research-article

Crowdsourcing through Cognitive Opportunistic Networks

Authors:

Matteo Mordacchini,

Andrea Passarella,

Stuart M. Allen,

Martin J. Chorley,

Gualtiero B. Colombo,

Vlad Tanasescu,

Roger M. WhitakerAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 10, Issue 2

Article No.: 13, Pages 1 - 29

https://doi.org/10.1145/2733379

Published: 09 June 2015 Publication History

Abstract

Until recently crowdsourcing has been primarily conceived as an online activity to harness resources for problem solving. However, the emergence of Opportunistic Networking (ON) has opened up crowdsourcing to the spatial domain. In this article, we bring the ON model for potential crowdsourcing in the smart city environment. We introduce cognitive features of the ON that allow users’ mobile devices to become aware of the surrounding physical environment. Specifically, we exploit cognitive psychology studies on dynamic memory structures and cognitive heuristics—mental models that describe how the human brain handles decision making among complex and real-time stimuli. Combined with ON, these cognitive features allow devices to act as proxies in their users’ cyberworlds and exchange knowledge to deliver awareness of places in an urban environment. This is done through tags associated with locations. They represent features that are perceived by humans about a place. We consider the extent to which this knowledge becomes available to participants using interactions with locations and other nodes. This is assessed taking into account a wide range of cognitive parameters. Outcomes are important because this functionality could support a new type of recommendation system that is independent of the traditional forms of networking.

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Andrade-Ruiz GCarrasco RPorcel CSerrano-Guerrero JMata FArias-Oliva M(2024)Emerging Perspectives on the Application of Recommender Systems in Smart CitiesElectronics10.3390/electronics1307124913:7(1249)Online publication date: 27-Mar-2024
https://doi.org/10.3390/electronics13071249
Lu HGao XChen G(2022)Efficient Crowdsourcing-Aided Positioning and Ground-Truth-Aided Truth Discovery for Mobile Wireless Sensor Networks in Urban FieldsIEEE Transactions on Wireless Communications10.1109/TWC.2021.310590621:3(1652-1664)Online publication date: Mar-2022
https://doi.org/10.1109/TWC.2021.3105906
Gao XChen SChen G(2022)MAB-Based Reinforced Worker Selection Framework for Budgeted Spatial CrowdsensingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299253134:3(1303-1316)Online publication date: 1-Mar-2022
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Index Terms

Crowdsourcing through Cognitive Opportunistic Networks
1. Networks
  1. Network protocols

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 10, Issue 2

June 2015

175 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/2790463

Editors:
Manish Parashar
Rutgers University, USA
,
Franco Zambonelli
University of Modena e Reggio Emilia, Italy

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Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 June 2015

Accepted: 01 February 2015

Revised: 01 January 2015

Received: 01 June 2014

Published in TAAS Volume 10, Issue 2

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Cited By

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https://doi.org/10.3390/electronics13071249
Lu HGao XChen G(2022)Efficient Crowdsourcing-Aided Positioning and Ground-Truth-Aided Truth Discovery for Mobile Wireless Sensor Networks in Urban FieldsIEEE Transactions on Wireless Communications10.1109/TWC.2021.310590621:3(1652-1664)Online publication date: Mar-2022
https://doi.org/10.1109/TWC.2021.3105906
Gao XChen SChen G(2022)MAB-Based Reinforced Worker Selection Framework for Budgeted Spatial CrowdsensingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299253134:3(1303-1316)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TKDE.2020.2992531
Ferrucci LMordacchini MCoppola MCarlini EKavalionak HDazzi PCoppola MFerrucci LKavalionak HMakris A(2021)Latency Preserving Self-optimizing Placement at the EdgeProceedings of the 1st Workshop on Flexible Resource and Application Management on the Edge10.1145/3452369.3463815(3-8)Online publication date: 25-Jun-2021
https://dl.acm.org/doi/10.1145/3452369.3463815
Mordacchini MFerrucci LCarlini EKavalionak HCoppola MDazzi P(2021)Self-organizing Energy-Minimization Placement of QoE-Constrained Services at the EdgeEconomics of Grids, Clouds, Systems, and Services10.1007/978-3-030-92916-9_11(133-142)Online publication date: 21-Sep-2021
https://dl.acm.org/doi/10.1007/978-3-030-92916-9_11
Mordacchini MConti MPassarella ABruno R(2020)Human-centric Data Dissemination in the IoPACM Transactions on Autonomous and Adaptive Systems10.1145/336637214:3(1-25)Online publication date: 10-Feb-2020
https://dl.acm.org/doi/10.1145/3366372
Albano MMordacchini MRicci L(2020)AoI-Based Multicast Routing Over Voronoi Overlays With Minimal OverheadIEEE Access10.1109/ACCESS.2020.30234798(168611-168624)Online publication date: 2020
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Dazzi PMordacchini M(2020)Scalable Decentralized Indexing and Querying of Multi-Streams in the FogJournal of Grid Computing10.1007/s10723-020-09521-3Online publication date: 1-Jul-2020
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Wang YLu JChen JGao XChen G(2019)Reinforced Reliable Worker Selection for Spatial Crowdsensing NetworksDatabase Systems for Advanced Applications10.1007/978-3-030-18576-3_15(244-259)Online publication date: 22-Apr-2019
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Bozanta AKutlu B(2018)Developing a Contextually Personalized Hybrid Recommender SystemMobile Information Systems10.1155/2018/32589162018(1-13)Online publication date: 23-Oct-2018
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