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    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.
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    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.
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    A Hybrid Trust Management Scheme for Wireless Sensor Networks
    (Springer New York LLC barbara.b.bertram@gsk.com, 2017) Karthik, N.; Ananthanarayana, V.S.
    Wireless sensor network (WSN) consists of wireless small sensor nodes deployed in the terrain for continuous observation of physical or environmental conditions. The data collected from the WSN is used for making decisions. The condition for making critical decision is to assure the trustworthiness of the data generated from sensor nodes. However, the approaches for scoring the sensed data alone is not enough in WSN since there is an interdependency between node and data item. If the overall trust score of the network is based on one trust component, then the network might be misguided. In this work, we propose the hybrid approach to address the issue by assigning the trust score to data items and sensor nodes based on data quality and communication trust respectively. The proposed hybrid trust management scheme (HTMS) detects the data fault with the help of temporal and spatial correlations. The correlation metric and provenance data are used to score the sensed data. The data trust score is utilized for making decision. The communication trust and provenance data are used to evaluate the trust score of intermediate nodes and source node. If the data item is reliable enough to make critical decisions, a reward is given by means of adding trust score to the intermediate nodes and source node. A punishment is given by reducing the trust score of the source and intermediate nodes, if the data item is not reliable enough to make critical decisions. Result shows that the proposed HTMS detects the malicious, faulty, selfish node and untrustworthy data. © 2017, Springer Science+Business Media, LLC.