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Indoor positioning and tracking services are garnering more attention. Recently, several state-of-the-art localization techniques have been proposed that use radio maps or the sensors readily available on smartphones. This paper presents a localization system called Indoor Localization using Physical maps and smartphone Sensors (ILPS), which is based on a building blueprint database and smartphone sensors. The blueprint database and access points (APs) provide a number of reference points that can be used to acquire the initial position and adjust the user position each time a reference point is detected. The proposed method is implemented on a smartphone and tested in real indoor environments. The experiments with ILPS demonstrate that using a static blueprint will avoid the costly database updates that are usually required in other approaches due to signal attenuation. Furthermore, ILPS performs better than existing work in term of accuracy and effectiveness for indoor localization.

This paper presents an efficient semantic service discovery scheme called UbiSearch for a large-scale ubiquitous computing environment. A semantic service discovery network in the semantic vector space is proposed where services that are semantically close to each other are mapped to nearby positions so that the similar services are registered in a cluster of resolvers. Using this mapping technique, the search space for a query is efficiently confined within a minimized cluster region while maintaining high accuracy in comparison to the centralized scheme. The proposed semantic service discovery network provides a number of novel features to evenly distribute service indexes to the resolvers and reduce the number of resolvers to visit. Our simulation study shows that UbiSearch provides good semantic searchability as compared to the centralized indexing system. At the same time, it supports scalable semantic queries with low communication overhead, balanced load distribution among resolvers for service registration and query processing, and personalized semantic matching.

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The popularity of Social Networking Service and the ubiquity of handheld devices improve chances of social interactions. Mobile social software emerges as a key part of this new trend. In order for users to enjoy this social experience, the resource state of member needs to be monitored so applications can adapt to dynamics of MANET and resource constraints on mobile devices. Previous work in resource monitoring for MANETs focuses on providing a general monitoring scheme. Therefore important group semantics, such as membership information, are not considered. This lack of consideration generates unnecessary traffic overhead and delay in responses. In this paper, we propose a resource monitoring scheme for group-based applications in MANETs. The proposed scheme is based on clusters of information that communicate each other using a group-based overlay. An evaluation shows that the proposed scheme shows shorter response time and smaller traffic overhead without accuracy degradation compared with previous work.

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