High Quality Sensor Placement for SHM Systems: Refocusing on Application Demands

There are heavy studies recently on applying wireless sensor networks for structural health monitoring. These works usually focus on the computer science aspect, and the considerations include energy consumption, network connectivity, etc. It is commonly believed that for the current resource limited wireless sensors, system design could be more efficient if the application requirements are incorporated. Nevertheless, we often find that, rather than integration, assumptions have to be made due to lack of knowledge of civil engineering; for example, to evaluate routing algorithms, the sensor placement is assumed to be random or on grids/trees. These may not be practically meaningful to the respective application demands, and make the great efforts by the computer science community on developing efficient methods from the sensor network aspect less useful. In this paper, we study the very first problem of the SHM systems: the sensor placement and focus on the civil requirements. We first study the current general framework of structure health monitoring. We redevelop the framework that includes a new sensor placement module. This module implements the most widely accepted sensor placement scheme from civil engineering but focusing on its usefulness for computer science. It provides such interfaces that can rank the placement quality of the candidate locations in a step by step manner. We then optimize system performance by considering network connectivity and data routing issues; with the objective on energy efficiency. We evaluate our scheme using the data from the structural health monitoring system on the Ting Kau Bridge, Hong Kong. We show that a uniform and a state-of-the-art placement are not very meaningful in placement quality. Our scheme achieves almost the same sensor placement quality with that of the civil engineering with five-fold improvement in system lifetime. We conduct an experiment on the in-built Guangzhou New TV Tower, China; and the results valid- - ate the effectiveness of our scheme.Department of ComputingDepartment of Civil and Environmental EngineeringRefereed conference pape

Monitoring wild animal communities with arrays of motion sensitive camera traps

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans

Optimizing Sensing: From Water to the Web

Where should we place sensors to quickly detect contamination in drinking water distribution networks? Which blogs should we read to learn about the biggest stories on the Web? Such problems are typically NP-hard in theory and extremely challenging in practice. The authors present algorithms that exploit submodularity to efficiently find provably near-optimal solutions to large, complex real-world sensing problems

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Leak localization in water distribution networks using pressure and data-driven classifier approach

Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses through leak detection and localization, timely and efficiently using advanced techniques is an urgent humanitarian need. There are numerous methods being used to localize water leaks in WDNs through constructing hydraulic models or analyzing flow/pressure deviations between the observed data and the estimated values. However, from the application perspective, it is very practical to implement an approach which does not rely too much on measurements and complex models with reasonable computation demand. Under this context, this paper presents a novel method for leak localization which uses a data-driven approach based on limit pressure measurements in WDNs with two stages included: (1) Two different machine learning classifiers based on linear discriminant analysis (LDA) and neural networks (NNET) are developed to determine the probabilities of each node having a leak inside a WDN; (2) Bayesian temporal reasoning is applied afterwards to rescale the probabilities of each possible leak location at each time step after a leak is detected, with the aim of improving the localization accuracy. As an initial illustration, the hypothetical benchmark Hanoi district metered area (DMA) is used as the case study to test the performance of the proposed approach. Using the fitting accuracy and average topological distance (ATD) as performance indicators, the preliminary results reaches more than 80% accuracy in the best cases.Peer ReviewedPostprint (published version