Distributed Data Collection in Age-Aware Vehicular Participatory Sensing Networks

Qin, Xiaoqi; Xia, Yangyang; Li, Hang; Zhang, Feng; Zhang, Ping

doi:10.1109/jiot.2021.3049999

Cited by 24 publications

(2 citation statements)

References 35 publications

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“…To be specific, Li et al [14] proposed a mathematical framework for analyzing vehicle social network age and considered joint optimization of information update rate and transmission probability. Qin et al [16] designed a distributed data collection strategy to address the sensing data sampling of the source vehicle and the data forwarding problem in vehicular sensing networks. The above work focuse on the minimization of average AoI.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Data Collection and Resource Allocation in Age-Aware IoV

Chen¹,

Liwang²,

Gao³

et al. 2022

Preprint

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<p>The emergence of Internet of Vehicles (IoV) has facilitated many attractive vehicular applications that require massive sensed data and timely data analysis. Data collection and resource allocation are critical issues in IoV for timely data processing in dynamic network environments. However, the energy consumption of IoV infrastructure and vehicles pose challenges to developing sustainable vehicular communication and networking infrastructure. Moreover, the communication and computing resources are generally insufficient to support the transmission and analysis of the excessive data. Vehicular data are often updated periodically, which makes much of which outdated, or useless for vehicular applications, and thus leads to large latency and tremendous energy consumption. To this end, age of Information (AoI) has been introduced as a novel metric to characterize data freshness. Satisfying the state update AoI constraint is of great significance to guarantee the freshness of the IoV system. In this paper, we design a sampling strategy with responsive transmission and computing (e.g., no waiting latency), and investigate an energy minimization problem under peak AoI constraints in age-aware vehicular networks. We decouple the problem as a minimum set cover problem, and a convex problem, and propose a joint sampling selection and resource allocation (JSRA) algorithm to obtain the approximate optimal solution. We evaluate the proposed sampling selection and resource allocation strategy and JSRA algorithm by experiments on the simulation of urban mobilty (SUMO). Numerical results show that the proposed sampling strategy and algorithm outperform existing methods in terms of energy consumption, especially for the scenario with dense vehicles and pedestrians.</p>

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient Data Collection and Resource Allocation in Age-Aware IoV

Chen¹,

Liwang²,

Gao³

et al. 2022

Preprint

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show abstract

“…Vehicles as a carrier of sensors for sensing the urban environment is a common solution. To accomplish the data collection from sensors deployed in different locations in urban, data forwarding schema based on the probability of a vehicle to reach a road side unit was proposed in the literature [15]; a distributed age-aware data collection scheme was proposed based on Lyapunov optimization technique in the literature [16], including a sampling method with a threshold at the source vehicle and a data forwarding strategy based on learning. For the traffic data collection in urban scene, P. Salvo et al proposed a traffic data collection method based on LTE and V2V [17], and the way of data collection can switch between LTE and V2V according to the vehicle density.…”

Section: Related Workmentioning

confidence: 99%

UAV-Assisted Sensor Data Dissemination in mmWave Vehicular Networks Based on Network Coding

Huang

et al. 2022

Wireless Communications and Mobile Computing

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Due to good maneuverability, UAVs and vehicles are often used for environment perception in smart cities. In order to improve the efficiency of sensor data sharing in UAV-assisted mmWave vehicular network (VN), this paper proposes a sensor data sharing method based on blockage effect identification and network coding. The concurrent sending vehicles selection method is proposed based on the availability of mmWave link, the number of target vehicles of sensor data packet, the distance between a sensor data packet and target vehicle, the number of concurrent sending vehicles, and the waiting time of sensor data packet. The construction method of the coded packet is put forward based on the status information about the existing packets of vehicles. Simulation results demonstrated that efficiency of the proposed method is superior to baseline solutions in terms of the packet loss ratio, transmission time, and packet dissemination ratio.

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A Markov chain–based IoT system for monitoring and analysis of urban air quality

Barthwal

2022

Environ Monit Assess

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Distributed Data Collection in Age-Aware Vehicular Participatory Sensing Networks

Cited by 24 publications

References 35 publications

Energy-Efficient Data Collection and Resource Allocation in Age-Aware IoV

Energy-Efficient Data Collection and Resource Allocation in Age-Aware IoV

UAV-Assisted Sensor Data Dissemination in mmWave Vehicular Networks Based on Network Coding

A Markov chain–based IoT system for monitoring and analysis of urban air quality

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