Cooperative approaches for dymanic wireless charging of Electric Vehicles in a smart city

In this paper, a method of electric vehicles charging with the use of large truck/bus vehicles moving along national highways and provincial roads is proposed and described. The method relies on charging vehicles from trucks while moving either with plug in electric connection or by electromagnetic induction via loosely coupled coils. Open research challenges and several avenues or opportunities for future research on Electric Vehicles Charging are outlined. The proposed method overcomes the disadvantages of the so far known techniques. The advantages of this method compared to the so far proposed methods are a) economical, easy and safe procedure, b) increase of the energy transfer efficiency factor, c) minimization of the delay in vehicle movement during the charging procedure and d) reduction of the environmental contamination with CO2 or electromagnetic radiation. © 2014 IEEE

New metrics for characterizing the significance of nodes in wireless networks via path-based neighborhood analysis

This paper considers the problem of finding the most central nodes in neighborhoods of a given network with directed or undirected links taking into account only local information.An algorithm that calculates ranking, taking into account the nhop neighborhood of each node is proposed. The method is compared to popular existing schemes for ranking, using Spearman's rank correlation coefficient and other metrics. An extension to a faster algorithm which reduces the size of the examined network is described as well. © 2009 IEEE

OCSVM model combined with K-means recursive clustering for intrusion detection in SCADA systems

© 2014 ICST.Intrusion detection in Supervisory Control and Data Acquisition (SCADA) systems is of major importance nowadays. Most of the systems are designed without cyber security in mind, since interconnection with other systems through unsafe channels, is becoming the rule during last years. The de-isolation of SCADA systems make them vulnerable to attacks, disrupting its correct functioning and tampering with its normal operation. In this paper we present a intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition) system, based on the combination of One-Class Support Vector Machine (OCSVM) with RBF kernel and recursive k-means clustering. The combination of OCSVM with recursive k-means clustering leads the proposed intrusion detection module to distinguish real alarms from possible attacks regardless of the values of parameters σ and ν, making it ideal for real-time intrusion detection mechanisms for SCADA systems. The OCSVM module developed is trained by network traces off line and detect anomalies in the system real time. The module is part of an IDS (Intrusion Detection System) system developed under CockpitCI project

Intrusion detection in SCADA systems using machine learning techniques

© 2014 The Science and Information (SAI) Organization.In this paper we present a intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition) system. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM (One-Class Support Vector Machine) is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automate SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detect anomalies in the system real time. The module is part of an IDS (Intrusion Detection System) system developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF (Intrusion Detection Message Exchange Format) messages that carry information about the source of the incident, the time and a classification of the alarm

Intrusion detection in SCADA systems using machine learning techniques

© 2014 The Science and Information (SAI) Organization.In this paper we present a intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition) system. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM (One-Class Support Vector Machine) is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automate SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detect anomalies in the system real time. The module is part of an IDS (Intrusion Detection System) system developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF (Intrusion Detection Message Exchange Format) messages that carry information about the source of the incident, the time and a classification of the alarm

Clustering in Urban environments: Virtual forces applied to vehicles

Clustering of Vanets is a technique for grouping nodes in geographical vicinity together, making the network more robust and scalable. Clustering of vehicles that is based on virtual forces has been recently introduced for highways. We propose a new algorithm, called Virtual Forces Vehicular Clustering (VFVC), to create stable clusters in Urban environments where mobility patterns of vehicles is more spatial. The algorithm uses combined metrics produced by vehicle's position, geometry, relative velocity and vehicle's lane in order to assign virtual forces among them and create clusters. The performance of the proposed algorithm is examined against two baseline algorithms and Spring clustering (Sp - Cl). Results obtained show that VFVC performs better with a significant increase in cluster stability. © 2013 IEEE

Enhanced spring clustering in VANETs with obstruction considerations

Vehicular networks have a diverse range of applications that varies from safety applications to comfort applications. Clustering in VANETs is of crucial importance for addressing the scalability problems of VANETs. The performance of communication protocols is greatly influenced by the existence of vehicles in the neighborhood; vehicles acting as obstacles change the behavior of protocols when different density, speed and car sizes scenarios are investigated since reliable communication range among vehicles varies. The Enhanced Spring Clustering is a new distributed clustering protocol, which forms stable clusters based on vehicle dimensions. An investigation of the performance of the Enhanced Spring Clustering in realistic environments is presented confirming its superiority over the examined, competing clustering protocol. © 2013 IEEE

OCSVM model combined with K-means recursive clustering for intrusion detection in SCADA systems

© 2014 ICST.Intrusion detection in Supervisory Control and Data Acquisition (SCADA) systems is of major importance nowadays. Most of the systems are designed without cyber security in mind, since interconnection with other systems through unsafe channels, is becoming the rule during last years. The de-isolation of SCADA systems make them vulnerable to attacks, disrupting its correct functioning and tampering with its normal operation. In this paper we present a intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition) system, based on the combination of One-Class Support Vector Machine (OCSVM) with RBF kernel and recursive k-means clustering. The combination of OCSVM with recursive k-means clustering leads the proposed intrusion detection module to distinguish real alarms from possible attacks regardless of the values of parameters σ and ν, making it ideal for real-time intrusion detection mechanisms for SCADA systems. The OCSVM module developed is trained by network traces off line and detect anomalies in the system real time. The module is part of an IDS (Intrusion Detection System) system developed under CockpitCI project

Distributed clustering in vehicular networks

Clustering in vanets is of crucial importance in order to cope with the dynamic features of the vehicular topologies. Algorithms that give good results in Manets fail to create stable clusters since vehicular nodes are characterized by their high mobility and the different mobility patterns that even nodes in proximity may follow. In this paper, we propose a distributed clustering algorithm which forms stable clusters based on force directed algorithms. The simulation results show that our Spring-Clustering (Sp-Cl) scheme has stable performance in randomly generated scenarios on a highway. It forms lesser clusters than Lowest-ID and it is better in terms of Cluster stability compared to Lowest-ID and LPG algorithms in the same scenarios. © 2012 IEEE