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A Survey on Social-Physical Sensing: An Emerging Sensing Paradigm that Explores the Collective Intelligence of Humans and Machine
[article]
2023
arXiv
pre-print
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Motivated by the complementary strengths of the two sensing modes, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that explores the collective intelligence of humans and machines ...
Meanwhile, social sensing is contriving as a pervasive sensing paradigm leveraging observations from human participants equipped with portable devices and ubiquitous Internet connectivity to perceive the ...
Acknowledgment This research is supported in part by the National Science Foundation under Grant IIS-2008228, CNS-1845639, CNS-1831669, Army Research Office under Grant W911NF-17-1-0409. ...
arXiv:2104.01360v2
fatcat:lb6azrdxubasteb3xl5zusykga
Mobile crowdsensing with mobile agents
2015
Autonomous Agents and Multi-Agent Systems
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A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. ...
Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants' devices. ...
We simulate extensively different aspects of mobile agents in crowdsensing in comparison with the existing crowdsensing approaches. ...
doi:10.1007/s10458-015-9311-7
fatcat:mbm4w4hs75h6lfssizcol6tgea
Understanding Human-Machine Networks: A Cross-Disciplinary Survey
[article]
2017
arXiv
pre-print
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Such human-machine networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. ...
The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. ...
This makes them robust to manipulation by a small group of individuals [Hanson et al. 2006; Wolfers and Zitzewitz 2004] . ...
arXiv:1511.05324v2
fatcat:ixawl5uo4rd5tbewgcpz3as4pm
2020 Index IEEE Transactions on Mobile Computing Vol. 19
2021
IEEE Transactions on Mobile Computing
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., +, TMC March 2020 552-565 Enabling Strong Privacy Preservation and Accurate Task Allocation for Mobile Crowdsensing. ...
., +, TMC June 2020 1299-1316 Free Market of Multi-Leader Multi-Follower Mobile Crowdsensing: An Incentive Mechanism Design by Deep Reinforcement Learning. ...
doi:10.1109/tmc.2020.3036773
fatcat:6puiux5lp5bfvjo47ey7ycwyfu
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
[article]
2023
arXiv
pre-print
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Federated Learning (FL) is a recent framework that has emerged as a promising approach for multiple learning agents to build an accurate and robust machine learning models without sharing raw data. ...
By allowing mobile handsets and devices to collaboratively learn a global model without explicit sharing of training data, FL exhibits high privacy and efficient spectrum utilization. ...
Federated Machine Learning FL was recently proposed by Google [65] as a promising approach for performing distributed ML tasks without relying on a centralized data center. ...
arXiv:2312.04688v1
fatcat:uwut2mfcrzdbdh5z5emhjg2iqa
JAM: The JavaScript Agent Machine for Distributed Computing and Simulation with reactive and mobile Multi-agent Systems – A Technical Report
[article]
2022
arXiv
pre-print
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This paper is a technical report with some tutorial aspects of the JavaScript Agent Machine (JAM) platform and the programming of agents with AgentJS, a sub-set of the widely used JavaScript programming ...
language for the programming of mobile state-based reactive agents. ...
To simplify the development and deployment of multi-agent systems in pervasive and ubiquitous applications connected by the Internet and deployed in strong heterogeneous environments including the IoT ...
arXiv:2207.11300v1
fatcat:slr4t6o6gzcvjm5wezeafhkclm
Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives
2022
Sensors
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This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment ...
In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless ...
tasks to be allocated ...
doi:10.3390/s22051824
pmid:35270970
pmcid:PMC8914857
fatcat:xqcgx676mbckfpc2a6rurbfooq
Federated Learning for Internet of Things: A Comprehensive Survey
[article]
2021
arXiv
pre-print
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Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing ...
Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need ...
FL for IoT Mobile Crowdsensing With the development of IoT, mobile crowdsensing is designed to take advantage of pervasive mobile devices for sensing and collecting data from physical environments to serve ...
arXiv:2104.07914v1
fatcat:b5wsrfcbynel7jqdxpfw4ftwh4
Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey
[article]
2016
arXiv
pre-print
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, coverage optimization, efficient task allocation, and security. ...
Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. ...
The task allocation problem is modeled as a multi-agent negotiation including: sensor agents, i.e., sellers, which perform tasks, external systems known as buyers who may require data from the sensor network ...
arXiv:1608.03475v1
fatcat:2jjbf3bw2vai5kqi74r56dviwe
The Future Internet convergence of IMS and ubiquitous smart environments: An IMS-based solution for energy efficiency
2012
Journal of Network and Computer Applications
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This thesis focuses on pervasive sensing systems to extract design guidelines as foundation of a comprehensive reference model for multi-tier Pervasive Sensing applications. ...
P Sensing is a recent research trend that aims at providing widespread computing and sensing capabilities to enable the creation of smart environments that can sense, process, and act by considering input ...
In the next chapter we will describe a logical model that provides a robust structure to design multi-tier Pervasive Sensing systems. ...
doi:10.1016/j.jnca.2011.05.003
fatcat:3iprgfdvmjfxxey67qtflilnri
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed
[article]
2024
arXiv
pre-print
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Additionally, the potential of UMBRELLA is outlined for future smart city and multi-robot crowdsensing applications enhanced by semantic communications and multi-agent planning. ...
UMBRELLA is a large-scale, open-access Internet of Things (IoT) ecosystem incorporating over 200 multi-sensor multi-wireless nodes, 20 collaborative robots, and edge-intelligence-enabled devices. ...
ACKNOWLEDGMENTS This work was supported by Toshiba Europe Ltd. and Bristol Research and Innovation Laboratory (BRIL). ...
arXiv:2401.13346v2
fatcat:a5nnlggtozf67bx3thhvexamci
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
2019
IEEE Communications Surveys and Tutorials
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Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. ...
This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. ...
The interaction among UAVs is cast as a dynamic game and solved by a multi-agent DRL framework. ...
doi:10.1109/comst.2019.2916583
fatcat:5owsswhhrbctnirdtxre6mhv24
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
[article]
2018
arXiv
pre-print
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Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. ...
This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. ...
The interaction among UAVs is cast as a dynamic game and solved by a multi-agent DRL framework. ...
arXiv:1810.07862v1
fatcat:qc3mqk2norazvc2xnynau6bqzu
Federated Learning for Internet of Things: A Comprehensive Survey
2021
IEEE Communications Surveys and Tutorials
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Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing ...
Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need ...
FL for IoT Mobile Crowdsensing With the development of IoT, mobile crowdsensing is designed to take advantage of pervasive mobile devices for sensing and collecting data from physical environments to serve ...
doi:10.1109/comst.2021.3075439
fatcat:ycq2zydqrzhibfqyo4vzloeoqy
Client-Based Intelligence for Resource Efficient Vehicular Big Data Transfer in Future 6G Network
[article]
2021
arXiv
pre-print
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In this work, we present a novel client-based opportunistic data transmission method for delay-tolerant applications which is based on a hybrid machine learning approach: Supervised learning is applied ...
As a side-effect of preferring more robust network conditions for the data transfer, the transmission-related power consumption is reduced by up to 73 %. ...
Since mobile and vehicular networks are inherently impacted by the interdependency of mobility and radio channel dynamics [32] , machine learningenabled anticipatory networking is a promising approach ...
arXiv:2102.08624v1
fatcat:4vsc7lp6mzatjens5mxjqkc6eu
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