Cost-and-Quality Aware Data Collection for Edge-Assisted Vehicular Crowdsensing

Liu, Luning; Wang, Luhan; Lu, Zhaoming; Liu, Yong; Jing, Wenpeng

doi:10.1109/tvt.2022.3151859

Cited by 21 publications

(6 citation statements)

References 51 publications

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“…In order to meet the requirements of ITS applications for accuracy and timeliness of sensor data, while achieving efficient use of bandwidth resources, W. Nie et al formalized the problem as the minimized communication overhead problem [18] and proposed two different solutions with mixed-integer linear programming and deviation-detection method. An adaptive vehicle clustering mechanism and online parameter adjustment method were designed in [19] to satisfy the ITS requirements for accuracy and timeliness of sensor data.…”

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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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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“…With these abilities, vehicles can provide multiple location-based services, such as realtime map building [1], traffic management [2], crowdsensing [3], and environmental monitoring [4,5], etc,. With the development of intelligent transportation system (ITS), a large number of perceptual data is continuously generated [6,7]. It is estimated that if 25% of all vehicles are connected, 400 million GB of data will be transmitted every month [7].…”

Section: Introductionmentioning

confidence: 99%

“…With the development of intelligent transportation system (ITS), a large number of perceptual data is continuously generated [6,7]. It is estimated that if 25% of all vehicles are connected, 400 million GB of data will be transmitted every month [7]. In *This work was supported in part by the NSF China under Grant 61801365, 61701365 and 61971327, in part by the National Natural Science Foundation of Shaanxi Province under Grant 2019JQ-152, in part by Postdoctoral Foundation in Shaanxi Province of China, and the Fundamental Research Funds for the Central Universities.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Content Dissemination Framework for Fog-Based Internet of Vehicles

Wu¹,

Wang²,

Zhang³

et al. 2023

Preprint

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As the fog-based internet of vehicles (IoV) is equipped with rich perception, computing, communication and storage resources, it provides a new solution for the bulk data processing. However, the impact caused by the mobility of vehicles brings a challenge to the content scheduling and resource allocation of content dissemination service. In this paper, we propose a time-varying resource relationship graph to model the intertwined impact of the perception, computation, communication and storage resources across multiple snapshots on the content dissemination process of IoV. Based on this graph model, the content dissemination process is modeled as a mathematical optimization problem, where the quality of service of both delay tolerant and delay sensitive services are considered. Owing to its NP-completeness, the optimization problem is decomposed into a joint link and subchannel scheduling subproblem and as well a joint power and flow control subproblem. Then, a cascaded low complexity scheduling algorithm is proposed for the joint link and subchannel scheduling subproblem. Moreover, a robust resource management algorithm is developed for the power and flow control subproblem, where the channel uncertainties in future snapshots are fully considered in the algorithm. Finally, we conduct simulations to show that the effectiveness of the proposed approaches outperforms other state-of-art approaches.

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“…For a vehicular application, much of the sensed data is duplicated. Finally, the sheer amount of redundant data may exhaust transmission and computing resources, resulting in heavy overhead [6]. To address the above challenges, avoiding unnecessary data collection and allocating resources appropriately to transmit and process data is critical.…”

Section: Introductionmentioning

confidence: 99%

“…[7] proposed the concept of Age of Information (AoI) to quantify the freshness of sensed data. AoI is defined as the elapsed time since the last received state information update data was generated, which has been extensively researched in various fields such as queuing models [8], [9], energy harvesting [10], and data forwarding [6]. However, these work focus on minimizing the average AoI.…”

Section: Introductionmentioning

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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Cost-and-Quality Aware Data Collection for Edge-Assisted Vehicular Crowdsensing

Cited by 21 publications

References 51 publications

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

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

A Cooperative Content Dissemination Framework for Fog-Based Internet of Vehicles

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

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