University of Oulu

Leppänen, T., Álvarez Lacasia, J., Tobe, Y. et al. Auton Agent Multi-Agent Syst (2017) 31: 1.

Mobile crowdsensing with mobile agents

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Author: Leppänen, Teemu1; Lacasia, José Álvarez2; Tobe, Yoshito3;
Organizations: 1Department of Computer Science and Engineering, University of Oulu, Oulu, Finland
2Institute of Industrial Science, University of Tokyo, Tokyo, Japan
3RealWorld Communication Laboratory, Aoyama Gakuin University, Tokyo, Japan
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
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Language: English
Published: Springer Nature, 2017
Publish Date: 2019-05-27


We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.

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Series: Autonomous agents and multi-agent systems
ISSN: 1387-2532
ISSN-E: 1573-7454
ISSN-L: 1387-2532
Volume: 31
Issue: 1
Pages: 1 - 35
DOI: 10.1007/s10458-015-9311-7
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
Copyright information: © The Author(s) 2015. This is a post-peer-review, pre-copyedit version of an article published in Autonomous Agents and Multi-Agent Systems Vol. 31 Issue 1. The final authenticated version is available online at: