This paper presents an innovative evaluation and comparison of several methods and techniques necessary to implement an indoor localization system based on audible sound. Experiments were conducted in a room with conditions very close to possible practical application demonstrating that time delay estimation using generalized cross-correlation phase transform provides the best estimate to the distance to fixed anchors, and highlight the benefits of a new localization method entitled circle shrinking based on an optimization methodology. Of the three optimization methods tested, Gauss-Newton proves to be the most adequate, and among the three medium access methods evaluated, code division multiple access acoustic transmission provided the best results. A localization system combining these components and using only off-the-shelf hardware reached an average accuracy of 1.3 cm in the central area of the test room with an excitation signal-to-noise ratio as low as 7.2 dB, an almost unperceivable noise like audio signal. These results represent an advance of the state-of-the-art in indoor localization systems, pointing towards the possibility of widespread practical implementation with everyday use components.
This paper presents a method that allows mobile devices to be globally self-localised in indoor localisation systems by transmitting to them data from position reference anchors. The objective is to establish a reliable one-way down-link communication through signals used in the localisation process in a typically strong fading and multipath channel environment. This is accomplished by using signal processing techniques, including coding and forward error correction, to transmit data using a specific transmission control protocol. Experimental results, using audio as the signal between anchors and the mobile device, demonstrate successful data transmission in realistic scenarios like a common noisy and reverberant room. Spread spectrum noise-like masked signals 4·9 dB below background noise were sufficient to attain correct data reception at four metres distance between a loudspeaker anchor and a mobile device's microphone.
K E Y
Decision support and optimization tools to be used in construction often require an accurate estimation of the cost variables to maximize their benefit. Heavy machinery is traditionally one of the greatest costs to consider mainly due to fuel consumption. These typically diesel-powered machines have a great variability of fuel consumption depending on the scenario of utilization. This paper describes the creation of a framework aiming to estimate the fuel consumption of construction trucks depending on the carried load, the slope, the distance, and the pavement type. Having a more accurate estimation will increase the benefit of these optimization tools. The fuel consumption estimation model was developed using Machine Learning (ML) algorithms supported by data, which were gathered through several sensors, in a specially designed datalogger with wireless communication and opportunistic synchronization, in a real context experiment. The results demonstrated the viability of the method, providing important insight into the advantages associated with the combination of sensorization and the machine learning models in a real-world construction setting. Ultimately, this study comprises a significant step towards the achievement of IoT implementation from a Construction 4.0 viewpoint, especially when considering its potential for real-time and digital twins applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.