Browsing by Author "Mamatha, K.M."

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Effect of Variations in Mn Content on Mechanical and Corrosion Characteristics of Cu-Al-Mn Shape Memory Alloys; УТИЦАЈ ВАРИЈАЦИЈА У САДРЖАЈУ МН НА МЕХАНИЧКЕ И КОРОЗИОНЕ КАРАКТЕРИСТИКЕ Cu-Аl-Мn ЛЕГУРА СА МЕМОРИЈОМ ОБЛИКА
(Belgrade University, 2024) Mamatha, K.M.; Mallik, U.S.; Koti, V.; Murthy, K.V.S.; Koppad, P.G.
In this work, the role of Mn on the shape memory effect and mechanical and corrosion behavior of Cu-Al-Mn shape memory alloys was studied. The composition of Al was fixed to 10 wt% while that of Mn was varied from 2 to 10 wt%. The strain recovery by SME was evaluated using the bend test, while the yield and ultimate tensile strength were obtained using the tension test. The corrosion behavior was studied using three different solutions: freshwater, substitute ocean water, and Hank’s solution. The yield and ultimate tensile strength of Cu-Al-Mn alloys increased with Mn content up to 6%, which was attributed to grain refinement and precipitation hardening, while the fracture analysis showed mixed mode failure for all alloys. The corrosion behavior of Cu-Al-Mn alloys was modified due to the addition of Mn. With the increase in Mn content, the alloys displayed better corrosion resistance and lower corrosion rates. The corroded surface analysis tested in freshwater showed pitting corrosion, while Cu-Al-Mn alloy with low Mn content was tested in substitute ocean water. Hank’s solution showed surface damage with an unstable surface layer. © Faculty of Mechanical Engineering, Belgrade. All rights reserved
Energy Efficient Coverage Optimization in Mobile Wireless Sensor Network Using Grey Wolf Algorithm
(Institute of Electrical and Electronics Engineers Inc., 2023) Gupta, A.; Mamatha, K.M.; Manjappa, M.
The issue of decreased coverage rate in Mobile Wireless Sensor Networks (MWSNs), caused by mobile sensor nodes being randomly placed inside a monitoring area. Additionally, it becomes extremely important to utilise a sensor node's energy very effectively due to the finite energy of sensor nodes. Hence, to provide optimised positions for the sensor nodes while using the energy of sensor nodes adeptly authors propose an energy efficient coverage algorithm. Initially, article focus on optimal placement of the sensor nodes within a area to achieve the maximum coverage and later authors have focused on improvising the network lifetime. Article presents a combination of Grey Wolf Optimization and Virtual Force algorithm for optimization of coverage in MWSN. Further, to improve the network lifetime, a GWO-based clustering algorithm is presented using distance and energy as a parameter. The algorithms are implemented and simulated on Matlab. The efficiency of the presented algorithm is observed comparing with other Swarm Intelligence (SI) based optimization algorithms, like GWO, VFA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant-lion Optimization (ALO) and the results of the GWO-based clustering is compared with the traditional LEACH algorithm and energy-balanced clustering based on PSO. Simulation results demonstrate that the presented algorithms outperform the considered algorithms. Â© 2023 IEEE.
Firefly algorithm for self organization of mobile wireless sensor network
(2020) Mamatha, K.M.; Kiran, M.
For a Wireless Sensor Network (WSN), designing low power scalable network remains a challenge for researchers. The sensor nodes find it difficult to gather and transfer data to sink node when they are deployed in hostile and unfavorable environment. Hence, establishing and maintaining connectivity among the mobile sensor nodes in decentralized network play an important role when the environment is unfavorable. Senor nodes self organize themselves in order to establish and maintain the connectivity. This paper proposes a nature based Swarm Intelligence (SI) technique, based on insect firefly to enhance connectivity among the sensor nodes for a decentralized mobile WSN in an energy efficient manner. The foraging feature of insect firefly is used in the proposed algorithm for which a multi-objective fitness function with parameter energy and distance has been designed. The proposed algorithm is theoretically analyzed and verified by simulation and the results show that the proposed algorithm leads energy consumption compared to existing firefly algorithm and prolongs the network lifetime significantly. 2020 Journal of Communications.
Firefly algorithm for self organization of mobile wireless sensor network
(Engineering and Technology Publishing, 2020) Mamatha, K.M.; Manjappa, M.
For a Wireless Sensor Network (WSN), designing low power scalable network remains a challenge for researchers. The sensor nodes find it difficult to gather and transfer data to sink node when they are deployed in hostile and unfavorable environment. Hence, establishing and maintaining connectivity among the mobile sensor nodes in decentralized network play an important role when the environment is unfavorable. Senor nodes self organize themselves in order to establish and maintain the connectivity. This paper proposes a nature based Swarm Intelligence (SI) technique, based on insect firefly to enhance connectivity among the sensor nodes for a decentralized mobile WSN in an energy efficient manner. The foraging feature of insect firefly is used in the proposed algorithm for which a multi-objective fitness function with parameter energy and distance has been designed. The proposed algorithm is theoretically analyzed and verified by simulation and the results show that the proposed algorithm leads energy consumption compared to existing firefly algorithm and prolongs the network lifetime significantly. © 2020 Journal of Communications.