Existing taxonomies such as that proposed by Hightower et al. The taxonomy we have chosen to propose has been constructed based on a literature study of 51 papers and articles. The analyses of four of the 30 systems are covered as case studies in Section 7.
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The analysis results for all of the 30 systems are available online at . The structure of the paper is as follows. The taxons of the proposed tax- onomy are discussed in Section 2. The individual taxons are then presented in Sections 3 to 6. Four case studies are afterwards presented in Section 7 and a discussion is given in Section 8.
Finally, conclusions are given in Section 9.
These were partly inspired by earlier work on taxonomies for location systems in general and from our literature study. The four taxons: scale, output, measurements, and roles describe general properties of LF systems.
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We mean by scale the size of the deployment area and by output the type of provided location information. Measurements means the types of measured network characteristics and roles means the division of responsibilities between wireless clients, base sta- tions, and servers. Only these four of our eleven taxons are covered by existing taxonomies such as Hightower et al. Estimation method and radio map describe the location estimation process.
Estimation method denote a method for predicting locations from a radio map and currently measured network characteristics and radio map a model of net- work characteristics in a deployment area. The division into estimation method and radio map is used by many papers about LF, for instance Youssef et al. Output Type of provided location information. Measurements Types of measured network characteristics.
Roles Division of responsibilities between wireless clients, base stations, and servers. Estimation Method Method for predicting locations from a radio map and cur- rently measured network characteristics.
Radio Map Model of network characteristics in a deployment area. How changing network characteristics over time, space and sensors can be handled is described by spatial, temporal and sensor variations. The spatial and temporal dimensions were introduced by Youssef et al. The focus of the proposed taxonomy is on methods for LF and therefore the taxonomy does not cover evaluation properties for LF systems.
Evaluation properties for all kinds of location systems have for instance been suggested by Muthukrishnan et al. The taxonomy proposed by Hightower et al. In our analysis we have included the following evaluation properties: precision, accuracy, evaluation setup, and limitations. These four were chosen because this information is available from most papers. Calibration, privacy, scalability, and scale are partly covered by our taxons scale, roles and collection method.
These four properties are also listed in our case studies in Section 7. The taxonomy does not cover non-functional system properties, because work has not yet matured in these directions for LF systems. Non-functional proper- ties of LF systems have been addressed by several recent papers, such as sys- tem robustness by Lorincz et al.
Also, the taxonomy does not cover the application of LF techniques to other types of sensor measurements such as sound and light. These taxons are shown including subtaxons in Figure 1. In this and the following sections when taxons are presented up to four references are given to papers or articles that propose systems that are grouped below the particular taxon. Therefore not all papers groupped under a taxon are listed, this type of information can be found online at . Many LF systems have been proposed for a building scale of deployment [11,12,13,14]. Some systems are limited to this scale because they assume knowledge about the physical layout of buildings [15,16,17,18]; oth- ers because they assume the installation of a special infrastructure [19,20].
City-wide systems [22,23,24] scale even further by not assuming that a system is deployed by or for a single organization. City wide systems could scale to any area that is covered by base stations.
Output denotes the type of provided location information. Spatial locations are described by a set of coordinates stated with respect to a spatial reference system. Many LF systems output spatial loca- tions [11,14,24,25] but systems have also been proposed that output descriptive 1 Some authors refer to this as symbolic locations. A Taxonomy for Radio Location Fingerprinting locations [16,18,21]. However, a location outputted as either of the two types can be mapped to the other type given a suitable location model. Measurements are the types of measured network characteristics. BSI is a unique name assigned to a base station.
The power level is information from the signal sender about current sending power. Roles denote the division of responsibilities between wireless clients, base stations, and servers. How roles are assigned impact both how systems are re- alized, but also important non-functional properties like privacy and scalability. The two main categories for roles are infrastructure-based and infrastructure- less. Infrastructure-based systems depend on a pre-installed powered infras- tructure of base stations. The infrastructure-less systems are divided into terminal-based and collaborative sys- tems.
Infrastructure-less LF-systems have to be optimized for the resource- weak wireless clients, which is addressed by the collaborative setup [9,26]. The two taxons are shown including subtaxons in Figure 3. Estimation method A central part of a LF system is the estimation method used for predicting locations from a radio map and currently measured network characteristics.
It would, however, be very challenging to taxonomize all possible methods because nearly all methods developed for machine learning see Witten et al. Here we follow Krishnakumar et al. Deterministic methods estimate location by considering 2 However, when only considering the basic method of each system, most can be realized in all of the three setups.
Radio map measurements only by their value [11,12,22,25]. Probabilistic methods estimate location considering measurements as part of a random process [5,15,16,18]. A comment is that many of the studied LF systems use more than one of the listed methods. A radio map provides a model of network characteristics in a deployment area.
Model-based methods use a model parame- terised for the LF-system covered area to construct radio maps [11,23,30,31]. Deterministic methods represent entries in a radio map as single values and probabilistic methods represent en- tries by probability distributions.
Both of these can be further subcategorised into aggregation and interpolation methods. Figure 5 illustrates two interpola- tion methods at the location marked with a circle using the square-marked and triangle-marked locations as nearby locations. Propagation can either be modeled by only considering the di- rect path between a location and a base station  or by considering multiple paths categorized as ray tracing . The representation of the generated radio map can either be deterministic using single values  or probabilistic using probability distributions .
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A Taxonomy for Radio Location Fingerprinting 5 Variation Taxons The three taxons for variations are: spatial variations, temporal variations, and sensor variations. The three taxons are shown including subtaxons in Figure 6. In the illustrated embodiment, all portions of the antenna array are configured to be substantially non-rotating, stationary, or fixed.
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Also in the illustrated embodiment, each of the directional antenna elements are positioned substantially equidistant from one another at an angle determined by dividing the number of antenna elements by degrees. It should be understood that a variety of other substantially non-rotating directional antenna arrays could be used. For example, greater or fewer than sixteen directional antenna elements could be used. Furthermore, other types of substantially non-rotating directional antenna elements could also be used, and could be adapted to a variety of operating frequencies of different wireless networks.
Additionally, different shapes and configurations of ground plane and support structures could be used. The substantially non-rotating directional antenna array need only provide a plurality of directional antenna structures having maximum signal reception in different directions over a desired detection range which, in the illustrated embodiment is degrees. Furthermore, a variety of refinements and modifications appropriate for commercial embodiments according to the present invention could be made to the embodiment illustrated in FIG.
This is also true of the embodiments illustrated and described elsewhere herein. As an example, directional patch antennas may be used instead of helices or combinations of both or other antennas may also be used. Furthermore, arrangement of the antenna elements in two staggered octagons is not essential. For example, a single ring of 16 antennas would be functionally equivalent and could be positioned about a hexadecagon or a circle or in a variety of other configurations.
The dimensions of the compound antenna illustrated in FIG.