Experimental Hardware

The hardware used for the experimental set-up can be summarised as follows: • 10 desktop workstations

These workstations ran the two applications (presence detection application and physical interaction data collection). These machines were distributed throughout the school build- ing to mostly cover different school buildings (namely Central, North, South and Tre- vitech buildings). Five of them work as servers (connected with central database) that are distributed throughout the school building to detect the mobility of the participants. The other five work as servers (with local database) for Bluetooth devices to receive the sys- tem files of Bluetooth motes by establishing FTP connection. Dongle Bluetooth devices were plugged in all the workstations to be able to perform their functionality (detecting presence of nearby Bluetooth devices, and receiving the file system from the wireless devices).

• 40 AIRcable Mini devices

Forty Bluetooth motes were prepared in small boxes to make them portable for users. Each box included the batteries plugged in a battery holder to support the power for the Bluetooth device. This is seen in Figures 3.10 and Figure 3.11. Each device has a unique code on the box that identifies different users and this code also renders anonymous the collected data from the system file.

• Central Database Server Machine

This machine is responsible for receiving all the tracking information that is stored sim- ultaneously by five distributed servers where the other server has its own local database to receive the system file from the wireless device. The central database was used to store real time user mobility during the experiment time.

The experiment deployment was delayed by three months due to a delay in the manufacturing of the AIRCable Mini devices in North America and devices with similar capabilities could not be sourced. This is bespoke research equipment that is not stored in large quantities in a supply chain.

3.6 Summary 51

Figure 3.10: Bluetooth-mote in Box.

Figure 3.11: Bluetooth-mote in Open Box.

3.6

Summary

This chapter presented, in detail, the design and development of an indoor mobility tracking system to meet all the user and system requirements that are generated for research purposes. It was challenging to create opportunistic networks and, from our knowledge, there are no oppor- tunistic network platforms available other than the prototype developed in the Haggle project. Therefore, we took a different approach and looked at developing a system to detect opportun- ities for peer to peer transmissions. A detailed framework of the system was proposed. The chapter demonstrated the requirements, design and configuration of the system for deployment and capture of mobility data. Bluetooth technology was chosen for its applicability of localizing

52 3.6 Summary

people indoor rather than GPS. It was challenging that smart phones limit the discoverability option for their Bluetooth services where the phone Bluetooth can be on discoverable mode for two minutes only and then it becomes undiscoverable.

An external Bluetooth device (AIRcable Mini) was chosen to support specific functionality for its users. In addition, this choice helped us to manage issues, such as using mobile phones that have limited Bluetooth functions and protecting the identity and privacy of individual par- ticipants. The device has some useful characteristics, such as the ability to be programmed, it is small in size, and can perform independently with battery support. The device is also able to automatically record interaction with devices carried by other users. Workstations with a local database were used to dump the system files from the Bluetooth devices. Dongle Bluetooth devices were plugged into these workstations to support the ability to establish FTP connection between Bluetooth devices and the servers when they come within range. FTP connection is es- tablished to send the system file to be dumped. The system files are dumped in the workstations by means of a new unique name to avoid overwriting any of these files while storing.

Another workstation that is connected with a central database was utilized to identify for any nearby Bluetooth devices. A Java application was developed to track the participant’s mobil- ity indoor while carrying the AIRcable Mini. The Java programming language was used to develop the tracking system as it supports the API that enables us to program Bluetooth oper- ations in the device. The Bluecove API was used to develop the system on desktop machines instead of mobile devices. Different hardware components were integrated together to address the two applications for mobility monitoring and interaction between devices. Communication protocols were used to provide data transfer between different components of the system. The reliability of the system was tested before starting the experimental study and the functionality of each component was tested separately. Finally, the performance of the system as a whole was validated through the pilot study.

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Chapter 4

Social Network Survey Design

Overview

Two types of data sets have been used to support the aim of this study (self-reported data, mobility tracking data). The aim of this study focuses on detecting the opportunistic network between the participants and comparing the human social network with the opportunistic net- work which will lead to addressing the study hypothesis. The mobility tracking data collection system (opportunistic network) was represented in Chapter Three, demonstrating the detailed steps in developing a framework for opportunistic network detection. This chapter is concerned with building up an ego-centric view of the social network by asking people about their own perceptions. This is therefore self-reported data and compared to data in Chapter Five which was collected from observation. This chapter focuses on the methodology for collecting the self-reported social network data in Section 4.1 and the design used for collection in Section 4.2. It includes the challenges faced during the collection stage. The chapter closes with a summary on the collected data.

4.1

Information Collection Methods

Our aim in this chapter is to uncover the social network that the population of participants have. This is hidden because it is based on users’ perceptions. Therefore, we require a research methodology that will uncover these perceptions. Generally, in order to collect qualitative in- formation from participants, there are six common methods for collection [11]. In this study, it was appropriate to collect information using an electronic questionnaire. This method enables us to explore social networks from an ego-centric viewpoint. This approach has been used in a number of other studies and is suitable for a large number of participants. For example, Eagle, Pentland, and Lazer have all used self-reported data in social network analysis and inferring the friendship ties [30].