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At present there is a lot of research being done simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry helps participants carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi floor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple

At present there is a lot of research being done simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry helps participants carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi floor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple

At present there is a lot of research being done simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry helps participants carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi floor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple

The El Farol Bar problem proposed by Arthur in [1] is a study of economic system. Though Arthur's main objective was to highlight how humans are more apt in making inductive reasoning for complex decision making process rather than deductive reasoning; the model has been widely used in analysis of economic systems, particularly when congestion issues arise. The original model is described as follows. A population of agents have to decide every week whether to go to the El Farol bar or not. If many agents attend the bar, for example more that 60%, it will be overcrowded and that results unpleasant experience for the attendees. The decision made by each agent is purely individual and based on a random subset of predictors. Arthur's simulation results showed that the system kept fluctuating near the 60% threshold and the agents divided themselves into a 60/40 ratio of bar attendance. In this research, we are interested in interaction-based decision making processes, which are lacking in Arthur's model. Several attempts have been made in the literature to introduce such interaction processes or communication mechanisms to the original model. Those extended models often involve a xed network/neighborhood structure over the agents and the system dynamics were mainly studied with computer simulations [4, 6, 7]. Our contribution is a novel mechanism of information exchange and decision making among the agents, resulting an extended model for the El Farol 1 bar problem. The idea is similar to social communication. Each agent randomly communicates with two other agents within the population to obtain information about the last bar attendance. Based on this information the agent makes a stochastic decision to go to the bar. The aim of the study is to experimentally and rigorously analyse how such a system behaves, in particular how the bar attendance varies. The first part of this thesis is dedicated to simulation results. We first investigate the system settings for which an

At present there is a lot of research being done simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry helps participants carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi floor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple