Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Adaptive Selection of Cryptographic Protocols in Wireless Sensor Networks using Evolutionary Game Theory
    (Elsevier B.V., 2016) Arora, S.; Singh, P.; Gupta, A.J.
    Game theory applies to scenarios wherein multiple players with contrary motives contend with each other. Various solutions based on Game theory have been recently proposed which dealt with security aspects of wireless sensor networks (WSNs). However, the nodes have limited capability of rationality and evolutionary learning which makes it unfavorable to apply conventional game theory in WSNs. Evolutionary Game Theory (EGT) relies on bounded rationality assumption which is in harmony with the wireless sensor networks characteristics. Based on EGT, authors propose an adaptive security model for WSNs for the selection of cryptographic protocols during runtime. The authors formulate this selection in WSNs with the help of an evolutionary game to obtain the evolutionarily stable strategy (ESS) for the system. In this model, the sensor nodes dynamically adapt their defensive strategies to attain the most efficient defense, corresponding to the attackers' varied strategies. Further, the simulations convey that the proposed system converges rapidly to the Evolutionary Stable Strategy. Not only the system converges, but also forms a stable system which was verified by deliberately destabilizing the system. Results show that the nodes quickly return to ESS even after perturbation. © 2016 The Authors.
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    Adaptive Selection of Cryptographic Protocols in Wireless Sensor Networks using Evolutionary Game Theory
    (Elsevier B.V., 2016) Arora, S.; Singh, P.; Gupta, A.J.
    Game theory applies to scenarios wherein multiple players with contrary motives contend with each other. Various solutions based on Game theory have been recently proposed which dealt with security aspects of wireless sensor networks (WSNs). However, the nodes have limited capability of rationality and evolutionary learning which makes it unfavorable to apply conventional game theory in WSNs. Evolutionary Game Theory (EGT) relies on bounded rationality assumption which is in harmony with the wireless sensor networks characteristics. Based on EGT, authors propose an adaptive security model for WSNs for the selection of cryptographic protocols during runtime. The authors formulate this selection in WSNs with the help of an evolutionary game to obtain the evolutionarily stable strategy (ESS) for the system. In this model, the sensor nodes dynamically adapt their defensive strategies to attain the most efficient defense, corresponding to the attackers' varied strategies. Further, the simulations convey that the proposed system converges rapidly to the Evolutionary Stable Strategy. Not only the system converges, but also forms a stable system which was verified by deliberately destabilizing the system. Results show that the nodes quickly return to ESS even after perturbation. © 2016 The Authors.
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    Performance measures of fuzzy C-means algorithm in wireless sensor networks
    (Inderscience Publishers, 2017) Kumar, P.; Chaturvedi, A.
    The major issues that govern performance of wireless sensor networks (WSNs) are efficient uses of limited resources and appropriate routing decisions of network paths under the severely constrained energy scenario. In this work, to address these issues uses of k-means and fuzzy C-means algorithms are investigated for clusters formation and subsequent selection of cluster heads (CHs). For all these newly formed clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) followed by estimation of Euclidean distances. Depending upon the Euclidean distance measures between the sink node and the estimated energy-centroid (EC) of clusters these clusters are classified into five types. The RES estimation is exercised for all the CHs and sensor nodes (SNs) of the network. Outcomes of simulation results indicate superior performance of fuzzy-c means algorithm compared to k-means algorithm. Further, a case study is presented, wherein the sink is allowed to have some movements in the service area. Here, different quadrant of service area exhibits different pattern of query spatial distribution. The optimal location of sink is sought to support energy efficient operational aspects of the WSNs. © © 2017 Inderscience Enterprises Ltd.
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    Optimizing Split Algorithm Performance: A Heuristic Method for Enhanced Tensor Product Matrix Computations
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bhowmik, B.; Kumar, S.; Raju, S.R.; Prakash, A.; Mense, O.
    Optimizing tensor product matrix computations is critical for enhancing computational efficiency in high-performance applications. Traditional algorithms, like the Split algorithm, often struggle due to the unique properties of each matrix involved. This paper presents a novel heuristic method that optimizes the selection of cutting points and matrix ar-rangement, significantly reducing redundant calculations and minimizing memory usage. The proposed approach adapts to the varying characteristics of tensor products, improving performance across different computational scenarios. Enhancing floating-point operation efficiency and CPU utilization delivers substantial speed and efficiency gains, particularly in large-scale tensor product matrix operations, offering a robust solution for complex computational tasks. © 2024 IEEE.