Opportunities in mobile crowd sensing

Ma, Huadóng; Zhang, Dong; Yuan, Peiyan

doi:10.1109/mcom.2014.6871666

Cited by 428 publications

(183 citation statements)

References 10 publications

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“…In the evaluation we consider the upper bound on execution time obtained through static profiling as the response time requirement of each app, and calculate the fraction of times 14. https://developer.att.com/video-optimizer this can be achieved.…”

Section: Generalizabilitymentioning

confidence: 99%

“…The key novelty in EMCO is the use of crowdsensed evidence traces to characterize the influence of different contextual parameters and other factors on offloading decisions [14]. Contrary to existing solutions, which either rely on static code profiling performed on individual devices [2], [3], [4], [5], [6], [11], [15], [16], [17] or on parametrized models that consider a handful of parameters such as network latency, remaining energy, and CPU speed [9], [18], the use of crowdsensing enables EMCO to quantify and characterize the effect of a wide range of parameters and how they vary over execution contexts.…”

Section: Introductionmentioning

confidence: 99%

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Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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Abstract-Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterising execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from GitHub.

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Section: Generalizabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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“…This become more popular in recent times. [8] Coverage area and transmission of data are improved because of human mobility. Figure 11: Crowd-sensing node In dedicated-deploying mode, the coverage ratio is nothing but ratio between total area and sensor's coverage area.…”

Section: Data-centric Inter Networkingmentioning

confidence: 99%

Challenges in Internet of Things Networking

Prakash¹,

Kumar²

2017

ijastems

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Abstract-Internet of Things(IoT) is the future trend for networks. It facilitates the physical devices like sensors, actuators, etc., buildings and many more modules to collect and barter data. In this paper, we analyse the problem of networking of IoT with respect to IoT architecture, structure of networking, and sensor network mode. Finally we explore the challenges involved in inter-networking of IoT.

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“…A second type of activity related to the use of new collaborative technologies for disasters is the emergence of the so-called 'crowd sensing' (Ma et al 2014). This activity involves citizens on the Web that can act as sensors and share their observations.…”

Section: Crowd Sensingmentioning

confidence: 99%

Crowdsourcing geographic information for disaster management and improving urban resilience: an overview of recent developments and lessons learned

Albuquerque¹,

Eckle²,

Herfort³

et al. 2016

European Handbook of Crowdsourced Geographic Information

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In the past few years, crowdsourced geographic information (also called volunteered geographic information) has emerged as a promising information source for improving urban resilience by managing risks and coping with the consequences of disasters triggered by natural hazards. This chapter presents a typology of sources and usages of crowdsourced geographic information for disaster management, as well as summarises recent research results and present lessons learned for future research and practice in this field.

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Opportunities in mobile crowd sensing

Cited by 428 publications

References 10 publications

Evidence-Aware Mobile Computational Offloading

Evidence-Aware Mobile Computational Offloading

Challenges in Internet of Things Networking

Crowdsourcing geographic information for disaster management and improving urban resilience: an overview of recent developments and lessons learned

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