Conference Papers
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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.