Estimation over Communication Networks: Performance Bounds and Achievability Results

This paper considers the problem of estimation over communication networks. Suppose a sensor is taking measurements of a dynamic process. However the process needs to be estimated at a remote location connected to the sensor through a network of communication links that drop packets stochastically. We provide a framework for computing the optimal performance in the sense of expected error covariance. Using this framework we characterize the dependency of the performance on the topology of the network and the packet dropping process. For independent and memoryless packet dropping processes we find the steady-state error for some classes of networks and obtain lower and upper bounds for the performance of a general network. Finally we find a necessary and sufficient condition for the stability of the estimate error covariance for general networks with spatially correlated and Markov type dropping process. This interesting condition has a max-cut interpretation

Green compressive sampling reconstruction in IoT networks

In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks

Investigating optimal internet data collection in low resource networks

Community networks have been proposed by many networking experts and researchers as a way to bridge the connectivity gaps in rural and remote areas of the world. Many community networks are built with low-capacity computing devices and low-capacity links. Such community networks are examples of low resource networks. The design and implementation of computer networks using limited hardware and software resources has been studied extensively in the past, but scheduling strategies for conducting measurements on these networks remains an important area to be explored. In this study, the design of a Quality of Service monitoring system is proposed, focusing on performance of scheduling of network measurement jobs in different topologies of a low-resource network. We also propose a virtual network testbed and perform evaluations of the system under varying measurement specifications. Our results show that the system is capable of completing almost 100% of the measurements that are launched by users. Additionally, we found that the error due to contention for network resources among measurements stays constant at approximately 34% with increasing number of measurement nodes

\u3ci\u3eBioinformatics and Biomedical Engineering\u3c/i\u3e

Editors: Francisco Ortuño, Ignacio Rojas Chapter, Identification of Biologically Significant Elements Using Correlation Networks in High Performance Computing Environments, co-authored by Kathryn Dempsey Cooper, Sachin Pawaskar, and Hesham Ali, UNO faculty members. The two volume set LNCS 9043 and 9044 constitutes the refereed proceedings of the Third International Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2015, held in Granada, Spain in April 2015. The 134 papers presented were carefully reviewed and selected from 268 submissions. The scope of the conference spans the following areas: bioinformatics for healthcare and diseases, biomedical engineering, biomedical image analysis, biomedical signal analysis, computational genomics, computational proteomics, computational systems for modelling biological processes, eHealth, next generation sequencing and sequence analysis, quantitative and systems pharmacology, Hidden Markov Model (HMM) for biological sequence modeling, advances in computational intelligence for bioinformatics and biomedicine, tools for next generation sequencing data analysis, dynamics networks in system medicine, interdisciplinary puzzles of measurements in biological systems, biological networks, high performance computing in bioinformatics, computational biology and computational chemistry, advances in drug discovery and ambient intelligence for bio emotional computing.https://digitalcommons.unomaha.edu/facultybooks/1323/thumbnail.jp

Performance comparison between iSCSI and other hardware and software solutions

We report on our investigations on some technologies that can be used to build disk servers and networks of disk servers using commodity hardware and software solutions. It focuses on the performance that can be achieved by these systems and gives measured figures for different configurations. It is divided into two parts : iSCSI and other technologies and hardware and software RAID solutions. The first part studies different technologies that can be used by clients to access disk servers using a gigabit ethernet network. It covers block access technologies (iSCSI, hyperSCSI, ENBD). Experimental figures are given for different numbers of clients and servers. The second part compares a system based on 3ware hardware RAID controllers, a system using linux software RAID and IDE cards and a system mixing both hardware RAID and software RAID. Performance measurements for reading and writing are given for different RAID levels.Comment: Paper associated to a poster from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 4 pages, LaTeX. PSN TUDP00