Safety assurance and rescue communication systems in high-stress environments: A mining case study

Misra, Prasant; Kanhere, Salil S.; Ostry, Diethelm; Jha, Sanjay

doi:10.1109/mcom.2010.5439078

Cited by 77 publications

(56 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is well understood that cross-correlation only solves a small part of the ranging problem, and therefore, our work is preliminary in the sense that we have not yet entirely evaluated the consequences of externalities. An intriguing question for future work is whether this framework can be useful with other challenges such as directional receivers, limited range and signal penetration, noisy background conditions and high-stress environments [34], scalability issues, etc. In addition, an indepth analysis of the properties of a Toeplitz matrix with respect to spare approximation and compressive sensing is also required for better design of sensing matrices.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Efficient cross-correlation via sparse representation in sensor networks

Misra

Yang

et al. 2012

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)

Self Cite

View full text Add to dashboard Cite

Cross-correlation is a popular signal processing technique used in numerous localization and tracking systems for obtaining reliable range information. However, a practical efficient implementation has not yet been achieved on resource constrained wireless sensor network platforms. We propose cross-correlation via sparse representation: a new framework for ranging based on 1 -minimization. The key idea is to compress the signal samples on the mote platform by efficient random projections and transfer them to a central device, where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain. Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework, together with the proposed correlation domain achieved up to two order of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling. Furthermore, compared to cross-correlation results, 30-40% measurements are sufficient to obtain precise range estimates with an additional bias of only 2-6 cm for high accuracy application requirements, while 5% measurements are adequate to achieve approximately 100 cm precision for lower accuracy applications.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Efficient cross-correlation via sparse representation in sensor networks

Misra

Yang

et al. 2012

2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, sensor nodes are vulnerable to failure because the WSNs are often deployed in harsh environments [18][19][20][21]. Outliers are commonly found in datasets collected by the WSNs installed in harsh environments [22,23]. Second, noise in wireless signals and malicious attacks both create outliers [24,25], which obviously reduce the WSN capabilities.…”

Section: Outlier Detection In Wsn Applicationsmentioning

confidence: 99%

Bayesian-Optimization-Based Peak Searching Algorithm for Clustering in Wireless Sensor Networks

Zhang

Zhao

Shin

et al. 2018

JSAN

View full text Add to dashboard Cite

Abstract:We propose a new peak searching algorithm (PSA) that uses Bayesian optimization to find probability peaks in a dataset, thereby increasing the speed and accuracy of clustering algorithms. Wireless sensor networks (WSNs) are becoming increasingly common in a wide variety of applications that analyze and use collected sensing data. Typically, the collected data cannot be directly used in modern data analysis problems that adopt machine learning techniques because such data lacks additional information (such as data labels) specifying its purpose of users. Clustering algorithms that divide the data in a dataset into clusters are often used when additional information is not provided. However, traditional clustering algorithms such as expectation-maximization (EM) and k-means algorithms require massive numbers of iterations to form clusters. Processing speeds are therefore slow, and clustering results become less accurate because of the way such algorithms form clusters. The PSA addresses these problems, and we adapt it for use with the EM and k-means algorithms, creating the modified PSEM and PSk-means algorithms. Our simulation results show that our proposed PSEM and PSk-means algorithms significantly decrease the required number of clustering iterations (by 1.99 to 6.3 times), and produce clustering that, for a synthetic dataset, is 1.69 to 1.71 times more accurate than it is for traditional EM and enhanced k-means (k-means++) algorithms. Moreover, in a simulation of WSN applications aimed at detecting outliers, PSEM correctly identified the outliers in a real dataset, decreasing iterations by approximately 1.88 times, and PSEM was 1.29 times more accurate than EM at a maximum.

show abstract

“…The most promising feature of this system is the early fire detection and the ability to locate a fire in the underground mine. Many existing studies [16,17] have implemented WSNs with focus on low power and low operational costs in real underground conditions to enhance safety and productivity. From the perspective of wireless routing, a minimum delay maximum lifetime (MDML) [18] is an energy efficient, cost-effective, and reliable routing algorithm which has been specifically designed for the emergency and non-emergency transmission in underground coal mines.…”

Section: Related Workmentioning

confidence: 99%

An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study

Khan

2017

Applied Sciences

View full text Add to dashboard Cite

Fatal accidents associated with underground coal mines require the implementation of high-level gas monitoring and miner's localization approaches to promote underground safety and health. This study introduces a real-time monitoring, event-reporting and early-warning platform, based on cluster analysis for outlier detection, spatiotemporal statistical analysis, and an RSS range-based weighted centroid localization algorithm for improving safety management and preventing accidents in underground coal mines. The proposed platform seamlessly integrates monitoring, analyzing, and localization approaches using the Internet of Things (IoT), cloud computing, a real-time operational database, application gateways, and application program interfaces. The prototype has been validated and verified at the operating underground Hassan Kishore coal mine. Sensors for air quality parameters including temperature, humidity, CH 4 , CO 2 , and CO demonstrated an excellent performance, with regression constants always greater than 0.97 for each parameter when compared to their commercial equivalent. This framework enables real-time monitoring, identification of abnormal events (>90%), and verification of a miner's localization (with <1.8 m of error) in the harsh environment of underground mines. The main contribution of this study is the development of an open source, customizable, and cost-effective platform for effectively promoting underground coal mine safety. This system is helpful for solving the problems of accessibility, serviceability, interoperability, and flexibility associated with safety in coal mines.

show abstract

Safety assurance and rescue communication systems in high-stress environments: A mining case study

Cited by 77 publications

References 9 publications

Efficient cross-correlation via sparse representation in sensor networks

Efficient cross-correlation via sparse representation in sensor networks

Bayesian-Optimization-Based Peak Searching Algorithm for Clustering in Wireless Sensor Networks

An Event Reporting and Early-Warning Safety System Based on the Internet of Things for Underground Coal Mines: A Case Study

Contact Info

Product

Resources

About