VJAGG -- A Thick-Client Smart-Phone Journey Detection Algorithm
In this paper we describe $Vja\dot{g}\dot{g}$, a battery-aware journey detection algorithm that executes on the mobile device. The algorithm can be embedded in the client app of the transport service provider or in a general purpose mobility data collector. The thick client setup allows the customer/participant to select which journeys are transferred to the server, keeping customers in control of their personal data and encouraging user uptake. The algorithm is tested in the field and optimised for both accuracy in registering complete journeys and battery power consumption. Typically the algorithm can run for a full day without the need of recharging and more than 88% of journeys are correctly detected from origin to destination, whilst 12% would be missing part of the journey.
Code (1)
Similar Papers 제목 키워드 기반
A review of smartphones based indoor positioning: challenges and applications
The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navig…
The Smart Parking Management System
With growing, Car parking increases with the number of car users. With the increased use of smartphones and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking Management …
ManagementA Crowdsourcing Smartphone Application for Swiss German: Putting Language Documentation in the Hands of the Users
This contribution describes an on-going projects a smartphone application called Voice {\~A}pp, which is a follow-up of a previous application called Dial{\"a}kt {\~A}pp. The main purpose of both apps is to identify the …
speech-recognitionSpeech RecognitionReLF: Scalable Remote Live Forensics for Android
The world has witnessed the proliferation of mobile technologies as well as smartphone-related cybercrimes in recent years. However, due to high mobility of smartphones and tablets and transient nature of those attacks, …
Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data
Mood instability is a key behavioral indicator of mental health, yet traditional assessments rely on infrequent and retrospective reports that fail to capture its continuous nature. Smartphone-based mobile sensing enable…
Federated Learning