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University of Minnesota

1 / 9May 2011

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

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Location-based Services: Now

Location-based traffic reports

Range query:How many cars in afreeway?

Shortest path query:What is the shortestpath to my destination?

Location-based store finder Nearest-neighbor query:Where is my

nearest restaurant?

Range query:What are the restaurantswithin one mile from my location?

Location-based advertisement Range query:Send e-coupons to all

customers within five miles from my store

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Future

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Energy Consumption inLocation-based Services

Power consumed by location-detection devices

It is crucial to minimize the powerconsumption of such devices to keep themalive longer

Power consumed at the databaseserver to answer location-basedqueries

Most of location-based queries areinherently continuous, which makes themexpensive to evaluate on the server side

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Energy Consumption in Transportation

Source: EIA. Annual Energy Review 2009.Table 2.1a Energy Consumption by Sector,

1949-2007.

The transportation sector consumes 29% of the US power

Source: BTS, National Transportation Statistics.Energy Consumption by Transportation Mode inthe United States, 1960-2007 (in Trillion BTUs)

Road accounts for about 80% of all the energy consumed by transportation inthe United States and this share has remained constant in time.

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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 updateonly 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 itwill affect the result of a given query

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Energy Saving at the Server Side

Minimize the work that the DBMS needs todo through a built-in structure

Power-Aware Evaluation of ContinuousQueries Two approaches for continuous query evaluation:

A set of consecutive snapshot queries,executed every ttime units

Incremental evaluation

Power-aware Cost models for incrementalevaluation and shared execution

Shared execution

Load Shedding

DBMS

GIS

LBS

Layered Approach

GIS Interface

LBS-Index

LBS QueryProcessing

DBMS

Built-in Approach

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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 andpreference-aware queries

Accurate traffic prediction Prediction and avoidance of traffic congestions save driving time

We need to devise long-term and accurate prediction techniquesthat 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 thatmostly consider the distance to consider the time of the day, andtime-aggregated graphs

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Thanks