Faculty Publications

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    Cluster Formation for Underwater Routing in UnetStack3
    (Springer Science and Business Media Deutschland GmbH, 2025) Nazareth, P.; Chandavarkar, B.R.; Das, A.P.
    Underwater Acoustics Sensor Networks (UASNs) are utilized in a range of underwater applications, including sea habitat monitoring, offshore research, and mineral exploration. Due to the underwater current, low bandwidth, high water pressure, fluctuations in link quality between nodes, propagation latency, and error probability, underwater communication is challenging. Because of these difficulties, data transmission in UASNs is unreliable during routing. One strategy to improve routing speed is to use an opportunistic routing technique. The sender will transmit the data to the set of neighbours in opportunistic routing such that at least one neighbour can receive and forward the data. The main processes in opportunistic routing include evaluating the adjacent nodes, picking the group of neighbours, and coordinating among the selected nodes to transfer the received data. The optimum next-hops during routing are picked. The numerous properties of neighbouring nodes are analysed and the neighbouring nodes are used for forming clusters that are utilised to choose the best next-hops. In this paper, a novel approach for sensor node clustering technique for UASNs is proposed. Here, it is assumed that the neighbours of a sender node are already ranked. A suitable algorithm like TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is employed to search for the best next-hops and determines a set of candidates to be considered for cluster formation. The protocol has been implemented and simulated in UnetStack3, an agent-based network stack for underwater communication. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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    Multi-Objective Energy Efficient Virtual Machines Allocation at the Cloud Data Center
    (Institute of Electrical and Electronics Engineers, 2019) Sharma, N.K.; Guddeti, R.M.R.
    Due to the growing demand of cloud services, allocation of energy efficient resources (CPU, memory, storage, etc.) and resources utilization are the major challenging issues of a large cloud data center. In this paper, we propose an Euclidean distance based multi-objective resources allocation in the form of virtual machines (VMs) and designed the VM migration policy at the data center. Further the allocation of VMs to Physical Machines (PMs) is carried out by our proposed hybrid approach of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) referred to as HGAPSO. The proposed HGAPSO based resources allocation and VMs migration not only saves the energy consumption and minimizes the wastage of resources but also avoids SLA violation at the cloud data center. To check the performance of the proposed HGAPSO algorithm and VMs migration technique in the form of energy consumption, resources utilization and SLA violation, we performed the extended amount of experiment in both heterogeneous and homogeneous data center environments. To check the performance of proposed HGAPSO with VM migration, we compared our proposed work with branch-and-bound based exact algorithm. The experimental results show the superiority of HGAPSO and VMs migration technique over exact algorithm in terms of energy efficiency, optimal resources utilization, and SLA violation. © 2019 IEEE.