Low-Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Hamrouni, Aymen; Ghazzai, Hakim; Alelyani, Turki; Massoud, Yehia

doi:10.1109/jiot.2021.3086410

Cited by 23 publications

(6 citation statements)

References 54 publications

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“…After winning the bids, they are supposed to complete the tasks and submit the data truthfully. Finally, they will the rewards to cover the cost of resources, effort, and time consumed by completing the tasks themselves [31] , [32] , [33] . There are two modes of workers’ participation in the task.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd

Tang

Fan

Xie

et al. 2023

Computer Communications

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Section: Related Workmentioning

confidence: 99%

“…The success of MCS applications depends on the massive data contributed by the workers [31] , [32] , [33] . In completing tasks, workers will have some costs, such as transportation fees, time-consuming, communication overhead, and device loss.…”

Section: Introductionmentioning

confidence: 99%

A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd

Tang

Fan

Xie

et al. 2023

Computer Communications

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“…In this context, combining the Graph Neural Networks paradigm (GNNs) with the Social IoT (SIoT) concept could be a promising solution to provide an efficient service discovery [4]. The SIoT refers to the social relationships established between IoT devices.…”

Section: Introductionmentioning

confidence: 99%

Service Discovery in Social Internet of Things using Graph Neural Networks

Hamrouni

Ghazzai

Massoud

2022

2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)

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Internet-of-Things (IoT) networks intelligently connect thousands of physical entities to provide various services for the community. It is witnessing an exponential expansion, which is complicating the process of discovering IoT devices existing in the network and requesting corresponding services from them. As the highly dynamic nature of the IoT environment hinders the use of traditional solutions of service discovery, we aim, in this paper, to address this issue by proposing a scalable resource allocation neural model adequate for heterogeneous large-scale IoT networks. We devise a Graph Neural Network (GNN) approach that utilizes the social relationships formed between the devices in the IoT network to reduce the search space of any entity lookup and acquire a service from another device in the network. This proposed approach surpasses standardization issues and embeds the structure and characteristics of the social IoT graph, by the means of GNNs, for eventual clustering analysis process. Simulation results applied on a real-world dataset illustrate the performance of this solution and its significant efficiency to operate on large-scale IoT networks.

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“…The figure illustrates how graph analysis and machine learning techniques can be used to perceive the SIoT system's structure via a cloud gateway to exchange the necessary data, such as the location and specifications of the devices. From this information and other IoT devices' features, graphs that model the different social relations between the devices can be determined and exploited to devise effective context-aware service discovery techniques in large-scale IoT networks [14].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

et al. 2022

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Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises compatibility and trustworthiness challenges, hindering conventional service discovery and network navigability processes. The IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices' status, such as battery capacity and computational power); traditional methods fail to learn and understand the evolving behavior of the network to enable real-time and contextaware service discovery in such diverse and large-scale topologies of IoT networks. The Social IoT (SIoT) concept, which defines the relationships among the connected objects, can be exploited to extract established relationships between devices and enable trustworthy and context-aware services. In fact, SIoT expresses the possible connections that devices can establish in the network and reflect compatibility, trustworthiness, and so on. In this paper, we investigate the service discovery process in SIoT networks by proposing a lowcomplexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery. Unlike the conventional graph-based techniques, the proposed approach simultaneously embeds the devices' features and their SIoT relations. Our simulations on a real-world IoT dataset show that the proposed GNN-based approach can provide more concise clusters compared to traditional techniques, namely the Louvain and Leiden algorithms. This allows a better IoT network learning and understanding and also, speeds up the service lookup search space. Finally, we discuss implementing the GNN-assisted context-service discovery processes in novel smart city IoT-enabled applications.

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Low-Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Cited by 23 publications

References 54 publications

A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd

A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd

Service Discovery in Social Internet of Things using Graph Neural Networks

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

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