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With the advent of wireless sensing technology and interest in tracking resources, researchers have developed advanced tracking algorithms by using one or more sensor systems for improved accuracy and reliability of tracking. The objective of this research lies in another aspect−deployment−of tracking that has received only little attention until now. The research explores a method for sensor deployment particularly designed for the building in which the sensors are used. To tailor our solution to a specific building, we integrate a building information model with an electromagnetic energy analysis. By using such a model, the system extracts the properties of building materials, which are used as parameters of sensor deployment optimization. Then, we find a method of optimizing the deployment of a Received Signal Strength Indication (RSSI)-based tracking sensors for reducing wireless energy dissipation during the operation of the tracking system. For the numerical validation of the proposed method, the High-Frequency Structural Simulator (HFSS) runs an electromagnetic simulation to generate comparison data of electromagnetic energy flow from optimized sensor deployment and random sensor deployment. The results indicate that the proposed method could produce results that are correlated to the HFSS results. In addition, the method shows clear evidence of a reduction in signal power loss. Finally, optimized sensor deployment through the proposed framework can use signals of electromagnetic energy more effectively and potentially improve the efficiency of the RSSI-based tracking system. [ABSTRACT FROM AUTHOR]

Past construction research has focused primarily on the realization of tracking systems but much less on their effectiveness. Despite significant achievements, little is known about the relationship between the environmental site conditions and deployed sensors and their influence on system effectiveness. To reduce this knowledge gap, we investigate the effect of cement-based material on signal energy loss using electromagnetic simulation. We take conventional electromagnetic equations and derive equations that quantitatively describe the signal behavior influenced by cement-based material. Then, we conduct high frequency structure simulator (HFSS) simulations and use the experimental measurements for benchmark comparison with our analytical solution. Results suggest that our analytical method accurately quantifies attenuated signal loss. Also, we construct an attenuation spectrum of energy loss for cement-based material, which can serve as a reference for sensor deployment. Therefore, the method should enable quantification of the signal loss of sensors without actually deploying them or measuring signal data, which should lead to the more effective deployment of tracking sensors. [ABSTRACT FROM AUTHOR]

The nature of an indoor construction site is dynamic and complex. This characteristic becomes even more evident when a multitude of interactions among workers, equipment, and materials exists. To assist various operations of construction, which are dynamic and complex in nature, previous research explored sensing technologies to discover advanced approaches to supporting construction operations. In particular, many researchers have focused on investigating the tracking of construction resources over the past several years. Despite the previous research efforts in radio-signal-strength-indication (RSSI)-based tracking systems, signal interference such as signal degradations, occlusions, obstructions, and multipath effects created by a complex and dynamic construction environment has not been clearly discussed. Furthermore, fundamental understanding of issues associated with signal interference is limited. This research aims to reduce the gap in knowledge of the RSSI-based tracking system when it is used in a highly noisy environment like a dynamic, complex, and large indoor construction site. This paper presents the development of a new probabilistic local search (PLS) algorithm using Bluetooth low energy network technology. The PLS is assessed with respect to accuracy, reliability, and robustness. Two conventional positioning methods are subjected to the same tests as a benchmark for comparison. The results indicate that PLS outperforms the other conventional methods when the signal interference is high and performs comparably when the signal interference is minimal. [ABSTRACT FROM AUTHOR]