Department of Computer Science and Information Systems
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Item Mmulcriapp: ML and MCDA based approach for energy efficient communication for wsn based resource constrained iot devices(IEEE, 2025-05) Haribabu, K.Wireless Sensor Networks (WSNs) play a crucial role in various domains like environmental monitoring, agriculture, home automation, and healthcare. However, they face challenges such as limited resources, dynamic environments, data routing issues, scalability, unreliable wireless communication, mobility, security concerns, limited bandwidth, and fault tolerance. Machine Learning (ML) techniques have been utilized to address these challenges. Additionally, Multi Criteria Decision Analysis (MCDA), a tool for making decisions involving multiple criteria, is helpful in scenarios like cluster head selection in WSNs. This paper proposes a hybrid approach that combines ML for initial rounds, followed by MCDA based mechanisms in later rounds. The approach is evaluated using metrics like energy consumption, node degree, remaining energy, sink node location, and distance metrics and shows better performance compared to the ML technique alone.Item 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.Item 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.Item 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.Item Internet of Things and Web Services for Handling Pandemic Challenges(Springer, 2021-10) Rao, Shreyas SureshWithin the past few months, the COVID-19 pandemic has disrupted millions of lives and caused unforeseen economic damage, whose impact is both significant and far-reaching. There is an immediate need to utilize emerging technologies across various industries to fight the pandemic in this light. Internet of Things (IoT) and Web Services (Cloud services) are two such technologies that provide promising solutions to combat the virus outbreak. To monitor, track, and control the spread of viruses during the pandemic, IoT and similar sensor-based technologies have been employed. Innovative technologies that enable monitoring of health delivers live observation by using smart devices to monitor the health and can handle remotely with support of cloud and Artificial Intelligence. The HMS establishes a secure remote monitoring system between patients and doctors, facilitating telehealth services to be rendered. For tracking, the HMS uses a combination of personal health data and social data in real-time, enabled through technologies such as Machine Learning, distributed Cloud computing, and AI-based speech recognition. Because of lightweight Application Programming Interfaces (APIs) and edge computing capacity, the IoT-enabled HMS is now accessible through mobile apps and web-based applications. Web services are playing an integral role in Industry’s response to fight the global pandemic. To access the data on the COVID-19 provided by World Health Organization a separate interface is provided over a web service. Some other RESTful APIs to track COVID-19 include: CORD-19, deployed on Vespa Cloud, that enables search and navigation on Open Research Dataset; CoronaTab that provides localized health information; COVID-19 India API sourced from the Ministry of Health and Family Welfare that retrieves case counts, testing statistics and hospital data from the Indian subcontinent. Cloud-based services are employed to support remote work-from-home operations, e-commerce, retail, healthcare, and entertainment segments, to name a few. Enterprises effectively use cloud services to build robust and disaster-averse networks worldwide to respond to a distributed workforce and protect data and business applications’ integrity. Another sector is the energy and utility verticals, which uses IT service management (PaaS and SaaS) and infrastructure (IaaS) for digital transformation during this pandemic. This chapter discusses how IoT and Web services support handling global COVID-19 challenges, especially in Healthcare, retail, and social sectors.