Mobility and transportation are considered one of the six dimensions of the smartness in Smart Cities [121]. Hernández-Muñoz et al. [122] state that sensors can be used to manage the mobility needs with appropriate intelligent transportation systems. Hence researchers have frequently worked on methods that enable members of the BPS (blind and partially-sighted) community to travel safely and independently in indoor and outdoor environments [123, 124].
The Bio-engineering department of Imperial College, London has implemented a system to capture navigational paths using sensors of mobile devices as well as measurement of Wi-Fi signals in buildings. These sensors also record contextual data that can affect the navigation of the BPS community (e.g. staircases, traffic lights, revolving doors). The data that is generated from these sensors are stored and queried using the WikiSensing API.
Figure 8.1: An example of a route instance for the trustworthiness assessment
Figure 8.1 illustrates a sample route taken between the main entrance of Imperial College, London library and the entrance of the London Science Museum. A route (represented using the dashed line) consists of a set of segments that are demarcated by the route designer. The route designer is a user who registers the
Imperial College, Library main entrance
Entrance of the Science Museum Segments
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route with the navigational path system and is usually familiar with this route. These routes are recorded for the distance and the number of turns using an accelerometer and a gyroscope embedded in a mobile device.
Identifying the trustworthiness of these route instances is important as they are used to provide directions for the visually handicapped. A route recorded by a user is considered trustworthy when it contains the distance, the number of turns and the necessary contextual data (e.g. obstacles, revolving doors, staircases, etc.) that approximately corresponds to the actual route. The level of approximation can be based on the level of tolerance acceptable to the blind or partially sighted person. In contrast an untrustworthy route recording usually does not correspond with the actual route and may not be suitable for a visually handicapped person. To assess the trustworthiness, metrics can be calculated using the information on the route instances as well as any map information on the actual route.
The map information is generally a rough idea or an approximation on the actual route based on data such as the distance, the number of turns, etc. This information can be calculated using geographical coordinates, maps or can even be based on previously recorded routes that are correct. It must be noted that this map information itself is not sufficient to consider when checking for valid routes as they do not correspond to the current status of the route. For example, it may not contain certain obstacles or changes to the route that is relevant on a more up-to- date basis.
The inputs to assess the trustworthiness of routes are the map information and one or more route instances recorded by the users, with the problem being to attach a trustworthiness rating for each of these routes. Clearly the distance of a segment in route can be compared with the map information (Figure 8.2) but does not guarantee that it’s trustworthy. Moreover the route can also be compared with other route instances to calculate similarities. Hence there is a need for new metrics and a methodology to calculate trust for these route instances. This contrasts with the trustworthiness calculations for environmental sensor data (discussed in chapter
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5) due to the complexity of the route data which is based on instances rather than a continuous stream of measurements.
The trust metrics for the route data can include conflicts with background information (map information) by comparing the length and number of turns with known information. It can also include metrics on conflicts with other instances of the same route based on distance and number of turns. Moreover metrics for example, on the correlation of the segments of a particular route can provide a representation of the linear independence of this data. It can also include contextual information that can specifically affect the trustworthiness for a blind person as well as information based on the views of other users.
An important feature of the route data is the ability to represent it as a multilevel of information e.g. a sensor is used to record multiple routes, routes can be split into several segments, etc. The motivation of such representation for trustworthiness is that it enables a whole new dimension of data items that can be used to validate information. While a route instance can be assessed for trustworthiness, its segment instances can also be individually assessed for trustworthiness. Hence it is constructive to identify how the collective trustworthiness of segments can be used to validate trustworthiness of the route.
Segment 1, 90 m Segment 2 Segment n
Segment 1, 100 m Segment 2 Segment n Route A1
Map Information for Route A
Segment 1, 92 m Segment 2 Segment n Route A2
Figure 8.2: Comparing a segment with map information and other instances of that route
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8.2. The Requirements (Challenges)
When trust is assessed based on multiple levels it is a challenge to compose or combine trust values of level L so that it is a correct reflection of the collective trustworthiness of level L-1. A multi-level of information exists when data can be subdivided into hierarchical levels. However not all types of data can have a multi- levelled structure (as discussed in section 8.5.2).
Figure 8.3 illustrates the different levels of information gathered when generating route data for the visually handicapped. Trustworthiness ratings can be calculated for users (U), sensors (S), routes (R) or segments (G). With a multilevel of information the number of trust metrics usually increases at lower levels as the data available can be divided into smaller components e.g. a route decomposed into a set of segments. Moreover certain contextual data becomes more relevant when information is partitioned, e.g. it is more appropriate to associate contextual data such as lifts and staircases with a certain part of a route (a segment) rather than the entire route.
Figure 8.3: Multiple layered structure of trust in routes for visually handicapped User Sensor Route Segment Abstraction of Detail Trust Metrics Concentration R1 G1 G2 G4 G5 R3 U1 S1 S2 U2 R4 G6 R2 G3 S3 Level 4 Level 3 Level 2 Level 1
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With the availability of trust metrics at different levels it must be possible to compose the trustworthiness (combine several trust ratings) of a certain level L to obtain the trustworthiness of level L-1. For instance, consider the division of a route into several segments. A methodology is needed to compose the trustworthiness for example, for segments G1 and G2 of Level 4 to determine the trustworthiness of route R1 of Level 3. Hence how can we compose the trustworthiness of the segments of a route? Are conventional aggregation techniques or methods based on voting adequate for such composition? This becomes further challenging when segments have different levels of trustworthiness and is represented continuously as opposed to being discrete.
Consider the example scenario illustrated in Figure 8.4 that shows a set of route instances and the trustworthiness of its segments (in discrete and continuous form). For example, a trust rating of 0.5 or greater can be used as threshold to consider for a segment or a route to be trustworthy. Clearly if the trustworthiness of the route is composed by averaging or by voting the route instances B1 and B4 can be considered trustworthy and not-trustworthy respectively. However such methods may not be suitable for determining the trustworthiness of route instances B2 and B3 as they contain segments with very low trust ratings.
Segment 1, T (0.9) Segment 2, T (0.8) Segment 3, T (0.9)
Segment 1, NT (0.3) Segment 2, T (0.9) Segment 3, T (1.0) Route B1 Route B2 Segment 1, NT (0.1) Segment 2, NT (0.2) Segment 3, T (1.0) Route B3 Segment 4, T (0.7) Segment 4, NT (0.4) Segment 4, T (1.0)
Figure 8.4: Trust composition route example Segment 2, T (0.9)
Segment 1, NT (0.3) Segment 3, NT (0.1) Route B4
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With trustworthiness ratings available at multiple levels a querying mechanism is needed to search and aggregate information from different levels that match querying criteria.