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We would like to welcome you to the first international workshop on mobile location-based service or MLBS 2011! The number of "smart" wireless devices such as mobile phones and iPad-like computers has been rapidly growing. Being able to keep track of locations of moving devices can enhance a number of applications. Location-Based Service (LBS) is quickly becoming the next ubiquitous technology for a wide range of mobile applications, such as location positioning, location navigation, location-aware search, social networks, and ads, just to name a few. LBS was first deployed in the turn of the century by Palm VII, Swisscom, Vodafone, and DoCoMo [1]. These first wave of deployment performed location positioning based on the locations of the nearest cell towers. The accuracy of such approach ranges from one hundred to a few thousand meters, depending on the density of cell towers. In 2004, Global Position System (GPS) was tested successfully to work with a mobile phone by Quadcomm [2]. GPS is now available on most smart phones. GPS can achieve outdoor location positioning with approximately ten-meter accuracy. Its major shortcomings are high power consumption, long TTFF (time to the first fix), and unavailability in urban tunnels and indoor. Some remedies such as AGPS and hybrid cells have recently been researched, experimented, and deployed. Indoor positioning and indoor navigation (IPIN) is a capability that is in high demand in Asia, where most commerce activities take place in high-rise buildings. A shopper may want to find nearby stores selling a particular merchandise, a store may want to deliver coupons to nearby shoppers, and a user may want to locate nearby friends, to name just a few. Existing cell-based, GPS-based and hybrid technologies cannot perform accurate indoor location positioning. Solutions using WIFI signals can achieve sub-five-meter accuracy, both indoor and outdoor. However, access-point density must be high. Furthermore, WIFI-based positioning suffers from both time-consuming site survey (to survey their locations and signal strengths) and signal disturbance [3]. New signal sources such as radar, sonar and inertial navigation systems are now being actively investigated [4]. These devices also suffer from their limitations. A good solution is likely to be one that combines signals from complementary sources. Besides hardware solutions, data-driven software solutions can assist location positioning. For instance, maps and POIs (points of interest) can be mined from users' traveling history. Google recently experiments with a transit alert system based on user moving patterns [5]. It was observed that in a city like Zurich, where transits are always on time and traffic is light, predicting bus arrival time is trivial. In a city like Taipei and Hangzhou, where every bus is equipped with a GPS, bus arrival time can be predicted fairly accurately by taking traffic congestion into consideration. In a city like Bangalore and Beijing, where limited signals are available with transits (to-date) and traffic can be insanely congested, the system can use the position of a user on a bus (assuming the route of the bus is known) to predict the bus's location. We believe that user moving patterns can provide useful information for applications such as information ranking and ad matching. While the opportunities are exciting, preserving user privacy is an issue that must be thoroughly addressed when historical data of users are processed and mined [6]. Another important research area of LBS is data storage and query processing. As location-cognizant devices become ubiquitous, the volume of such data poses unprecedented challenges for LBS providers. Fresh data must be stored and indexed as they arrive, and historical data archived before main memory is full. Spatial query processing of LBS must be both fast and accurate (location updates are reflected in real-time). Traditional spatial data structures such as Quad-trees may not be able to handle such high-frequency current updates and queries. Furthermore, user privacy, including both location privacy and query privacy, must be preserved. In summary, there are at least six key areas of research and development to enhance LBS: Location signal acquisition and processing, Signal fusion, Spatial query processing, Privacy-preserved data mining, Power-conserving algorithms, and LBS-enabled system evaluation. We are excited to organize this first international workshop on mobile location-based service (MLBS). The workshop accepted twelve papers covering the aforementioned areas. We believe MLBS will be an active research direction in the next decade because of the momentum of smart mobile devices, the set of exciting problems to be tackled, and applications to be developed.