Towards Context-Aware Mobile Crowdsensing in Vehicular Social Networks

Hu, Xiping; Leung, Victor C. M.

doi:10.1109/ccgrid.2015.155

Cited by 9 publications

(4 citation statements)

References 21 publications

(24 reference statements)

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“…The relation between social networks activity and mobile user positioning is a demonstrated fact [27], and thus, it has been greatly studied. Some works aim to apply social network information to provide context awareness to crowdsensing applications [28,29], in order to improve the richness of the collected data. Other approaches are focused on applying the social network data to model different aspects of cellular networks.…”

Section: Related Workmentioning

confidence: 99%

Statistical Model for Mobile User Positioning Based on Social Information

et al. 2021

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In spite of the vast set of measurements provided by current mobile networks, cellular operators have problems pinpointing problematic locations because the origin of such measurements (i.e., user location) is usually not registered. At the same time, social networks generate a huge amount of data that can be used to infer population density. In this work, a data-driven model is proposed to deduce the statistical distribution of connections, exploiting the knowledge of network layout and population density in the scenario. Due to the absence of GPS measurements, the proposed method combines data from radio connection traces stored in the network management system and geolocated posts from social networks. This information is enriched with user context information inferred from data traffic attributes. The method is tested with a large trace dataset from a live Long Term Evolution (LTE) network and a database of geotagged posts from social networks collected in real-time.

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Section: Related Workmentioning

confidence: 99%

Statistical Model for Mobile User Positioning Based on Social Information

et al. 2021

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“…Some efforts in existing research propose semantic approaches to data integration and creating the standards for metadata schema associated with the heterogeneity of data sources. In [75]- [77], they make integration of a formal ontology with an implicit one reflected in a database schema or in a communication protocol specification to retrieve information from different data sources and provide cross-domain crowdsourcing platforms. For such heterogeneous data sources produced by both human and devices, another solution is to have a middleware platform for facilitating collaboration and information exchange.…”

Section: ) Mobile Sensing and Gathering Layermentioning

confidence: 99%

A Review of Mobile Crowdsourcing Architectures and Challenges: Toward Crowd-Empowered Internet-of-Things

Phuttharak

Loke

2019

IEEE Access

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Crowdsourcing using mobile devices, known as mobile crowdsourcing, is a powerful approach incorporating human wisdom into mobile computations to solve problems while exploiting the advantages of mobility and context-awareness. The problems that can be tackled include the use of geographically distributed tasks, and mobile sensing using the collective wisdom of the crowd. However, the implementation of mobile crowdsourcing applications has been found to be challenging to users due to the nature of dynamic sensing, crowd engagement with data distribution, and a process of data verification. In this paper, we provide an extensive survey of the literature on mobile crowdsourcing research, highlighting the aspects of particular concerns in terms of implementation needs during the development, architectures, and key considerations for their development. We present a taxonomy based on the key issues in mobile crowdsourcing and discuss the different approaches applied to these issues. We also provide a critical analysis of some challenges and suggest directions for future work. In particular, with the future Internet-of-Things in view, we generalize the notion of mobile crowdsourcing to thing crowdsourcing, where crowdsourcing can be issued from smart Internet-connected things that need to harness the human resources to solve problems.

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“…Joints. Crowdsourced-based applications [30][31][32][33][34][35][36][37] have been increasingly developed by collecting data and reducing the cost in the Internet field. For the macrolevel network, the work [30] proposed a crowdsensing-oriented mobile cyber-physical system to provide the practical usage of the vita.…”

Section: Crowdsourced Wifi Signals and Skeletonmentioning

confidence: 99%

HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data

Guo

Wang

Liu

et al. 2018

Wireless Communications and Mobile Computing

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The joint of WiFi-based and vision-based human activity recognition has attracted increasing attention in the human-computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi-based and Kinect-based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi-based activity recognition dataset named WiAR to provide a benchmark for WiFi-based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton-based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than 93% using WiAR dataset.

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Towards Context-Aware Mobile Crowdsensing in Vehicular Social Networks

Cited by 9 publications

References 21 publications

Statistical Model for Mobile User Positioning Based on Social Information

Statistical Model for Mobile User Positioning Based on Social Information

A Review of Mobile Crowdsourcing Architectures and Challenges: Toward Crowd-Empowered Internet-of-Things

HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data

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