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Master Thesis: An Algorithm to Improve the Reliability of Proactive Location-Based Services


An Algorithm to Improve the Reliability of Proactive Location-Based Services


With the rise of smartphones, mobile services became a vital part of daily life activities. Due to the diversity of available services, users rely heavily on mobile services in their everyday routines. Usually, a user is interested only in the content related to his current location. Such interest was the main motivation behind the emergence of the so-called Location-based Services (LBS). LBS refers to services which consider location for filtering the delivered content to users. Mostly, any service responds with the proper information upon an explicit user request. Reactive LBS replies to explicit user requests with location-specific content. With the introduction of background tracking and geofencing technologies, another type of LBS arose which supports the user proactively by tracking his location. Proactive LBS sends location-related content to users upon entering or exiting a predefined geographical area are called a geofence. Currently, configuring a proactive LBS geofence is based primarily on human effort. To guarantee a correctly configured geofence before deploying a proactive LBS, an expert is required. Such requirement acts as a barrier for the popularity of proactive LBS. One way to overcome this barrier is to automate the evaluation of geofences configuration. Such automated evaluation assists non-experts to configure geofences in a correct manner making proactive LBS more reliable. This work presents an algorithm for estimating the reliability of a proactive LBS by evaluating its geofence configuration. The algorithm considers the proactive LBS background tracking properties, underlying traffic network and deployment environment. The algorithm gives an approximated probability indicates the reliability of a proactive LBS relies on the geofence under evaluation. This algorithm is intended to enable the introduction of future proactive LBS, in which candidate geofences are inferred dynamically from data obtained of diverse sources such as environmental sensors. The algorithm ensures the validity of each candidate geofence to offer a reliable proactive LBS. To evaluate the algorithm, the outcome of a real-world proactive LBS with a given parametrization is compared with the approximation resulting from the algorithm with the exact same parameters.


Supervisor: Sandro Rodriguez Garzon, Bersant Deva

Type:  Master Thesis

Duration: 6 months

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TU Berlin - Service-centric Networking - TEL 19
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