A Probabilistic Approach for Maximizing Travel Journey WiFi Coverage Using Mobile Crowdsourced Services

Said, Ahmed Ben; Erradi, Abdelkarim

doi:10.1109/access.2019.2924434

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…Machine learning has been established as an efficient approach to support future BIoV. As a basis of artificial intelligence, machine learning has been used in numerous areas, such as speech recognition, medical diagnosis, and computer vision [499]- [503]. It has also revolutionized BIoV services because it enables them to learn from training data and derive data-driven conclusions, provide decision support, and predict improvements in network performance.…”

Section: B Machine Learning With Biovmentioning

confidence: 99%

Blockchain Technology for Intelligent Transportation Systems: A Systematic Literature Review

Dhib²,

et al. 2022

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The use of Blockchain technology has recently become widespread. It has emerged as an essential tool in various academic and industrial fields, such as healthcare, transportation, finance, cybersecurity, and supply chain management. It is regarded as a decentralized, trustworthy, secure, transparent, and immutable solution that innovates data sharing and management. This survey aims to provide a systematic review of Blockchain application to intelligent transportation systems in general and the Internet of Vehicles (IoV) in particular. The survey is divided into four main parts. First, the Blockchain technology including its opportunities, relative taxonomies, and applications is introduced; basic cryptography is also discussed. Next, the evolution of Blockchain is presented, starting from the primary phase of pre-Bitcoin (fundamentally characterized by classic cryptography systems), followed by the Blockchain 1.0 phase, (characterized by Bitcoin implementation and common consensus protocols), and finally, the Blockchain 2.0 phase (characterized by the implementation of smart contracts, Ethereum, and Hyperledger). We compared and identified the strengths and limitations of each of these implementations. Then, the state of the art of Blockchain-based IoV solutions (BIoV) is explored by referring to a large and trusted source database from the Scopus data bank. For a well-structured and clear discussion, the reviewed literature is classified according to the research direction and implemented IoV layer. Useful tables, statistics, and analysis are also presented. Finally, the open problems and future directions in BIoV research are summarized.

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Section: B Machine Learning With Biovmentioning

confidence: 99%

Blockchain Technology for Intelligent Transportation Systems: A Systematic Literature Review

Dhib²,

et al. 2022

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“…In this context, intelligent transportation systems have become an important source of ubiquitous data, e.g., Internet of Vehicles (IoV). For example, authors in [101]- [104] used the GPS data from taxis and bikes as an input to their CNN models to forecast the traffic flow and predict the potential congestions. Learning from GPS data can be categorized as outdoor localization, which is also called location aware DL services.…”

Section: Iiid1 Intelligent Vehicles Robots and Dronesmentioning

confidence: 99%

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

Baccour,

Mhaisen,

Abdellatif

et al. 2021

Preprint

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Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-oflife. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy concerns, scalability, and latency requirements. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

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“…Alabduljabbar et al [7] studied a dynamic approach that exploits task-quality ontology to select the most suitable quality control mechanism for a given task based on its type. Ben Said et al [16] proposed an algorithm to determine the best public transport journey plan offering based on the quality of service of available WiFi mobile crowdsourced WiFi coverage along the journey.…”

Section: Related Workmentioning

confidence: 99%

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Hamrouni¹,

Ghazzai²,

Alelyani³

et al. 2021

Preprint

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Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this paper, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: i) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and ii) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its Social Network (SN) neighbors. We first formulate the recruitment as an Integer Linear Program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: level of expertise, social relationship strength, recruitment cost, and recruiter's confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on Graph Neural Networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers' search space and afterwards, exploiting a meta-heuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world dataset illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.

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A Probabilistic Approach for Maximizing Travel Journey WiFi Coverage Using Mobile Crowdsourced Services

Cited by 14 publications

References 23 publications

Blockchain Technology for Intelligent Transportation Systems: A Systematic Literature Review

Blockchain Technology for Intelligent Transportation Systems: A Systematic Literature Review

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

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