Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smartphones do include a GPS receiver, it's frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. Our design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used. Our method automatically determines the dynamic accuracy requirement for mobile search-based applications. As the user moves, both the accuracy requirements and the location sensor errors change. A-Loc continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments are performed with Android G1 and AT&T Tilt phones, on paths that include outdoor and indoor locations, using war-driving data from Google and Microsoft. The experiments show that a-Loc not only provides significant energy savings, but also improves the accuracy achieved, because it uses multiple sensors.

We present DIP, a data discovery and dissemination protocol for wireless networks. Prior approaches, such as Trickle or SPIN, have overheads that scale linearly with the number of data items. For T items, DIP can identify new items with O(log(T)) packets while maintaining a O(1) detection latency. To achieve this performance in a wide spectrum of network configurations, DIP uses a hybrid approach of randomized scanning and tree-based directed searches. By dynamically selecting which of the two algorithms to use, DIP outperforms both in terms of transmissions and speed. Simulation and testbed experiments show that DIP sends 20-60% fewer packets than existing protocols and can be 200% faster, while only requiring O(log(log(T))) additional state per data item.

Sensor network applications tend to exhibit significant high-level commonalities along several major dimensions that have heretofore been underexposed, particularly in the areas of collection and dissemination. We have developed a component library, sdlib, which presents the fundamental abstractions of collection and dissemination as part of a dataflow sytem. This allows application developers to rapidly develop applications at the nesC level. This means that sdlib maintains significant expressivity while operating efficiently.We have built four applications, each faithful to a mature monolithic application, on top of sdlib to compare its performance to that of original. We find that applications implemented with sdlib are much simpler to write, just as resource efficient, and perform comparably to monolithic implementations.