Faculty Publications

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Author: search.filters.author.Shetty D, D.P.Has files: No

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    K-distinct strong minimum energy topology problem in wireless sensor networks
    (Springer Verlag, 2015) Panda, B.S.; Shetty D, D.P.; Pandey, A.
    Given a set of sensors, the strong minimum energy topology (SMET) problem is to assign transmit power to each sensor such that the resulting topology containing only bidirectional links is strongly connected and the total energy of all the nodes is minimized. The SMET problem is known to be NP-hard. Currently available sensors in the market support a finite set of transmission ranges. So we consider the k- Distinct-SMET problem, where only k transmission power levels are used. We prove that the k-Distinct-SMET problem is NP-complete for k ≥ 3. However, on the positive side, we show that the 2-Distinct- SMET problem can be solved in polynomial time. The energy cost of transmitting a bit is higher than the cost of computation, and hence it may be advantageous to organize the sensors into clusters and form a hierarchical structure. This motivated the study of k-Distinct-rStrong Minimum Energy Hierarchical Topology (k-Distinct-rSMEHT) problem: Given a sensor network consisting of n sensors, and integers k and r, assign transmit powers to all sensors out of the k distinct power levels such that (i) the graph induced using only the bi-directional links is connected, (ii) at most r sensors are connected to two or more sensors by a bidirectional link and (iii) the sum of the transmit powers of all the sensors is minimum. We Propose a(formula presented.) approximation algorithm for the k-Distinct-rSMEHT problem for any fixed r and arbitrary k. © Springer International Publishing Switzerland 2015.
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    Algorithms for minimizing the receiver interference in a wireless sensor network
    (Institute of Electrical and Electronics Engineers Inc., 2016) Shetty D, D.P.; Lakshmi, M.P.
    Limiting Interference between the nodes in a Wireless Sensor Network (WSN) is of considerable importance for energy-efficiency of the network. Minimizing the interference in a WSN minimizes the overall energy consumption of the network by reducing the number of conflicting transmissions. We consider Receiver interference minimization problem. Two types of interference are defined in a WSN, namely Sender interference and Receiver interference. In this paper we consider the Receiver interference problem, where the objective is to minimize the maximum Receiver interference. The problem of minimizing the maximum Receiver interference is proved to be NP-hard. In this paper we propose two algorithms named MinMax-RIP and a modified version of the same to minimize the maximum Receiver interference in a WSN. We evaluate the performance of our algorithms through simulation. We then consider the interference minimization problem in a broadcast network. We propose MinMax-BRIP algorithm for optimal range assignment which gives minimum total Receiver interference for connectivity predicate Broadcast. © 2016 IEEE.
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    Total Power Minimization Using Dual Power Assignment in Wireless Sensor Networks
    (Institute of Electrical and Electronics Engineers Inc., 2016) Sisodiya, N.; Shetty D, D.P.
    To minimize the energy consumption in a wireless sensor network (WSN) is very important task as the sensors are generally very small and have a power battery attached to it which cannot have very large power capacity. In a wireless sensor network any sensor node must be able to retrieve information from any other sensor node in the network, so the connectivity of the network is also very important concern. The range assignment problem in WSN is to assign transmission power levels to the nodes of a WSN such that some constraints like connectivity is satisfied. In practice it is usually impossible to assign arbitrary power levels to the sensor nodes in a wireless sensor network. Most sensors available in the market operate with discrete power levels. So in this paper we consider the sensor nodes with only two possible power levels assigned to them. Our aim is to minimize the total power consumption while maintaining the strong connectivity between the sensor nodes in the network. We present a nearly optimal heuristic algorithm and also the experimental results. Our SCDPA algorithm gives an average approximation of 1.51. We also present a heuristic algorithm to minimize total power using 3 power levels. © 2015 IEEE.
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    Study of novel COVID-19 data using graph energy centrality: a soft computing approach
    (Inderscience Publishers, 2022) Mahadevi, S.; Kamath, S.S.; Shetty D, D.P.
    The propagation of the new pandemic COVID-19 is more likely linked to human social relations and activities. A social network can be used to describe these human relationships and activities. Understanding the dynamic properties of disease dissemination through diverse social networks is critical for effective and efficient infection prevention and control. With the frequent emergence and spread of infectious diseases and their impact on large areas of the population, there is growing interest in modelling these complex epidemic behaviour. Such an approach could provide a stronger decision-making method to tackle and control disease. In this paper, a transmission network is developed using the South Korean data, and the study of the network is carried out using graph energy centrality. This measure of centrality allows us to recognise the primary cause of the spread of the virus within the established network by ranking the nodes of the network based on graph energy. The identified primary cause can then be isolated, which can prevent further spread of infection. We have also considered the Novel Corona Virus 2019 Dataset from Johns Hopkins University to analyse epidemiological data around the world. © © 2022 Inderscience Enterprises Ltd.
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    Optimal sensors placement scheme for targets coverage with minimized interference using BBO
    (Springer Science and Business Media Deutschland GmbH, 2022) Naik, C.; Shetty D, D.P.
    A Wireless Sensor Network (WSN) consists of a group of energy-constrained tiny devices called sensors which have sensing, processing, and communicating capabilities. These sensors are deployed in a region of interest for monitoring targets or detecting events, and forwarding the processed data to the sink nodes or gateways. In any wireless network scenario, the targets are to be covered by at least one sensor in the network in order to detect certain events. Maximizing coverage along with improving energy efficiency of the network is a fundamental issue in WSN. Therefore, a Biogeography Based Optimization (BBO) meta-heuristic technique is employed to place sensors in the region of interest. The proposed scheme solves a multi-objective problem using classical weighted sum approach. A fitness function is derived from combination of conflicting objectives, minimum interference, maximum target coverage, and selection of minimum number of sensor nodes along with connectivity of the network as a constraint. The scheme selects minimum number of sensors to deploy in the field of interest which maximizes the target coverage by minimizing the interference of sensors. The proposed scheme is tested on random and grid deployment scenarios. Finally, the scheme is compared with Genetic Algorithm and Random Scheme. The average interference energy loss on BBO-based scheme is found to be 16% less than that of the GA-based scheme, and 60% less than that of a Random-based scheme. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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    Multi-attribute decision making approach for energy efficient sensor placement and clustering in wireless sensor networks
    (Springer, 2025) Naik, C.; Shetty D, D.P.
    Energy conservation is the most critical problem in wireless sensor networks due to its battery-operated tiny devices called sensors. These sensors are placed randomly in a region of interest to monitor certain events and targets. The random placement of sensors creates interference among them and leads to a quick energy drain of sensors. Minimizing interference while maintaining target coverage and connectivity in wireless sensor networks is less studied in the literature. There are many studies on clustering in wireless sensor network using different schemes and techniques to handle energy problems in wireless sensor networks. However, these studies never consider the interference during the sensor placement and clustering. The interference of nodes causes a message drop and results in quick energy drain during data transfer between member nodes and cluster heads. Therefore, in the proposed work, a novel interference-aware sensor deployment scheme is developed followed by a clustering technique on deployed sensors. The parameters such as interference, coverage, and connectivity of the sensors are considered for the sensor deployment. In clustering, the cluster heads are identified using various parameters like energy of the nodes, distance between the nodes and base station, communication range of the nodes, average distance between the nodes to their member nodes. Both the sensor deployment and the clustering adopt a well known multi-attribute decision making method E_TOPSIS for ranking potential positions for deployment of the sensors and ranking the sensor nodes for electing cluster heads. The sensor deployment scheme is compared with TOPSIS and SAW methods and the clustering technique is compared with TOPSIS, SAW, and Modified LEACH for stability period and network lifetime. The results show that the stability period for clustering using E_TOPSIS is 34.1%, 73.65%, and 83.5% better than TOPSIS, SAW, and Modified LEACH methods respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.