Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

Li, Tan; Li, Congduan; Luo, Jijun; Song, Linqi

doi:10.23919/icn.2020.0005

Cited by 28 publications

(16 citation statements)

References 86 publications

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“…In this paper, we only discuss the health data with identical formats. However, data format variety is one of the key characteristics of big data applications [47][48][49][50][51][52]. Therefore, we will further improve our anomaly detection algorithm by accommodating the diversity of health data types or formats in future.…”

Section: Discussionmentioning

confidence: 99%

Private anomaly detection of student health conditions based on wearable sensors in mobile cloud computing

et al. 2022

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With the continuous spread of COVID-19 virus, how to guarantee the healthy living of people especially the students who are of relative weak physique is becoming a key research issue of significant values. Specifically, precise recognition of the anomaly in student health conditions is beneficial to the quick discovery of potential patients. However, there are so many students in each school that the education managers cannot know about the health conditions of students in a real-time manner and accurately recognize the possible anomaly among students quickly. Fortunately, the quick development of mobile cloud computing technologies and wearable sensors has provided a promising way to monitor the real-time health conditions of students and find out the anomalies timely. However, two challenges are present in the above anomaly detection issue. First, the health data monitored by massive wearable sensors are often massive and updated frequently, which probably leads to high sensor-cloud transmission cost for anomaly detection. Second, the health data of students are often sensitive enough, which probably impedes the integration of health data in cloud environment even renders the health data-based anomaly detection infeasible. In view of these challenges, we propose a time-efficient and privacy-aware anomaly detection solution for students with wearable sensors in mobile cloud computing environment. At last, we validate the effectiveness and efficiency of our work via a set of simulated experiments.

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Section: Discussionmentioning

confidence: 99%

Private anomaly detection of student health conditions based on wearable sensors in mobile cloud computing

et al. 2022

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“…In addition to recommender systems in domains such as personalized travel [40], wireless networks [41], data-driven clustering [42], and anchors on live streaming platforms [43], personality-aware recommender systems have shown great success in identifying similar users based on their personality types. In relation to personality-aware recommendations in the domains of education and academia, many researchers have worked in this area.…”

Section: Personality-aware Recommender Systemsmentioning

confidence: 99%

CoSPARP: Collaborative and Social-Personality Aware Recommendation of Programmes

Asabere

2022

Mobile Information Systems

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Globally, the selection of tertiary programmes for higher education in a university by prospective applicants is a daunting task. Different universities offer a wide range of programmes using different education delivery modes for teaching and learning. This creates information overload in the context of tertiary programmes. To tackle the information overload problem of tertiary programmes in the context of higher education institutions (HEIs), this paper, therefore, proposes a novel recommendation model called Collaborative and Social-Personality Aware Recommendation of Programmes (CoSPARP) for tertiary programme selection. CoSPARP utilizes a hybrid filtering system that incorporates the computation of similarities relating to the CF, personality traits, and the tie strength of users (prospective applicants) to generate effective programme recommendations for a tertiary programme applicant (TPA). The proposed CoSPARP recommendation method employs the above recommendation entities to create profiles of the TPAs as a basis of profile similarity for tertiary programme recommendations. Results of benchmarking experiments showed that CoSPARP overcomes cold-start due to the proposed (innovative) hybridization process. Additionally, using a relevant real-world dataset and suitable evaluation metrics such as precision, recall, and F-measure, CoSPARP produces more favourable outcomes in comparison to other state-of-the-art methods.

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“…The vehicular communication networks enable the safety message exchanges in advanced intelligent transportation system applications [1,2] , through vehicle-Ping Wang and Shuai Wang are with the College of Information Science and Technology, Donghua University, Shanghai 201620, China. E-mail: pingwang@dhu.edu.cn; 2161187@mail.dhu.edu.cn.…”

Section: Introductionmentioning

confidence: 99%

A Fairness-Enhanced Intelligent MAC Scheme Using Q-Learning-Based Bidirectional Backoff for Distributed Vehicular Communication Networks

Wang

2023

Tsinghua Sci. Technol.

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With the increasing attention to front-edge vehicular communication applications, distributed resource allocation is beneficial to the direct communications between vehicle nodes. However, in highly dynamic distributed vehicular networks, quality of service (QoS) of the systems would degrade dramatically because of serious packet collisions in the absence of sufficient link knowledge. Focusing on the fairness optimization, a Q-learning-based collision avoidance (QCA) scheme, which is characterized by an ingenious bidirectional backoff reward model R QCA corresponding to arbitrary backoff stage transitions, has been proposed in an intelligent distributed media access control protocol. In QCA, an intelligent bidirectional backoff agent based on the Markov decision process model can actively motivate each vehicle agent to update itself toward an optimal backoff sub-intervel BSI opt through either positive or negative bidirectional transition individually, resulting in the distinct fair communication with a proper balance of the resource allocation. According to the reinforcement learning theory, the problem of goodness evaluation on the backoff stage self-selection policy is equal to the problem of maximizing Q function of the vehicle in the current environment. The final decision on BSI opt related to an optimal contention window range was solved through maximizing the Q value or Q max . The "-greedy algorithm was used to keep a reasonable convergence of the Q max solution. For the fairness evaluation of QCA, four kinds of dynamic impacts on the vehicular networks were investigated: mobility, density, payload size, and data rate with a network simulator NS2. Consequently, QCA can achieve fair communication efficiently and robustly, with advantages of superior Jain's fairness index, relatively high packet delivery ratio, and low time delay.

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Wireless recommendations for Internet of vehicles: Recent advances, challenges, and opportunities

Cited by 28 publications

References 86 publications

Private anomaly detection of student health conditions based on wearable sensors in mobile cloud computing

Private anomaly detection of student health conditions based on wearable sensors in mobile cloud computing

CoSPARP: Collaborative and Social-Personality Aware Recommendation of Programmes

A Fairness-Enhanced Intelligent MAC Scheme Using Q-Learning-Based Bidirectional Backoff for Distributed Vehicular Communication Networks

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