Summary and Contributions

9.1.1. Summary

This research has introduced a new collaborative approach for sensor data management known as WikiSensing. The thesis presented an architectural design and described the implementation details for a collaborative sensor data management system. The advantage of WikiSensing is based on incorporating online collaboration into sensor data management. Online collaboration is used to annotate, update and share sensor information as well as in creating virtual sensors. The concept of virtual sensors is an extremely useful feature that provides sensor readings using existing sensor data streams. Some of the main challenges in sensor data management with online collaboration are due to the large amounts of heterogeneous, real time of sensor data as well as the need to demonstrate trust of the shared information.

This research investigated the challenges of managing trustworthiness in collaborative sensor systems. A framework and methodology was presented based on a generic probabilistic definition of trust, and described how to capture and calculate metrics for different types of available evidence. Trustworthiness was defined as a probability as it provides a good indication of uncertainty as well as to be used in future predictions. The approach is extensible allowing incorporating metrics based on other probabilistic models if needed, e.g. by using binomial models to calculate trust based on historical interactions with individual sensors as in other work [63]. A number of experiments were also presented to demonstrate and verify the use of the framework and models. Furthermore different representational models were compared and studied as to how early untrustworthy behaviour of sensors could be detected.

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The WikiSensing system was one of the data stores that supported the

Hackathon event at the Urban Prototyping London (uplondon.org) festival in April

2013. During the event, a workshop was held where contestants were given air pollution data similar to the one used in this thesis. They used WikiSensing’s anomaly and conflict detection tools to obtain different metrics and to assign their own trustworthiness scores to sensor measurements at different time frames. They were also provided with various visualization tools to explore such data.

The practical experience from the workshop not only provided valuable feedback but also highlighted the opportunity for developing new metrics to extend the trust model easily. For example, it is not difficult to include metrics based on correlation values [127] between sensor measurements across different time periods (as shown in the metric calculation for the route data in section 8.5). It is also not difficult to include ones that explore the evolution of trust over time for individual sensors or those that capture trust propagation information [54] between different sensors. Although guidance was provided in compiling threshold values of β for metric calculations it has to be explicitly set by the user, based on discretion and knowledge. In future it is preferable to automate the estimates of β values taking into account available sensor information and previous trust scores.

9.1.2. Contributions

In this thesis, the architecture and a system for collaborative sensor data management as well as a model and a framework for trustworthiness management were presented. The central problems that this thesis concerns can be summarised as follows:

To find a means for managing collaborative sensor data (Big data) and to standardise the ways users can trust data in such environments.

The research work presented here covers a range of different areas pertaining to the data management of sensor data, the organising of collaborative information and

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the management and assessment of trustworthiness of the sensor data. The contributions of this research can be categorised as follows:

Collaborative Sensor Data Management: Managing sensor data and organising

collaborative information with the aim of addressing the Big data challenges of volume, velocity and variety.

Trustworthiness Management: Managing and assessing the trustworthiness of

sensor data with the aim of addressing the Big data challenge of veracity.

The key contributions of this research are based on the WikiSensing system that provides collaborative sensor data management. Furthermore it also provides trustworthiness management with the ability to assess trust on a multilevel of information. The contributions are summarised as follows:

An architecture and implementation of a collaborative sensor data management system known as WikiSensing

The distinct features of WikiSensing include a hybrid data storage, support for online collaboration and virtual sensors. The system is also used as a testbed to develop a framework to manage trustworthiness of sensor data based on a novel probabilistic model.

A generic probabilistic definition of trust in sensor data

A generic mathematical definition is provided to relate the general concept of trustworthiness with trustworthiness of sensor data.

A framework and model to determine the trustworthiness of data in a collaborative sensor data management system

The thesis describes a framework to capture, calculate and represent the metrics needed to determine the trustworthiness. The methodology of determining the trustworthiness is based on Bayesian probabilistic modelling. This work also describes the architecture and implementation of this framework based on a software system in order to implement the methodology.

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An adaptation of the trustworthiness model when data that can be represented in a multilevel

The trust model is extended to assess trustworthiness when data is represented in a multilevel of information. The aim is to use this methodology as a generic solution to determine trust of data in other collaborative sensor data domains.