59 research outputs found
Artificial Intelligence in Road Traffic Accident Prediction
The rapid development of AI shows its power and great development potential in practical engineering applications. Critical issues and potential solutions can reduce road traffic accidents and application of AI in road accident prediction. The published use of AI for road accident prediction is reviewed, presented, and represented as the main objective. The methods are collecting article data, quotations, presentation, and representation. The article data collection was obtained from 671 conference and journal articles in 2019-2023, but the suitability of articles that can be used is 69. Quotation produces a grouping of approaches, models, predictions, and benefits. The presentation showed that most approaches used were machine learning, the most used model was random forest, the prediction was mostly about severity, and the most benefit was about number reduction. Representation produces road accidents and related factors into factors in road accident predictions using artificial intelligence, so strategies and anticipation can be made to overcome them to improve road safety. AI in road accident prediction plays an important role in building predictive models with the hope that road accidents can be identified early, risk factors can be reduced, and effective preventive measures can be taken to improve road safety
Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective
Machine Learning models are being deployed as parts of real-world systems
with the upsurge of interest in artificial intelligence. The design,
implementation, and maintenance of such systems are challenged by real-world
environments that produce larger amounts of heterogeneous data and users
requiring increasingly faster responses with efficient resource consumption.
These requirements push prevalent software architectures to the limit when
deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging
concept that equips systems better for integrating ML models. DOA extends
current architectures to create data-driven, loosely coupled, decentralised,
open systems. Even though papers on deployed ML-based systems do not mention
DOA, their authors made design decisions that implicitly follow DOA. The
reasons why, how, and the extent to which DOA is adopted in these systems are
unclear. Implicit design decisions limit the practitioners' knowledge of DOA to
design ML-based systems in the real world. This paper answers these questions
by surveying real-world deployments of ML-based systems. The survey shows the
design decisions of the systems and the requirements these satisfy. Based on
the survey findings, we also formulate practical advice to facilitate the
deployment of ML-based systems. Finally, we outline open challenges to
deploying DOA-based systems that integrate ML models.Comment: Under revie
A Generic Framework for Constraint-Driven Data Selection in Mobile Crowd Photographing
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile crowd photographing (MCP) is an emerging area of interest for researchers as the built-in cameras of mobile devices are becoming one of the commonly used visual logging approaches in our daily lives. In order to meet diverse MCP application requirements and constraints of sensing targets, a multifacet task model should be defined for a generic MCP data collection framework. Furthermore, MCP collects pictures in a distributed way in which a large number of contributors upload pictures whenever and wherever it is suitable. This inevitably leads to evolving picture streams. This paper investigates the multiconstraint-driven data selection problem in MCP picture aggregation and proposes a pyramid-tree (PTree) model which can efficiently select an optimal subset from the evolving picture streams based on varied coverage needs of MCP tasks. By utilizing the PTree model in a generic MCP data collection framework, which is called CrowdPic, we test and evaluate the effectiveness, efficiency, and flexibility of the proposed framework through crowdsourcing-based and simulation-based experiments. Both the theoretical analysis and simulation results indicate that the PTree-based framework can effectively select a subset with high utility coverage and low redundancy ratio from the streaming data. The overall framework is also proved flexible and applicable to a wide range of MCP task scenarios
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Social Media Text Processing and Semantic Analysis for Smart Cities
With the rise of Social Media, people obtain and share information almost
instantly on a 24/7 basis. Many research areas have tried to gain valuable
insights from these large volumes of freely available user generated content.
With the goal of extracting knowledge from social media streams that might be
useful in the context of intelligent transportation systems and smart cities,
we designed and developed a framework that provides functionalities for
parallel collection of geo-located tweets from multiple pre-defined bounding
boxes (cities or regions), including filtering of non-complying tweets, text
pre-processing for Portuguese and English language, topic modeling, and
transportation-specific text classifiers, as well as, aggregation and data
visualization.
We performed an exploratory data analysis of geo-located tweets in 5
different cities: Rio de Janeiro, S\~ao Paulo, New York City, London and
Melbourne, comprising a total of more than 43 million tweets in a period of 3
months. Furthermore, we performed a large scale topic modelling comparison
between Rio de Janeiro and S\~ao Paulo. Interestingly, most of the topics are
shared between both cities which despite being in the same country are
considered very different regarding population, economy and lifestyle.
We take advantage of recent developments in word embeddings and train such
representations from the collections of geo-located tweets. We then use a
combination of bag-of-embeddings and traditional bag-of-words to train
travel-related classifiers in both Portuguese and English to filter
travel-related content from non-related. We created specific gold-standard data
to perform empirical evaluation of the resulting classifiers. Results are in
line with research work in other application areas by showing the robustness of
using word embeddings to learn word similarities that bag-of-words is not able
to capture
Geospatial Information Research: State of the Art, Case Studies and Future Perspectives
Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
Machine learning (ML) is widely used for key tasks in Connected and Automated
Vehicles (CAV), including perception, planning, and control. However, its
reliance on vehicular data for model training presents significant challenges
related to in-vehicle user privacy and communication overhead generated by
massive data volumes. Federated learning (FL) is a decentralized ML approach
that enables multiple vehicles to collaboratively develop models, broadening
learning from various driving environments, enhancing overall performance, and
simultaneously securing local vehicle data privacy and security. This survey
paper presents a review of the advancements made in the application of FL for
CAV (FL4CAV). First, centralized and decentralized frameworks of FL are
analyzed, highlighting their key characteristics and methodologies. Second,
diverse data sources, models, and data security techniques relevant to FL in
CAVs are reviewed, emphasizing their significance in ensuring privacy and
confidentiality. Third, specific and important applications of FL are explored,
providing insight into the base models and datasets employed for each
application. Finally, existing challenges for FL4CAV are listed and potential
directions for future work are discussed to further enhance the effectiveness
and efficiency of FL in the context of CAV
Security and Privacy Preservation in Mobile Crowdsensing
Mobile crowdsensing (MCS) is a compelling paradigm that enables a crowd of individuals to cooperatively collect and share data to measure phenomena or record events of common interest using their mobile devices. Pairing with inherent mobility and intelligence, mobile users can collect, produce and upload large amounts of data to service providers based on crowdsensing tasks released by customers, ranging from general information, such as temperature, air quality and traffic condition, to more specialized data, such as recommended places, health condition and voting intentions. Compared with traditional sensor networks, MCS can support large-scale sensing applications, improve sensing data trustworthiness and reduce the cost on deploying expensive hardware or software to acquire high-quality data.
Despite the appealing benefits, however, MCS is also confronted with a variety of security and privacy threats, which would impede its rapid development. Due to their own incentives and vulnerabilities of service providers, data security and user privacy are being put at risk. The corruption of sensing reports may directly affect crowdsensing results, and thereby mislead customers to make irrational decisions. Moreover, the content of crowdsensing tasks may expose the intention of customers, and the sensing reports might inadvertently reveal sensitive information about mobile users. Data encryption and anonymization techniques can provide straightforward solutions for data security and user privacy, but there are several issues, which are of significantly importance to make MCS practical. First of all, to enhance data trustworthiness, service providers need to recruit mobile users based on their personal information, such as preferences, mobility pattern and reputation, resulting in the privacy exposure to service providers. Secondly, it is inevitable to have replicate data in crowdsensing reports, which may possess large communication bandwidth, but traditional data encryption makes replicate data detection and deletion challenging. Thirdly, crowdsensed data analysis is essential to generate crowdsensing reports in MCS, but the correctness of crowdsensing results in the absence of malicious mobile users and service providers become a huge concern for customers. Finally yet importantly, even if user privacy is preserved during task allocation and data collection, it may still be exposed during reward distribution. It further discourage mobile users from task participation.
In this thesis, we explore the approaches to resolve these challenges in MCS. Based on the architecture of MCS, we conduct our research with the focus on security and privacy protection without sacrificing data quality and users' enthusiasm. Specifically, the main contributions are, i) to enable privacy preservation and task allocation, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation. In SPOON, the service provider recruits mobile users based on their locations, and selects proper sensing reports according to their trust levels without invading user privacy. By utilizing the blind signature, sensing tasks are protected and reports are anonymized. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users; ii) to improve communication efficiency while guaranteeing data confidentiality, we propose a fog-assisted secure data deduplication scheme, in which a BLS-oblivious pseudo-random function is developed to enable fog nodes to detect and delete replicate data in sensing reports without exposing the content of reports. Considering the privacy leakages of mobile users who report the same data, the blind signature is utilized to hide users' identities, and chameleon hash function is leveraged to achieve contribution claim and reward retrieval for anonymous greedy mobile users; iii) to achieve data statistics with privacy preservation, we propose a privacy-preserving data statistics scheme to achieve end-to-end security and integrity protection, while enabling the aggregation of the collected data from multiple sources. The correctness verification is supported to prevent the corruption of the aggregate results during data transmission based on the homomorphic authenticator and the proxy re-signature. A privacy-preserving verifiable linear statistics mechanism is developed to realize the linear aggregation of multiple crowdsensed data from a same device and the verification on the correctness of aggregate results; and iv) to encourage mobile users to participating in sensing tasks, we propose a dual-anonymous reward distribution scheme to offer the incentive for mobile users and privacy protection for both customers and mobile users in MCS. Based on the dividable cash, a new reward sharing incentive mechanism is developed to encourage mobile users to participating in sensing tasks, and the randomization technique is leveraged to protect the identities of customers and mobile users during reward claim, distribution and deposit
Deep Learning in Mobile and Wireless Networking: A Survey
The rapid uptake of mobile devices and the rising popularity of mobile
applications and services pose unprecedented demands on mobile and wireless
networking infrastructure. Upcoming 5G systems are evolving to support
exploding mobile traffic volumes, agile management of network resource to
maximize user experience, and extraction of fine-grained real-time analytics.
Fulfilling these tasks is challenging, as mobile environments are increasingly
complex, heterogeneous, and evolving. One potential solution is to resort to
advanced machine learning techniques to help managing the rise in data volumes
and algorithm-driven applications. The recent success of deep learning
underpins new and powerful tools that tackle problems in this space.
In this paper we bridge the gap between deep learning and mobile and wireless
networking research, by presenting a comprehensive survey of the crossovers
between the two areas. We first briefly introduce essential background and
state-of-the-art in deep learning techniques with potential applications to
networking. We then discuss several techniques and platforms that facilitate
the efficient deployment of deep learning onto mobile systems. Subsequently, we
provide an encyclopedic review of mobile and wireless networking research based
on deep learning, which we categorize by different domains. Drawing from our
experience, we discuss how to tailor deep learning to mobile environments. We
complete this survey by pinpointing current challenges and open future
directions for research
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