Journal Articles
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Item Differential evolution meta-heuristic scheme for k-coverage and m-connected optimal node placement in wireless sensor networks(Machine Intelligence Research (MIR) Labs contact@mirlabs.org, 2019) Naik, C.; Shetty D, D.A wireless sensor network (WSN) faces a wide range of issues, which includes coverage of the given set of targets under specified connectivity constraint. There is a need to monitor different targets in the sensor field for effective information communication to the base station from each wireless sensor node which monitors the target by maintaining required connectivity among them. The problem of ensuring every target covered by at least k sensors and each sensor directly communicate with m sensors is termed as k-coverage and m-connectivity problem in wireless sensor networks. As the wireless sensor nodes are battery driven and have limited energy, the primary challenge is to have an optimal placement of sensor nodes in the field of deployment to minimize energy consumption. The objective of this work is to deploy the optimal number of sensor nodes with k-coverage and m-connectivity constraints in an area of interest. In the last few years, many meta-heuristic algorithms have been proposed to solve different problems like clustering and localization in WSN. In this paper, we introduce a meta-heuristic based differential evolution algorithm to solve k-coverage and m-connectivity problem in WSN. The simulation result shows that the proposed meta-heuristic method out performs the genetic algorithm. © MIR Labs.Item FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks(Springer, 2022) Naik, C.; Shetty D, P.D.Stability of the wireless sensor network (WSN) is the most critical factor in real-time and data-sensitive applications like military and surveillance systems. Many energy optimization techniques and algorithms have been proposed to extend the stability of a wireless sensor network. Clustering is a well regarded method in the research communities among them. Hence, this paper presents hybrid hierarchical artificial intelligence based clustering techniques, named FLAG and I-FLAG. The first phase of these algorithms use game-theoretic technique to elect suitable cluster heads (CHs) and later phase of the algorithms use fuzzy inference system to select appropriate super cluster heads (SCHs) among CHs. The I-FLAG is an improved version of FLAG where additional parameters like energy and distance are considered to elect CHs. Simulations are performed to check superiority of the proposed algorithms over the existing protocols like LEACH, CHEF, and CROSS. Simulation results show that the average stability period of WSN is better in FLAG and I-FLAG compared to other protocols, and so is the throughput of WSN during the stability period. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item 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.Item Factors influencing paddy farmers' choice of marketing channels: study of a nationally representative survey from India(Emerald Publishing, 2024) Naik, C.; Mohan, B.C.Purpose: This study aims to examine the factors that impact the choice of paddy marketing channels in India at the farm level and household contingencies. Design/methodology/approach: Employing multinomial logistic regression, the analysis utilizes the National Sample Survey Office (NSSO) 77th round Situation Assessment Survey (SAS) data from the 2018 to 2019 period, specifically for the paddy Kharif season, to determine the factors determining the choice of marketing channels. The significant independent variables include minimum support price (MSP) awareness, access to and adoption of technical advice, input agency, social group, farm size of farmers, region, age and education of the household head. Findings: Awareness of MSP and adoption of technical advice from experts can enhance the probability of selecting government channels for paddy. The reliance on government input agencies has a favourable impact on the choice of government channels. Government channels are more likely preferred by higher social groups and those with higher land-holdings. There has been a state-wise variation in access to regulated marketing channels for paddy. Research limitations/implications: Transaction cost associated with marketing channel choice is an important factor, not incorporated in this study due to the unavailability in the NSS data. Originality/value: The research uses the latest unit-level data of the NSSO 77th round, published by the Ministry of Statistics and Programme Implementation (MoSPI), the Government of India. © 2024, Emerald Publishing Limited.Item Role of agricultural marketing channels in price realization: an empirical analysis of selected crops in India(Emerald Publishing, 2025) Naik, C.; Mohan, B.C.Purpose: The provision of fair and remunerative prices to farmers through government intervention is one of the key debates to address the farmers' distress in India. This article identifies how different marketing channels are responsible for higher price realization over the officially announced minimum support price (MSP). Design/methodology/approach: The study uses the NSSO-SAS, 2012–13 and NSSO-SAS, 2018–19 for Aggregate level data and Unit Level Data on the Situation Assessment Survey of Farmers' households. It uses logit regression to determine the factors responsible for better price realization. Findings: Our major findings indicate that two factors importantly determine better price realization than MSP. Firstly, government agencies provide better prices for crops covered by MSP, such as paddy, wheat and cotton. However, the probability of receiving higher prices increases for some crops if the farmers belong to the upper land size classes and upper social category. Secondly, jowar, bajra, maize and ragi, other important crops that don't benefit from government agencies, may require higher levels of procurement at the state level. Research limitations/implications: The present study only analyzes selected major crops. Distance is an important factor in choosing a marketing channel that is not incorporated due to unavailability in NSS Data. Originality/value: The study is based on the latest original empirical evidence and sheds light on the variation in price realization in different agricultural marketing channels in India. © 2023, Emerald Publishing Limited.Item 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.