BITS Faculty Publications

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Subject: search.filters.subject.Clustering

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    A novel approach for energy-efficient communication in a constrained IoT environment
    (IEEE, 2024-07) Haribabu, K.
    In the realm of the Internet of Things (IoTs) and wireless sensor networks (WSNs), two key concerns are improving security and energy efficiency. One approach to enhancing network longevity is through the implementation of clustering, which involves managing cluster heads. In this study, the authors proposed two variants of a novel algorithm for energy efficient communication in a constrained IoT environment. One variant considers the node degree while the other doesn’t consider it to improve the round speed by eliminating mandatory re-election processes. Both variants also eliminate the selection of zero cluster heads problem, specifically at the beginning or towards the end of the network. Additionally, the authors tested the performance of proposed variants against several well known algorithms based on various factors such as operating nodes, number of clusters, transmission energy, remaining energy using MATLAB simulation environment. These comparisons will give us a crucial insight into the working of the proposed algorithm and question its applicability in the real world. The results of this comparison are promising, as the proposed variant with node degree outperforms other algorithms.
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    An energy efficient data transmission approach in smart IOT systems
    (IEEE, 2024-07) Haribabu, K.
    Improving energy efficiency and maximizing network longevity are two pressing issues in the Internet of Things (IoT) and wireless sensor networks (WSN). Clustering aids in enhancing energy efficiency and extending network life. A cluster head is selected in each cluster to collect and aggregate data from its cluster members. While electing appropriate nodes as cluster heads is important, associating nodes with the elected cluster heads is another component that can aid improve the network’s longevity. In this study, the authors proposed a new algorithm belonging to the family of local search problems for performing connection migration of nodes between different cluster heads. Furthermore, the simulation environment and the toolkit developed to evaluate several Cluster Head algorithms in this simulation environment have both been presented in detail.
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    Energy efficient data communication for WSN based resource constrained IoT devices
    (Elsevier, 2024-10) Haribabu, K.
    In the Internet of Things (IoTs) and wireless sensor networks (WSNs), improving security and energy efficiency are key concerns. Clustering, which involves managing cluster heads, plays a pivotal role in extending network lifetime. The selection of a cluster head, responsible for data transfer between nodes, is a key aspect of network management. This paper proposes two variants of a novel algorithm designed for energy efficient communication in a resource constrained IoT environments. One variant considers remaining energy, distance, and node degree for cluster head selection, while the other focuses on remaining energy and distance only. Including node degree ensures cluster heads do not waste energy by remaining idle or performing unnecessary tasks such as the cluster head selection process in every round. The authors tested these variants against several well known algorithms using MATLAB simulation environment, evaluating factors such as operating nodes, number of clusters, transmission energy, and remaining energy. The proposed algorithm extends network lifetime by maintaining more operating nodes for longer, not changing clusters or cluster heads frequently, minimizing energy consumption for transmission, and conserving more remaining energy. Consequently, the proposed algorithm outperforms existing approaches by addressing issues like zero cluster head selection, compulsory cluster head selection in every round, avoiding cluster heads that connect to no nodes, and preventing network destabilization due to unnecessary re-elections.
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    A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta
    (Elsevier, 2025-06) Pasari, Sumanta
    Air pollution poses a significant challenge to public health and the global environment. The Industrial Revolution, advancing technology and society, led to elevated air pollution levels, contributing to acid rain, smog, ozone depletion, and global warming. Poor air quality increases risks of respiratory inflammation, tuberculosis, asthma, chronic obstructive pulmonary disease (COPD), pneumoconiosis, and lung cancer. In this context, developing reliable air pollution forecasting models is imperative for guiding effective mitigation strategies and policy interventions. This study presents a daily air pollution prediction model focusing on Jakarta's sulfur dioxide (SO₂) and carbon monoxide (CO) levels, leveraging a hybrid methodology that integrates Clustering Large Applications (CLARA) with the Fuzzy Time Series Markov Chain (FTSMC) approach. The analysis revealed five distinct clusters, with medoid selection refined iteratively to ensure stabilization. A 5 × 5 Markov transition probability matrix was subsequently constructed for modeling the data. Predicted values for SO₂ and CO in Jakarta using the CLARA-FTSMC hybrid method showed strong alignment with the actual data. Forecasting accuracy results for SO₂ and CO in Jakarta, based on Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), showed excellent performance, underscoring the efficacy of the CLARA-FTSMC hybrid approach in predicting air pollution levels.
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    Cluster analysis of breast cancer data using Genetic Algorithm and Spiking Neural Networks
    (IEEE, 2015) Viswanathan, Sangeetha
    Breast cancer is taking a large toll in the present scenario. Many computer aided diagnosis are been developed to detect breast cancer. The detected breast cancer is also classified according to their subtypes. In the absence of a class definition, analyzing the cancer types is huge some task. Clustering the breast cancer data is a process that merges the feature selection process and the process of defining the class labels for the data. The proposed work has four stages which include preprocessing, feature selection, feature clustering and cluster validation. This paper uses a Spiking Neural Network that is been trained with an Evolution topology algorithm and Genetic Algorithm is used to select the features from the dataset. The result of the network will cluster that classifies the data into abrupt types. The clusters are then validated using DB index
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    An Adaptive Hierarchical Method for Anytime Set-wise Clustering of Variable and High-Speed Data Streams
    (IEEE, 2023) Challa, Jagat Sesh; Goyal, Poonam; Goyal, Navneet
    Set-wise Clustering is a clustering technique for data streams that groups sets of objects based on distribution patterns, applicable in contexts like retail chain clustering, text-based community clustering, restaurant categorization, etc. The existing set-wise clustering method cannot handle variable and high-speed streams with reasonable accuracy. This paper presents an Anytime Set-wise Clustering method for data streams known as ANYSETCLUS. The method handles the variable inter-arrival rates of stream objects using a proposed indexing structure called AnySetClusTree, which stores a hierarchy of micro-clusters of multi-set entities at varying granularity. ANYSETCLUS is highly adaptive as it supports incremental model updates, segregates outliers, enables outlier-to-concept transition, and captures concept drift. The method also enables anytime offline clustering wherein it can generate multiple clusterings of varying granularity and purity depending upon the available time allowance for final clustering. The experimental results affirm the superior efficacy of the proposed method in handling variable and high-speed streams compared to the state-of-the-art method. The experimental results also showcase its effectiveness in achieving significantly higher micro-cluster purity for low and high-speed streams. This contrasts with the state-of-the-art method, which is unable to generate valid clustering results for high-speed streams. The experiments further validate the proposed method’s capability for anytime offline clustering.
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    Fusion of multivariate time series meteorological and static soil data for multistage crop yield prediction using multi-head self attention network
    (Elsevier, 2023-09) Goyal, Poonam; Goyal, Navneet
    Yield prediction is helpful for timely harvest management, crop planning, and food security. It depends on many factors like location, climate, soil characteristics, genotype, etc. The data used in yield prediction is a typical mix of highly dynamic time series (meteorological) and static (soil) data. We effectively integrate the two data categories to train a deep-learning model. We introduce a novel attribute selection algorithm to select the most discriminating soil features and modified it for depth-level selection which suggests the most appropriate depth of soil factors for a given crop. We have also introduced a novel approach for modeling the problem where spatiality is handled by clustering locations based on their meteorological and soil characteristics which allow our model to learn spatial patterns. The variation in sowing and harvesting time across locations is taken care of by using padded crop cycle data. We have also taken several other design decisions and validated them on existing models. We experimented with NC94 data of the US with three major crops soybean, wheat, and corn, and predicted yield at the county-level. We have also modified our model to perform in-season and multi-time horizon prediction. The results of our proposed YieldPredictNet show that it outperforms competing techniques.
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    HACCS: Heterogeneity-Aware Clustered Client Selection for Accelerated Federated Learning
    (IEEE, 2022) Kumar, Dhruv
    Federated Learning is a machine learning paradigm where a global model is trained in-situ across a large number of distributed edge devices. While this technique avoids the cost of transferring data to a central location and achieves a strong degree of privacy, it presents additional challenges due to the heterogeneous hardware resources available for training. Furthermore, data is not independent and identically distributed (IID) across all edge devices, resulting in statistical heterogeneity across devices. Due to these constraints, client selection strategies play an important role for timely convergence during model training. Existing strategies ensure that each individual device is included, at least periodically, in the training process. In this work, we propose HACCS, a Heterogeneity-Aware Clustered Client Selection system that identifies and exploits the statistical heterogeneity by representing all distinguishable data distributions instead of individual devices in the training process. HACCS is robust to individual device dropout, provided other devices in the system have similar data distributions. We propose privacy-preserving methods for estimating these client distributions and clustering them. We also propose strategies for leveraging these clusters to make scheduling decisions in a federated learning system. Our evaluation on real-world datasets suggests that our framework can provide 18% −38% reduction in time to convergence compared to the state of the art without any compromise in accuracy.
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    A Randomized Scheduling Algorithm For Multiprocessor Environments
    (World Scientific, 2012) Mishra, Abhishek
    In this paper, we propose a randomized scheduling algorithm on a fully connected homogeneous multiprocessor environment. This is a randomized version of our earlier algorithm in which we used priority of modules that was dependent on the computation and the communication times associated with the modules. First we propose a generalization of our earlier scheduling algorithm with restricted number of clusters to reduce the time complexity. Then we apply randomization to the generalized algorithm and demonstrate its superiority over our previous work. We show the complexity of our proposed algorithm as O(ab |V| (|V|+|E|) log (|V|+|E|)). Here a is the number of randomization steps, and b is a limit on the number of clusters formed. If we use a and b as constants, then this gives a better complexity in comparison with the complexity of our previous algorithm that was O(|V|2(|V|+|E|) log (|V|+|E|)). In comparison with our previous work we get a performance improvement of up to 6.63% and a performance improvement of up to 12.56% when compared with Sarkar's Edge Zeroing algorithm.
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    A Randomized Scheduling Algorithm for Multiprocessor Environments Using Local Search
    (World Scientific, 2016) Mishra, Abhishek
    The LOCAL(A, B) randomized task scheduling algorithm is proposed for fully connected multiprocessors. It combines two given task scheduling algorithms (A, and B) using local neighborhood search to give a hybrid of the two given algorithms. Objective is to show that such type of hybridization can give much better performance results in terms of parallel execution times. Two task scheduling algorithms are selected: DSC (Dominant Sequence Clustering as algorithm A), and CPPS (Cluster Pair Priority Scheduling as algorithm B) and a hybrid is created (the LOCAL(DSC, CPPS) or simply the LOCAL task scheduling algorithm). The LOCAL task scheduling algorithm has time complexity O(|V||E|(|V |+|E|)), where V is the set of vertices, and E is the set of edges in the task graph. The LOCAL task scheduling algorithm is compared with six other algorithms: CPPS, DCCL (Dynamic Computation Communication Load), DSC, EZ (Edge Zeroing), LC (Linear Clustering), and RDCC (Randomized Dynamic Computation Communication). Performance evaluation of the LOCAL task scheduling algorithm shows that it gives up to 80.47 % improvement of NSL (Normalized Schedule Length) over other algorithms.