RFID indoor location identification for construction projects

Montaser, Ali; Moselhi, Osama

doi:10.1016/j.autcon.2013.06.012

Cited by 132 publications

(40 citation statements)

References 20 publications

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“…Wang [64] looked at RFID as part a quality management system. Montaser and Moselhi [65] used RFID and 'received signal strength' as a proxy for distances to locate people and materials inside buildings using Ultra High Frequency (UHF) radio waves. Cai et al [66] achieved a similar ambition using GPS outside a building.…”

Section: An Evolutionary View Of Digital Construction Solutionsmentioning

confidence: 99%

Digital construction: From point solutions to IoT ecosystem

Woodhead

Stephenson

Morrey

2018

Automation in Construction

239

176

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This paper takes a longitudinal view of literature to explain the current period as disruptive technology drives an evolutionary adaptation of the construction industry in a historical socio-technological process. The authors argue the way Internet of Things (IoT) solutions are conceived as singularly focused "point solutions" undermine future opportunities. An evolutionary view is overlooked because extant literature describes technology in a particular epoch. An ecosystem perspective needs to influence IT strategy as an emerging "digital layer" transcends a smart city and continues to function long after a traditional construction project completes. We describe innovation as a succession of transformational waves in an evolutionary process that is currently manifesting as "Industry 4.0" and changing expectations for the construction industry. The paper concludes by listing emerging trends and warns existing UK construction companies must understand the transformational process they are in and learn how to adapt with a stronger drive for R&D.

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Section: An Evolutionary View Of Digital Construction Solutionsmentioning

confidence: 99%

Digital construction: From point solutions to IoT ecosystem

Woodhead

Stephenson

Morrey

2018

Automation in Construction

239

176

View full text Add to dashboard Cite

show abstract

“…Such unsynchronized information flows generate delays and do not allow an adequate reaction for problem-solving [8,40]. However, apart from partial RFID implementations [41], there are currently no comprehensive systems for real-time monitoring on construction sites.…”

Section: State Of the Art And Research Objectivementioning

confidence: 99%

A Lean Approach for Real-Time Planning and Monitoring in Engineer-to-Order Construction Projects

2018

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Engineer-to-order (ETO) construction companies are characterized by an off-site and on-site production. Often, budget deviations for installation works on-site are identified in a late stage when improvement actions cannot be applied anymore. Consequently, installation tasks are often affected by significant delays and/or reworks. This work proposes a "real-time" capable approach for planning and monitoring in construction and a corresponding information technology (IT) framework. The core is represented by the so-called "pitching" concept known from lean management, which breaks down large job orders into smaller controllable parts. It can be considered as the main enabler for gathering management information in real-time and to identify problems and their causes on time. The most noticeable consequence lies in smaller jobs and a software-aided punctual control that allows a better rescheduling capability and, thus reduced, delays. A case study is provided, showing how the model was applied and validated in an ETO façade supplier company.

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“…According to the general engineering practice that P 0 = 0.6, and the effective interval of RSSI value is Equation (9). Figure 5 shows the RSSI value of a tag that is collected at a fixed point several times: The final output of the Gaussian process is the arithmetic mean of all the RSSI values within the interval [ 2 3.8 , 2 6.3 ], and is given by…”

Section: Data Gaussian Filteringmentioning

confidence: 99%

“…Furthermore, Wang et al [8] have successfully designed an indoor personnel tracking and positioning system based on RFID technology combined with images. Similarly, Montaser et al [9] present a low-cost indoor positioning and material tracking methodology for construction projects using passive UHF RFID technology. Goodrum et al [10] also explored the positioning and material tracking methodology for construction projects using passive UHF RFID technology.…”

Section: Introductionmentioning

confidence: 99%

Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine

Wang

Shi

2017

Symmetry

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Nowadays, the increasing demands of location-based services (LBS) have spurred the rapid development of indoor positioning systems (IPS). However, the performance of IPSs is affected by the fluctuation of the measured signal. In this study, a Gaussian filtering algorithm based on an extreme learning machine (ELM) is proposed to address the problem of inaccurate indoor positioning when significant Received Signal Strength Indication (RSSI) fluctuations happen during the measurement process. The Gaussian filtering method is analyzed and compared, which can effectively filter out the fluctuant signals that were caused by the environment effects in an RFID-based positioning system. Meanwhile, the fast learning ability of the proposed ELM algorithm can reduce the time consumption for the offline and online service, and establishes the network positioning regression model between the signal strengths of the tags and their corresponding positions. The proposed positioning system is tested in a real experimental environment. In addition, system test results demonstrate that the positioning algorithms can not only provide higher positioning accuracy, but also achieve a faster computational efficiency compared with other previous algorithms.

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RFID indoor location identification for construction projects

Cited by 132 publications

References 20 publications

Digital construction: From point solutions to IoT ecosystem

Digital construction: From point solutions to IoT ecosystem

A Lean Approach for Real-Time Planning and Monitoring in Engineer-to-Order Construction Projects

Intelligent RFID Indoor Localization System Using a Gaussian Filtering Based Extreme Learning Machine

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