Statistical Analysis to Extract Effective Parameters on Overall Energy Consumption of Wireless Sensor Network (WSN)

In this paper, we use statistical tools to analysis dependency between Wireless Sensor Network (WSN) parameters and overall Energy Consumption (EC). Our approach has two main phases: profiling, and effective parameter extraction. In former, a sensor network simulator is re-run 800 times with different values for eight WSN parameters to profile consumed energy in nodes; then in latter, three statistical analyses (p-value, linear and non-linear correlation) are applied to the outcome of profiling phase to extract the most effective parameters on WSN overall energy consumption.Comment: 5-pages. This paper has been accepted in PDCAT-2012 conference (http://www.pdcat2012.org/

An Energy Driven Architecture for Wireless Sensor Networks

Most wireless sensor networks operate with very limited energy sources-their batteries, and hence their usefulness in real life applications is severely constrained. The challenging issues are how to optimize the use of their energy or to harvest their own energy in order to lengthen their lives for wider classes of application. Tackling these important issues requires a robust architecture that takes into account the energy consumption level of functional constituents and their interdependency. Without such architecture, it would be difficult to formulate and optimize the overall energy consumption of a wireless sensor network. Unlike most current researches that focus on a single energy constituent of WSNs independent from and regardless of other constituents, this paper presents an Energy Driven Architecture (EDA) as a new architecture and indicates a novel approach for minimising the total energy consumption of a WS

Forecasting and Conditional Projection Using Realistic Prior Distributions

This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates.We provide unconditional forecasts as of 1982:12 and 1983:3.We also describe how a model such as this can be used to make conditional projections and to analyze policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12.While no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, which may help inevaluating causal hypotheses, without containing any such hypotheses themselves.

Active data-centric framework for data protection in cloud environment

Cloud computing is an emerging evolutionary computing model that provides highly scalable services over highspeed Internet on a pay-as-usage model. However, cloud-based solutions still have not been widely deployed in some sensitive areas, such as banking and healthcare. The lack of widespread development is related to users&rsquo; concern that their confidential data or privacy would leak out in the cloud&rsquo;s outsourced environment. To address this problem, we propose a novel active data-centric framework to ultimately improve the transparency and accountability of actual usage of the users&rsquo; data in cloud. Our data-centric framework emphasizes &ldquo;active&rdquo; feature which packages the raw data with active properties that enforce data usage with active defending and protection capability. To achieve the active scheme, we devise the Triggerable Data File Structure (TDFS). Moreover, we employ the zero-knowledge proof scheme to verify the request&rsquo;s identification without revealing any vital information. Our experimental outcomes demonstrate the efficiency, dependability, and scalability of our framework.<br /

Delayed hepatic uptake of multi-phosphonic acid poly(ethylene glycol) coated iron oxide measured by real-time Magnetic Resonance Imaging

We report on the synthesis, characterization, stability and pharmacokinetics of novel iron based contrast agents for magnetic resonance imaging (MRI). Statistical copolymers combining multiple phosphonic acid groups and poly(ethylene glycol) (PEG) were synthesized and used as coating agents for 10 nm iron oxide nanocrystals. In vitro, protein corona and stability assays show that phosphonic acid PEG copolymers outperform all other coating types examined, including low molecular weight anionic ligands and polymers. In vivo, the particle pharmacokinetics is investigated by monitoring the MRI signal intensity from mouse liver, spleen and arteries as a function of the time, between one minute and seven days after injection. Iron oxide particles coated with multi-phosphonic acid PEG polymers are shown to have a blood circulation lifetime of 250 minutes, i.e. 10 to 50 times greater than that of recently published PEGylated probes and benchmarks. The clearance from the liver takes in average 2 to 3 days and is independent of the core size, coating and particle stability. By comparing identical core particles with different coatings, we are able to determine the optimum conditions for stealth MRI probes.Comment: 19 pages 8 figures, RSC Advances, 201