Most of the previous work on power optimization regarded the capacity of battery power as an ideal constant value. In fact, experiments showed that 30% of the total battery capacity was wasted by improper discharge pattern [1]. In this letter, a battery-aware task scheduling protocol which harnesses one of the typical characteristics of batteries, i.e., battery recovery, is proposed to extend usage time for smart phones. The key idea is to adjust the working schedule of the components in smart phones for more energy recovering. Experiments show that when the proposed protocol is applied in an online music application, as much as 9% lifespan extension for batteries can be obtained.

This letter considers a dual-hop multiuser MIMO amplify-and-forward relay broadcast system with multi-antenna nodes. A unified scheme is addressed to jointly optimize the linear transceiver based on the sum mean-square error (MSE) and the sum rate criterion. The solutions are iteratively obtained by deriving the gradients of the objective functions for a gradient descent algorithm. Simulation results demonstrate the performance improvements in terms of the BER and the sum rate.

We describe in this paper our experience of developing a large-scale, highly distributed multi-agent system using wireless-networked sensors. We provide solutions to the problems of localization (position estimation) and dynamic, real-time mobile object tracking, which we call PET problems for short, using wireless sensor networks. We propose system architectures and a set of distributed algorithms for organizing and scheduling cooperative computation in distributed environments, as well as distributed algorithms for localization and real-time object tracking. Based on these distributed algorithms, we develop and implement a hardware system and software simulator for the PET problems. Finally, we present some experimental results on distance measurement accuracy using radio signal strengths of the wireless sensors and discuss future work.