Teoría de los núcleos múltiples Harris y Ullman

As the objective of this thesis is to predict occupant locations within smart buildings, the ideal test dataset would be data from an actual tracking system in such a building. Datasets which were considered include (McNett et al. 2003) and (Henderson et al. 2008), in both of which location datasets are collected on a college campus using Wi-Fi localisation. However, both of these datasets track people’s movements between multiple buildings and, due to relying on wireless access points for localisation, have difficulty pinpointing exact locations. In particular in the 2008 work, a user is only considered to have moved if they connect to access points more than 50m apart, as even stationary occupants could appear to be connecting to and disconnecting from APs in different buildings. A well known dataset is the Reality Mining dataset (Eagle et al. 2006), in which occupants were tracking using a combination of cell towers and Bluetooth sightings. As some of the Bluetooth devices sighted in the dataset are static, such as desktop pcs, part of the dataset could provide a location trace of the participants, however this would be limited to those locations demarked by static Bluetooth devices.

The Augsburg Indoor Location Tracking Benchmarks (Petzold 2004) is a location dataset in which the exact room-level location of a set of occupants is recorded throughout the working day in a university department. While the data was collected manually, it is exactly the type of data we would expect from an occupant tracking system using one of the technologies discussed in section 1.1. The approach developed in this thesis could be applied to the other datasets referenced above, including predictions across multiple buildings, however as the Augsburg dataset provides the type of data the approach is intended for it is the external dataset used when evaluating the approach.

The Augsburg dataset was collected by 4 occupants in the University of Augsburg via a PDA application which they used to record their movements. Every time an occupant changed rooms, they tapped their new room on the PDA, and the time was logged, resulting in a timestamped trace of the occupant’s movements. The

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occupants gathered several weeks of data in two time periods, the summer season and fall season.

Occupant Duration Locations

A 5 weeks 4

B 7 weeks 9

C 6 weeks 8

D 11 weeks 9

Table 3-1 – Total amount of data collected by each occupant in the Augsburg dataset (5 day weeks)

OccApriori predicts occupants’ locations during specific timeslots, as opposed to approaches which predict what the occupant’s next location will be whenever they move. As the Augsburg dataset collected the exact times when occupants moved, it had to be converted into a dataset which would state the occupant’s location in each timeslot. The evaluations in this thesis use half-hour timeslots, and consider an occupant’s location in a timeslot to be the location they were in for the majority of that timeslot. The arrival times recorded in the Augsburg dataset were used to determine each occupant’s location in each timeslot and record it in a timeslot based format. Table 3-1 shows the total duration of the data for each occupant, and the number of locations per occupant, in the Augsburg dataset. The number of locations is lower than in the original dataset as some locations, for example ‘kitchen’, were visited for such short durations that they did not become any occupant’s location in any timeslot.

While the Augsburg dataset is the most suitable existing dataset for evaluating OccApriori, several attributes make it less than ideal. First and foremost it features a small number of occupants who collected a relatively small amount of data. While occupant D collected nearly 3 months of data, the other three occupants collected less than 2. It has been shown that the amount of data is available is sufficient for good next location prediction using some approaches, for example (Petzold et al. 2006), however other approaches may prefer more training data to make these or other types of predictions. The data is also collected in two different seasons, rather than being a single contiguous dataset, requiring approaches to make predictions on movements in fall based in whole or in part on occupant movements

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in the summer. Ideally predictions would be based on occupants’ recent patterns of movement, not those from several months before. The occupants in the dataset also do not tend to move in patterns which relate the time of day, which is the basis on which OccApriori predicts their location in the future. While it is useful to evaluate how OccApriori performs on datasets that do not have occupants moving in ideal types of patterns, it is of course also important to evaluate it on occupants who do have such patterns. Finally, the Augsburg dataset contains no extra data which may be useful for predicting the occupants’ locations. OccApriori is designed to be able to incorporate any extra data which can be provided, the most obvious example of which is schedule data for occupants who have scheduled events. While this extra data is not essential, it would be preferable to evaluate its effect.