Respirable Dust Monitoring in Construction Sites and Visualization in Building Information Modeling Using Real-time Sensor Data

Construction activities, involving cutting, drilling, and grinding of materials, often produce toxic respirable dust that can cause fatal diseases and illnesses. To protect workers from breathing excessive amounts of respirable dust at job sites, superintendents should continuously monitor the level of respirable dust in workspaces and make timely interventions for overexposed workers. However, current practices of respirable dust monitoring have critical drawbacks, and superintendents cannot accurately estimate workers’ exposures to respirable dust or make prompt decisions to protect the workers. Therefore, there is a need for real-time air dust monitoring that can be deployed ubiquitously at a construction site and be integrated as part of daily construction management. In this research, we developed a real-time dust monitoring system that comprises a network of low-cost mobile dust sensors and visualization in building information modeling (BIM). Single-board computers and dust sensors were integrated as field deployment units. Inaccurate sensors were calibrated automatically on the basis of an accurate ground truth sensor. A BIM-based visualization system was developed to present the data collected from dust sensors in real time. A prototype system was developed and tested in a controlled environment

Feasibility of LoRa for Smart Home Indoor Localization

With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization includingWiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates

Improving angle and time estimation for concurrent Ultra-wideband localization through transmitter-side techniques

Many internet of things (IoT) applications nowadays rely on positioning systems to optimize their decision making. GPS is one of the most used location providers in many of these systems. However, it lacks in accuracy and precision inside buildings. Ultra-wideband (UWB) is becoming a significant localization technology enabler for indoor environments with an accuracy of ∼10 cm. Recent approaches focused on scalability and efficiency by using anchor-side concurrent transmissions (TX) and extracting the time and phase information from the channel impulse response (CIR) of the UWB signal on the receiver side (tag). Concurrent time-based and phase-based localization represent the state-of-the-art techniques for UWB localization. However, when combined with concurrency, they can face many challenges. Concurrent time-based methods currently lack in accuracy due to hardware timing limitations related to channel impulse response (CIR) granularity and transmission (TX) scheduling uncertainty problem. Phase-based techniques solved the accuracy issue as they are independent from timing uncertainties. However, they require the localized targets to have dual-chip devices to calculate the Angle of Arrival (AoA). With a large number of targets to localize, the cost and complexity of the system are expected to increase. In this dissertation, we focus on three main challenges: (1) simplifying the tag complexity through designing a single-antenna tag AoA estimation system by measuring the phase difference from the CIR resulting from intra-anchor concurrency; (2) improving ToA-based concurrent solutions that use large transmission delays by mitigating the pairwise RX precision loss resulting from the TX scheduling uncertainty problem using new transmitter-side techniques in inter-anchor concurrency; (3) Combine inter-anchor and intra-anchor concurrency to provide an accurate, efficient and scalable phase-based and time-based estimation solution that offloads cost and complexity to anchors. In this research, we designed different algorithms and systems. We also proved their performance in real-world via implementation and evaluation on state-of-the-art platforms

Respirable Dust Monitoring in Construction Sites and Visualization in Building Information Modeling Using Real-time Sensor Data

Construction activities, involving cutting, drilling, and grinding of materials, often produce toxic respirable dust that can cause fatal diseases and illnesses. To protect workers from breathing excessive amounts of respirable dust at job sites, superintendents should continuously monitor the level of respirable dust in workspaces and make timely interventions for over-exposed workers. However, current practices of respirable dust monitoring have critical drawbacks, and superintendents cannot accurately estimate workers??? exposures to respirable dust or make prompt decisions to protect the workers. Therefore, there is a need for real-time air dust monitoring that can be deployed ubiquitously at a construction site and be integrated as part of daily construction management. In this research, we developed a real-time dust monitoring system that comprises a network of low-cost mobile dust sensors and visualization in building information modeling (BIM). Single-board computers and dust sensors were integrated as field deployment units. Inaccurate sensors were calibrated automatically on the basis of an accurate ground truth sensor. A BIM-based visualization system was developed to present the data collected from dust sensors in real time. A prototype system was developed and tested in a controlled environment

Feasibility of LoRa for Smart Home Indoor Localization

With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization including WiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates