We study sensor networks with energy harvesting nodes. The generated energy at a node can be stored in a buffer. A sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time at the node. For such networks we develop efficient energy management policies. First, for a single node, we obtain policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable suboptimal policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay. Next using the results for a single node, we develop efficient MAC policies.

This paper presents the design, implementation and evaluation of new parallelization schemes for performing dense disparity estimation based on non-parametric rank transform and semi-global matching on Graphics Processing Units (GPUs). A detailed analysis of the performance limitating factors (memory throughput, instruction throughput, etc.) for each part of the parallel implementation is performed. Thus, a highly optimized mapping for each parallelization scheme onto the resources of the GPU is obtained. The resulting implementation performs disparity estimation at 27 frames per second for 1024×768 pixel images with 128 disparity levels on a Nvidia Tesla C2050 GPU.