Effect of security and trustworthiness for a fuzzy cluster management system in VANETs

Abstract Recently, smart cities and Internet of Things (IoT) applications, such as Vehicular Ad-hoc Networks (VANETs) and Opportunistic networks have been deeply investigated. However, these kinds of wireless networks have security problems. Also, the vehicles can be not trustworthy, which brings different communication problems. In this work, we present a Fuzzy Cluster Management System (FCMS) for VANETs. We present and compare two fuzzy-based system models: FCMS1 and FCMS2 for clustering of vehicles in VANETs. For FCMS1, we use three input parameters: Vehicle Relative Speed with Vehicle Cluster (VRSVC), Vehicle Degree of Centrality (VDC) and Vehicle Security (VS). The output parameter is Vehicle Remain or Leave Cluster (VRLC). For FCMS2, we consider four input parameters by adding Vehicle Trustworthiness (VT) as a new parameter. We evaluate both systems by simulations. The simulation results show that vehicles with the same VRSVC and with high VDC, VS and VT values have higher possibility to remain in the cluster. By comparing FCMS1 and FCMS2, we found that the FCMS2 can manage better the vehicles in the cluster than FCMS1.

[1]  Fatos Xhafa,et al.  A comparison study for two fuzzy-based systems: improving reliability and security of JXTA-overlay P2P platform , 2016, Soft Comput..

[2]  Leonard Barolli,et al.  FACS-MP: A fuzzy admission control system with many priorities for wireless cellular networks and its performance evaluation , 2015, J. High Speed Networks.

[3]  L. Zadeh,et al.  Fuzzy Logic for the Management of Uncertainty , 1992 .

[4]  F. Martin McNeill,et al.  Fuzzy Logic: A Practical Approach , 1994 .

[5]  Zehang Sun,et al.  On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Hong Wen,et al.  A Novel Framework for Message Authentication in Vehicular Communication Networks , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[7]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[8]  Hannes Hartenstein,et al.  A tutorial survey on vehicular ad hoc networks , 2008, IEEE Communications Magazine.

[9]  Leonard Barolli,et al.  Performance evaluation of a QoS-aware fuzzy-based CAC for LAN access , 2016, Int. J. Space Based Situated Comput..

[10]  Montserrat Ros,et al.  A Comparative Survey of VANET Clustering Techniques , 2017, IEEE Communications Surveys & Tutorials.

[11]  Abraham Kandel,et al.  Fuzzy Expert Systems , 1991 .

[12]  Ahmad Khademzadeh,et al.  VWCA: An efficient clustering algorithm in vehicular ad hoc networks , 2011, J. Netw. Comput. Appl..

[13]  Giovanni Pau,et al.  VANET via Named Data Networking , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[14]  B. Noble,et al.  On certain integrals of Lipschitz-Hankel type involving products of bessel functions , 1955, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[15]  Leonard Barolli,et al.  A comparison study of two fuzzy-based systems for selection of actor node in wireless sensor actor networks , 2015, J. Ambient Intell. Humaniz. Comput..

[16]  Leonard Barolli,et al.  A Secure-Aware Call Admission Control Scheme for Wireless Cellular Networks Using Fuzzy Logic and Its Performance Evaluation , 2015, J. Mobile Multimedia.

[17]  Wujun Zhang,et al.  Cooperative downloading with privacy preservation and access control for value-added services in VANETs , 2016, Int. J. Grid Util. Comput..

[18]  Leonard Barolli,et al.  Improving reliability of JXTA-Orverlay platform: evaluation for e-learning and trustworthiness , 2015 .

[19]  Hung-Yu Chien,et al.  ABAKA: An Anonymous Batch Authenticated and Key Agreement Scheme for Value-Added Services in Vehicular Ad Hoc Networks , 2011, IEEE Transactions on Vehicular Technology.

[20]  Sherali Zeadally,et al.  Performance comparison of media access control protocols for vehicular ad hoc networks , 2012, IET Networks.

[21]  Toshinori Munakata,et al.  Fuzzy systems: an overview , 1994, CACM.

[22]  Xiang Cheng,et al.  Wideband Channel Modeling and Intercarrier Interference Cancellation for Vehicle-to-Vehicle Communication Systems , 2013, IEEE Journal on Selected Areas in Communications.

[23]  Paolo Santi.  Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks , 2012 .

[24]  Leonard Barolli,et al.  A Message Suppression Controller for Vehicular Delay Tolerant Networking , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications.

[25]  Feng Xia,et al.  A Cooperative Watchdog System to Detect Misbehavior Nodes in Vehicular Delay-Tolerant Networks , 2015, IEEE Transactions on Industrial Electronics.

[26]  Tomoyuki Ishida,et al.  Delay tolerant networks-based vehicle-to-vehicle wireless networks for road surveillance systems in local areas , 2016, Int. J. Space Based Situated Comput..

[27]  Nobuo Funabiki,et al.  Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system , 2017, Int. J. Space Based Situated Comput..

[28]  H. Zimmermann,et al.  Fuzzy Set Theory and Its Applications , 1993 .

[29]  Panwit Tuwanut,et al.  A survey on internet of things architecture, protocols, possible applications, security, privacy, real-world implementation and future trends , 2015, 2015 IEEE 16th International Conference on Communication Technology (ICCT).

[30]  Leonard Barolli,et al.  Two Fuzzy-Based Systems for Selection of Actor Nodes inWireless Sensor and Actor Networks: A Comparison Study Considering Security Parameter Effect , 2016, Mob. Networks Appl..

[31]  ALI ÇALHAN,et al.  A fuzzy logic based clustering strategy for improving vehicular ad-hoc network performance , 2015 .

[32]  Fatos Xhafa,et al.  Improvement of JXTA-Overlay P2P Platform: Evaluation for Medical Application and Reliability , 2015, Int. J. Distributed Syst. Technol..

[33]  Mónica Aguilar-Igartua,et al.  Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[34]  Leonard Barolli,et al.  A Fuzzy-Based CAC Scheme for Cellular Networks Considering Security , 2014, 2014 17th International Conference on Network-Based Information Systems.

[35]  Antonio Iera,et al.  LTE for vehicular networking: a survey , 2013, IEEE Communications Magazine.

[36]  Eylem Ekici,et al.  Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions , 2011, IEEE Communications Surveys & Tutorials.

[37]  Ebrahim H. Mamdani,et al.  A linguistic self-organizing process controller , 1979, Autom..

[38]  Mohan M. Trivedi,et al.  Learning to Detect Vehicles by Clustering Appearance Patterns , 2015, IEEE Transactions on Intelligent Transportation Systems.

[39]  Fatos Xhafa,et al.  Trustworthiness in P2P: performance behaviour of two fuzzy-based systems for JXTA-overlay platform , 2014, Soft Comput..

[40]  Vinton G. Cerf,et al.  Delay-tolerant networking: an approach to interplanetary Internet , 2003, IEEE Commun. Mag..

[41]  Leonard Barolli,et al.  An Enhanced Message Suppression Controller for Vehicular-Delay Tolerant Networks , 2016, 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA).