Department of Computer Applications

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Browsing Department of Computer Applications by Author "Alphonse, P.J.A."

Now showing 1 - 6 of 6
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    A Hybrid Cryptosystem to Enhance Security in IoT Health Care System
    (Cambridge University Press, 2019-01-08) Kavitha, S; Alphonse, P.J.A.
    Internet of Things (IoT) based health care system provides an essential interface between tiny devices and customers, who require customary checking by remedial focus. The technological innovation is important to guarantee the data protection and security among customer and devices, though it is vulnerable by various security assaults. The cryptographic technique is a prominent method for data protection in a healthcare management system. Single cryptographic algorithm based solution suffered to provide efficient security as its high probability of attacks. So this paper proposes a hybrid cryptographic algorithm that secures the sensitive medicinal information in IoT health care system. The proposed hybrid algorithm is dealing with various security attacks in a proficient way, and also increases its performance when compared to other cryptographic algorithms.
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    Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation
    (Springer Nature, 2021-05-01) Alphonse, P.J.A.; Sriharsha, K.V.
    In recent years, with increase in concern about public safety and security, human movements or action sequences are highly valued when dealing with suspicious and criminal activities. In order to estimate the position and orientation related to human movements, depth information is needed. This is obtained by fusing data obtained from multiple cameras at different viewpoints. In practice, whenever occlusion occurs in a surveillance environment, there may be a pixel-to-pixel correspondence between two images captured from two cameras and, as a result, depth information may not be accurate. Moreover use of more than one camera exclusively adds burden to the surveillance infrastructure. In this study, we present a mathematical model for acquiring object depth information using single camera by capturing the in focused portion of an object from a single image. When camera is in-focus, with the reference to camera lens center, for a fixed focal length for each aperture setting, the object distance is varied. For each aperture reading, for the corresponding distance, the object distance (or depth) is estimated by relating the three parameters namely lens aperture radius, object distance and object size in image plane. The results show that the distance computed from the relationship approximates actual with a standard error estimate of 2.39 to 2.54, when tested on Nikon and Cannon versions with an accuracy of 98.1% at 95% confidence level.
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    Energy-Aware Multilevel Clustering Scheme for Underwater Wireless Sensor Networks
    (Institute of Electrical and Electronics Engineers Inc., 2022-05-25) Chinnasamy, Sivaraj; Naveen, J; Alphonse, P.J.A.; Dhasarathan, Chandramohan; Sambasivam, G
    The expansion of wireless sensor networks in the underwater environment resulted in underwater wireless sensor networks. It has dramatically impacted the research arena because of its widespread and real-time applications. But successful implementation of underwater wireless sensor networks faces many issues. The primary concern in the underwater sensor network is sensor nodes' energy depletion problem. In this paper, to improve the lifetime of the underwater wireless sensor network, an Energy-Aware Multi-level Clustering Scheme is proposed. The underwater network region is considered 3D concentric cylinders with multiple levels. Further, each level is divided into various blocks, representing one cluster. The proposed algorithm follows vertical communication mode from the sea bed to the surface area in a bottom-up fashion. Multiple levels with varying heights overcome the communication issues due to high water pressure towards the sea bed. Simulations are carried out to show the efficiency of the proposed algorithm, which performs better in terms of a prolonged network lifetime and average residual energy. The simulation result shows significant improvement in the network lifetime compared with current algorithms.
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    Energy-efficient and load distributed clustering algorithm for dense wireless sensor networks
    (Modern Education and Computer Science Press, 2017-05-08) Sivaraj, C; Alphonse, P.J.A.; Janakiraman T.N.
    Wireless sensor networks (WSNs) consist of a large number of tiny sensors with sensing, processing and transmission capabilities. Reducing energy consumption of nodes is one of the major objectives in the design of wireless sensor networks, as sensors have low power batteries. As data collection is the primary objective of WSNs and it consumes more energy, energy-efficient routing is a prominent solution to reduce the sensors energy consumption. This paper proposes an Energy-efficient and Load Distributed Clustering Algorithm (ELDCA) for routing in dense wireless sensor networks to reduce and distribute the sensors' energy consumption. The network is divided into number of virtual congruent square grids of defined sizes. The algorithm constructs optimal and load balanced clusters at every inner crossing point of grid cells using defined cluster fitness value. As early energy depletion is a major design issue in clustering protocols, the proposed algorithm provides a local substitution for energy suffering clusterheads (CHs). To prove the excellence of the proposed algorithm, extensive simulation experiments are performed under different network scenario. The results are compared with latest routing algorithms in terms of network lifetime, energy dissemination, and energy utilization.
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    Self-Load balanced clustering algorithm for routing in wireless sensor networks
    (Modern Education and Computer Science Press, 2017-09-08) Chinnasamy, Sivaraj; Alphonse, P.J.A.; Janakiraman, T.N.
    Energy-efficient routing is an extremely critical issue in unattended, tiny and battery equipped Wireless Sensor Networks (WSNs). Clustering the network is a promising approach for energy aware routing in WSN, as it has a hierarchical structure. The Connected Dominating Set (CDS) is an appropriate and prominent approach for cluster formation. This paper proposes an Energy-efficient Self-load Balanced Clustering algorithm (SLBC) for routing in WSN. SLBC has two phases: The first phase clusters the network by constructing greedy connected dominating set and the nodes are evenly distributed among them, using the defined parent fitness cost. The second phase performs data manipulations and new on-demand re-clustering. The efficiency of the proposed algorithm is analysed through simulation study. The obtained results show that SLBC outperforms than the recent algorithms like GSTEB and DGA-EBCDS in terms of network lifetime, CDS size, load dissemination, and efficient energy utilization of the network.
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    Using computational fluid dynamics and deep learning for leak detection and localization in a smart water management system
    (Springer Science and Business Media B.V., 2024-11-05) Pandian, C; Alphonse, P.J.A.
    Water leakages in distribution systems offer significant challenges, resulting in water loss, infrastructure damage, and environmental hazards. As urban populations grow and water resources become scarcer, water management systems’ efficiency becomes increasingly important. Traditional leak detection methods are inadequate and labour-intensive, making them unsuitable for large-scale infrastructure. In addition, limitations in the data representing pipes of varying diameters and materials under various leak scenarios leads to complexity in automatic water leak detection and localization. In response to this challenge, this research effort combines Computational Fluid Dynamics (CFD) and Deep Learning (DL) to enhance leak detection and localization. The study simulates water flow in pipes of varying diameters and materials under various leak scenarios using CFD. Consequentially deep learning models like the Bi-Layered ReLU Encoder + Softmax and Tri-Layered ReLU Encoder + Softmax are trained and tested. The results indicate improvements in detection accuracy by 2.4% and localization by 5.6%, highlighting the potential of this hybrid approach to enhance the reliability and efficiency of water management systems, ultimately promoting water conservation and reducing infrastructure damage.