Tecnología de Información y Comunicación:

Users can express their views on the form of rating, by voting or even by commenting. When deciding on the trustworthiness of sensors, users can provide ratings based on their knowledge of the sensor, its deployed environment as well as observation on the sensor measurements. This can be an important factor that would help determine the actual trustworthiness of a sensor or a sensor measurement.

The work by [116] contains a useful definition on predicting rating that is based on the heuristic that people who agreed in the past will probably agree again. This is an important aspect in sensor ratings as this thesis considers the profiles of other metrics that were present when the user rated a particular measurement.

Recommendation or collaborative filtering can be applied to rated sensors or measurements in order to understand the preferences of the users e.g. whether users might accept or reject the sensors or the sensor measurements. Breese et al. [48] define the task of collaborative filtering to predict the utility of items for a particular user (the active user) based on a database of user votes from a sample or population of other users (the user database). The approach used in this research estimates and extrapolates the views of expert ratings for a sensor measurement using previous ratings obtained by other users. Breese et al. also explain the concepts of explicit and implicit voting. The former refers to the user expressing preferences on a title based on a discrete numerical scale and the latter relates to a ratio of information access patterns carried out by the user. Explicit voting is used as the mode of feedback in WikiSensing as it concentrates on the user’s preference on the data and not their access patterns.

̅ | | ∑ (7.1) ̅ ∑ ̅ (7.2)

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Memory based algorithms discussed by Breese et al. predict (formula 7.2) the preference of user a for item j which is based on user votes (by user i on item j), the mean vote of user i for a set of items I (formula 7.1) and a set of weights ( )). This is applied to the total number of users in the collaborative filtering database (n) and subjected to a normalising factor k. The weighting ( ) used in this algorithm is based on either correlation or vector similarity.

The views of experts metric for sensor data are based on ratings of a specific sensor for a measurement instance which compares to an item in the memory mapped algorithm. Moreover the distinction of the user is not significant as the focus of this thesis is to estimate a user’s trust rating for a sensor measurement. One of the other methodologies for estimating the views of experts used here is based on vector similarities of metric patterns. The overall aim of this method is to estimate the trustworthy ratings of sensors or sensor measurements based on previous user ratings which are similar to the votes ( ) used in the memory based algorithm.

To summarise the way WikiSensing determine the views of expert metric is similar to the concept of collaborative filtering as it deals with missing or unavailable data. However the intension here is to estimate the ratings of other unrated measurements and not to make any recommendations to users. The options that are discussed for estimating the views of experts are based on extrapolating data, modelling existing patterns and adding default ratings.

7.2.1. Extrapolate Views of Expert Metric with Sensor

When the data to calculate the views of expert’s metric is unavailable, already obtained ratings are used to estimate it by extrapolation. Two options are explained on the nature of the extrapolation. While the first option extrapolates values to the entire sensor the second option extrapolates values from the time frame of the available user rating.

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Option A: The first option extrapolates the rating of the sensor in its entirety instead

of a measurement instance. For example, when a measurement of a sensor is rated by users to be ‘Negative’ (Not-Trustworthy) all its measurement instances will be rated as ‘Negative’. By default (before any rating is set by user) a sensor has the V metric set as ‘Positive’.

(7.3)

The formula 7.3 symbolises this option where a label of at least one negative V will result in the entire sensor being untrustworthy. Here the represents the views of expert metric that is assigned to the entire sensor. is calculated by extrapolating the rating of given to a sensor i, measurement j by user u.

Option B: The second option extrapolates the rating of the sensor measurement

instances from the time frame of the actual user rating. Hence a label of at least one negative V will result in the sensor being untrustworthy from the time frame of that annotation. (7.4)

The formulae 7.4 symbolises this option where the metric V is extrapolated from the time frame of the rated measurement y. A ratio obtained in case where conflicting ratings are provided by multiple users. Here the represents the views of expert metric that is assigned to all sensor measurements from time frame j. is calculated by extrapolating the rating of for all measurements where j > y.

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7.2.2. Estimating Views of Expert Metric by Modelling Similarities

This method requires identifying patterns of other metrics of the sensor measurements that are already rated by users. Similarities based on these metric patterns can be used to label other measurement instances that do not have views of experts rating. For example, if a measurement instance has the views of expert rating as ‘Negative’ has the pattern of the H metric as ‘1’, O metric as ‘1’ and B metric as ‘1’ then metric instances with similar metric patterns can be labelled as ‘Negative’. By default a sensor has the V metric set as ‘Positive’.

(7.5)

The formulae 7.5 estimates the user rating based on the profile of other metrics. Here the represents the estimated views of expert metric that is assigned to the sensor measurements j. is estimated by observing the similarities of its profile ( ) with a profile ( ) of a rated ( ) measurement. A ratio is obtained to manage similar profiles that have conflicting ratings.

7.2.3. The Inclusion of a Third State of ‘Unknown’

This strategy assigns a default state of ‘Unknown’ to the V metric to all sensor measurements that are not rated by the users. Ratings are not estimated or extrapolated in this strategy but are purely based on the user rating it as ‘Positive’ or ‘Negative’. Initially, (7.6) Once rated,

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Formulae 7.6 symbolises this method by initially assigning views of metric rating for the measurement as ‘Unknown’. Once the user ratings are available is set to the ratio of the ratings.

7.2.4. Incorporating Views of Expert Metrics with Trust Model

H V X O F1 F2 Fn Trustworthiness ... ... B ... ... Vm C Vs Vs

Figure 7.1: Incorporating the different views of expert metrics in the trust model

The above model (Figure 7.1) illustrates the representation of the views of expert (V) metrics (V metric extrapolated or estimated for the sensor measurement), (V metric extrapolated to the sensor) and (V metric set to a default value) in trustworthiness model. Each type of V metric is grouped under the views of expert metric in the trust model to obtain a clear classification of the metric that are based on expert ratings. This representation has the advantage of natural classification when represented as an instance or individual of the trustworthiness ontology.

Clearly the trustworthiness model can be easily extended to represent multiple types of V metrics. Moreover in situations when multiple V metrics are available the user can decide on the metric to be included to assess trustworthiness. The experimental evaluation described in the next section demonstrates use of the different V metric types to assess sensor measurement trustworthiness.

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