Efficient deployment of sensors is an important issue in wireless sensor networks. If we deploy sensors randomly by our hands or carriers only, some unlucky places are not covered forever. In this paper, we propose distributed self-deployment schemes of mobile sensors. At first, sensors are deployed randomly. They then calculate the next positions locally by utilizing the proposed schemes and move to them. The locations of the sensors are adjusted round by round so that the coverage is gradually improved. By using Voronoi diagram and centroid (geometric center), we design two schemes, named Centroid (centroid-based scheme) and Dual-Centroid (dual-centroid based scheme). We also measure the performance of the proposed schemes and the existing schemes, and show that the proposed schemes get better results.

Desktop Grid has recently received the strong attraction for executing high throughput applications as CPU, storage and network capacities improve and become cheaper. Desktop Grid is different from Grid in many respects, but there is no general survey or taxonomy for desktop Grid. Therefore, we propose a new comprehensive taxonomy and survey of desktop Grid in order to characterize and categorize desktop Grid.

This paper proposes a restoration scheme based on mobility learning and prediction in the presence of the failure of mobility databases in personal communication systems (PCSs). In PCSs, mobility databases must maintain the current location information of users to provide a fast connection for them. However, the malfunction of mobility databases may cause some location information to be lost. As a result, without an explicit restoration procedure, incoming calls to users may be rejected. Therefore, an explicit restoration scheme against the malfunction of mobility databases is needed to guarantee continuous service availability to users. Introducing mobility learning and prediction into the restoration process allows systems to locate users after a failure of mobility databases. In failure-free operations, the movement patterns of users are learned by a neuro-fuzzy inference system (NFIS). After a failure, an inference process of the NFIS is initiated and the users' future location is predicted. This is used to locate lost users after a failure. This proposal differs from previous approaches using a checkpoint because it does not need a backup process nor additional storage space to store checkpoint information. In addition, simulations show that our proposal can reduce the cost needed to restore the location records of lost users after a failure when compared to the checkpointing scheme.