Sigmoid utility function formulation for handoff reducing Access model in cognitive radio

Dahi, Soumaya; Tabbane, Sami

doi:10.1109/iscit.2013.6645843

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…In contrast, the plots for all variations of our bonus method show clearly increased annotation throughput for peak demands 5 . Furthermore, Figure 4f shows that the combination of high reward and long notice results in extreme annotation peaks in response to the demand peaks, compared to a rather low base annotation throughput due to lower participation numbers (see Table 1).…”

Section: Temporal Annotation Characteristicsmentioning

confidence: 81%

“…Time utility functions were designed to express the temporal demand and reward or penalize the output depending on the desired execution time limit. They are widely used in real time computing [22] for resource management such as package scheduling in wireless networks [5] or video transcoding [35]. In contrast to these works, we are the first to exploit time-based utility functions with different properties in streaming crowdsourcing scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…where sc is a scaling factor and st determines the steepness of the curve. Such sigmoidal utility functions are also employed in the context of wireless networks [5,36].…”

Section: Completion Factormentioning

confidence: 99%

“…These observations can be taken into account when planning the bonus based on a demand scenario: our demand scenario could be matched more closely by awarding the bonus slightly earlier and longer. 5 Except for one hour in Figure 4f. Activity during bonus hours was so high in the high bonus setting, in fact, that our application could not handle the load at some points, which resulted in artifacts in the data.…”

Section: Temporal Variations Around Bonusesmentioning

confidence: 99%

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Rokicki

Zerr

Siersdorfer

2016

Proceedings of the 25th International Conference on World Wide Web

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Many modern data analytics applications in areas such as crisis management, stock trading, and healthcare, rely on components capable of nearly real-time processing of streaming data produced at varying rates. In addition to automatic processing methods, many tasks involved in those applications require further human assessment and analysis. However, current crowdsourcing platforms and systems do not support stream processing with variable loads. In this paper, we investigate how incentive mechanisms in competition based crowdsourcing can be employed in such scenarios. More specifically, we explore techniques for stimulating workers to dynamically adapt to both anticipated and sudden changes in data volume and processing demand, and we analyze effects such as data processing throughput, peakto-average ratios, and saturation effects. To this end, we study a wide range of incentive schemes and utility functions inspired by real world applications. Our large-scale experimental evaluation with more than 900 participants and more than 6200 hours of work spent by crowd workers demonstrates that our competition based mechanisms are capable of adjusting the throughput of online workers and lead to substantial on-demand performance boosts.

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Section: Temporal Annotation Characteristicsmentioning

confidence: 81%

Section: Related Workmentioning

confidence: 99%

“…where sc is a scaling factor and st determines the steepness of the curve. Such sigmoidal utility functions are also employed in the context of wireless networks [5,36].…”

Section: Completion Factormentioning

confidence: 99%

Section: Temporal Variations Around Bonusesmentioning

confidence: 99%

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Rokicki

Zerr

Siersdorfer

2016

Proceedings of the 25th International Conference on World Wide Web

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“…Results clearly show that proactive spectrum handoff takes less time than the reactive spectrum handoff. Soumaya Dahi, Sami Tabbane, [6] have used sigmoid a S-shaped curve to represent the life cycles of the cognitive radios. It has three distinct phases: an incipient or starting phase, a maturing phase, and a declining or aging phase.…”

Section: Different Spectrum Handoff Techniquesmentioning

confidence: 99%

A Survey on Spectral Handoff Mechanisms for the Cognitive Radio Network

Vinay¹

2015

IJRITCC

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Abstract-In a cognitive radio network, the cognitive radio (CR) devices also called as secondary users (SU's) need to change their operating frequency due to the inclusion of primary user (PU) in that frequency band. Thus when a PU arrives in a frequency band and asks for a channel in that band, it gets that band and occupies the channel which may be occupied by a SU. In this situation, the SU needs to find another channel in a different frequency band which leads to the spectral handoff. Thus in addition to the location based handoff's for the SU, spectral handoff also occur. This spectral handoff may be done several times for the SU. Thus this situation leads to the study of handoff mechanism. This paper carries out a survey of the handoff types and their mechanisms which have been already conceptualized.

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Spectrum mobility management in cognitive two‐tier networks

Sethom

Pujolle

2018

Int J Network Mgmt

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Summary Recently, new type of small Base Station known as “femtocell” has emerged. Femtocells are deployed by customers with minimal or no radio frequency planning in arbitrary locations. Thus, operators must use new approaches rather than the classic network planning and optimization. Self‐organization techniques will allow femtocells to integrate themselves into the network, learn about their environment, and adjust their parameters (power, frequency) accordingly. In this paper, a new spectrum mobility management algorithm is proposed. It allows intelligent use of the spectrum by secondary user (femtocell guest user) accessing spectrum holes unoccupied by primary users (subscriber user) and handing over to new channels or femtocells when primary user appears. Our solution aims to have more adaptive and aware communication system, which can make better use of available natural resources.

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Sigmoid utility function formulation for handoff reducing Access model in cognitive radio

Cited by 7 publications

References 3 publications

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A Survey on Spectral Handoff Mechanisms for the Cognitive Radio Network

Spectrum mobility management in cognitive two‐tier networks

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