In addition to simulations, we have evaluated the performance of the different
predictors also by using the implemented service prototype, built on top of our
middleware, and by moving 4 laptop clients among the campus wireless localities
during streaming provisioning. Even if the number of considered in-the-field
handovers is largely lower than the simulated one (and, thus, less relevant from
the statistical point of view), in-the-field performance results confirm the
simulation-based ones. The prototype has achieved great average results for ABS,
SH%, and ABD, also due to the lower number of APs considered, and the consequent
simplification of correct handover prediction.
Table 3 presents predictor performance indicators collected with Orinoco Gold and Cisco Aironet 350 PCMCIA wireless
cards either on a Windows XP SP2 and a Linux Fedora Core 2 laptop.
Orinoco Gold wireless card performs SP handover strategy. Regardless the operating system either ABS, SH% and ABD shows great performances,
validating our handover predictor.
Cisco Aironet 350 wireless card performs HP handover strategy when ACU (Aironet Client Utility) Manager imposes "Periodically Scan for a Better Access Point". However it is not
possible to perform AP scanning through NDISUIO driver when ACU Manager is turn on. In order to
perform AP scanning it is mandatory to impose "Use Another Application to Configure My Wireless
Settings"; in this case handover strategy is reactive. This is the reason because in table 3 we
define
Reactive Cisco 350 card handover strategy. Also in
this case of reactive handover strategy our predictor shows great performance; moreover again GM-filtered based predictor performs better than actual-RSSI based one!
Minor comment: Cisco card on Linux OS does not return RSSI in dB; we exploit a Cisco table to transform RSSI in dB.
(for details see "You Believe You Understand What You Think I Said..." by Joshua Bardwell)
Wireless Card |
HO strategy |
Operating System |
Exploited RSSI |
ABS
(KB) |
SH% |
ABD
(s) |
pre-Ho
buffer fulfill |
post-Ho
buffer fulfill |
Orinoco Gold |
SP |
Windows XP SP2 |
Actual |
145 |
80 |
5.01 |
193 |
9 |
GM-filtered |
131 |
100 |
6.97 |
199 |
12 |
Linux FC2 |
Actual |
109 |
100 |
4.70 |
199 |
13 |
GM-filtered |
100 |
100 |
8.07 |
199 |
12 |
Cisco Aironet 350 |
Reactive |
Windows XP SP2 |
Actual |
172 |
100 |
4.22 |
199 |
13 |
GM-filtered |
169 |
100 |
5.99 |
199 |
12 |
Linux FC2 |
Actual |
165 |
100 |
4.79 |
199 |
12 |
GM-filtered |
160 |
100 |
7.13 |
199 |
13 |
Table 3. In-the-field performance indicators.
Predictor parameters may differ from simulation to in In-The-Field experiments; see Table 4 for further details.
Card |
OS |
FST |
FIT |
HST |
HIT |
Orinoco |
Windows |
76 |
80 |
10 |
6 |
|
Linux |
72 |
76 |
10 |
6 |
Cisco |
Windows |
--- |
--- |
10 |
6 |
|
Linux |
--- |
--- |
10 |
6 |
Table 4. Predictor parameters in In-The-Field experiments.
Finally let us stress occasionally we have experienced a
significant degradation of performance indicators in the case of extreme RSSI
fluctuations, e.g., when a client follows a trajectory in strict proximity of
relevant obstacles, such as the reinforced concrete walls of our campus
buildings. In those case we claim handover prediction is not possible.