|
|
|
| Wireless recommendations for internet of vehicles: Recent advances, challenges, and opportunities |
Tan Li( ),Congduan Li(),Jingjing Luo(),Linqi Song*() |
∙ Department of Computer Science, City University of Hong Kong, Kowloon 999077, China
City University of Hong Kong Shenzhen Research Institute, Shenzhen 518057, China.
∙ School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, China.
∙ School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China. |
|
|
Abstract Internet of Vehicles (IoV) is a distributed network of connected cars, roadside infrastructure, wireless communication networks, and central cloud platforms. Wireless recommendations play an important role in the IoV network, for example, recommending appropriate routes, recommending driving strategies, and recommending content. In this paper, we review some of the key techniques in recommendations and discuss what are the opportunities and challenges to deploy these wireless recommendations in the IoV.
|
|
Received: 11 February 2020
Online: 17 June 2020
|
| Fund: National Natural Science Foundation of ChinaNSFC(61901534);Guangdong Basic and Applied Basic Research Foundation(2019B1515120032);Science, Technology and Innovation Commission of Shenzhen Municipality(JCYJ20190807155617099);Hong Kong RGC ECS(21212419) |
|
Corresponding Authors:
Linqi Song
E-mail: tanli6-c@my.cityu.edu.hk;licongd@mail.sysu.edu.cn;luojingjing@hit.edu.cn;linqi.song@cityu.edu.hk
|
| About author: Tan Li received the BS degree from Central South University, Changsha, China in 2016, and the MS degree from University of Science and Technology of China, Hefei, China in 2019. She is currently working toward the PhD degree at the Department of Computer Science, City University of Hong Kong. Her research interests lie in the edge computing, distributed computing, and machine learning for wireless communication.|Congduan Li received the BS degree from University of Science and Technology Beijing, China in 2008, the MS degree from Northern Arizona University, AZ, USA in 2011, and the PhD degree from Drexel University, PA, USA in 2015, all in electrical engineering. From Oct 2015 to Aug 2018, he was a postdoctoral research fellow at the Institute of Network Coding, Chinese University of Hong Kong and the Department of Computer Science, City University of Hong Kong. He is currently an associate professor at the School of Electronics and Communication Engineering, Sun Yat-sen University, China. His research interests lie in the broad areas related with networks, such as coding, security, wireless, storage, and caching.|Jingjing Luo is currently an assistant professor at the School of Electronic and Information Engineering, Harbin Institute of Technology (Shenzhen), China. She received the BS and PhD degrees from Huazhong University of Science and Technology, Wuhan, China in 2010 and 2015, respectively. From 2016 to 2018, she was a post-doctoral fellow at the Department of Information Engineering, Chinese University of Hong Kong. Her research interests lie in the broad areas related with wireless networks, such as edge computing, caching, and machine learning.|Linqi Song is an assistant professor at the Department of Computer Science, City University of Hong Kong. He received the BS and MS degrees in electronic engineering from Tsinghua University, China in 2006 and 2009, respectively, and the PhD degree in electrical engineering from University of California, Los Angeles (UCLA), USA in 2017. After that, he was a postdoctoral scholar at the Electrical and Computer Engineering Department, University of California, Los Angeles, USA. He received a UCLA fellowship for his graduate studies. His research interests are in coding techniques, algorithms, big data, and machine learning. |
|
|
| [1] |
He W., Yan G. J., and Xu L. D., Developing vehicular data cloud services in the IoT environment, IEEE Trans. Ind. Inform., vol. 10, no. 2, pp. 1587-1595, 2014.
|
| [2] |
Zhang W. Y., Zhang Z. J., and Chao H. C., Cooperative fog computing for dealing with big data in the internet of vehicles:?Architecture and hierarchical resource management, IEEE Commun. Mag., vol. 55, no. 12, pp. 60-67, 2017.
|
| [3] |
Zhang J. P., Wang F. Y., Wang K. F., Lin W. H., Xu X., and Chen C., Data-driven intelligent transportation systems: A survey, IEEE Trans. Intell. Transp. Syst., vol. 12, no. 4, pp. 1624-1639, 2011.
|
| [4] |
Sladkowski A. and Pamula W., Intelligent Transportation Systems-Problems and Perspectives. Cham, Germany: Springer, 2016.
|
| [5] |
Liu Y. B., Wang Y. H., and Chang G. H., Efficient privacy-preserving dual authentication and key agreement scheme for secure V2V communications in an IoV paradigm, IEEE Trans. Intell. Transp. Syst., vol. 18, no. 10, pp. 2740-2749, 2017.
|
| [6] |
Chen S. Z., Hu J. L., Shi Y., Peng Y., Fang J. Y., Zhao R., and Zhao L., Vehicle-to-everything (V2X) services supported by LTE-based systems and 5G, IEEE Commun. Stand. Mag., vol. 1, no. 2, pp. 70-76, 2017.
|
| [7] |
Dar K., Bakhouya M., Gaber J., Wack M., and Lorenz P., Wireless communication technologies for ITS applications [Topics in Automotive Networking], IEEE Commun. Mag., vol. 48, no. 5, pp. 156-162, 2010.
|
| [8] |
Butakov V. A. and Ioannou P., Personalized driver assistance for signalized intersections using V2I communication, IEEE Trans. Intell. Transp. Syst., vol. 17, no. 7, pp. 1910-1919, 2016.
|
| [9] |
Hussein A., Garcia F., Armingol J. M., and Olaverri-Monreal C., P2V and V2P communication for pedestrian warning on the basis of autonomous vehicles, in Proc. IEEE 19th Int. Conf. Intelligent Transportation Systems, Rio de Janeiro, Brazil, 2016, pp. 2034-2039.
|
| [10] |
Nahri M., Boulmakoul A., Karim L., and Lbath A., IoV distributed architecture for real-time traffic data analytics, Procedia Comput. Sci., vol. 130, pp. 480-487, 2018.
|
| [11] |
Xu X. L., Xue Y., Qi L. Y., Yuan Y., Zhang X. Y., Umer T., and Wan S. H., An edge computing-enabled computation offloading method with privacy preservation for Internet of connected vehicles, Future Gener. Comput. Syst., vol. 96, pp. 89-100, 2019.
|
| [12] |
Ning Z. L., Feng Y. F., Collotta M., Kong X. J., Wang X. J., Guo L., Hu X. P., and Hu B., Deep learning in edge of vehicles: Exploring trirelationship for data transmission, IEEE Trans. Ind. Inform., vol. 15, no. 10, pp. 5737-5746, 2019.
|
| [13] |
Sodhro A. H., Luo Z. W., Sodhro G. H., Muzamal M., Rodrigues J. J. P. C., and de Albuquerque V. H. C., Artificial intelligence based QoS optimization for multimedia communication in IoV systems, Future Gener. Comput. Syst., vol. 95, pp. 667-680, 2019.
|
| [14] |
Lv C., Liu Y. H., Hu X. S., Guo H. Y., Cao D. P., and Wang F. Y., Simultaneous observation of hybrid states for cyber-physical systems: A case study of electric vehicle powertrain, IEEE Trans. Cybern., vol. 48, no. 8, pp. 2357-2367, 2017.
|
| [15] |
Lv C., Hu X. S., Sangiovanni-Vincentelli A., Li Y. T., Martinez C. M., and Cao D. P., Driving-style-based codesign optimization of an automated electric vehicle: A cyber-physical system approach, IEEE Trans. Ind. Electron., vol. 66, no. 4, pp. 2965-2975, 2019.
|
| [16] |
Xiong K., Leng S. P., He J. H., Wu F., and Wang Q., Recouping efficient safety distance in IoV-Enhanced transportation systems, in Proc. IEEE 54th Int. Conf. Communication, Shanghai, China, 2019, pp. 1-6.
|
| [17] |
Chang B. J. and Chiou J. M., Cloud computing-based analyses to predict vehicle driving shockwave for active safe driving in intelligent transportation system, IEEE Trans. Intell. Transp. Syst., vol. 21, no. 2, pp. 852-866, 2020.
|
| [18] |
Silva A., Mendon?a R., and Santana P., Monocular trail detection and tracking aided by visual SLAM for small unmanned aerial vehicles, J. Intell. Robot. Syst., vol. 97, no. 3, pp. 531-551, 2020.
|
| [19] |
Nourinejad M., Bahrami S., and Roorda M. J., Designing parking facilities for autonomous vehicles, Transp. Res. Part B-Methodol., vol. 109, pp. 110-127, 2018.
|
| [20] |
Hannoun G. J., Murray-Tuite P., Heaslip K., and Chantem T., Facilitating emergency response vehicles’ movement through a road segment in a connected vehicle environment, IEEE Trans. Intell. Transp. Syst., vol. 20, no. 9, pp. 3546-3557, 2019.
|
| [21] |
Zhao J. D., Guo Y. J., and Duan X. H., Dynamic path planning of emergency vehicles based on travel time prediction, J. Adv. Transp., vol. 2017, p. 9184891, 2017.
|
| [22] |
Li T., Tian S. J., Liu A. F., Liu H. L., and Pei T. R., DDSV: Optimizing delay and delivery ratio for multimedia big data collection in mobile sensing vehicles, IEEE Internet Things J., vol. 5, no. 5, pp. 3474-3486, 2018.
|
| [23] |
Herlocker J. L., Konstan J. A., Borchers A., and Riedl J., An algorithmic framework for performing collaborative filtering, in Proc. 22nd Annu. Int. ACM SIGIR Conf. Research and Development in Information Retrieval, Berkeley, CA, USA, 1999, pp. 230-237.
|
| [24] |
Sarwar B., Karypis G., Konstan J., and Riedl J., Item-based collaborative filtering recommendation algorithms, in Proc. 10th Int. Conf. on World Wide Web, Hong Kong, China, 2001, pp. 285-295.
|
| [25] |
Linden G., Smith B., and York J., Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Internet Comput., vol. 7, no. 1, pp. 76-80, 2003.
|
| [26] |
Melville P., Mooney R. J., and Nagarajan R., Content-boosted collaborative filtering for improved recommendations, in Proc. 18th National Conf. on Artificial Intelligence, Menlo Park, CA, USA, 2002, pp. 187-192.
|
| [27] |
Tso-Sutter K. H., Marinho L. B., and Schmidt-Thieme L., Tag-aware recommender systems by fusion of collaborative filtering algorithms, in Proc. 2008 ACM Symp. on Applied Computing, Fortaleza, Brazil, 2008, pp. 1995-1999.
|
| [28] |
Massa P. and Avesani P., Trust-aware collaborative filtering for recommender systems, in Proc. 2004 OTM Confederated Int. Conf., Agia Napa, Cyprus, 2004, pp. 492-508.
|
| [29] |
Hu Y. F., Koren Y., and Volinsky C., Collaborative filtering for implicit feedback datasets, in Proc. 8th IEEE Int. Conf. on Data Mining, Pisa, Italy, 2008, pp. 263-272.
|
| [30] |
Adomavicius G. and Tuzhilin A., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734-749, 2005.
|
| [31] |
Koren Y., Bell R., and Volinsky C., Matrix factorization techniques for recommender systems, Computer, vol. 42, no. 8, pp. 30-37, 2009.
|
| [32] |
Cui G., Luo J., and Wang X., Personalized travel route recommendation using collaborative filtering based on GPS trajectories, Int.J. Digit. Earth, vol. 11, no. 3, pp. 284-307, 2018.
|
| [33] |
Koren Y., Factorization meets the neighborhood: A multifaceted collaborative filtering model, in Proc. 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Las Vegas, NV, USA, 2008, pp. 426-434.
|
| [34] |
Chen T. Q., Zhang W. N., Lu Q. X., Chen K. L., Zheng Z., and Yu Y., SVDFeature: A toolkit for feature-based collaborative filtering, J. Mach. Learn. Res., vol. 13, no. 1, pp. 3619-3622, 2012.
|
| [35] |
Rendle S., Factorization machines, in Proc. 10th IEEE Int. Conf. on Data Mining, Sydney, Australia, 2010, pp. 995-1000.
|
| [36] |
Ma H., Yang H. X., Lyu M. R., and King I., SoRec: Social recommendation using probabilistic matrix factorization, in Proc. 17th ACM Conf. on Information and Knowledge Management, Napa Valley, CA, USA, 2008, pp. 931-940.
|
| [37] |
Rendle S., Freudenthaler C., Gantner Z., and Schmidt-Thieme L., BPR: Bayesian personalized ranking from implicit feedback, in Proc. 25th Conf. on Uncertainty in Artificial Intelligence, Montreal Quebec, Canada, 2009, pp. 452-461.
|
| [38] |
Ram P. and Gray A. G., Maximum inner-product search using cone trees, in Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Beijing, China, 2012, pp. 931-939.
|
| [39] |
Hsieh C. K., Yang L. Q., Cui Y., Lin T. Y., Belongie S., and Estrin D., Collaborative metric learning, in Proc. 26th Int. Conf. on World Wide Web, Perth, Australia, 2017, pp. 193-201.
|
| [40] |
He X. N., Liao L. Z., Zhang H. W., Nie L. Q., Hu X., and Chua T. S., Neural collaborative filtering, in Proc. 26th Int. Conf. on World Wide Web, Perth, Australia, 2017, pp. 173-182.
|
| [41] |
Chen W. Y., Cai F., Chen H. H., and de Rijke M., Joint neural collaborative filtering for recommender systems, ACM Trans. Inform. Syst., vol. 37, no. 4, pp. 1-30, 2019.
|
| [42] |
Zhang Y. F., Ai Q. Y., Chen X., and Croft W. B., Joint representation learning for top-N recommendation with heterogeneous information sources, in Proc. 2017 ACM Conf. on Information and Knowledge Management, Singapore, 2017, pp. 1449-1458.
|
| [43] |
Du X. Y., He X. N., Yuan F. J., Tang J. H., Qin Z. G., and Chua T. S., Modeling embedding dimension correlations via convolutional neural collaborative filtering, ACM Trans. Inform. Syst., vol. 37, no.4, pp. 1-22, 2019.
|
| [44] |
Li T., Kaewtip K., Feng J. X., and Lin L., IVAS: Facilitating safe and comfortable driving with intelligent vehicle audio systems, in Proc. 2018 IEEE Int. Conf. on Big Data, Seattle, WA, USA, 2018, pp. 5381-5382.
|
| [45] |
Goyal P. and Ferrara E., Graph embedding techniques, applications, and performance: A survey, Knowl. Based Syst., vol. 151, pp. 78-94, 2018.
|
| [46] |
Ying R., He R. N., Chen K. F., Eksombatchai P., Hamilton W. L., and Leskovec J., Graph convolutional neural networks for web-scale recommender systems, in Proc. 24th ACM SIGKDD Int. Conf. on Knowledge Discovery & Data Mining, London, UK, 2018, pp. 974-983.
|
| [47] |
Li L. H., Chu W., Langford J., and Schapire R. E., A contextual-bandit approach to personalized news article recommendation, in Proc. 19th Int. Conf. on World Wide Web, Raleigh, NC, USA, 2010, pp. 661-670.
|
| [48] |
Qin L. J., Chen S. Y., and Zhu X. Y., Contextual combinatorial bandit and its application on diversified online recommendation, in Proc. 2014 SIAM Int. Conf. on Data Mining, Philadelphia, PA, USA, 2014, pp. 461-469.
|
| [49] |
Song L. Q., Tekin C., and van der Schaar M., Online learning in large-scale contextual recommender systems, IEEE Trans. Serv. Comput., vol. 9, no. 3, pp. 433-445, 2016.
|
| [50] |
Shani G., Heckerman D., and Brafman R. I., An MDP-based recommender system, J. Mach. Learn. Res., vol. 6, pp. 1265-1295, 2005.
|
| [51] |
Auer P., Cesa-Bianchi N., and Fischer P., Finite-time analysis of the multiarmed bandit problem, Mach. Learn., vol. 47, nos. 2&3, pp. 235-256, 2002.
|
| [52] |
Slivkins A., Contextual bandits with similarity information, J. Mach. Learn. Res., vol. 15, no. 1, pp. 2533-2568, 2014.
|
| [53] |
Lu T., Pál D., and Pál M., Contextual multi-armed bandits, in Proc. 13th Int. Conf. Artificial Intelligence and Statistics, Sardinia, Italy, 2010, pp. 485-492.
|
| [54] |
Agrawal S. and Goyal N., Thompson sampling for contextual bandits with linear payoffs, in Proc. 30th Int. Conf. Machine Learning, Atlanta, GA, USA, 2013, pp. 127-135.
|
| [55] |
Dai J., Yang B., Guo C. J., and Ding Z. M., Personalized route recommendation using big trajectory data, in Proc. IEEE 31st Int. Conf. on Data Engineering, Seoul, South Korea, 2015, pp. 543-554.
|
| [56] |
Dai J. M., Liu T. A. J., and Lin H. Y., Road surface detection and recognition for route recommendation, in Proc. 2017 IEEE Intelligent Vehicles Symp., Los Angeles, CA, USA, 2017, pp. 121-126.
|
| [57] |
Kong J. T., Huang J., Yu H. K., Deng H. Q., Gong J. X., and Chen H., RNN-based default logic for route planning in urban environments, Neurocomputing, vol. 338, pp. 307-320, 2019.
|
| [58] |
Gao Y., Jiang D., and Xu Y., Optimize taxi driving strategies based on reinforcement learning, Int.J. Geogr. Inf. Sci., vol. 32, no. 8, pp. 1677-1696, 2018.
|
| [59] |
You C. X., Lu J. B., Filev D., and Tsiotras P., Highway traffic modeling and decision making for autonomous vehicle using reinforcement learning, in Proc. 2018 IEEE Intelligent Vehicles Symp., Changshu, China, 2018, pp. 1227-1232.
|
| [60] |
Liu L., Xu J., Liao S. S., and Chen H. P., A real-time personalized route recommendation system for self-drive tourists based on vehicle to vehicle communication, Expert Syst. Appl., vol. 41, no. 7, pp. 3409-3417, 2014.
|
| [61] |
Lei T., Wang S. G., Li J. L., and Yang F. C., A cooperative route choice approach via virtual vehicle in IoV, Veh. Commun., vol. 9, pp. 281-287, 2017.
|
| [62] |
Bishop R., Intelligent vehicle applications worldwide, IEEE Intelligent Systems and their Applications, vol. 15, no. 1, pp. 78-81, 2000.
|
| [63] |
Kuderer M., Gulati S., and Burgard W., Learning driving styles for autonomous vehicles from demonstration, in Proc. 2015 IEEE Int. Conf. on Robotics and Automation, Seattle, WA, USA, 2015, pp. 2641-2646.
|
| [64] |
Liu H. L., Taniguchi T., Tanaka Y., Takenaka K., and Bando T., Visualization of driving behavior based on hidden feature extraction by using deep learning, IEEE. Trans. Intell. Transp. Syst., vol. 18, no. 9, pp. 2477-2489, 2017.
|
| [65] |
Li G. F., Li S. E., Cheng B., and Green P., Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities, Transp. Res. Part C: Emerg. Technol., vol. 74, pp. 113-125, 2017.
|
| [66] |
Wang J. Q., Zhang L., Zhang D. Z., and Li K. Q., An adaptive longitudinal driving assistance system based on driver characteristics, IEEE Trans. Intell. Transp. Syst., vol. 14, no. 1, pp. 1-12, 2013.
|
| [67] |
Zhang K. R., Wang J. X., Chen N., Cao M. C., and Yin G. D., An algorithm of cooperative V2V trajectory planning on a winding road considering the drivers’ characteristics, in Proc. 2019 IEEE Intell. Transp. Syst. Conf., Auckland, New Zealand, 2019, pp. 2477-2482.
|
| [68] |
Elleuch I., Makni A., and Bouaziz R., Cooperative intersection collision avoidance persistent system based on V2V communication and real-time databases, in Proc. IEEE/ACS 14th Int. Conf. on Computer Systems and Applications, Hammamet, Tunisia, 2017, pp. 1082-1089.
|
| [69] |
Dang R. N., Ding J. Y., Su B., Yao Q. C., Tian Y. M., and Li K. Q., A lane change warning system based on V2V communication, in Proc. 17th Int. IEEE Conf. on Intelligent Transportation Systems, Qingdao, China, 2014, pp. 1923-1928.
|
| [70] |
Chen Y. F., Liu M., Everett M., and How J. P., Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning, in Proc. 2017 IEEE Int. Conf. on Robotics and Automation, Singapore, 2017, pp. 285-292.
|
| [71] |
Chen C., Xiang H. Y., Qiu T., Wang C., Zhou Y., and Chang V., A rear-end collision prediction scheme based on deep learning in the Internet of Vehicles, J. Parallel. Distrib. Comput., vol. 117, pp. 192-204, 2018.
|
| [72] |
Nkenyereye L., Liu C. H., and Song J., Towards secure and privacy preserving collision avoidance system in 5G fog based Internet of Vehicles, Future Gener. Comput. Syst., vol. 95, pp. 488-499, 2019.
|
| [73] |
Yu Z. W., Zhou X. S., and Zhang D. Q., An adaptive in-vehicle multimedia recommender for group users, in Proc. IEEE 61st Vehicular Technology Conf., Stockholm, Sweden, 2005, pp. 2800-2804.
|
| [74] |
Lin F. H., Zhou Y. T., You I., Lin J. Z., An X. S., and Lü X., Content recommendation algorithm for intelligent navigator in fog computing based IoT environment, IEEE Access, vol. 7, pp. 53 677-53 686, 2019.
|
| [75] |
Pandey M. K. and Subbiah K., Social networking and big data analytics assisted reliable recommendation system model for internet of vehicles, in Proc. 3rd Int. Con. on Internet of Vehicles, Nadi, Fiji, 2016, pp. 149-163.
|
| [76] |
Zhou Z. Y., Gao C. X., Xu C., Zhang Y., Mumtaz S., and Rodriguez J., Social big-data-based content dissemination in internet of vehicles, IEEE Trans. Ind. Inform., vol. 14, no. 2, pp. 768-777, 2018.
|
| [77] |
Silva C. M., Silva F. A., Sarubbi J. F. M., Oliveira T. R., Meira W. Jr, and Nogueira J. M. S., Designing mobile content delivery networks for the internet of vehicles, Veh. Commun., vol. 8, pp. 45-55, 2017.
|
| [78] |
Xu C. Q., Quan W., Vasilakos A. V., Zhang H. K., and Muntean G. M., Information-centric cost-efficient optimization for multimedia content delivery in mobile vehicular networks, Comput. Commun., vol. 99, pp. 93-106, 2017.
|
| [79] |
Li Z., Chen Y. T., Liu D. L., and Li X., Performance analysis for an enhanced architecture of IoV via content-centric networking, EURASIP J. Wirel. Commun. Netw., vol. 2017, p. 124, 2017.
|
| [80] |
Amadeo M., Campolo C., and Molinaro A., Priority-based content delivery in the internet of vehicles through named data networking, J. Sens. Actuator Netw., vol. 5, no. 4, p. 17, 2016.
|
| [81] |
Xu C. and Zhou Z. Y., Vehicular content delivery: A big data perspective, IEEE Wirel. Commun., vol. 25, no. 1, pp. 90-97, 2018.
|
| [82] |
Wang X. N. and Li Y. L., Content delivery based on vehicular cloud, IEEE Trans. Veh. Technol., vol. 69, no. 2, pp. 2105-2113, 2020.
|
| [83] |
Dai Y. Y., Xu D., Lu Y. L., Maharjan S., and Zhang Y., Deep reinforcement learning for edge caching and content delivery in internet of vehicles, in Proc. 8th Int. Conf. on Communications in China, Changchun, China, 2019, pp. 134-139.
|
| [84] |
Ndikumana A., Tran N. H., and Hong C. S., Deep learning based caching for self-driving car in multi-access edge computing, arXiv preprint arXiv: 1810.01548, 2018.
|
| [85] |
Zhang K., Leng S. P., He Y. J., Maharjan S., and Zhang Y., Cooperative content caching in 5G networks with mobile edge computing, IEEE Wirel. Commun., vol. 25, no. 3, pp. 80-87, 2018.
|
| [86] |
Su Z., Hui Y. L., Xu Q. C., Yang T. T., Liu J. Y., and Jia Y. J., An edge caching scheme to distribute content in vehicular networks, IEEE Trans. Veh. Technol., vol. 67, no. 6, pp. 5346-5356, 2018.
|
| [87] |
Dai Y. Y., Xu D., Maharjan S., Qiao G. H., and Zhang Y., Artificial intelligence empowered edge computing and caching for internet of vehicles, IEEE Wirel. Commun., vol. 26, no. 3, pp. 12-18, 2019.
|
| [88] |
Tan L. T. and Hu R. Q., Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning, IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 10 190-10 203, 2018.
|
| [89] |
Dai Y. Y., Xu D., Zhang K., Maharjan S., and Zhang Y., Permissioned blockchain and deep reinforcement learning for content caching in vehicular edge computing and networks, in Proc. 11th Int. Conf. on Wireless Communications and Signal Processing, Xi’an, China, 2019, pp. 1-6.
|
| [90] |
Darwish T. S. J. and Bakar K. A., Fog based intelligent transportation big data analytics in the Internet of vehicles environment: Motivations, architecture, challenges, and critical issues, IEEE Access, vol. 6, pp. 15 679-15 701, 2018.
|
| [91] |
Datta S. K., Haerri J., Bonnet C., and Da Costa R. F., Vehicles as connected resources: Opportunities and challenges for the future, IEEE Veh. Technol. Mag., vol. 12, no. 2, pp. 26-35, 2017.
|
| [92] |
Wang M., Wu J., Li G. L., Li J. H., Li Q., and Wang S., Toward mobility support for information-centric IoV in smart city using fog computing, in Proc. 5th IEEE Int. Conf. on Smart Energy Grid Engineering, Oshawa, Canada, 2017, pp. 357-361.
|
| [93] |
Contreras-Castillo J., Zeadally S., and Guerrero-Iba?ez J. A., Internet of vehicles: Architecture, protocols, and security, IEEE Internet Things J., vol. 5, no. 5, pp. 3701-3709, 2018.
|
| [94] |
Song H. M., Kim H. R., and Kim H. K., Intrusion detection system based on the analysis of time intervals of can messages for in-vehicle network, in Proc. 30th Int. Conf. on Information Networking, Kota Kinabalu, Malaysia, 2016, pp. 63-68.
|
| [95] |
Liu J. J., Zhang S. B., Sun W., and Shi Y. P., In-vehicle network attacks and countermeasures: Challenges and future directions, IEEE Netw., vol. 31, no. 5, pp. 50-58, 2017.
|
| [96] |
Woo S., Jo H. J., and Lee D. H., A practical wireless attack on the connected car and security protocol for in-vehicle CAN, IEEE Trans. Intell. Transp. Syst., vol. 16, no. 2, pp. 993-1006, 2015.
|
| [97] |
Kang J. W., Yu R., Huang X. M., and Zhang Y., Privacy-preserved pseudonym scheme for fog computing supported internet of vehicles, IEEE Trans. Intell. Transp. Syst., vol. 19, no. 8, pp. 2627-2637, 2018.
|
| [98] |
Kang M. J. and Kang J. W., A novel intrusion detection method using deep neural network for in-vehicle network security, in Proc. IEEE 83rd Vehicular Technology Conf., Nanjing, China, 2016, pp. 1-5.
|
| [99] |
Kwon C., Liu W. Y., and Hwang I., Security analysis for cyber-physical systems against stealthy deception attacks, in Proc. 2013 American Control Conf., Washington, DC, USA, 2013, pp. 3344-3349.
|
| [100] |
Gantsou D. and Sondi P., Toward a honeypot solution for proactive security in vehicular ad hoc networks, in Future Information Technology, Park J. J. J. H., Stojmenovic I., Choi M., and Xhafa F., eds. Berlin, Germany: Springer, 2014, pp. 145–150.
|
| [101] |
Argyroudis P. G. and O’Mahony D., Secure routing for mobile ad hoc networks, IEEE Commun. Surv. Tutor., vol. 7, no. 3, pp. 2-21, 2005.
|
| [102] |
Song L. Q. and Fragouli C., Making recommendations bandwidth aware, IEEE Trans. Inf. Theory, vol. 64, no. 11, pp. 7031-7050, 2018.
|
| [103] |
Bar-Yossef Z., Birk Y., Jayram T., and Kol T., Index coding with side information, IEEE Trans. Inf. Theory, vol. 57, no. 3, pp. 1479-1494, 2011.
|
| [104] |
Song L. Q., Fragouli C., and Shah D., Interactions between learning and broadcasting in wireless recommendation systems, in Proc. 2019 IEEE Int. Symp. Information Theory, Paris, France, 2019, pp. 2549-2553.
|
| [105] |
Song L. Q., Fragouli C., and Shah D., Recommender systems over wireless: Challenges and opportunities, in Proc. 2018 IEEE Inf. Theory Workshop, Guangzhou, China, 2018, pp. 1-5.
|
| [106] |
Chen L. X., Song L. Q., Chakareski J., and Xu J., Collaborative content placement among wireless edge caching stations with time-to-live cache, IEEE Trans. Multimed., vol. 22, no. 2, pp. 432-444, 2020.
|
| [107] |
Song L. Q. and Xu J., Dynamic edge caching with popularity drifting, in Proc. 2018 IEEE Information Theory Workshop, Guangzhou, China, 2018, pp. 1-5.
|
| [108] |
Luo J. J., Zhang J. B., Cui Y., Yu L., and Wang X. B., Asymptotic analysis on content placement and retrieval in MANETs, IEEE/ACM Trans. Netw., vol. 25, no. 2, pp. 1103-1118, 2017.
|
| [109] |
Karmoose M., Song L. Q., Cardone M., and Fragouli C., Privacy in index coding: k-limited-access schemes, IEEE Trans. Inf. Theory, vol. 66, no. 5, pp. 2625-2641, 2020.
|
| [110] |
Yang M. W., Song L. Q., Xu J., Li C. D., and Tan G. Z., The tradeoff between privacy and accuracy in anomaly detection using federated XGBoost, arXiv preprint arXiv: 1907.07157, 2019.
|
| [111] |
Bai Y., Chen L. X., Song L. Q., and Xu J., Bayesian stackelberg game for risk-aware edge computation offloading, in Proc. 6th ACM Workshop on Moving Target Defense, London, UK, 2019, pp. 25-35.
|
| [112] |
Data D., Song L. Q., and Diggavi S., Data encoding methods for byzantine-resilient distributed optimization, in Proc. 2019 IEEE Int. Symp. Information Theory, Paris, France, 2019, pp. 2719-2723.
|
| [113] |
Data D., Song L. Q., and Diggavi S., Data encoding for byzantine-resilient distributed gradient descent, in Proc. 56th Annu. Allerton Conf. on Communications, Control, and Computing, Monticello, IL, USA, 2018, pp. 863-870.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
