Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Data trustworthiness in wireless sensor networks(Institute of Electrical and Electronics Engineers Inc., 2016) Karthik, N.; Ananthanarayana, V.S.Wireless Sensor Networks (WSN) comprises of tiny wireless sensor nodes installed in the terrain for continuous observation of physical or environmental conditions. Finding data trustworthiness is a prime pre-processing action in WSN because of harsh environment producing faulty data and insecure data transfer over WSN. The trustworthy of the data generated from sensor nodes play an important role to make critical decision. In this work, we propose a Data Trust Management Scheme (DTMS) to address the issue by assigning the trust score to data items. The proposed DTMS detects the data fault with the help of temporal and spatial correlations. The provenance data is used to evaluate the trust score of data item by similarity of value and provenance. The data trust score is utilized for making decision. Implementation of the proposed DTMS is done by simulations. Results show that the proposed DTMS detects untrustworthy data and score the data items which are useful for taking critical decisions. © 2016 IEEE.Item Sensor data modeling for data trustworthiness(Institute of Electrical and Electronics Engineers Inc., 2017) Karthik, N.; Vs, A.Wireless sensor networks (WSNs) are installed in the terrain for observing the physical and environmental parameters. The nodes in the network are resource constrained in nature and faces several challenges for producing the data from the unfriendly environment. Large amount of data is generated from WSN and suffers from data fault, inaccuracy and inconsistency. To increase the reliability of application, several data trust management schemes are introduced to ensure the trustworthiness of data in decision making process. Apart from these schemes, in the absence of ground truth, sensor data models are used to find the trustiness of the sensor data. The data generated from the simulation of data model is used as a metric to evaluate the degree of trustiness of sensor data. The existing sensor data models suffer from high energy consumption for data trustiness detection and it becomes inaccurate when the data fault rate is high. In this paper, we are proposing an energy efficient sensor data model for evaluating the sensor data trustworthiness and reconstruct the sensor data in case of any data loss and data fault. The proposed data model is hybrid in nature and it works at low level sensor nodes and also at sink node. Results show that the proposed data model is able to detect the untrustworthy data and gives remedy to untrustworthy and missing data with the help of data reconstruction in an energy efficient way and it is able to identify the events in reliable fashion. © 2017 IEEE.Item Data trust model for event detection in wireless sensor networks using data correlation techniques(Institute of Electrical and Electronics Engineers Inc., 2017) Karthik, N.; Ananthanarayana, V.S.A wireless sensor network (WSN) is a conglomeration of scattered self organized sensor nodes to agreeably monitor the physical and surrounding conditions. These sensor nodes are equipped with limited resources such as memory, processing capability, battery power and transceiver for monitoring, processing and communicating the observed phenomena to make critical decisions with respect to collected data. Evaluating the trustworthiness of data is a primary preprocessing process of event detection in WSN. The trustworthy data which is free from data fault, inaccuracy and inconsistency is used to identify the interesting events and critical decision making in WSN. In this paper, we present our current work on data trust model that focuses on data fault detection, data reconstruction, data quality estimation for reliable event detection in WSN. The aim of this paper is to propose a novel data trust model for harsh environment of WSN to identify the events and strange environmental data behavior. This proposed framework combines different data processing methods through data correlation techniques to mitigate the data security risks of pervasive environments. © 2017 IEEE.Item An Ontology Based Trust Framework for Sensor-Driven Pervasive Environment(Institute of Electrical and Electronics Engineers Inc., 2018) Karthik, N.; Ananthanarayana, V.S.Pervasive computing is an environment consisting set of sensor nodes with the characteristics of perception, computation and communication capabilities. Wireless sensor nodes are deployed in various pervasive computing applications to observe the happenings in the surroundings. Data gathered from such wireless sensor nodes are utilized for critical decision making in context-aware environment. The Frequent incorrect data sampling, missing values, untrustworthy data, misbehavior and selfishness of nodes are common in pervasive applications since they are deployed in unfriendly and harsh environment. Moreover, the increasing number of sensor node fabricator leads to interoperability problems in context aware pervasive applications because they are represented in different formats and processed using different techniques. In this paper, we propose an ontology based trust framework for sensor driven pervasive environment for evaluating node and data trustworthiness which suffers from heterogeneity and interoperability problems. The proposed approach is capable of handling all types of nodes in pervasive environment and heterogeneous data generated from harsh and unfriendly environments of context aware pervasive applications. The proposed method comprises of semantic sensor data model and an ontology which is written in OWL language and implemented in protege. The proposed ontology is validated against use case and used for finding the trustworthiness of different sensor nodes and its data using a generic TRUST ontology. © 2017 IEEE.Item A Trust Model for Lightweight Semantic Annotation of Sensor Data in Pervasive Environment(Institute of Electrical and Electronics Engineers Inc., 2018) Karthik, N.; Ananthanarayana, V.S.Pervasive computing application consists of various types of sensors, actuators and smart devices for monitoring physical, environmental circumstances and happenings by collecting data and act autonomously to serve user. Due to recent advancements of sensors and wireless technologies, pervasive computing is bringing heterogeneous sensors into our everyday life for providing better services. Data collected from heterogeneous sensors and raising number of sensor node manufacturers leads to data heterogeneity problem in pervasive computing applications. The generated data from various sensors depict more conflict in types, formats and representations which arises problem for nodes to process and infer. Because of data heterogeneity, the data cannot be shared with other application which leads to interoperability problem among pervasive environment. To overcome this, Semantic Web Technologies (SWT) are used for semantic annotation of sensor data. Annotating the sensor data with SWT is an important process in making interoperable pervasive applications. Due to resource restriction, harsh and open environments, data generated from sensor network suffers from noisy, faulty data and missing data. Annotating the faulty data with SWT causes unwanted resource consumption, network traffic and affects application performance. To solve these problems, a trust model is proposed to remove noisy, faulty data and reconstruct the missing data. Trust model ensures the annotation process of trustworthy sensor data alone and reduces resource consumption. In order to find the efficiency of proposed approach, we carried out a set of experimentation on medical sensor network prototype of pervasive healthcare application. Results show that the proposed approach is lightweight in semantic data annotation process and suitable for resource restricted nodes in pervasive environment. © 2018 IEEE.Item Towards an Upper Ontology and Hybrid Ontology Matching for Pervasive Environments(Springer Verlag service@springer.de, 2020) Karthik, N.; Ananthanarayana, V.S.Pervasive environments include sensors, actuators, handheld devices, set of protocols and services. The specialty of this environment is its power to manage with any device at any time anywhere and work autonomously for providing customized services to user. The different entities of pervasive environment collaborate with each other to accomplish an objective by sharing data among them. It raises an interesting problem called semantic heterogeneity. To address this problem, a hybrid ontology matching technique which combines direct and indirect matching techniques is proposed in this paper. To share and integrate data semantically, ontology matching technique establishes a semantic correspondence among various entities of pervasive application ontologies. To find the efficiency of proposed approach, we carried out set of experiments with real world pervasive applications. Experimental results prove that the proposed approach shows excellent performance in hybrid ontology matching. Results also proved that the use of background knowledge has influence over the performance of ontology matching technique. © 2020, Springer Nature Switzerland AG.Item Cardiovascular Diseases Divination using Artificial Neural Network with Ensemble Models(Institute of Electrical and Electronics Engineers Inc., 2023) Pabitha, B.; Sanshi, S.; Karthik, N.Health is wealth, but nowadays, wealth is health, where humans keep running their day-to-day activities without caring about their health for various reasons. Every human being in this world suffers from one or other diseases. Recently, cardiovascular diseases like heart attacks are prevalent in all age groups. Addressing cardiovascular diseases is essential before the disease reaches a crucial stage. Nowadays, artificial intelligence algorithms have been used to detect diseases in their early stages. In this piece of writing, a model of an artificial neural network is utilised to analyze, detect and predict the likelihood of cardiovascular disease in the early stages. In this proposed work, feed forward propagation, forward the input data to learn and map the relationships between inputs and outputs, and backward propagation is used to reduce the errors in the data. Further, an ensemble learning stacked model is used to achieve high accuracy in the prediction of diseases. To verify the correctness of the model, ensemble learning to stack is executed with three different models, namely Model 1, Model 2, and Model 3, with varying sets of feature selections. The experiment results show an accuracy rate of 93% in their predictions. © 2023 IEEE.Item Multi-factor Authentication and Data Integrity for WBAN Using Hash-Based Techniques(Springer Science and Business Media Deutschland GmbH, 2024) Pabitha, B.; Vani, V.; Sanshi, S.; Karthik, N.In recent days, a wireless body area network (WBAN) has been developed as part of the Internet of Things (IoT) with sensors and actuators in three different modes, building its network, i.e., in-body sensors, wearable sensors, and on-body sensors. The doctor’s access the data recorded and monitored by the sensor embedded in the patient to treat critical situations immediately. Maintaining data integrity and guarding against threats is necessary to secure sensitive patient information. Several people have proposed schemes for authenticating data access through formal and informal verification. In this research work, we carry out multi-factor authentication extensively using zero-knowledge proofs. The anomaly detection of the sensors is detected using machine learning algorithms, which help tune the sensors to their correct working conditions. The work aims to concentrate on sensor working conditions promptly and to handle attacks like masquerade, forgery, and key escrow attacks. To assess whether performance metrics are superior in computing cost, storage overhead, and communication overhead, utilize the BAN logic tool. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.