BeTelGeuse: A Platform for Gathering and Processing Situational Data

Kukkonen, Joonas; Lagerspetz, Eemil; Nurmi, Petteri; Andersson, Mikael

doi:10.1109/mprv.2009.23

Cited by 46 publications

(22 citation statements)

References 13 publications

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“…However, it does not constitute a ready to deploy solution and is suitable only for developers. [Miluzzo et al 2008] X X BeTelGeuse [Kukkonen et al 2009] X X Jigsaw X X X X EmotionSense [Rachuri et al 2010] X X X X X ISC SociableSense [Rachuri et al 2011] X X X X X ISC AmbientDynamix [Carlson and Schrader 2012] X X X X CC BY-NC Auditeur [Nirjon et al 2013] X X inference engines and identifies a) the type of information that is extracted by each framework, b) the development of an energy efficient approach and c) the software license of the framework.…”

Section: An Overview Of Mobile Social Signal Processingmentioning

confidence: 99%

A Survey on Mobile Social Signal Processing

et al. 2016

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Understanding human behaviour in an automatic but non-intrusive manner is an important area for various applications. This requires the collaboration of information technology with human sciences to transfer existing knowledge of human behaviour into self-acting tools. These tools will reduce human error that is introduced by current obtrusive methods such as questionnaires. To achieve unobtrusiveness, we focus on exploiting the pervasive and ubiquitous character of mobile devices.In this article, a survey of existing techniques for extracting social behaviour through mobile devices is provided. Initially we expose the terminology used in the area and introduce a concrete architecture for social signal processing applications on mobile phones, constituted by sensing, social interaction detection, behavioural cues extraction, social signal inference and social behaviour understanding. Furthermore, we present state-of-the-art techniques applied to each stage of the process. Finally, potential applications are shown while arguing about the main challenges of the area.

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Section: An Overview Of Mobile Social Signal Processingmentioning

confidence: 99%

A Survey on Mobile Social Signal Processing

et al. 2016

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“…Enabled by the sensing on the small and cheap, developments in mobile sensing have been tremendous in recent years [19]. Stemming as a natural evolution of the last decade of work in wireless sensor networks [20] and smart spaces [21], efforts to personalize mobile sensing and bring it to commodity mobile devices are only a handful of years old [22][23][24]. This availability of mobile sensing capability has already engendered powerful and readily available libraries that are part of standard software development kits for common mobile platforms [25].…”

Section: Trendsmentioning

confidence: 99%

Software engineering for mobility: reflecting on the past, peering into the future

Picco

Julien

Murphy

et al. 2014

Future of Software Engineering Proceedings

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At the end of the second millennium, mobility was a hot research topic. Physical mobility of devices was becoming commonplace with the availability of cheap wireless cards, the first attempts to transform phones into personal do-it-all devices were beginning to appear, and mobile ad hoc networks were attracting a huge interest from many research communities. Logical mobility of code was still going strong as a design option for distributed systems, with the Java language providing some of the ready-to-use building blocks. In 2000, when we put forth a research "roadmap" for software engineering for mobility, the challenges posed by this dynamic scenario were many.A decade and a half later, many things have changed. Mobility is no longer exotic: we juggle multiple personal devices every day while on the move, plus we grab and update applications on a whim from virtual stores. Indeed, some trends and visions we considered in our original paper materialized, while others faded, disappeared, or morphed into something else. Moreover, some players unexpected at the time (e.g., cloud computing and online social networks) appeared on the scene as game changers.In this paper we revisit critically our original vision, reflecting on the past and peering into the future of the lively and exciting research area of mobility. Further, we ask ourselves to what extent the software engineering community is still interested in taking up the challenges mobility bears.

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“…The existing related work focuses mainly on movement prediction and the use of information about the current location to infer human activities (such as cooking is very probable in a kitchen). The problem of optimizing the uploading process has not been explored yet also because mobile sensing systems based on smart phones, like MyExperience [8], CenceMe [15], Nericell [16] and BeTelGeuse [12], are very recent. Recently, Wang et al have proposed a framework called EEMSS [25] for optimizing the duty cycles of the sensors for mobile sensing applications.…”

Section: Related Workmentioning

confidence: 99%

“…Mobile sensing applications [15,12] are being developed for new sensor-enabled mobile phones (e.g., Apple iPhone and Nokia N95) and new sensing approaches are emerging based on participatory [17] and people-centric sensing [4] paradigms, where people carrying sensor-enabled mobile phones are central to the sensing process (i.e., they are active producers and consumers of sensed data). A wide set of sensing systems are envisioned where phones are used not only to retrieve presence information about individuals, but also to sense external environmental conditions in real-time, such as traffic, road conditions and air quality [16,7].…”

Section: Introductionmentioning

confidence: 99%

Supporting Energy-Efficient Uploading Strategies for Continuous Sensing Applications on Mobile Phones

Musolesi

Piraccini

Fodor

et al. 2010

Lecture Notes in Computer Science

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Abstract. Continuous sensing applications (e.g., mobile social networking applications) are appearing on new sensor-enabled mobile phones such as the Apple iPhone, Nokia and Android phones. These applications present significant challenges to the phone's operations given the phone's limited computational and energy resources and the need for applications to share real-time continuous sensed data with back-end servers. System designers have to deal with a trade-off between data accuracy (i.e., application fidelity) and energy constraints in the design of uploading strategies between phones and back-end servers. In this paper, we present the design, implementation and evaluation of several techniques to optimize the information uploading process for continuous sensing on mobile phones. We analyze the cases of continuous and intermittent connectivity imposed by low-duty cycle design considerations or poor wireless network coverage in order to drive down energy consumption and extend the lifetime of the phone. We also show how location prediction can be integrated into this forecasting framework. We present the implementation and the experimental evaluation of these uploading techniques based on measurements from the deployment of a continuous sensing application on 20 Nokia N95 phones used by 20 people for a period of 2 weeks. Our results show that we can make significant energy savings while limiting the impact on the application fidelity, making continuous sensing a viable application for mobile phones. For example, we show that it is possible to achieve an accuracy of 80% with respect to ground-truth data while saving 60% of the traffic sent over-the-air.

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BeTelGeuse: A Platform for Gathering and Processing Situational Data

Cited by 46 publications

References 13 publications

A Survey on Mobile Social Signal Processing

A Survey on Mobile Social Signal Processing

Software engineering for mobility: reflecting on the past, peering into the future

Supporting Energy-Efficient Uploading Strategies for Continuous Sensing Applications on Mobile Phones

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