Dr. Anil Rose | Chandigarh College of Engineering and Technology (original) (raw)

Dr. Anil Rose

Supervisors: Dr. Arun Khosla and Dr. J. S. Saini
Phone: +91-9416234853

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Papers by Dr. Anil Rose

Range-free 3D node localization in anisotropic wireless sensor networks

IPSec performance on Wimax Networks

Computational Intelligence Based Algorithm for Node Localization in Wireless Sensor Networks

A MESH BASED TRIANGULATED APPROACH TO RECONSTRUCT 3-D SURFACES

Journal of Emerging Trends in …, Jan 1, 2012

Abstract In this paper we describe triangulated approach to reconstruct 3-D surface from noisy an... more Abstract In this paper we describe triangulated approach to reconstruct 3-D surface from noisy and unoriented data sets. Due to increasing availability of point cloud data sets the purpose of surface reconstruction is to obtain a digital representation which is good ...

Accurate location of target nodes is highly desirable in a Wireless Sensor Network (WSN) as it ha... more Accurate location of target nodes is highly desirable in a Wireless Sensor Network (WSN) as it has a strong impact on overall performance of the WSN. This paper proposes the application of H-Best Particle Swarm Optimization (HPSO) and Biogeography Based Optimization (BBO) algorithms for distributed optimal localization of randomly deployed sensors. The proposed HPSO algorithm is modeled for fast and mature convergence, though previous PSO models had only fast convergence but less mature. Biogeography is a school work (collective learning) of geographical allotment of biological organisms. BBO has a new inclusive vigor based on the science of biogeography and employs migration operator to share information between different habitats, i.e., problem solutions. WSN localization problem is formulated as an NP-Hard optimization problem because of its size and complexity. In this work, an error model is described for estimation of optimal node location in a manner such that the location error is minimized using HPSO and BBO algorithms. Proposed HPSO and BBO algorithms are matured to optimize the sensors' locations and perform better as compared to the existing optimization algorithms such as Genetic Algorithms (GAs), and Simulated Annealing Algorithm (SAA). Comparative study reveals that the HPSO yields improved performance in terms of faster, matured, and accurate localization as compared to global best (gbest) PSO. The performance results on experimental sensor network data demonstrate the effectiveness of the proposed algorithms by comparing the performance in terms of the number of nodes localized, localization accuracy and computation time.

nguyendangbinh.org

Wireless sensor networks are increasingly becoming viable solutions to many challenging problems ... more Wireless sensor networks are increasingly becoming viable solutions to many challenging problems and will successively be deployed in many areas in the future. However, deploying new technology without security in mind has often proved to be unreasonably dangerous. In this paper a well structured rule using Reinforcement Learning (RL) is proposed. An agent interacts with the network environment and map the policy for the network security. This paper is organized into five Sections. In Section 1 basic introduction of the sensor network with its architecture and RL is discussed. Section 2 describes the requirement of sensor network security. In Section 3 security management based on policy, its architecture and policy language is discussed. Framework of well structured rule using reinforcement learning agent is proposed in Section 4. Finally conclusions are drawn in Section 5.