sensor Networks”, International Journal of Advanced Research in Computer Science, Vol. 5, No. 5, pp. 1-12, 2014.
- M. Rao and N. Singh, “Energy Efficient QoS Aware Hierarchical KF-MAC Routing Protocol in MANET”, Wireless Personal Communications, Vol. 101, pp. 635-648, 2018.