Location-based queries are quickly becoming ubiquitous. However, traditional search engines perform poorly for a significant fraction of location-based queries, which are nonfactual (i.e., subjective, relative, or multi-dimensional). As an alternative, we investigate the feasibility of answering locationbased queries by crowdsourcing over Twitter. More specifically, we study the effectiveness of employing location-based services (such as Foursquare) for finding appropriate people to answer a given location-based query. Our findings give insights for the feasibility of this approach and highlight some research challenges in social search engines.

Abstract. This paper describes the design, implementation and deployment of LineKing (LK), a crowdsourced line wait-time monitoring service. LK consists of a smartphone component (that provides automatic, energy-efficient, and accurate wait-time detection), and a cloud backend (that uses the collected data to provide accurate wait-time estimation). LK is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in our university campus. The novel wait-time estimation algorithms deployed at the cloud backend provide mean absolute errors of less than 2-3 minutes.

This article describes LineKing, a crowdsensing system for monitoring and forecasting coffee shop line wait times. LineKing consists of a smartphone component that provides automatic and accurate wait-time detection, and a cloud backend that uses the collected data to provide accurate wait-time estimation. LineKing is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in the University at Buffalo, SUNY. The novel wait-time estimation algorithms of LineKing deployed at the cloud backend provide median absolute errors of less than 3 minutes. © 2002-2012 IEEE

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Abstract-The proximity alert service on Android is important as an enabler of ubiquitous location-based services, however, it is also limited in this role due to its excessive energy expenditure. In this paper, we present the design and implementation of an energy-efficient proximity alert service for Android. Our method utilizes the distance to the point of interest and the user's transportation mode in order to dynamically determine the location-sensing interval and the location providers (GPS, GSM, or Wi-Fi) to be used. We implement our method as a middleware service in the Android open source project. Our service, for a realistic scenario, reduces GPS usage by 96.66% and increases battery life time by 75.71% compared to the baseline proximity alert in Android.

The proximity alert service on Android is important as an enabler of smart cities, however, it is also limited in this role due to its excessive energy expenditure. In this paper, we present the design and implementation of an energy-efficient proximity alert service for both high-precision and low-precision applications. Our methods utilize the distance to the point of interest and the user's transportation mode in order to dynamically determine the location-sensing interval and the location providers (GPS or Network) to be used. We implement our methods as a middleware service in the Android open source project. Our service for realistic scenarios, on average, increases the battery life time of baseline proximity alert in Android by 62% for highprecision and 71% for low-precision applications.