1. Energy-Efficient Location-based Services Mohamed F. Mokbel Department of Computer Science and Engineering University of Minnesota www.cs.umn.edu/~mokbel mokbel@cs.umn.edu

2. 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

3. Future

4. 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

5. Energy Consumption in Transportation • The transportation sector consumes 29% of the US power • Road accounts for about 80% of all the energy consumed by transportation in the United States and this share has remained constant in time. Source: BTS, National Transportation Statistics. Energy Consumption by Transportation Mode in the United States, 1960-2007 (in Trillion BTUs) Source: EIA. Annual Energy Review 2009. Table 2.1a Energy Consumption by Sector, 1949-2007.

6. 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

7. LBS GIS DBMS Energy Saving at the Server Side Layered Approach • 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 Built-in Approach GIS Interface DBMS LBS Query Processing LBS-Index

8. 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

9. Thanks