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3.3 QoSIS: Collaborative Sharing of QoS-information
3.3.1 Introduction
In this section we explain a paradigm of collaborative sharing of QoS-information for mobile service users. Firstly in Section 3.3.2, we explain a mechanism of ‘virtual tag’ as a basic mechanism, upon which we envision an operation of our system for collaborative sharing of information for mobile service users. This system we name Quality of Service Information System, called QoSIS. In Section 3.3.3 we provide different cases of virtual tags usage by QoSIS. In Section 3.3.4 we reflect on user mobility patterns and QoSIS.
3.3.2 Virtual Tags
From the dawn of time people were placing marks (tags) at different geographical locations of interest, serving either to inform others of some important aspects of a particular place (e.g. “dangerous zone”) or to simply fulfil their vanity (“I’ve been here – John”). In most cases, these tags were persistent at this geographical location even if the particular aspects of the location have changed over time and the information contained in the tag became obsolete (Want, Fishkin et al. 1999).
Nowadays, with the emergence of new wireless access network technologies more and more applications evolve from ‘stationary’ (i.e., wired) use to mobile (i.e., wireless) use (Hansmann, Merk et al. 2003).
These applications operate based on the underlying mobile services. Hence, if the application is used ‘on eth move’, there is a need to provide mobility related information to its underlying services. Indeed many applications provide location-based services to mobile users. However, many of these services are unidirectional: the mobile user’s location is considered as an
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application context information used to enhance and filter predefined content delivery (Rao and Minakakis 2003; Bellavista, Küpper et al. 2008).
So far, it is not possible for the user to enrich this context information, by, for example, placing a virtual tag with user-defined content at the current geographical location and, by means of this tag, providing information to other users.
Today with the emergence of miniaturized personalized mobile devices (e.g. PDAs, mobile phones) with integrated location determination technologies (Nokia 2004; ABI Research 2007), and the wide availability of numerous and ubiquitous wireless access network technologies, we can extend the existing unidirectional location-based services with user-provided ‘fine-grained’ location-based information. This can be done by combining high precision location determination technologies (e.g.
EGNOS/Galileo), various wireless access network technologies (e.g. WLAN, 2.5G/3/3.5G) (Bellavista, Küpper et al. 2008) and context provisioning tools that create bi-directional location-based services. A mobile user (or mobile device on his behalf) is able to place a ‘virtual tag’ at different locations of interest.
3.3.3 QoS Virtual Tags and QoS Prediction Map
As we explain in the following sections of this thesis, ‘virtual tag’ is a basic mechanism, upon which we envision an operation of system for collaborative sharing of QoS-information for mobile service users.
Technically, a ‘virtual tag’ is a set of data values, including user geographical location, time and values of QoS measures of interest. A
‘virtual tag’ includes either a) QoS-information placed by mobile devices to QoSIS, i.e., QoS information about QoS provided at the given user geographical location and time, for a given WNP used; or it includes b) QoS prediction information collected by mobile devices from QoSIS, based on which device makes a decision which WNP to use where and when. Particularly, we name these ‘virtual tags’ as QoS Virtual Tags (QVT). QVTs are placed and collected by mobile devices on behalf of mobile service users.
For the QVTs placed by mobile devices, it is important to notice that in our proposal the QoS-information concerns the QoS provided (and technically speaking measured by the mobile device) at the application-level; none of the measurements are done inside the WNP itself.
Based on the placed QoS-information, QoSIS derives QoS prediction for given geographical location and time. Therefore, in return to the provided QoS-information, mobile devices collect QVTs containing predictions of the QoS provided at a given geographical location and time interval and for a set of WNPs available there. These predictions can be presented in form of prediction map (stored on the user’s mobile device)
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denoting user’s current location and his nearby locations along with QoS prediction for these locations in different times. The prediction map is automatically updated by mobile device collecting new QVTs. This update occurs either at regular time intervals or at the moment when user starts using his mobile service on the device. The predictions information in the map allows mobile device to get QoS prediction even if the user is out of coverage of any WNP; although, these QoS prediction can be outdated depending when the QVTs were collected for the last time. In the situation when user is out of coverage of any WNP, his mobile device can still determine (and communicate to the user), where the closest available WNPs are. If a service provided to a mobile user is critical, e.g. it is a mobile health telemonitoring service provided to Sophie, the user can be explicitly warned via sound and vibration generated on the mobile device, before he gets out of coverage of any WNP. Figure 3-2 presents an example prediction map for Swisscom and Sunrise WNPs in Switzerland for a fictitious user trajectory and fictitious prediction values of a QoS measure.
In the following sections, we provide the following scenarios to illustrate the main operational principles of the QVTs usage. Namely, we illustrate the principle of mobile devices placing and collecting the QVTs to / from QoSIS.
Single Mobile Service User, Single WNP
A mobile service user moves along the trajectory from the geographical location A to B (Figure 3-3). To access the Internet, his device uses the wireless access network of one WNP, as available along the trajectory. His mobile device attempts to collect QVTs from QoSIS for the given geographical area and time interval. However, this user is first in this area.
Figure 3-2
Example QoS prediction map at a mobile device
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At the geographical location A, user’s mobile device requests QVT with QoS prediction. It occurs that there is no QoS prediction available for this location, therefore the device places to QoSIS QVT1 containing its QoS-information for this given geographical location and time. This is repeated throughout the trajectory. The mobile device posted to QoSIS numerous QVTs (QVT1 ,QVT2,..., QVTn) along this trajectory.
Multiple Mobile Service Users, Single WNP
A mobile service user moves along the trajectory from geographical location X to Y (a dashed line in Figure 3-4). There has been a lot of QoS-information posted to QoSIS by device of another user moving along trajectory from location A to B (a solid line).
Figure 3-3 QVTs use for a single mobile service user, single WNP
Figure 3-4
QVT use for multiple mobile service users, single WNP
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At the geographical location X, the mobile device attempts to collect from QoSIS a QVT with QoS prediction. It occurs that there is no QoS prediction available for this location, therefore the device posts QVT1 containing its QoS information for this given geographical location and time. At the next point, the device attempts to collect from QoSIS a new QVT, specifying a new location area. It occurs that there is a QVT (QVT2) with QoS prediction derived based on the QVT posted by a device of the previous user in this location area and time interval. Therefore, his mobile device collects from QoSIS QVT with QoS prediction, and at the same time his device posts to QoSIS the QVT2 based on the QoS information for user’s given location and time.
Multiple Mobile Service Users, Multiple WNPs and Wireless Technologies
A mobile service user moves along the trajectory from geographical location A to B (Figure 3-5). There are different wireless access networks of multiple WNPs available along the user’s trajectory. Moreover, there has been a lot of QoS-information posted to QoSIS by devices of another users moving along different trajectories overlapping with the trajectory from location A to B and using these different WNPs and wireless access technologies for mobile services.
At the geographical location X, the mobile device attempts to collect from QoSIS a QVT with QoS prediction. It occurs that there is QVT available at this location, for two different MNOs. Based on these QVT, the device makes an informed choice of an operator (red dotted), which provides a service with a quality matching the one required by the service
Figure 3-5
QVT use for multiple mobile service users, multiple WNPs and technologies
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user. The device also places to QoSIS its QVT1 in this location. This is repeated throughout the complete trajectory from location A to B. The QVTs placed to QoSIS by the devices of other users enable deriving accurate QoS prediction for different WNPs for devices of other users in this location area. The devices may seamlessly handover between WNPs, while assuring that the QoS requirements of their mobile service users will be fulfilled.
3.3.4 User-Mobility Patterns
Current state of the art research shows that mobile service users tend to have highly predictable mobility patterns, e.g. home, office, shopping-centre, and trajectories in between these locations, traversed at particular days of the week and particular hours. Namely, authors of (Gonzalez, Hidalgo et al. 2008) show that GSM voice service users tend to stay in top four locations. Moreover, when moving, 45 % of them stay in a location area of a radius of approximately 10 km, while 73 % of them within location area of radius of approximately 30 km. The authors show that GSM voice service users’ mobility patterns can be learned from only 3 months of history.
We hypothesise that the fact that mobile service users tend to have highly predictable mobility patterns, will influence the accuracy of the QoS predictions provided by QoSIS. Namely, if these mobility patterns are highly predictable (e.g. every day from geographical location A representing home to B representing office, and back), a mobile device of this user places to QoSIS large amount of QoS-information for WNPs used in particular geographical locations at particular times or at locations traversed along the trajectories in a given time interval. The collected QoS-information facilitates deriving QoS prediction collected from QoSIS by this and other users at given geographical locations and time intervals.