Vehicle-cluster-based opportunistic relays for data collection in intelligent transportation systems

被引:5
作者
Sun, Sheng [1 ]
Zhang, Zengqi [1 ,2 ]
Pan, Quyang [4 ]
Liu, Min [1 ,2 ,3 ]
Li, Zhongcheng [1 ,2 ]
机构
[1] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
[2] Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China
[3] Zhongguancun Lab, Beijing, Peoples R China
[4] Xidian Univ, Xian, Peoples R China
基金
中国国家自然科学基金;
关键词
Intelligent transportation system; Channel allocation; Device association; MACHINE-TYPE COMMUNICATION; RESOURCE-ALLOCATION; DATA AGGREGATION; NETWORKS; TRANSMISSION;
D O I
10.1016/j.comnet.2022.109509
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Intelligent transportation system (ITS) is envisioned to greatly improve traffic and enhance safety on roads. ITS relies on a huge amount of data generated from roadside sensor devices to make decisions. Due to the limited channel resources, the base station (BS) that connects with an ITS server can only collect data from parts of sensor devices at a time. To improve the number of device associations, we regard vehicle clusters as relays to collect data, which can avoid high capital expenditure and operating expenses from dedicated relays. Due to the frequently varying channel interference and the limited communication coverage of a vehicle cluster relay (VCR), it is challenging to guarantee the transmission rates and the fairness of sensor devices, which will affect the decisions of ITS. For this reason, we propose a Movement-and Fairness-Aware Heuristic (MFAH) algorithm to tackle the above challenges. MFAH sequentially conducts two novel channel allocation schemes, i.e., exclusive channel allocation scheme and compatible channel allocation scheme, to fast allocate channels and improve the channel utilization, which increases the number of device associations while guaranteeing the transmission rates. Regarding the fairness of each device's associations, we propose a device association scheme based on the cumulative number of device associations and the distance from the target VCR to select appropriate sensor devices to upload data. We theoretically analyze the lower bound of the obtained network utility. Extensive simulations show that compared with benchmarks, the proposed MFAH algorithm converges fast and effectively improves the network utility (i.e., increasing the number of device associations while guaranteeing the fairness of device associations).
引用
收藏
页数:13
相关论文
共 35 条
[21]   Data Uploading in Hybrid V2V/V2I Vehicular Networks: Modeling and Cooperative Strategy [J].
Ni, Yuanzhi ;
He, Jianping ;
Cai, Lin ;
Bo, Yuming .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (05) :4602-4614
[22]   A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing [J].
Nie, Jiangtian ;
Luo, Jun ;
Xiong, Zehui ;
Niyato, Dusit ;
Wang, Ping .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2019, 18 (01) :724-738
[23]   NOMA-Based Coordinated Direct and Relay Transmission With a Half-Duplex/Full-Duplex Relay [J].
Pei, Xinyue ;
Yu, Hua ;
Wen, Miaowen ;
Mumtaz, Shahid ;
Al Otaibi, Sattam ;
Guizani, Mohsen .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2020, 68 (11) :6750-6760
[24]   Parked Cars are Excellent Roadside Units [J].
Reis, Andre B. ;
Sargento, Susana ;
Tonguz, Ozan K. .
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2017, 18 (09) :2490-2502
[25]   Cooperative Data Aggregation and Dynamic Resource Allocation for Massive Machine Type Communication [J].
Salam, Tabinda ;
Rehman, Waheed Ur ;
Tao, Xiaofeng .
IEEE ACCESS, 2018, 6 :4145-4158
[26]   Designing Secure User Authentication Protocol for Big Data Collection in IoT-Based Intelligent Transportation System [J].
Srinivas, Jangirala ;
Das, Ashok Kumar ;
Wazid, Mohammad ;
Vasilakos, Athanasios V. .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (09) :7727-7744
[27]   User-centric content sharing via cache-enabled device-to-device communication [J].
Sun, Sheng ;
Liu, Min ;
Jiao, Zhenzhen ;
Pang, Xiao ;
Chen, Shuang .
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2018, 115 :103-115
[28]   Location-Dependent Task Allocation for Mobile Crowdsensing With Clustering Effect [J].
Tao, Xi ;
Song, Wei .
IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (01) :1029-1045
[29]   Energy-efficient data collection in strip-based wireless sensor networks with optimal speed mobile data collectors [J].
Vishnuvarthan, R. ;
Sakthivel, R. ;
Bhanumathi, V. ;
Muralitharan, K. .
COMPUTER NETWORKS, 2019, 156 :33-40
[30]   Mobile Service Amount Based Link Scheduling for High-Mobility Cooperative Vehicular Networks [J].
Xiong, Ke ;
Zhang, Yu ;
Fan, Pingyi ;
Yang, Hong-Chuan ;
Zhou, Xianwei .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2017, 66 (10) :9521-9533