Conference Papers
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Item Energy aware routing protocol for resource constrained wireless sensor networks(Association for Computing Machinery acmhelp@acm.org, 2016) Prasad, S.; Jaiswal, S.; Shet, N.S.V.; Sarwesh, P.Wireless sensor network (WSN) is a developing technology that improves the resource utilization in various fields such as home automation, e-health, smart grid, precision agriculture etc. It consists of sensor devices, which works autonomously with its sensing, communication and computation capabilities. In most of the sensor network applications the nodes will be deployed in remote areas (land slide monitoring, wildlife monitoring etc.), so replacing the battery often is impossible in many WSN scenarios. Hence energy is considered as a valuable resource in resource constrained wireless sensor networks. In this paper, we propose a new routing methodology to improve the energy efficiency in resource constrained WSN. The proposed technique uses SNR (Signal to Noise Ratio), node degree and residual energy as routing parameters, to find an energy efficient and reliable path for data transmission. The combination of the parameters used in the proposed routing technique is done based on weighted sum approach. The proposed framework has been implemented on and compared with a number of standard energy efficient protocols and our results show considerable improvement in comparison to the existing techniques. © 2016 ACM.Item An energy-efficient static multi-hop (ESM) routing protocol for wireless sensor network in agriculture(Institute of Electrical and Electronics Engineers Inc., 2018) Dubey, A.K.; Upadhyay, D.; Santhi Thilagam, P.S.Energy efficient routing is a crucial area of research for wireless sensor network(WSN). The communication between the wireless sensor nodes is handled by the routing protocols. The nature of the link, low power and limited recourse makes it a difficult task to design an energy and performance efficient routing protocol for WSN. This paper proposes an energy-efficient route selection protocol for a multi-hop network with a static link for deployment in the field of agriculture. An energy model is also given in this paper to evaluate the energy consumption of the network. The proposed energy-efficient static multi-hop(ESM) routing protocol is evaluated based on the performance parameters for energy efficiency, network lifetime and pack loss and compared with an existing scheme. The simulation results represent that the proposed protocol increases the network throughput and the lifetime. © 2018 IEEE.Item A framework for residual energy model in unetstack simulator for underwater sensor networks(Institute of Electrical and Electronics Engineers Inc., 2020) Chandavarkar, B.R.; Gadagkar, A.V.In recent years, Underwater Acoustic Sensor Networks (UASN) has gained much attention from researchers because of its diverse applications. UASNs face several issues and challenges like limited bandwidth, high propagation delay, 3D topology, media access control, routing, resource utilization, and energy constraints. Unlike the nodes in terrestrial wireless sensor networks (TWSNs), UASNs suffer from energy constraints, severely affecting the network lifetime and throughput. Simulation of UASNs is a common aspect of researchers. It facilitates analysis of the working and performance of a UASN before it is implemented and deployed, which incurs substantial time and cost. Among the different simulation platforms available for simulating UASNs, UnetStack is one, which is an efficient and well-known tool available for simulating UASN, with significant benefits. But, the present UnetStack does not provide direct functionality for monitoring the energy of nodes during simulations, which is crucial. This paper presents the design and implementation of the residual energy model framework in UnetStack. Additionally, through the experimental simulations, the number of frames transmitted received, and the depletion of node energy over time presented. Further, the implemented energy model framework able the researchers in the design of energy-aware routing protocols and load balancing methods. © 2020 IEEE.Item Quantum Inspired Multiobjective Optimization in Clustered Homogeneous Wireless Sensor Networks for Improving Network Lifetime and Coverage(Springer Science and Business Media Deutschland GmbH, 2021) Kanchan, P.; Shetty D, D.S.; Attea, B.A.The optimization technique in which many objectives are simultaneously optimized is called multiobjective optimization. A wireless sensor network (WSN) consists of many sensors forming a network. These sensor nodes mainly run on battery which deteriorates with time. Our aim is to optimize coverage and lifetime of the network. One of the most effective methods for minimizing energy and increasing lifetime of nodes is clustering. In this paper, we integrate the two objectives of improving network lifetime and increasing coverage. We use quantum bits or qubits in our representation instead of bits. A qubit can be in 0 state, 1 state or a super position of these two states at the same time. This is what makes quantum computing-based algorithms more powerful as we can have more diversity. The proposed algorithm, quantum inspired multiobjective evolutionary algorithm based on decomposition (QMOEAD) is compared with LEACH, SEP, NSGAII and MOEA/D on the basis of coverage and network lifetime. The results show that QMOEAD outperforms the other algorithms mentioned above. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Quantum Optimizer Using MOEAD for WSN’s(Springer Science and Business Media Deutschland GmbH, 2024) Kanchan, P.; Shetty D, P.; Attea, B.A.Optimization of Wireless sensor networks is done with respect to several parameters like energy efficiency, coverage, etc. A WSN is an inter-related collection of sensors. A WSN can be Homogeneous or Heterogeneous. All nodes in homogeneous WSNs have comparable characteristics like energy of the nodes or radius of sensing, etc. In Heterogeneous WSNs, some of these properties differ. MultiObjective Opimization (MOO) simultaneously optimizes more than one objective. The Multi Objective Evolutionary Algorithm with Decomposition (MOEAD) splits/decomposes a problem into subproblems and all these subproblems are simultaneously optimized. In classical computing, a bit is usually represented by 0 or 1. In Quantum Computing, a bit is 0, 1 or a superposition of 0 and 1. In our research, we use MOEAD with quantum computing to optimize the multiple goals of network lifetime along with coverage for WSNs. These WSNs can be homogeneous or heterogeneous. We contrast our methodology with some of the standard methodologies. Simulations show the upsides of our methodology over different techniques referenced here. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.