University of Minnesota 1 / 9 May 2011 Energy-Efficient Location-based Services Mohamed F. Mokbel Department of Computer Science and Engineering University of Minnesota

May / 9 Location-based Services: Now Location-based traffic reports  Range query: How many cars in a freeway?  Shortest path query: What is the shortest path to my destination? ■ Location-based store finder  Nearest-neighbor query: Where is my nearest restaurant?  Range query: What are the restaurants within one mile from my location? ■ Location-based advertisement  Range query: Send e-coupons to all customers within five miles from my store

May / 9 Future

May / 9 Energy Consumption in Location-based Services Power consumed by location- detection devices  It is crucial to minimize the power consumption of such devices to keep them alive longer ■ Power consumed at the database server to answer location-based queries  Most of location-based queries are inherently continuous, which makes them expensive to evaluate on the server side

May / 9 Energy Consumption in Transportation Source: EIA. Annual Energy Review Table 2.1a Energy Consumption by Sector, The transportation sector consumes 29% of the US power Source: BTS, National Transportation Statistics. Energy Consumption by Transportation Mode in the United States, (in Trillion BTUs) Road accounts for about 80% of all the energy consumed by transportation in the United States and this share has remained constant in time.

May / 9 Energy Saving in Mobile Devices Most of the energy consumption in mobile devices is consumed in detecting/uploading the user location Approaches of energy saving  Sampling. Update the location information every t time units  Prediction. Send the predicted future trajectory, then, send an update only if different from the predicted trajectory Need to go beyond data-driven techniques to query-driven techniques where the location will be uploaded only if it will affect the result of a given query

May / 9 Energy Saving at the Server Side ■ Minimize the work that the DBMS needs to do through a built-in structure ■ Power-Aware Evaluation of Continuous Queries  Two approaches for continuous query evaluation:  A set of consecutive snapshot queries, executed every t time units  Incremental evaluation  Power-aware Cost models for incremental evaluation and shared execution  Shared execution  Load Shedding DBMS GIS LBS Layered Approach GIS Interface LBS-Index LBS Query Processing DBMS Built-in Approach

May / 9 Energy Saving in Transportation Personalization:  Giving the right answer is essential in saving driving time  We need to go beyond the traditional nearest-neighbor queries that are solely based on distance to consider more context and preference-aware queries Accurate traffic prediction  Prediction and avoidance of traffic congestions save driving time  We need to devise “long-term” and “accurate” prediction techniques that send alerts about possible congestions Shortest path queries  Finding the right shortest path route significantly affect driving time  We need to go beyond the typical shortest path algorithms that mostly consider the distance to consider the time of the day, and time-aggregated graphs

May / 9 Thanks