The Methodology and Implementation

The methodology has three main stages (Figure 5.3). The first stage involves the collection of sensor data (measurements, sensor properties, etc.), user feedback and values for input parameters for the trustworthiness models. The second stage manages the collected data and utilizes the trustworthiness models and formulae to calculate the metrics and trustworthiness values. The third stage outputs calculated trustworthiness and supporting information.

Decide on what metrics to be included and the formulae for

the calculations Sensor Data, Meta-data, User Feedback and Formulae Trustworthiness and Supporting Information Manage Data Provenance Data

Collection Organization and Processing

Information Output

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During the first stage the required data for the trustworthiness assessment is collected. This data comprises of sensor data, user feedback and the input parameters. The sensor data includes sensor measurements, meta-data (sensor properties) and geographical details of the deployment of the sensors. The user feedback contains user ratings or any type of positive or negative remarks. The input parameters are used by the formulae that calculate the trust metrics that are discussed in detail in section 5.5.3.

The second stage manages the unstructured data and utilizes the formulae to calculate the metrics. The user can also provide additional metrics and formulae for their calculations as well as their own formulae for the existing metrics. Curating the data is a prerequisite for the metric calculations e.g. sampling different sensor reading frequencies and converting sensor measurements into a common unit of measure. The second phase also involves calculating the trustworthiness rating for the sensor. In order to calculate the trustworthiness of a sensor or sensor measurement a set of metrics are formulated. These metrics are representations of data that can include historical information (H), information on conflicts (C) between the sensor measurements with other sensors (O), conflicts with background information (B), contextual information (X) (e.g. calibration) and information provided by the views of other users (V). Information on these calculations is also stored for provenance. Further this process can be reinitiated on the same sensor at a later time or when new information becomes available. This model highlights the importance of data provenance as the trustworthiness of a sensor may change over time as well as when new information becomes available. The third phase outputs the calculated trustworthiness and all supporting information. This supporting information is used to explain the calculations and the parameters used in the calculation of the metrics and the final trustworthiness rating.

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5.4.1. The Architecture

The WikiSensing architecture described in section 3.2 is extended in order to support this generic framework by introducing new components highlighted in bold in Figure 5.4. It is noted that the components themselves are implemented in a generic way and can be accessibly plugged into a sensor data management system other than WikiSensing.

Non-relational

Database Relational Database

Sensor Data Ontology Data

Sensor /Trust Ontology Database Layer Application Layer Client Layer Wiki Data File

Server ArticlesWiki Manage Trustworthiness Wiki Pages Web Interface XML Relational Database Manage Trustworthiness Metrics Manage Ontology Add Trust Concepts

Trust Data

WikiSensing Core

Manage Collaboration Assess Trustworthiness

Strategies for Assessing Trustworthiness Manage History Log History Query Data API Services Data Management

Filter History Query Ontology Calculate Metrics

Figure 5.4: The architecture of the trustworthiness management framework The overall architecture is based on a layered model with a data layer that includes a database for trustworthiness data. The algorithms for trustworthiness management reside in the application tier. Moreover the metric calculation is done in the Assess Trustworthiness component and is invoked by API services. The Business Logic

Layer

Data Management Layer

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metric calculation requires the functionalities of the Manage Trustworthiness,

Manage History, Manage Ontology as well as the WikiSensing Core components.

The database layer contains the databases for the sensor, trustworthiness, wiki and the ontological data. The client layer provides a web interface for sensor data management and Wiki pages for collaboration with XML being used as a medium for the exchange of data.

The application tier contains two sub layers, a business logic layer (top) and a data management layer (bottom) and components with thick borders are specifically responsible for trustworthiness management. The data management layer provides functionality for data manipulation and the business logic layer contains algorithms for resolving conflicts and assessing trustworthiness. The

Assess Trustworthiness module uses the WikiSensing Core components and the

data management layer to obtain information from the databases for metric calculations.

Once the metrics are calculated it is then represented as ontology and the calculations and data are stored in history for provenance. For instance, when a trustworthiness assessment request is made by the API Services, the Assess

Trustworthiness module obtains the strategies (formulae) for the metric calculation.

It then obtains the necessary data (sensor data, meta-data, user ratings, etc.) and calculates the trust metrics. All calculation details and metrics are logged using the

Manage History module. The Manage Ontology then represents this information in

the trustworthiness ontology as individuals based on the defined ontology schema. The metric calculations usually require data from the sensor database that includes current and historical measurements, spatial information (e.g. geographical coordinates) and sensor types. It also requires data on sensor properties and context (represented as ontology) as well as user rating information (recorded in wiki pages). All the current metric values as well as their historical values are stored in the trust database.

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5.4.2. Representing Trustworthiness Metrics as an Ontology

The trustworthiness metrics, the contextual data and the sensor information are stored as ontology in order to maintain a common vocabulary. This research extends the OntoSensor ontology [66] to contain sensor trustworthiness data.

OntoSensor is an extension of SUMO (Suggested Upper Merged Ontology) [65] a

top-level ontology for computer based information systems that provides concepts that are general throughout the knowledge domain. The OntoSensor ontology is a comprehensive ontology that maps a subset of the SensorML [64] concepts into

OWL [67]. The WikiSensing trustworthiness ontology is available on the internet

under the section Trustworthiness API at wikisensing.org.