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Wireless sensor networks constitute the platform of a broad range of applications related to national security, surveillance, military, health care, and environmental monitoring. The coverage problem for Wireless Sensor Network (WSN) has been studied extensively in recent years, especially when combined with connectivity and energy efficiency. This paper focuses on the sensor replacement problem in wireless sensor networks consists of mobile sensors. Mobility equipped sensors are utilized to recover and maintain the overall coverage using the dynamic cluster concept. The proposed fault repair solution does not assume the localization information is available. Mobile sensor nodes make use of simple geometric operation to locate and replace dying nodes to recover or increase the existing coverage and connectivity.

The visualization of high resolution video on small mobile devices is still a great challenge today. Most critical are the limited display resolution and different aspect ratios of handheld mobile devices. So far, there is no retargeting algorithm available that guarantees good results for all videos. We introduce a new video retargeting approach that reduces the resolution while preserving as much of the relevant content as possible. A central component of the system selects the most suitable algorithm to adapt a given shot. We have implemented two retargeting algorithms: a region of interest (ROI) based technique, and a fast implementation of seam carving for size adaptation of videos (FSCAV). The ROI-based retargeting detects important regions like faces, objects, text, and contrast-based saliency regions. A rectangular window within the larger frame is selected that defines the visible area of the target video. If several relevant regions are detected, an artificial camera motion (pan, tilt, or zoom) may change the selected view within a shot. For seam carving, we present two extensions: The first reduces the distortion of straight lines (lines may become curved or disconnected); the second avoids jitter in the target video, limits the large memory requirements and computational effort of seam carving, and makes it applicable to video retargeting. In addition, we present a heuristic that estimates the visual quality of the target video. If the quality drops below a threshold, the ROI-based retargeting is used for this shot. User evaluations confirm a very high visual quality of our approach.

The curvature scale space (CSS) technique, which is also part of the MPEG-7 standard is a robust method to describe complex shapes.The central idea is to analyze the curvature of a shape and derive features from inflection points. A major drawback of the CSS method is its poor representation of convex segments: Convex objects cannot be represented at all due to missing inflection points. We have extended the CSS approach to generate feature points for concave and convex segments of a shape. This generic approach is applicable to arbitrary objects. In the experimental results, we evaluate as a comprehensive example the automatic recognition of characters in images and videos.

Fingerprinting is a popular technology for 802.11based positioning systems: Radio characteristics from different access points are measured at various positions and stored in a database. The database is copied to all mobile devices, and when a position is needed, the devices compares its currently measured radio characteristics with the database entries. In this paper, we present two on-demand fingerprint selection algorithms to avoid the cumbersome and time-consuming approach of manually copying all fingerprints. Our algorithms only request those fingerprints from the database that are currently required to compute a position. The two algorithms differ in the way they shape the region for which fingerprints are requested. On-demand selection also allows storagerestricted mobile devices to utilize the positioning system. We carefully evaluate our algorithms in a real-world experiment. The results show that our algorithms do not harm the position accuracy of the positioning system. In addition, we analyze the space requirements of our algorithms and show that the typical constraints of mobile devices are met.

Positioning systems are one of the key elements required by location-based services. This paper presents the design, implementation and analysis of a positioning system called COMPASS which is based on 802.11-compliant network infrastructure and digital compasses. On the mobile device, COMPASS samples the signal strength values of different access points in its communication range and utilizes the orientation of the user to preselect a subset of the training data. The remaining training data is used by a probabilistic positioning algorithm to determine the position of the user. While prior systems show limited accuracy due to blocking effects caused by the human body, we apply digital compasses to detect the orientations of the users so that we can deal with these blocking effects. After a short period of training our COMPASS system achieves an average error distance of less than 1.65 meters in our experimental environment of 312 square meters.

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