COSINE: Collaborator Selector for Cooperative Multi-Device Sensing and Computing

Huber Flores, Agustin Zuniga, Farbod Faghihi Berenjegani, Xin Li, Samuli Hemminki, Sasu Tarkoma, Pan Hui, Petteri Nurmi

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Pervasive availability of programmable smart devices is giving rise to sensing and computing scenarios that involve collaboration between multiple devices. Maximizing the benefits of collaboration requires careful selection of devices with whom to collaborate as otherwise collaboration may be interrupted prematurely or be sub-optimal for the characteristics of the task at hand. Existing research on collaborative scenarios has mostly focused on providing mechanisms that can establish and harness collaboration, without considering how to maximally benefit from it. In this paper, we contribute by developing COSINE as a novel approach for selecting collaborators in multi-device computing scenarios. COSINE identifies and recommends collaborators based on a novel information theoretic measure based on Markov trajectory entropy. Rigorous experimental benchmarks carried out using a large-scale dataset of device-to-device encounters demonstrate that COSINE can significantly improve collaboration benefits compared to current state-of-the-art solutions, increasing expected duration of collaboration and reducing variability of
Original languageEnglish
Title of host publication2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)
Number of pages10
Publication date2020
ISBN (Electronic)978-1-7281-4657-7
Publication statusPublished - 2020
MoE publication typeA4 Article in conference proceedings
EventIEEE International Conference on Pervasive Computing and Communications (PerCom) - Austin
Duration: 23 Mar 202027 Mar 2020

Fields of Science

  • 113 Computer and information sciences

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