Destination Prediction-Based Scheduling Algorithms for Message Delivery in IoVs

Destination related applications are playing an important role in Internet of Vehicles(IoVs), which can provide people with convenience or business profit, such as traffic jam warning or parking guide. However, in reality, people hesitate to share their destination information to other people due to operation inconvenience, which requires service providers to predict vehicles’ destinations in advance in order to deliver them destination related messages. Some papers have considered the delivery scheduling problem of destination related information. But, they neglect the destination prediction problem with the assumption that vehicle’s destinations are known in advance. In this paper, we target the delivery scheduling problem of destination related information in the case of destinations unknown to others in IoVs. First, a realtime destination prediction framework with machine learning models is proposed, with which a vehicle’s destination can be predicted while traveling. Then, we propose a delivery profit maximization algorithm for service providers to select a proper location to deliver destination related information to each vehicle. Simulations with real vehicle trajectories show that our scheduling algorithm performs well and can successfully select a proper location to disseminate destination related information.

[1]  Xiaohua Jia,et al.  Design of Analytical Model and Algorithm for Optimal Roadside AP Placement in VANETs , 2016, IEEE Transactions on Vehicular Technology.

[2]  Xing Xie,et al.  Solving the data sparsity problem in destination prediction , 2015, The VLDB Journal.

[3]  Xiang Li,et al.  T-DesP: Destination Prediction Based on Big Trajectory Data , 2016, IEEE Transactions on Intelligent Transportation Systems.

[4]  Jiguo Yu,et al.  Achieving Personalized $k$-Anonymity-Based Content Privacy for Autonomous Vehicles in CPS , 2020, IEEE Transactions on Industrial Informatics.

[5]  Miao Pan,et al.  Secure Roadside Unit Hotspot Against Eavesdropping Based Traffic Analysis in Edge Computing Based Internet of Vehicles , 2018, IEEE Access.

[6]  Arobinda Gupta,et al.  Event Notification in VANET With Capacitated Roadside Units , 2016, IEEE Transactions on Intelligent Transportation Systems.

[7]  Xiaohua Jia,et al.  Delay-bounded minimal cost placement of roadside units in vehicular ad hoc networks , 2015, 2015 IEEE International Conference on Communications (ICC).

[8]  Zhongliang Deng,et al.  Location monitoring approach: multiple mix-zones with location privacy protection based on traffic flow over road networks , 2018, Multimedia Tools and Applications.

[9]  Leonidas J. Guibas,et al.  Urban Travel Time Prediction using a Small Number of GPS Floating Cars , 2017, SIGSPATIAL/GIS.

[10]  Jianping Pan,et al.  Joint Roadside Unit Deployment and Service Task Assignment for Internet of Vehicles (IoV) , 2019, IEEE Internet of Things Journal.

[11]  Izhak Rubin,et al.  Understanding Spurious Message Forwarding in VANET Beaconless Dissemination Protocols: An Analytical Approach , 2016, IEEE Transactions on Vehicular Technology.

[12]  Jia Yuan Yu,et al.  A Reinforcement Learning Technique for Optimizing Downlink Scheduling in an Energy-Limited Vehicular Network , 2017, IEEE Transactions on Vehicular Technology.

[13]  Tao Xiang,et al.  Secure and Efficient Data Communication Protocol for Wireless Body Area Networks , 2016, IEEE Transactions on Multi-Scale Computing Systems.

[14]  Jun Zheng,et al.  An RSU on/off scheduling mechanism for energy efficiency in sparse vehicular networks , 2015, 2015 International Conference on Wireless Communications & Signal Processing (WCSP).

[15]  Sang-Sun Lee,et al.  Long-term prediction of vehicle trajectory based on a deep neural network , 2017, 2017 International Conference on Information and Communication Technology Convergence (ICTC).

[16]  Jiguo Yu,et al.  A Differential-Private Framework for Urban Traffic Flows Estimation via Taxi Companies , 2019, IEEE Transactions on Industrial Informatics.

[17]  Philippe C. Besse,et al.  Destination Prediction by Trajectory Distribution-Based Model , 2016, IEEE Transactions on Intelligent Transportation Systems.

[18]  David R. Karger,et al.  Tackling the Poor Assumptions of Naive Bayes Text Classifiers , 2003, ICML.

[19]  Guojun Xie,et al.  Delay-Bounded and Cost-Limited RSU Deployment in Urban Vehicular Ad Hoc Networks , 2018, Sensors.

[20]  Christian S. Jensen,et al.  Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models , 2013, Proc. VLDB Endow..

[21]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[22]  Mohsen Guizani,et al.  A Prediction Method for Destination Based on the Semantic Transfer Model , 2019, IEEE Access.

[23]  Chung Choo Chung,et al.  Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[24]  Peng Li,et al.  BCDP: Budget Constrained and Delay-Bounded Placement for Hybrid Roadside Units in Vehicular Ad Hoc Networks , 2014, Sensors.

[25]  Arobinda Gupta,et al.  A publish-subscribe based framework for event notification in vehicular environments , 2013, 2013 Fifth International Conference on Communication Systems and Networks (COMSNETS).