MobiSens: A Versatile Mobile Sensing Platform for Real-World Applications

Wu, Pang; Zhu, Jiang; Zhang, Joy Ying

doi:10.1007/s11036-012-0422-y

Cited by 54 publications

(21 citation statements)

References 15 publications

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“…Lane et al [1] in a recent survey paper discussed the importance of continuous sensing among different mobile platforms. Various mobile sensing frameworks have been designed that provide continuous sensing, like MobiSens [2], EmotionSense [3], Funf [4] and AIRS [5]. However, these platforms are currently limited to work on Android or Nokia Maemo phones, limiting the sampling space of users participating in different studies.…”

Section: Introductionmentioning

confidence: 99%

SensingKit: Evaluating the Sensor Power Consumption in iOS Devices

Katevas

Haddadi

Tokarchuk

2016

2016 12th International Conference on Intelligent Environments (IE)

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Abstract-Todays smartphones come equipped with a range of advanced sensors capable of sensing motion, orientation, audio as well as environmental data with high accuracy. With the existence of application distribution channels such as the Apple App Store and the Google Play Store, researchers can distribute applications and collect large scale data in ways that previously were not possible. Motivated by the lack of a universal, multiplatform sensing library, in this work we present the design and implementation of SensingKit, an open-source continuous sensing system that supports both iOS and Android mobile devices. One of the unique features of SensingKit is the support of the latest beacon technologies based on Bluetooth Smart (BLE), such as iBeacon TM and Eddystone TM . We evaluate and compare the power consumption of each supported sensor individually, using an iPhone 5S device running on iOS 9. We believe that this platform will be beneficial to all researchers and developers who plan to use mobile sensing technology in large-scale experiments.

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Section: Introductionmentioning

confidence: 99%

SensingKit: Evaluating the Sensor Power Consumption in iOS Devices

Katevas

Haddadi

Tokarchuk

2016

2016 12th International Conference on Intelligent Environments (IE)

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“…In [12] it is proposed to preprocess sensor data on the mobile device in order to achieve context-aware event filtering, but sophisticated event processing rules are still executed on the server. Sensors of mobile phones are also exploited in [15] to diagnose the actual behavior of the user. But in contrast to our approach, specialized activity recognition algorithms are used instead of CEP.…”

Section: Related Workmentioning

confidence: 99%

Intelligent event processing for emergency medical assistance

Billhardt

Lujak

Ossowskí

et al. 2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

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The main objective of Emergency Medical Assistance services is to attend patients with sudden diseases at any possible location within an area of influence. Especially for severe emergency patients, the potential of such systems to reduce mortality is directly related to the response time, e.g., the time a patient has to wait for an ambulance. An efficient coordination of the ambulance fleet is crucial for reducing the response times of a service. And this requires complete, real-time information about the current state of the ambulance fleet. Such information is usually transmitted by the ambulance crew members. However, due to the often stressful work of those professionals, the information is frequently not sent in a timely manner. In this paper we present an approach that addresses this problem. We use a Complex Event Processing architecture to automatically identify and transmit incidents and changes in the operational states of ambulances. As a result, the availability of information in the control centre and, thus, the effectiveness of the service is improved. The system is inspired by the operational model of SUMMA112, the Emergency Medical Coordination Centre of the Autonomous Region of Madrid in Spain.

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“…In order to collect mobile sensing data, several frameworks have been proposed, such as MobiSens [9], Funf [10], and Ubiqlog [11]. These frameworks are data collecting and analyzing platforms or pure data-collecting systems.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Fine-Grained Mobile Sensing Data Collection and Behavior Analysis in an Energy-Configurable Way

Shi

Yang

et al. 2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

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In recent years, mobile sensing data are widely used for analyzing human's activities, usage patterns, emotions, health conditions and social relationships. In order to understand and analyze human's behaviors, several frameworks have been proposed to collect mobile sensing data. In this paper we extend previous works and design StarLog, which is a distributed and energy-configurable framework for both mobile data collecting and analyzing. It collects fine-grained sensing data of five categories, reflecting user's locations, activities, interactions with smart phone, social contacts and device setting habits. Data analyses are developed on both client side and server side to understand individual as well as crowd behaviors. Besides, StarLog proposes optional modes for collecting sensory data from GPS, accelerometer, gyroscope and magnetometer to make it configurable for battery concern.

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MobiSens: A Versatile Mobile Sensing Platform for Real-World Applications

Cited by 54 publications

References 15 publications

SensingKit: Evaluating the Sensor Power Consumption in iOS Devices

SensingKit: Evaluating the Sensor Power Consumption in iOS Devices

Intelligent event processing for emergency medical assistance

A Framework of Fine-Grained Mobile Sensing Data Collection and Behavior Analysis in an Energy-Configurable Way

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