A Knowledge Fusion Approach for Context Awareness in Vehicular Networks

Vehicular ad-hoc networks (VANETs) are a challenging Internet of Things scenario. While research is proposing increasingly sophisticated hardware and software solutions for on-board context detection, probably high-level context information sharing has not been adequately addressed so far. This paper proposes a novel logic-based framework enabling a contextual data management and mining in VANETs. It grounds on a knowledge fusion algorithm based on nonstandard, nonmonotonic inference services in Description Logics, adopting standard Semantic Web languages. Ontology-referred context annotations produced by individual VANET nodes are merged with automatic reconciliation of inconsistencies. An efficient information dissemination protocol complements the proposal. The approach has been implemented in a vehicular network simulator and early experimental results proved its effectiveness and feasibility.

[1]  Yan Zhang,et al.  Modeling Prioritized Broadcasting in Multichannel Vehicular Networks , 2012, IEEE Transactions on Vehicular Technology.

[2]  Véronique Berge-Cherfaoui,et al.  Detecting Road Events Using Distributed Data Fusion: Experimental Evaluation for the Icy Roads Case , 2016, IEEE Transactions on Intelligent Transportation Systems.

[3]  Eugenio Di Sciascio,et al.  Semantic Matchmaking for Location-Aware Ubiquitous Resource Discovery , 2012 .

[4]  James Llinas,et al.  High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges , 2012, IEEE Aerospace and Electronic Systems Magazine.

[5]  Fakhri Karray,et al.  Attention Assist: A High-Level Information Fusion Framework for Situation and Threat Assessment in Vehicular Ad Hoc Networks , 2016, IEEE Transactions on Intelligent Transportation Systems.

[6]  Eugenio Di Sciascio,et al.  A Mobile Matchmaker for the Ubiquitous Semantic Web , 2014, Int. J. Semantic Web Inf. Syst..

[7]  Reinhard German,et al.  Bidirectionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis , 2011, IEEE Transactions on Mobile Computing.

[8]  Marco Schaerf,et al.  Arbitration (or How to Merge Knowledge Bases) , 1998, IEEE Trans. Knowl. Data Eng..

[9]  Eric Lefevre,et al.  Methods using belief functions to manage imperfect information concerning events on the road in VANETs , 2016 .

[10]  Gunnar Teege.  Making the Difference: A Subtraction Operation for Description Logics , 1994, KR.

[11]  Fuad A. Ghaleb,et al.  Data Verification and Misbehavior Detection in Vehicular Ad-hoc Networks , 2015 .

[12]  Min Chen,et al.  A 5G Cognitive System for Healthcare , 2017, Big Data Cogn. Comput..

[13]  Eugenio Di Sciascio,et al.  Concept abduction and contraction in semantic-based P2P environments , 2011, Web Intell. Agent Syst..

[14]  Antonio F. Gómez-Skarmeta,et al.  Providing Adapted Contextual Information in an Overlay Vehicular Network , 2010, Int. J. Digit. Multim. Broadcast..

[15]  Moumena Chaqfeh,et al.  A survey on data dissemination in vehicular ad hoc networks , 2014, Veh. Commun..

[16]  Mohsen Guizani,et al.  Deep Features Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network , 2017 .

[17]  Ahmed Helmy,et al.  IMPORTANT: a framework to systematically analyze the Impact of Mobility on Performance of Routing Protocols for Adhoc Networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[18]  Huirong Fu,et al.  An IEEE 802.11p-Based Multichannel MAC Scheme With Channel Coordination for Vehicular Ad Hoc Networks , 2012, IEEE Transactions on Intelligent Transportation Systems.

[19]  Victor C. M. Leung,et al.  S-Aframe: Agent-Based Multilayer Framework With Context-Aware Semantic Service for Vehicular Social Networks , 2015, IEEE Transactions on Emerging Topics in Computing.

[20]  C. L. Chou,et al.  NCTUns tool for wireless vehicular communication network researches , 2009, Simul. Model. Pract. Theory.

[21]  Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .

[22]  Liviu Iftode,et al.  A Comparative Study of Data Dissemination Models for VANETs , 2006, 2006 3rd Annual International Conference on Mobile and Ubiquitous Systems - Workshops.

[23]  Fakhri Karray,et al.  Recent Advances on Context-Awareness and Data/Information Fusion in ITS , 2014, International Journal of Intelligent Transportation Systems Research.

[24]  Peter F. Patel-Schneider,et al.  OWL 2 Web Ontology Language Manchester Syntax (Second Edition) , 2012 .

[25]  Weiru Liu,et al.  The basic principles of uncertain information fusion. An organised review of merging rules in different representation frameworks , 2016, Inf. Fusion.

[26]  Jong Hyuk Park,et al.  Block-VN: A Distributed Blockchain Based Vehicular Network Architecture in Smart City , 2017, J. Inf. Process. Syst..

[27]  Gustavo Cevolani.  Truth approximation, belief merging, and peer disagreement , 2014, Synthese.

[28]  Dominique Gruyer,et al.  A New Decentralized Bayesian Approach for Cooperative Vehicle Localization Based on Fusion of GPS and VANET Based Inter-Vehicle Distance Measurement , 2015, IEEE Intelligent Transportation Systems Magazine.

[29]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .

[30]  Eugenio Di Sciascio,et al.  A Swarm of Mini-MEs: Reasoning and Information Aggregation in Ubiquitous Multi-agent Contexts , 2015, ORE.

[31]  Yan Zhang,et al.  Optimal Resource Sharing in 5G-Enabled Vehicular Networks: A Matrix Game Approach , 2016, IEEE Transactions on Vehicular Technology.

[32]  Anis Laouiti,et al.  VANet security challenges and solutions: A survey , 2017, Veh. Commun..

[33]  Min Chen,et al.  Disease Prediction by Machine Learning Over Big Data From Healthcare Communities , 2017, IEEE Access.

[34]  Thierry Delot,et al.  A Data Management Perspective on Vehicular Networks , 2015, IEEE Communications Surveys & Tutorials.

[35]  Antonio F. Gómez-Skarmeta,et al.  A complex event processing approach to perceive the vehicular context , 2015, Inf. Fusion.

[36]  M. Ruta,et al.  A Mobile Knowledge-based System for On-Board Diagnostics and Car Driving Assistance , 2010 .