Edge-Assisted Mobile Crowdsensing in Internet of Vehicles

♦ Introduction and Background

     With the advances of sensing, mobile computing and communication, mobile crowdsensing has emerged as a new sensing and computing paradigm. Recently, intelligent vehicles are usually equipped with a vast array of sensors (such as cameras and radars), having more powerful computation and storage capability, and providing larger movement patterns. This makes them ideal to sense up-to-date and fine-grained information for large areas. So we focus on mobile crowdsensing system in internet of vehicles. We mainly study the problems of vehicle recruitment, vehicle trajectory prediction, perception data de-redundancy, data transmission process, etc.

♦ Research results

  • We propose a collaborative data collection architecture based on edge intelligence, where non-dedicated and dedicated vehicles cooperate to carry out large-scale and fine-grained data collection with the assistance of the edge server. In order to improve the spatiotemporal evenness of collected data and guarantee the coverage rate, two scheduling algorithms, namely, offline scheduling and online scheduling, have been proposed to guide the movement of dedicated vehicles.

Fig 1. System Functional Architecture

  • We propose an incentive-aware vehicle recruitment scheme for edge-assisted mobile crowdsensing. In particular, we first design an incentive mechanism to motivate cooperation among the edge server and the intelligent vehicles, and apply the Nash bargaining theory to obtain the optimal cooperation decision. Furthermore, to weigh the contribution of vehicles, a practical and efficient scheme is also proposed, in terms of the vehicular spatiotemporal availability, the vehicular reputation and the priority of regions.

Fig 2. Edge-Assisted Vehicular Crowdsensing System

  • We propose CHRT, a clustering-based hybrid re-routing system for traffic congestion avoidance. CHRT develops a multi-layer hybrid architecture. The central server accesses the global view of traffic, and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead. Then, a clustering-based priority mechanism is proposed, which sets priorities for clusters based on real-time traffic information to avoid secondary congestion.

Fig 3. CHRT system architecture

  • We propose an online vehicle recruitment mechanism based on deterministic trajectory. This mechanism periodically analyzes the spatio-temporal availability of trajectory to address the problem of matching vehicles with their assigned tasks while optimizing recruitment costs. Specifically, we study the interaction between trajectory and task, and establish the assessment to analyze the spatio-temporal availability of trajectory in terms of coverage and redundancy. Finally, we design and implement an efficient algorithm (VRP-PC) based on the online vehicle recruitment mechanism.

Fig 4. Vehicle trajectory and area division

  • We are also building some visualization platforms for perception data and simulation results to provide more intuitive display.

Fig 5. NGSIM road data set visualization result

♦ Publications

  • Papers

[1] Liu L, Lu Z, Wang L, et al. Evenness-Aware Data Collection for Edge-Assisted Mobile Crowdsensing in Internet of Vehicles[J]. IEEE Internet of Things Journal, 2021.

[2] Liu L, Wen X, Wang L, et al. Incentive-Aware Recruitment of Intelligent Vehicles for Edge-Assisted Mobile Crowdsensing[J]. IEEE Transactions on Vehicular Technology, 2020.

[3] Liu L, Lu Z, Wang L, et al. Large-volume data dissemination for cellular-assisted automated driving with edge intelligence[J]. Journal of Network and Computer Applications, 2020, 155: 102535.

[4] Liu L, Chen X, Lu Z, et al. Mobile-edge computing framework with data compression for wireless network in energy internet[J]. Tsinghua Science and Technology, 2019, 24(3): 271-280.

[5] Liu L, Wang L, Wen X. Joint Network Selection and Traffic Allocation in Multi-Access Edge Computing-Based Vehicular Crowdsensing[C]//IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2020: 1184-1189.

[6] Zhang Y, Liu L, Lu Z, et al. Robust autonomous intersection control approach for connected autonomous vehicles[J]. IEEE Access, 2020, 8: 124486-124502.

[7] Lu G, Liu L, Wang L, et al. Efficient Online Vehicle Recruitment Based on Deterministic Trajectory in Mobile Crowd Sensing[C]//2021 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2021: 1-7.

[8] Wang Z, Liu L, Wang L, et al. Privacy-Protecting Reputation Management Scheme in IoV-based Mobile Crowdsensing[C]//2020 16th International Conference on Mobility, Sensing and Networking (MSN). IEEE, 2020: 337-343.

[9] Huo J, Wen X, Liu L, et al. CHRT: Clustering-based hybrid re-routing system for traffic congestion avoidance[J]. China Communications, 2021, 18(7): 86-102.

  • Standardization

[1] Proposal for adding definition of the functional entities in Y.NDA-arch, 2019.

[2] Proposal on initiating a new work item for use cases and hierarchical decision architecture for network-assisted automatic driving of cellular networks, 2018.

  • Patents

[1] L. Wang, C. Liu, L. Liu, G. Wang, B. Fu, “Vehicle data collection method, device, electronic equipment and readable storage medium”, CN202011280243.7

[2] Z. Lu, J. H, L. Liu, X. Wen, L. Wang, “Method, device, electronic equipment and readable storage medium for distributing vehicle data”, CN201910545052.X

[3] L. Wang, G. Lu, L. Liu, G. Wang, B. Fu, “Trajectory-based vehicle recruitment method, system, equipment and scale storage medium”, CN202011270090.8

[4] L. Wang, X. Wu, L. Liu, Z. Lu, X. Wen, “Wireless access method, electronic equipment and readable storage medium”, CN201910538840.6