Mobile applications require software reconfiguration to improve resource usage and availability. We propose a power-aware reconfiguration scheme that (1) moves energy-demanding applications to proxy servers, and (2) adjusts the fidelity of mobile applications as resources diminish. We formulate a cooperative reconfiguration plan which determines when, where, and which components should be deployed and have their fidelity controlled, so as to minimize the power consumption of mobile devices and to utilize the system resources of servers efficiently. We then construct a graph-theoretic model of the cost of migrating components to one proxy server or to a cluster of servers. In this model, changes to the residual energy of mobile devices, available server resources, and the wireless network bandwidth can all accelerate or decelerate the migration and fidelity control of applications. We suggest an approximation algorithm that achieves a near-optimal solution in terms of energy consumption. Our proposal will support mobile applications which require large amount of computation and need to maintain their services for an extended time such as video conferencing, multimedia e-mail, and real-time navigation. Simulation-based experiments verify that our scheme is an efficient way to extend the battery life of mobile devices and to improve the response time of mobile applications.

To guarantee the high reliability of video services, video servers usually adopt parity-encoding techniques in which data blocks and their associated parity blocks form a parity group. For real-time video service, all the blocks in a parity group are prefetched in order to cope with a possible disk failure, thereby incurring a buffering overhead. In this paper, we propose a new scheme called Round-level Parity Grouping (RPG) to reduce the buffer overhead while restoring VBR video streams in the presence of a faulty disk. RPG allows variable parity group sizes so that the exact amount of data is retrieved during each round. Based on RPG, we have developed a storage allocation algorithm for effective buffer management. Experimental results show that our proposed scheme reduces the buffer requirement by 20% to 25%.

Rapid advances in wireless sensor networks require routing protocols which can accommodate new types of power source and data of differing priorities. We describe a QoS-aware geographic routing scheme based on a solar-cell energy model. It exploits an algorithm (APOLLO) that periodically and locally determines the topological knowledge range (KR) of each node, based on an estimated energy budget for the following period which includes the current energy, the predicted energy consumption, and the energy expected from the solar cell. A second algorithm (PISA) runs on each node and uses its knowledge range to determine a route which meets the objectives of each priority level in terms of path delay, energy consumption and reliability. These algorithms maximize scalability and minimize memory requirements by employing a localized routing method which only uses geographic information about the host node and its adjacent neighbors. Simulation results confirm that APOLLO can determine an appropriate KR for each node and that PISA can meet the objectives of each priority level effectively.