Empirical Analysis of Water-Main Failure Consequences

Modern urban societies depend greatly on critical lifeline systems such as drinking water supply. Water supply systems in the United States comprise about one million mile length of interconnected pipelines that transport water from sources to consumption points with the support of treatment plants, pumping stations, storage tanks and valves. While depleting freshwater sources in some regions is an alarming concern, supply infrastructure woes exacerbate the problem of meeting supply reliability targets. Evidenced by the “D” or lower grade it has been receiving over the past few ASCE infrastructure report cards, the quality of water supply infrastructure has degraded to an extent where 240,000 water mains fail annually in the U.S. A majority of these failures result in significant economic, environmental and societal consequences. Pro-active rehabilitation of deteriorated infrastructure will avoid these unwarranted failure consequences. This paper employs empirical analysis of the economic, environmental and societal consequences of large-diameter water main failures to estimate their overall impact cost. Data on the impacts of 11 large-diameter water main failures has been gathered and synthesized. The results of this paper will aid in predicting the future water main failure consequences to enable risk-based, long-term capital improvement planning of water supply systems

A Prediction Algorithm for Coexistence Problem in Multiple-WBAN Environment

The coexistence problem occurs when a single wireless body area network (WBAN) is located within a multiple-WBAN environment. This causes WBANs to suffer from severe channel interference that degrades the communication performance of each WBAN. Since a WBAN handles vital signs that affect human life, the detection or prediction of coexistence condition is needed to guarantee reliable communication for each sensor node of a WBAN. Therefore, this paper presents a learning-based algorithm to efficiently predict the coexistence condition in a multiple-WBAN environment. The proposed algorithm jointly applies PRR and SINR, which are commonly used in wireless communication as a way to measure the quality of wireless connections. Our extensive simulation study using Castalia 3.2 simulator based on the OMNet++ platform shows that the proposed algorithm provides more reliable and accurate prediction than existing methods for detecting the coexistence problem in a multiple-WBAN environment.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Hindawi Publishing Corporation. The published article can be found at: http://www.hindawi.com/journals/ijdsn/

Transmission Power Control with the Guaranteed Communication Reliability in WSN

In a wireless sensor network, sensor nodes are deployed in an ad hoc fashion and they deliver data packets using multihop transmission. However, transmission failures occur frequently in the multihop transmission over wireless media. Thus, a loss recovery mechanism is required to provide end-to-end reliability. In addition, because the sensor nodes are very small devices and have insufficient resources, energy-efficient data transmission is crucial for prolonging the lifetime of a wireless sensor network. This paper proposes a transmission power control mechanism for reliable data transmission, which satisfies communication reliability through recovery of lost packets. The proposed method calculates packet reception rate (PRR) of each hop to maintain end-to-end packet delivery rate (PDR), which is determined based on the desired communication reliability. Then, the transmission power is adjusted based on the PRR to reduce energy consumption. The proposed method was evaluated through extensive simulations, and the results show that it leads to more energy-efficient data transmission compared to existing methods.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Hindawi Publishing Corporation. The published article can be found at: http://www.hindawi.com/journals/ijdsn

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages