Faculty Publications
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Publications by NITK Faculty
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Item Optimizing Network Lifetime and Energy Consumption in Homogeneous Clustered WSNs Using Quantum PSO Algorithm(Springer Science and Business Media Deutschland GmbH, 2021) Kanchan, P.; Shetty D, D.S.A Wireless Sensor Network (WSN) is a group of sensors which communicate with each other and perform some specific task. Clustering is used to conserve energy in a WSN. In this work, the aim is to minimize the energy consumption and maximize the network lifetime of a homogeneous WSN using PSO (Particle Swarm Optimization) based Clustering algorithm in conjunction with quantum computing. In quantum computing, a bit is known as a qubit and it can exist in the following states: a ‘0’, a ‘1’ or a superposition of ‘0’ and ‘1’. In this chapter, the Quantum Computing based PSO clustering algorithm for Optimizing Energy consumption and Network lifetime (QCPOEN) algorithm for homogeneous wireless sensor networks is proposed. The proposed algorithm is compared with the PSO-ECHS algorithm and the LEACH algorithm. The superiority of the algorithm can be verified from the results. © 2021, Springer Nature Singapore Pte Ltd.Item Comparative Study of PI, PID controller for Buck-Boost Converter tuned by Bio-Inspired Optimization Techniques(Institute of Electrical and Electronics Engineers Inc., 2021) Vittal K, K.; Bhanja, S.; Keshri, A.In this paper the Buck-Boost converter was modelled using state-space averaging approach and simulated in MATLAB/Simulink. Buck-Boost converter with closed loop control, operated with PI and also with PID controller for good voltage regulation. Bio-inspired optimization techniques e.g. GreyWolves optimization Technique (GWO), Genetic Algorithm(GA), Particle Swarm optimization (PSO), Ant-Lion optimization (ALO), Whale optimization Algorithm (WOA) were used for tuning PI and also PID controller based Buck-Boost Converter. In order to find out the performances of PI and PID in the Buck-Boost converter, comparison between optimal values of PI parameters $(\text{K}-{\text{p}},\ \text{K}-{\text{i}})$ and PID parameters $(\text{K}-{\text{p}},\ \text{K}-{\text{i}},\ \text{K}-{\text{d}})$ obtained by all the above mentioned optimization techniques were performed. The transient behaviour for each optimal values of PI and PID controller was investigated when the system subjected to a load disturbance. Also, for each optimal PI and PID controller error performance indices e.g. Integral Squared Error and Integral Absolute Error were evaluated. The comparison proved that the PID is most suitable controller for Buck-Boost Converter as it is damping out the oscillations caused due to load disturbance 87.56% faster than PI controller. Moreover, based on the evaluated values of error performance indices and dynamic behaviour, it has also been proven that GA is best optimization technique among others for tuning PID in a Buck-Boost Converter. © 2021 IEEE.Item Performance analysis of PSO for solving coverage problem in WSN(Institute of Electrical and Electronics Engineers Inc., 2023) Bhat, P.P.; Geetha, V.A Wireless Sensor Network consists of a large number of nodes which gather information from the surroundings and transmit it to other sensor nodes in the network. To maximize this area covered is the main task in a wireless sensor network. The gaps created due to the deployment of nodes leads to coverage holes, which leads towards the non area coverage and communication coverage. In this paper, we explore different deployment methods and use Particle Swarm Optimization (PSO) to maximize area coverage of a given region. Our work explores the deterministic and random deployment techniques for sensor nodes to achieve maximum coverage with respect to both area and target and usage of PSO to optimize the solution. The results are compared with PSO with deterministic deployment of sensors in a given region. © 2023 IEEE.Item Nature inspired based pitch controller design of an UAV(2012) Jada, C.; Paul, K.; Omkar, S.N.; Tutika, S.In this paper a longitudinal control system has been designed for a 2 Kg payload Unmanned Aerial Vehicle using classical PID controller. To overcome the difficulty in tuning the PID gains to the required values, different nature inspired techniques have been used, for the Integral Squared Error (ISE) parameter optimization on UAV dynamic data at a specified trim condition. For this purpose, the penalty function that has been chosen includes time domain specifications as constraints. Finally all the results are compared for optimum PID parameters.Item A quantum inspired PSO algorithm for energy efficient clustering in wireless sensor networks(Cogent OA info@CogentOA.com, 2018) Kanchan, P.; Shetty D, S.D.Clustering is done in wireless sensor networks (WSN) to conserve the energy of sensor nodes in the network. Decreasing the energy consumption of nodes prolongs the lifetime of the WSN. A quantum bit can exist in “0” state, “1” state or a linear superposition of “0” and “1” states, unlike the binary bit which can exist in only “0” state or “1” state. In this paper, we propose a Quantum inspired PSO (particle swarm optimization) called Quantum-inspired PSO for Energy Efficient Clustering (QPSOEEC). The algorithm is tested by giving different values to the number of sensor nodes and cluster heads, varying the base station position, etc. Then, our results are compared to existing algorithms that demonstrate the superiority of our algorithm. © 2018, © 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.Item Inverse estimation of heat flux under forced convection conjugate heat transfer in a vertical channel fully filled with metal foam(Elsevier Ltd, 2022) Trilok, G.; Vishweshwara, P.S.; Gnanasekaran, G.In this work, for the first time, a heat flux at the boundary is estimated for a conjugate heat transfer under forced convection in the presence of high porosity metal foams. For the forward problem a vertical channel experimental set up reported in the literature is considered. The metal foam placed in the vertical channel is subjected to constant heat flux through aluminum plate and airflow of various velocities is passed through vertical channel for removal of heat from the high porosity metal foam placed in the vertical channel. Six different velocities are considered and the required temperature distribution of the aluminum plate is obtained by solving Darcy extended Forchheimer and Local Thermal non-equilibrium models for metal foams. The forward problem, created using computational fluid dynamics in ANSYS-FLUENT, is substituted with Neural Network for faster computation of the forward problem. The maximum errors between the computational fluid dynamics and Artificial Neural Network models for the heat flux values of 466.66, 666.66 and 1133.3 W/m2 are found to be 0.086, 0.043, 0.092 respectively. The heat flux to the forward problem is treated as unknown and the same is estimated using an inverse method that couples Particle Swarm Optimization with Bayesian framework. The result of inverse estimation of exact temperature data shows that for a heat flux of 1266.64 W/m2 the error is found to be 1.6e−4%. Similarly, for the noise added temperature data, the absolute % error in heat flux of 599.985, 733.315 and 1266.635 W/m2 is 4.80e−2%, 2.20e−2%, 2.30e−2% respectively. © 2022 Elsevier LtdItem Nature-inspired query optimisation models for medical information retrieval with relevance feedback(Inderscience Publishers, 2023) Jayasimha, A.; Mudambi, R.; Kamath S․, S.S.Medical information retrieval (MedIR) involves retrieving relevant medical-related information from a set of medical documents for a particular user query. As the volume of medical records grows, the challenging problem is determining those documents which best suiting a given query by considering user satisfaction. Statistical term weighting and probabilistic approaches for this purpose have several limitations. The gap between information need and user query can be addressed through query optimisation and relevance feedback. In this paper, we propose a document retrieval framework that incorporates query optimisation using techniques like genetic algorithm, particle swarm optimisation (PSO), and global swarm optimisation (GSO). Further, we use relevance feedback methods to reformulate the user query. The proposed techniques are applied to datasets with predefined relevance judgments to perform quantitative validation. Experimental results using the relevance judgements available in the University of Glasgow's Medline collection underscored the significant improvement achieved using BM25 scores as the fitness function. © 2023 Inderscience Enterprises Ltd.