RTS: road topology‐based scheme for traffic condition estimation via vehicular crowdsensing

Shao, Lu; Wang, Cheng; Liu, Lu; Jiang, Changjun

doi:10.1002/cpe.3778

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…The rapid development of communication technology realizes the information collection and dissemination from various mobile crowd sensing (MCS) services in Human-driven Edge Computing (HEC) environment [1]. However, the data collected from large-scale sensing usually includes a variety of modalities [2].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Spatial Mapping for Multimodal Data Fusion in Mobile Crowd Sensing

Zhou

Ren

et al. 2020

IEEE Access

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Human-driven Edge Computing (HEC) integrates the elements of humans, devices, Internet and information, and mobile crowd sensing become an important means of data collection. In HEC, the data collected from large-scale sensing usually includes a variety of modalities. These different modality data contain unique information and attributes, which can be complementary. Combining data from many different modalities will get more information. However, current deep learning is usually only for bimodal data. In order for artificial intelligence to make further breakthroughs in understanding our real world, it needs to be able to process data in different modalities together. The key step is to be able to map these different modalities data into the same space. In order to process multimodal data better, we propose a fusion and classification method for multimodal data. First, a multimodal data space is constructed, and data of different modalities are mapped into the multimodal data space to obtain a unified representation of different modalities data. Then, through bilinear pooling, the representations of different modality are fused, and the fused vectors are used in the classification task. Through the experimental verification on the multi-modal data set, it proves that the multi-modal fusion representation is effective, and the classification effect is more accurate than the single-modal data. INDEX TERMS Multimodal data, unified representation, mobile crowd sensing, human-driven edge computing.

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Section: Introductionmentioning

confidence: 99%

Two-Stage Spatial Mapping for Multimodal Data Fusion in Mobile Crowd Sensing

Zhou

Ren

et al. 2020

IEEE Access

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“…Such that, accurate advertisement could be directed to target users at proper time and location, which could be expected to save considerably high amount of money and time. However, with the increased mobile users, it is difficult to involve such large number of users with incentive mechanisms …”

Section: Introductionmentioning

confidence: 99%

Optimizing the interested area coverage with efficient mobile advertisement user selection

Yang

2017

Concurrency and Computation

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Summary Mobile advertisement distribution effects are vitally important for advertisers as well as users. Status quo studies are focusing on efficient distribution especially when user mobilities are involved. Unfortunately, previous studies have shown the interested area property during mobile advertisement propagation. In achieving efficient and effective mobile advertisement applications, this work advocates the concept of location‐centric mobile crowdsourcing network, where locations are vitally important for advertisement distribution, and mobile users need to be carefully selected for efficiency considerations. Different from traditional user‐centric and platform‐centric crowdsourcing networks, this work focuses on the mobile advertisement user selection problem when interested area coverage is considered. There are several fundamentally important challenges needed to be addressed before developing a location‐centric scheme for mobile advertisement user selection. First of all, we need to deal with the spatio‐temporal features for each user, where the interested area coverage ratio needs to be effectively evaluated. Even worse, budget constraint makes this problem intractable. In tackling aforementioned challenges, this work makes the following efforts: First, a budget‐constrained user selection problem is formulated when location sensitive mobile advertisement applications are considered, which is proved NP‐hard. Second, the submodularity feature is explored, and a simple but efficient heuristic algorithm is presented with guaranteed approximation ratio false(1−1efalse). Finally, extensive simulation results show that, our scheme could effectively improve the propagation effects for mobile advertisement with 125%. When considering the user's interest to different advertisements, the real user interest data set has also been used to validate that our proposed method could achieve improved performance than the random method.

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“…Related research challenges and possible solutions are discussed. Shao et al use crowdsensing in vehicular networks to predict the traffic condition in intelligent transportation systems. This solves the problem of traditional approaches being inefficiency ineffectiveness in data uploading and usage.…”

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confidence: 99%

Internet of People

2016

Concurrency and Computation

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Internet of People (IoP) refers to digital connectivity of people through the Internet infrastructure forming a network of collective intelligence and stimulating interactive communication among people. The purpose of the special issue is to collate a selection of representative articles that were primarily presented at the IEEE International Conference on Internet of People (IoP 2015) on August 10 to 14, 2015, in Beijing, China. The special issue was also made open to public submissions for a wide inclusion.The scope of the special issue is broad and represents a multidisciplinary nature of IoP. It covers topics from the network enabling technologies at the physical layer to services at the application layer. It is reported that around 40% of the world population has an Internet connection today, and the number of Internet users reached 3 billion in 2014. The sheer size of the Internet population has led to big data challenges of volume, velocity, variety, and veracity of the digital data generated. For this purpose, this special issue also includes a few articles on high performance computing techniques that can speed up the computation process in analyzing big volumes of data.Mobile and wireless networks facilitate the communication among people. In the work of Huang et al.[1] they present a multiple-user partner selection algorithm to improve the overall secrecy rate of the network. Zhong et al. Crowdsourcing enables IoP by soliciting contributions from a large group of people connected by the Internet. Wang et al.[5] conduct a survey on mobile crowdsourcing from the aspects of realtime and location-sensitive crowdsourced tasks. Related research challenges and possible solutions are discussed. Shao et al.[6] use crowdsensing in vehicular networks to predict the traffic condition in intelligent transportation systems. This solves the problem of traditional approaches being inefficiency ineffectiveness in data uploading and usage. Guo et al. [7] extract information from external-related attributes and improve latent Dirichlet allocation to build a topic mining model to facilitate services in telephone call centers. Knowledge diffusion as a component of crowdsourcing also plays an increasing role in modern communication networks and social networks. Zhang et al.[8] study the topological structure and research a new knowledge diffusion model taking into account both learning and forgetting attributes. The results from this research reveal that the social networks with a high degree of heterogeneity well suit for knowledge diffusion. In a mobile environment in IoP, network latency would have a significant impact on the communication of mobile services. For this purpose, Ding et al. [9] target at service composition so that mobile services can be optimized and provisioned to users with low communication latency. In the work of Gopalakrishna et al.[10] they assess relevance in cyber-physical systems. For this purpose, a new metric called relevance score is proposed for evaluation of a number of machine learning t...

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RTS: road topology‐based scheme for traffic condition estimation via vehicular crowdsensing

Cited by 6 publications

References 26 publications

Two-Stage Spatial Mapping for Multimodal Data Fusion in Mobile Crowd Sensing

Two-Stage Spatial Mapping for Multimodal Data Fusion in Mobile Crowd Sensing

Optimizing the interested area coverage with efficient mobile advertisement user selection

Internet of People

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