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A novel social deep autoencoder NMF incentive scheme to detect a selfish node in delay tolerant network

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Abstract

Detection of the selfish node in a delay tolerant network (DTN) can sharply reduce the loss incurred in a network. The algorithm's current pedigree mainly focuses on the rely on nodes, records, and delivery performance. The community structure and social aspects have been overlooked. Analysis of individual and social tie preferences results in an extensive detection time and increases communication overhead. In this article, a heterogeneous DTN topology with high-power stationary nodes and mobile nodes on Manhattan's accurate map is designed. With the increasing complexity of social ties and the diversified nature of topology structure, there need for a method that can effectively capture the essence within the speculated time. In this article, a novel deep autoencoder-based nonnegative matrix factorization (DANMF) is proposed for DTN topology. The topology of social ties projected onto low-dimensional space leads to effective cluster formation. DANMF automatically learns an appropriate nonlinear mapping function by utilizing the features of data. Also, the inherent structure of the deep autoencoder is nonlinear and has strong generalization. The membership matrices extracted from the DANMF are used to design the weighted cumulative social tie that eventually, along with the residual energy, is used to detect the network's selfish node. The testing of the designed model is carried out on the real dataset of MIT reality. The proficiency of the developed algorithm has been well tested and proved at every step. The methods employed for social tie extraction are NMF and DANMF. The methodology is rigorously experimented on various scenarios and has improved around 80% in the worst-case scenario of 40% nodes turning selfish. A comprehensive comparison is made with the other existing state-of-the-art methods which are also incentive-based approaches. The developed method has outperformed and has shown the supremacy of the current methods to capture the latent, hidden structure of the social tie.

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Authors and Affiliations

  1. Department of Information Technology, Seth Jai Parkash Mukand Lal Institute of Engineering and Technology (JMIT), Radaur, Yamunanagar, India

    Rakhi Sharma

  2. Uttarakhand Technical University, Dehradun, India

    Rakhi Sharma

  3. Department of Computer Science and Applications, Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand, India

    Shail Kumar Dinkar

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All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

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Correspondence to Shail Kumar Dinkar.

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Sharma, R., Dinkar, S.K. A novel social deep autoencoder NMF incentive scheme to detect a selfish node in delay tolerant network. J Supercomput 78, 15014–15041 (2022). https://doi.org/10.1007/s11227-022-04423-5

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