<i>Crowdsourcing</i>, by Daren C. Brabham

Livingstone, Randall M.

doi:10.1080/01972243.2016.1212621

Cited by 1 publication

(3 citation statements)

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“…Using too small of a γ does not result in much of an improvement in efficiency, but using too large of a γ wastes queries. The resulting average of 1 round in Figure 5 and Figure 6 were all obtained with γ having the value of approximately 1 σ , as for each of the k • 1 σ , queries randomly chooses a region with 1 σ chance of being positive.…”

Section: Discussionmentioning

confidence: 99%

“…Netlogo (https://ccl.northwestern.edu/netlogo/ [Last accessed 25 June 2020]) has been used for simulating two different aspects of crowd spatial networks in natural and social phenomena experiments: crowd task propagation and energy consumption. The purpose of the simulation presented in this section is two-fold: (1) to analyze the relationship between the parameters of spatial crowdsourcing and the number of responses, and (2) to study the cost and energy consumption characteristics of P2P spatial crowdsourcing, especially in a large scale ad-hoc network.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Crowdsourcing, simply known as the power of the crowd, involves seeking knowledge, goods, or services from a larger group of people. These people submit their ideas in response to online requests made through social media, smartphone apps, or dedicated crowdsourcing platforms [1][2][3][4]. Recently, the rapid adoption of mobile devices has provided a new data collection paradigm, often termed as mobile crowdsourcing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Iterative Spatial Crowdsourcing in Peer-to-Peer Opportunistic Networks

Phuttharak

Loke

2020

Electronics

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Spatial crowdsourcing is a potentially powerful method for incorporating human wisdom into mobile computations to solve problems while exploiting the advantages of mobility and context-awareness. This paper proposes and investigates task assignments and recruitment in iterative spatial crowdsourcing processes to find regions of particular interest among a collection of regions. We consider cases where associations between regions can be exploited to reduce costs and increase efficiency in crowdsourcing. We describe five approaches, incorporated into crowdsourcing algorithms, for reducing the cost (the number of queries required) and increasing the efficiency (reducing the number of rounds of querying required) in using such spatial crowdsourcing. We demonstrate the performance improvements gained using these approaches based on simulation scenarios. The findings show the interplay and relationships among our proposed approaches using a range of metrics including responses, energy consumption, costs, and time usage. These metrics are demonstrated via a range of scenarios, showing that our proposed approaches can lead to improved performance over randomly choosing regions for inquiry.

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Section: Discussionmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%