research-article

Collaboration Stability: Quantifying the Success and Failure of Opportunistic Collaboration

Authors:

Agustin Zuniga,

Leonardo Tonetto,

Petteri NurmiAuthors Info & Claims

Computer, Volume 55, Issue 8

Pages 70 - 81

https://doi.org/10.1109/MC.2021.3112850

Published: 01 August 2022 Publication History

Abstract

We quantify and derive a general model for the collaboration stability of human mobility and demonstrate its importance for networking applications. Our results demonstrate that collaboration opportunities are highly dependent on the context where they take place, with diurnal patterns and spatial characteristics being particularly important.

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Cited By

Olapade MHasanli TOttun AAkintola ALiyanage MFlores H(2024)Pervasive Chatbots: Investigating Chatbot Interventions for Multi-Device ApplicationsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659570(290-300)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659570
Dar FLiyanage MRadeta MYin ZZuniga AKosta STarkoma SNurmi PFlores H(2022)Upscaling Fog Computing in Oceans for Underwater Pervasive Data Science Using Low-Cost Micro-CloudsACM Transactions on Internet of Things10.1145/35758014:2(1-29)Online publication date: 8-Dec-2022
https://dl.acm.org/doi/10.1145/3575801

Index Terms

Collaboration Stability: Quantifying the Success and Failure of Opportunistic Collaboration
1. General and reference
  1. Cross-computing tools and techniques
2. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

Index terms have been assigned to the content through auto-classification.

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0018-9162 © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 August 2022

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Cited By

Olapade MHasanli TOttun AAkintola ALiyanage MFlores H(2024)Pervasive Chatbots: Investigating Chatbot Interventions for Multi-Device ApplicationsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659570(290-300)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659570
Dar FLiyanage MRadeta MYin ZZuniga AKosta STarkoma SNurmi PFlores H(2022)Upscaling Fog Computing in Oceans for Underwater Pervasive Data Science Using Low-Cost Micro-CloudsACM Transactions on Internet of Things10.1145/35758014:2(1-29)Online publication date: 8-Dec-2022
https://dl.acm.org/doi/10.1145/3575801

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