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This paper presents the threat modeling approach for pervasive environment's security. In pervasive computing, a user might be part of various security domains at any particular instant of time having various authentication mechanisms and different privileges in different security domains. A number of threat modeling approaches and methods have been defined in literature and are in use. However, because of the nature of the pervasive computing and ubiquitous networks, these approaches do not handle the inherent security problems and perspective of pervasive computing. The paper examines in detail the threat modeling and analysis approaches being developed at Microsoft and other methods used for threat modeling. The paper present a novel approach for addressing the threat modeling in pervasive computing and presents the model for threat modeling and risk analysis in pervasive environment.

Context awareness enables smart service discovery and adaptation for mobile and wireless hosts. The contextual data is acquired from sensors present in the smart space, which may be absent. The inherent noisy nature of wireless environments does not guarantee the gathering of correct data. A history module is thus required in conjunction with existing context-aware systems that overcomes these limitations by predicting the data. We present a modular approach that when coupled with existing context managers will be able to provide user preference on the basis of usage history.

Context Awareness is the task of inferring contextual data acquired through sensors present in the environment. ‘Context’ encompasses all knowledge bounded by a scope and includes attributes of machines and users. A general context aware system is composed of context gathering and context inference modules. This paper proposes a Context Inference Engine (CiE) that classifies the current context as one of several recorded context activities. The engine follows a distance measure based classification approach with standard deviation based ranks to identify likely activities. The paper presents the algorithm and some results of the context classification process.

Context aware systems strive to facilitate better usability through advanced devices, interfaces and systems in day to day activities. These systems offer smart service discovery, delivery and adaptation all based on the current context. A context aware system must gather the context prior to context inference. This gathered context is then stored in a tagged, platform independent format using Extensible Markup Language (XML) or Web Ontology Language (OWL). The hierarchy is enforced for fast lookup and contextual data organization. Researchers have proposed and implemented different contextual data organizations a large number of which has been reviewed in this chapter. The chapter also identifies the tactics of contextual data organizations as evident in the literature. A qualitative comparison of these structures is also carried out to provide reference to future research.