Principales Microorganismos Causantes De Infecciones

Several techniques have been devised to take advantage of radio beacons that are already widely deployed. Individual systems are described in the following sections, classified by technology; this section provides an overview of the possible mechanisms which can be applied regardless of the radio source used.

2.8.3.1 Proximity

Proximity-based systems are the simplest conceptually. They assume that if a target is in range of a fixed base station then it must be approximately co-located with that

base; this can be extended to assume that the target is co-located with the base from which it receives the strongest signal. Their resolution is therefore determined by the radio range, making this technique well-suited only to relatively short-range technologies such as Bluetooth if, as for the personal energy meter, indoor, approximate room-level location is required. Such systems cannot provide coordinate locations for the targets, but the major advantage is that no information beyond the locations of the base stations is required.

2.8.3.2 Signal propagation models

A more sophisticated alternative to simple proximity-based systems is to attempt to infer the distance from a base station from its received signal strength; if several base stations at known locations are visible, multilateration can be used to calculate a co-ordinate position. The main problem with this approach is that signal propagation in busy building environments is very different from what models predict for free space;

furniture, doors and people all cause changes in received signal strength that are very difficult to anticipate.

2.8.3.3 Fingerprinting

Fingerprinting is one of the most prominent and successful techniques for building location systems. Since predicting the signal strength from a base station at any given point is very difficult, it relies on building up in advance a radio map of the entire tracking area consisting of measurements of the signal strengths from a number of fixed base stations at each location; to position a mobile target, its measurements of these signal strengths are compared in some way to those at each location on the map and the target is assumed to be at the position whose recorded measurements most closely match its own. This can be very accurate, but has a number of disadvantages, the most significant of which is the ongoing effort required to build and keep up-to-date the radio map.

Even the proponents of fingerprint-based systems acknowledge that this calibration pro-cess is likely to hinder adoption. Castro et al. described map-building as “tedious” [26];

Matic et al. rated it “laborious and time-consuming” [142] while Schwaighofer et al. stated

“taking calibration measurements is a very costly process, in particular if larger areas need to be covered” [184]. In one of the few large-scale deployments of an RF-based location system, 28 man-hours were required to construct a radio map covering a 12,000 m² build-ing [77]. Several techniques have been suggested to mitigate this problem, though each introduces its own issues.

Ocana et al. used a robot capable of autonomously collecting WiFi signal strength mea-surements in different locations [153]. In addition, they proposed a number of strategies to reduce the calibration effort by optimising the number of collected training samples,

thus decreasing the time spent on calibration. This is useful, but a robot is unlikely to be practical in most settings.

Woodman and Harle used an inertial pedestrian tracking system (see Section 2.7.4) to speed up the process of building a radio map for a building by doing away with the need to annotate positions where measurements were taken by hand [208]. Instead, a user walked around the building taking signal strength measurements continuously while his position was tracked. Using this method they constructed a map for a large (8,725 m²) three-storey building in 2 hours and 28 minutes, travelling a total distance of 8.7 km.

This is a significant improvement, but requires an expensive and unusual tracking system and is still an arduous process; data collection had to take place on a Sunday to avoid disturbing building occupants.

Alternatively, maps can be built up by users themselves. Matic et al. developed a spon-taneous recalibration mechanism which works by having the mobile device capture a new fingerprint when it is in a known location, such as a docking station or charger, that can be detected by other means, such as the presence an external power supply; the change is then applied to other points based on a radio propagation model [142]. Their evaluation used five defined reference points, and improved the median error of a FM-based system from 1.45 m to 1.2 m after one month’s degradation. In practice, this method is unlikely to provide sufficient accurate data points to make a significant difference, and even the example of charging is prone to introduce more error if the phone is ever plugged in in a different location from that configured.

The most practical suggestion is that radio maps could be crowd-sourced directly by asking end users to take measurements in locations that are not well mapped. This is similar to the technique adopted for outdoor WiFi-based localisation by Google for its Android phones, which, every time they fix their position using GPS, also record a WiFi fingerprint and submit it automatically to the central database. Redpin is one such system, consisting of software deployed on mobile phones that could capture fingerprints using GSM, Bluetooth and WiFi, though it was not deployed or evaluated on a large scale [19]. Lee et al. used a similar system to evaluate the feasibility of crowd-sourcing a radio map, concluding that it begins to offer reasonable accuracy when the number of fingerprints in the database is larger than 5 for a typical office of 30 m² but the recognition accuracy decreases beneath 70% when about 7% of fingerprints are incorrect.

Barry et al. conducted one of the most thorough evaluations of such a system across the five buildings of a college campus over the course of a year, involving more than 200 users [14]. They found that uptake was good, motivated in part by a colleague finding application, with 95% of users contributing and over 1,000,000 location updates in total.

They assessed that the system could localise to within 10 m in 94% of cases, although this figure was based only on 57 user estimates of error. These results are very encour-aging, but the paper does highlight some significant remaining problems. Changes in the

MAC addresses and locations of access points invalidate collected fingerprints and their results may not be indicative of buildings in general since all the users were engineering students or staff who might be assumed to be highly technically literate and all were using institution-issued laptops, mitigating problems with diverse chipsets reporting different RSSI values. Most importantly, initial training is still required to produce a minimally-usable system to which users can be encouraged to add data. There is a chicken-and-egg problem: without good manual training data, users will not use (and therefore train) the system—but without users using it the system will not improve its accuracy. Google addresses this issue in its outdoor system described earlier by recording WiFi fingerprints continuously from the cars used to gather data for Street View. The authors estimated this manual training required 1–3 minutes per location. One improvement suggested is to use shared calendars as a source of training data to help solve the cold start prob-lem [15]. In the same environment they found it yielded similar accuracy to the pure crowd-sourcing approach but in a much shorter time, though it can only be applied in offices where shared calendars with meeting location are the norm.