An Efficient Trusted Framework for Context Aware Sensor driven Pervasive Applications and their Integration using Ontologies

Abstract

Pervasive computing application consists of various types of sensors, actuators, set of protocols and services for monitoring physical, environmental circumstances and happenings by collecting data and act autonomously to serve the user. The pervasive computing is established on recent advancements of mobile computing, distributed computing, wireless communications, embedded systems and context-aware computing that makes computing devices smaller and earns more ability for perception, communication and computation operations. Sensor nodes play an important role in a pervasive computing environment. These sensor nodes are expected to be installed in various pervasive applications for detecting real-world events and respond consequently. Tiny sensor nodes are embedded in everyday objects invisibly that provides ubiquitous access to information services. Due to recent advancements of sensors and wireless technologies, pervasive computing is bringing heterogeneous sensors into our everyday life for providing better services. Massive amount of data is generated from sensor nodes of a pervasive environment, which is forwarded to the sink node through the gateway for data analysis and event detection. The sensed data from pervasive computing application suffers from data fault, missing data, due to the unfriendly, harsh environment and resource restriction. In most of the cases, the generated data can be shared among different applications in the pervasive environment for increasing the user comfortableness, reliability of the application and achieving the full potential of the application. The shared data plays a vital role in critical decision making. The generated data from various sensors depict conflict in types, formats, and representations which arises problem for nodes to process and infer. Various types of sensor nodes and other devices would lead to the generation of heterogeneous data which constrains pervasive application to understand data and use efficaciously. Data interoperability problem occurs when different pervasive applications interact with each other. Furthermore, with the rise of several sensor node manufacturers, pervasive computing faces the problem in the data integration process. Because of data heterogeneity, the data cannot be shared with other application which leads to interoperability problem in the pervasive environment. The objective of the thesis is to share the trustworthy data and offer interoperability across different trusted context-aware pervasive applications. To deal with data faults, data loss and event detection, Trust Management Schemes (TMS) are proposed. To solve interoperability problem, hybrid ontology matching technique is proposed. Sensor data modeling is the basis for all TMS in sensor netowrks. An energy efficient hybrid sensor data modelingfor data fault detection, data reconstruction and event detection is proposed and analysis of energy consumption of data fault detection in various environment is also given. This thesis introduces the Trust-based Data Gathering (TDG) in sensor networks, which focuses on trust-based data collection, trust-based data aggregation, and trustbased data reconstruction to show that the absence of trust in a sensor-driven harsh pervasive environment consumes more energy and delay for handling untrustworthy data, untrustworthy node and affects the normal functionality of the application. This thesis presents the Hybrid Trust Management Scheme (HTMS) for sensor networks, which assign the trust score to node and data based on interdependency property. The correlation metric and provenance data are used to score the sensed data. The data trust score is utilized for making a decision. The communication trust and provenance data are used to evaluate the trust score of intermediate nodes and the source node. The Context-Aware Trust Management Scheme (CATMS) is introduced in pervasive healthcare systems for data fault detection, data reconstruction and medical event detection. It employs heuristic functions, data correlation, and contextual information based algorithms to identify data faults and events. It also reconstructs the data faults and data loss for detecting events reliably. This work aims to alert the caregiver and raise the alarm only when the patient enters into a medical emergency. Finally, this thesis investigates the hybrid ontology matching using upper ontology for solving semantic heterogeneity and interoperability problems. It combines direct and indirect matching techniques with upper ontology to share and integrate data semantically and establishes a semantic correspondence among various entities of pervasive application ontologies. To find the efficiency of the proposed framework, we carried out experiments with INTEL Berkeley lab dataset, sensorscope dataset and data samples collected by medical sensor network prototype of pervasive healthcare application. The experimental results show that the proposed framework shares trustworthy data and offers interoperability across different trusted context-aware pervasive applications.