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As portable devices have become a part of our everyday life, more people are unknowingly participating in a pervasive computing environment. People engage with not a single device for a specific purpose but many devices interacting with each other in the course of ordinary activity. With such prevalence of pervasive technology, the interaction between portable devices needs to be continuous and imperceptible to device users. Pervasive computing requires a small, scalable and robust network which relies heavily on the middleware to resolve communication and security issues. In this paper, we present the design and implementation of S-MARKS which incorporates device validation, resource discovery and a privacy module.

In this paper, we present a cloaking algorithm called DirectedCloaking to protect user's location privacy. There is a location anonymizer (LA) to perform this cloaking. Once this cloaked location data is provider by LA, location based service provider (LBSP) can minimize and give relevant result-candidates for a given query. LBSP can also constrain the maximum possible candidates for a given query because of our definition of a PossibleSpace. We define, for the first time, Landmark Influence Space (LIS) and show that cloaking in LIS can give the above-mentioned performance benefit to the user.

Handheld devices in a pervasive computing environment are prone to security as well as privacy violations, while discovering, sharing and accessing services and contents. Trust models are devised to fight against such violations and breaches. Although initial trust assignment is an important issue in evolving overall trust, a little amount of work has been done in this field so far. In pervasive smart space, similar type of contexts exhibits significant correlations to each other. However, this fact is not taken into consideration while computing the initial trust values. In this paper, we describe a new mechanism to assign initial trust: CCTB (Context Correlation for Trust Bootstrapping), which takes advantage of the presence of correlations among different contexts in a context-ontology. We evaluate the effectiveness of CCTB by simulating in two different scenarios. We show that CCTB offers better initial trust values than the other models considered here. We also implement a prototype for performance measurement using .NET Compact Framework.

We design and implement a low cost, easy to deploy and lightweight location tracking solution that operates without the need for a fixed infrastructure. We call it "WiFi radar," which uses the signal strength of radio transmissions to determine the position (distance and direction) of devices with near linear approximation. Notwithstanding the challenges that radio frequency signals pose for location determination, the accuracy and precision of our system is relatively high. Our application is user-friendly, customizable and shows graphical as well as list views of the located objects. To the best of our knowledge, our system is the first endeavor that implements a location tracking system without the use of any fixed infrastructure and it may be the cheapest solution built so far.