The number of smartphones in use is overwhelmingly increasing every year.
These devices rely on connectivity to the Internet for the majority of
their applications. The ever-increasing number of deployed 802.11 wireless
access points and the relatively high cost of other data services make the
case for opportunistic communication using free WiFi hot-spots. However,
this requires effective management of the WLAN interface, because by
design the energy cost of WLAN scanning and interface idle operation is
high and energy is a primary resource on mobile devices.
This thesis studies the WLAN interface management problem on mobile
devices. First, I consider the hypothetical scenario where future
knowledge of wireless connectivity opportunities is available, and present
a dynamic programming algorithm that finds the optimal schedule for the
interface. In the absence of future knowledge, I propose several heuristic
strategies for interface management, and use real-world user traces to
evaluate and compare their performance against the optimal algorithm.
Trace-based simulations show that simple static scanning with a suitable
interval value is very effective for delay-tolerant, background
applications. I attribute the good performance of static scanning to the
power-law distribution of the length of the WiFi opportunities of
mobile users, and provide guidelines for choosing the scanning interval
based on the statistical properties of the traces. I improve the
performance of static scanning, by 46% on average, using a local cache of
previous scan results that takes advantage of the location hints provided
by the set of visible GSM cell towers
