Chirp-spread-spectrum-based real time location system for construction safety management: A case study

Observing the working procedure of construction workers is an effective means of maintaining the safety performance of a construction project. It is also difficult to achieve due to a high worker-to-safety-officer ratio. There is an imminent need for the development of a tool to assist in the real-time monitoring of workers, in order to reduce the number of construction accidents. The development and application of a real time locating system (RTLS) based on the Chirp Spread Spectrum (CSS) technique is described in this paper for tracking the real-time position of workers on construction sites. Experiments and tests were carried out both on- and off-site to verify the accuracy of static and dynamic targets by the system, indicating an average error of within one metre. Experiments were also carried out to verify the ability of the system to identify workers’ unsafe behaviours. Wireless data transfer was used to simplify the deployment of the system. The system was deployed in a public residential construction project and proved to be quick and simple to use. The cost of the developed system is also reported to be reasonable (around 1800USD) in this study and is much cheaper than the cost of other RTLS. In addition, the CCS technique is shown to provide an economical solution with reasonable accuracy compared with other positioning systems, such as ultra wideband. The study verifies the potential of the CCS technique to provide an effective and economical aid in the improvement of safety management in the construction industry.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

Enhancing the trust of location acquisition systems for critical applications and location-based security services

This paper identifies a number of critical infrastructure applications that are reliant on location services from cooperative location technologies such as GPS and GSM. We show that these location technologies can be represented in a general location model, such that the model components can be used for vulnerability analysis. We perform a vulnerability analysis on these components of GSM and GPS location systems as well as a number of augmentations to these systems.

A pilot study on the effect of indoor particle sources on indoor particle concentration in residential houses

Characterization of indoor particle sources from 14 residential houses in Brisbane, Australia, was performed. The approximation of PM2.5 and the submicrometre particle number concentrations were measured simultaneously for more than 48 h in the kitchen of all the houses by using a photometer (DustTrak) and a condensation particle counter (CPC), respectively. From the real time indoor particle concentration data and a diary of indoor activities, the indoor particle sources were identified. The study found that among the indoor activities recorded in this study, frying, grilling, stove use, toasting, cooking pizza, smoking, candle vaporizing eucalyptus oil and fan heater use, could elevate the indoor particle number concentration levels by more than five times. The indoor approximation of PM2.5 concentrations could be close to 90 times, 30 times and three times higher than the background levels during grilling, frying and smoking, respectively.

Indoor exposure to submicrometer particles and PM2.5 in residential houses in Brisbane, Australia

As part of a large study investigating indoor air in residential houses in Brisbane, Australia, the purpose of this work was to quantify indoor exposure to submicrometer particles and PM2.5 for the inhabitants of 14 houses. Particle concentrations were measured simultaneously for more than 48 hours in the kitchens of all the houses by using a condensation particle counter (CPC) and a photometer (DustTrak). The occupants of the houses were asked to fill in a diary, noting the time and duration of any activity occurring throughout the house during measurement, as well as their presence or absence from home. From the time series concentration data and the information about indoor activities, exposure to the inhabitants of the houses was calculated for the entire time they spent at home as well as during indoor activities resulting in particle generation. The results show that the highest median concentration level occurred during cooking periods for both particle number concentration (47.5´103 particles cm-3) and PM2.5 concentration (13.4 mg m-3). The highest residential exposure period was the sleeping period for both particle number exposure (31%) and PM2.5 exposure (45.6%). The percentage of the average residential particle exposure level in total 24h particle exposure level was approximating 70% for both particle number and PM2.5 exposure.