The case for crowd computing

Murray, Derek G.; Yoneki, Eiko; Crowcroft, Jon; Hand, Steven

doi:10.1145/1851322.1851334

Cited by 77 publications

(42 citation statements)

References 18 publications

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“…Many papers proposed similar concepts termed as crowd sensing [31], crowd computing [46] and crowd-sourced sensing [15]. The general idea of these propositions is to take advantage of the computing/sensing abilities of mobile devices, preferably combined with human interaction or intervention.Such systems could be very useful to achieve great parallelism and accomplish tasks by utilize such parallelism.…”

Section: Crowd Computing and Crowd Sensingmentioning

confidence: 99%

“…Besides, given the potential of adapting powerful processing, storage and communication capabilities, these mobile devices can either serve as a bridge to other everyday objects, or generate information about the environment themselves. [46] analyse encounter traces to place an upper bound on the amount of computation that is possible in an opportunistic network of mobile devices. They also investigate a practical taskfarming algorithm that approaches this upper bound.…”

Section: Crowd Computing and Crowd Sensingmentioning

confidence: 99%

“…Community-Based Crowd Computing [46] shows an interesting result of crowd computing, which is social awareness can greatly raise the performance of crowd computing, in other words, achieve greater parallelism. This may point researchers to interest directions such as how to best exploit the community structure/attributes to achieve greater parallelism.…”

Section: Social Crowd Computingmentioning

confidence: 99%

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DRAP: A decentralized public resourced cloudlet for Ad-hoc networks

Agarwal

Nayak

2015

2015 IEEE 4th International Conference on Cloud Networking (CloudNet)

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Handheld devices are becoming increasingly common, and they have varied range of resources. Mobile Cloud Computing (MCC) allows resource constrained devices to offload computation and use storage capacities of more resourceful surrogate machines.This enables creation of new and interesting applications for all devices.We propose a scheme that constructs a high-performance de-centralized system by a group of volunteer mobile devices which come together to form a resourceful unit (cloudlet). The idea is to design a model to operate as a public-resource between mobile devices in close geographical proximity. This cloudlet can provide larger storage capability and can be used as a computational resource by other devices in the network. The system needs to watch the movement of the participating nodes and restructure the topology if some nodes that are providing support to the cloudlet fail or move out of the network. In this work, we discuss the need of the system, our goals and design issues in building a scalable and reconfigurable system. We achieve this by leveraging the concept of virtual dominating set to create an overlay in the broads of the network and distribute the responsibilities in hosting a cloudlet server. We propose an architecture for such a system and develop algorithms that are requited for its operation. We map the resources available in the network by first scoring each device individually, and then gathering these scores to determine suitable candidate cloudlet nodes.We have simulated cloudlet functionalities for several scenarios and show that our approach is viable alternative for many applications such as sharing GPS, crowd sourcing, natural language processing, etc.ii Acknowledgements Acknowledgements are due to the many people who have supported me, either directly or indirectly, in completing this work.Firstly, I would like to acknowledge and thank Professor Amiya Nayak, my thesis adviser, for all his help, guidance, advice and support for the past one and a half year that I have worked with him.

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Section: Crowd Computing and Crowd Sensingmentioning

confidence: 99%

Section: Crowd Computing and Crowd Sensingmentioning

confidence: 99%

Section: Social Crowd Computingmentioning

confidence: 99%

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DRAP: A decentralized public resourced cloudlet for Ad-hoc networks

Agarwal

Nayak

2015

2015 IEEE 4th International Conference on Cloud Networking (CloudNet)

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“…Since surrounding mobile users may be viewed as a cloud of resources to be exploited for computation and retrieval, mechanisms and languages have been developed for spreading computations across mobile nodes [20]-somewhat similar to data center operation with a Map/Reduce approach where a master assigns tasks to workers-and for declarative programming in such an environment [26]. The performance when spreading computation requests in an opportunistic fashion to mobile nodes, including estimating the number of workers to be assigned to a given task to achieve a certain success probability, has been studied in [20,22].…”

Section: Introductionmentioning

confidence: 99%

“…The performance when spreading computation requests in an opportunistic fashion to mobile nodes, including estimating the number of workers to be assigned to a given task to achieve a certain success probability, has been studied in [20,22].…”

Section: Introductionmentioning

confidence: 99%

Opportunistic content sharing applications

Ott

Kangasharju

2012

Proceedings of the 1st ACM Workshop on Emerging Name-Oriented Mobile Networking Design - Architecture, Algorithms, and Applicat

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Opportunistic communication between mobile nodes allows for asynchronous content sharing within groups. Limiting the spread of information to a geographic area creates an infrastructure-less variant of digital graffiti, a social network with coupling in space and limited decoupling in time. Due to its nature, this kind of a communication network lends itself readily to name-oriented abstractions. In this paper, we extend our previous work on floating content, extract its fundamental characteristics, and define a system model and a simple API with a set of basic programming elements to support applications in leveraging opportunistic content sharing as a generic communication facility. We validate our API through application examples and show how their communication needs are mapped to our model. In addition, we also implement our API in our simulator and demonstrate the feasibility of these kinds of applications.

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