Department of Computer Science and Information Systems
Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928
Browse
4 results
Search Results
Item A review on WSN based resource constrained smart IoT systems(Springer, 2025) Haribabu, K.In Wireless Sensor Network (i.e. WSN) based resource constrained Internet of Things (i.e. IoT) environments, efficient data forwarding is achieved through cluster based mechanisms, where cluster heads facilitate communication among themselves and with the sink node. Data collected by each cluster head is temporarily buffered before being transmitted to the sink via multi-hop communication. The integration of advanced wireless technologies, such as 5th Generation (i.e. 5G) networks, offers significant benefits, including reduced latency, extensive coverage, improved spectral efficiency, and higher data transmission rates. Incorporating Device-to-Device (i.e. D2D) communication further enhances energy efficiency and offloads data traffic, addressing critical IoT requirements such as low latency, increased network capacity, and improved spectral and energy efficiency. Software Defined Networking (i.e. SDN) addresses diverse IoT network needs across domains like smart grids, healthcare, traffic signaling, agriculture, and smart homes by enabling efficient communication, network management, and innovative control procedures. However, SDN’s application for anomaly detection and primary defense against security threats in IoT systems remains underexplored. This research investigates the potential of the design of an intelligent mechanism for energy efficient, privacy preserving, and secure communication in WSN based resource constrained IoT systems. The proposed approach leverages advanced technologies such as SDN, Machine Learning (i.e. ML), Deep Learning (i.e. DL), D2D communication, Computer Vision, and Network Function Virtualization (i.e. NFV). Additionally, it emphasizes assessing and offloading specific IoT application functions onto the network’s edge to enhance performance. Moreover, the development of lightweight security mechanisms for secure communication in resource constrained IoT environments is also identified as a crucial research domain.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 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 Dyswitch: dynamic switching to enable secure and energy efficient data communication in resource constrained iot environmentattack detection in data plane(Springer, 2025-04) Haribabu, K.Numerous applications spanning smart health, smart cities, smart parking, smart agriculture, smart homes, and smart transportation rely extensively on internet of things (IoT) systems. These systems depend on the periodic sensing of the physical environment, employing wireless sensor networks (WSNs) to collect vast amounts of data. Given the importance of safeguarding this data against diverse attacks, traditional security mechanisms may prove impractical for resource constrained WSN devices. Lightweight cryptographic algorithms emerge as a fitting solution for such environments. This paper introduces a system proposed to dynamically transition between available lightweight cryptographic algorithms, guided by factors such as the desired security level, network status (e.g. bit error rate), and user requests. Through this dynamic adaptive approach, the proposed system ensures swift adaptability to evolving security requirements and network conditions. Moreover, this methodology highlights a nuanced integration of cryptographic algorithms, catering to the evolving needs of modern IoT environments.