The ad hoc network is composed of multiple sensor nodes to serve various applications, such as data collection or environmental monitoring. In many applications, the sensor nodes near the boundary of the deployment region provide biased or low-quality information because they have limited number of neighboring nodes and only partial information is available. Hence, the boundary recognition is an important issue in the ad hoc networks. By the statistical approach in high node density networks, Fekete's pioneer work identified the boundary node by number of neighboring nodes and using a specific threshold. By exploiting the number of nodes in the two-hop region, our proposed algorithm has significant improvement of boundary recognition contrasted with Fekete's algorithm in the low-density network. Given the information topology and the cost function, the analyses provide a framework to obtain the optimal threshold for boundary recognition. Besides, the simulation results reveal the proposed algorithm has greater than 90% detection rate and lower than 10% false alarm rate.

The ad hoc sensor network has been widely applied to various applications, such as environmental data collection and surveillance. To enable these applications, the accurate localization of sensor nodes is crucial. The DV-Hop provides a basic scheme to retrieve the localization information without GPS. The DV-Hop scheme requires the anchor nodes to be localized in advance, and the locations of the anchor nodes are used to localize other unknown nodes. The hop count between two anchor nodes can be exchanged through multi-hop routing. Using the hop counts and locations of other anchor nodes, the anchor nodes obtain the average distances per hop among one another. The distance estimation errors are caused by the uncertainties in per-hop distance estimation and the communication ranges. We propose a scheme where a node ranks the orders of its neighbor nodes through exchanging neighbor information locally. The neighbor information with orders provides useful information for node localization. Besides, the analysis reveals the probability among the order distance and the node density. Therefore, the distance estimation is improved by using the order information of neighbor nodes. The simulation results show more than 22% error reduction compared with the DV-Hop.

This work presented a novel vision-based augmented-reality system for applications in multiuser collaborative environments. The kernel technology of this vision-based system locates the cameras that are utilized to point and simulate the positions of multiple viewers. Camera calibration based on computer vision is employed during the camera's locating process. The applications in multiuser collaborative environments allow the viewers to view various positions and in numerous directions. However, traditional calibration approaches are not suitable for these cases sufficiently. A novel calibration pattern based on pseudo-random arrays is designed for multiuser collaborative applications. The pattern has a simple and regular structure, easily extracts features, achieves robust recognition using local information, and does not limit viewer positions and directions. Experimental results indicate that the proposed system provides a effective platform for applications in multiuser collaborative environments.