Pavlos Basaras

Detecting Influential Spreaders in Complex, Dynamic Networks

A hybrid of node degree and k-shell index is more effective at identifying influential spreaders and has less computational overhead than either of these traditional measures.

Exploiting vehicular communications for reducing CO2 emissions in urban environments

In the overall effort of reducing CO2 emissions especially in large cities vehicular communications can play an important role. Intelligent transportation systems, which aim to use information and communication technology are considered to be a major factor in this effort. Eco-routing is already used to suggest most environmental-friendly routes in order to reduce overall mileage and CO2 emissions based on historical data. In this paper we propose a real time system based on Dedicated short-range communication (DSRC) capabilities in order to reroute vehicles to the most ecological route, avoiding congested roads and minimizing the overall travel time and C02 emissions.

Analyzing cooperative lane change models for connected vehicles

This paper examines intelligent lane change models based on the cooperation among connected vehicles for traffic management and travel time optimization. Lane change decisions and speed controls could be coordinated and optimized to reduce the overall braking and achieve greater traffic throughput. In an effort to design a distributed cooperative lane change assistant (D-CLCA) within the European Commissions project REDUCTION*, this paper describes the requirements associated with an optimal lane change behavior, and evaluate existing lane change models based on these requirements. These models are evaluated for travel times, fuel consumption, number of lane changes and the overall braking globally for all vehicles on the considered road segment. We have developed traffic simulations using different traffic densities for both symmetric and asymmetric lane changes and different levels of cooperation among vehicles. Our empirical analysis shows that an optimal lane change model should optimize the conflicting requirements of maintaining desired speeds and reducing the number of lane changes and fuel consumption for all vehicles simultaneously. These results will be used to develop an intelligent distributed and cooperative lane change assistant.

A Robust Eco-Routing Protocol against Malicious Data in Vehicular Networks

Vehicular networks have a diverse range of applications that vary from safety, to traffic management and comfort. Vehicular communications (VC) can assist in the ecorouting of vehicles in order to reduce the overall mileage and CO2 emissions by the exchange of data among vehicle-entities. However, the trustworthiness of these data is crucial as false information can heavily affect the performance of applications. Hence, the devising of mechanisms that reassure the integrity of the exchanged data is of utmost importance. In this article we investigate how tweaked information originating from malicious nodes can affect the performance of a real time eco-routing mechanism that uses Dedicated Short Ranged Communications (DSRC), namely Erou Ve. We improve the routing decision mechanism of the original algorithm and also develop and evaluate defense mechanisms that exploit vehicular communications in order to filter out tweaked data. We prove that our proposed mechanisms can restore the performance of the Erou Ve to near its optimal operation and can be used as a basis for protecting other similar traffic management systems.

Detecting Influential Nodes in Complex Networks with Range Probabilistic Control Centrality

Dynamic complex networks illustrate how ‘agents’ interact by exchanging information, in a network that is constantly changing; an example of such networks is a vehicular ad hoc network. This article investigates the issue of influence propagation in dynamic, complex networks, and in particular, it proposes a method for identifying influential nodes in a network with probabilistic links. Based on control-theoretic concepts, we develop the range probabilistic control centrality (RPCC). For evaluation purposes, we used the susceptible, infected, recovered (SIR) model, which is simple model for epidemic spreading assuming no births or deaths, accepting that the incubation period of the infectious agent is instantaneous, and that the duration of infectivity is same as length of the disease; it also assumes a completely homogeneous population with no age, spatial, or social structure. Our experimentation shows that the proposed identification method is able to recognize very effective spreaders. The key feature of these nodes is that they are positioned at the beginning of ‘strong’ paths, upon which paths a large number of other nodes lies.

Experimental evaluation of functional splits for 5G cloud-RANs

Centralized RAN processing has been identified as one of the major enablers for 5G mobile network access. By moving the baseband units (BBU) to the Cloud, multiple instances can be instantiated on the fly, serving several Remote Radio Head (RRH) units. The goal is to satisfy the existing demand of particular geographical areas, whereas drastically reducing the overall CAPEX and OPEX costs of the mobile operators. In this work, we present an experimental study of real Cloud-RAN deployments, with respect to different functional splits. We use as a reference architecture the 3GPP LTE stack, and argue about the functional split applicability in contemporary networks. We evaluate Layer 2 functional splits, that can be used for the convergence of multiple heterogeneous wireless technologies in an all-in-one unit. By deploying our approach in a real testbed setup, we extract the backhaul network transfer requirements for the different splits and present our experimental findings, compared with the respective simulation results.

Hadoop MapReduce Performance on SSDs for Analyzing Social Networks

Abstract The advent of Solid State Drives (SSDs) stimulated a lot of research to investigate and exploit to the extent possible the potentials of the new drive. The focus of this work is on the investigation of the relative performance and benefits of SSDs versus hard disk drives (HDDs) when they are used as underlying storage for Hadoops MapReduce. In particular, we depart from all earlier relevant works in that we do not use their workloads, but examine MapReduce tasks and data suitable for performing analysis of complex networks which present different execution patterns. Despite the plethora of algorithms and implementations for complex network analysis, we carefully selected our “benchmarking methods” so that they include methods that perform both local and network-wide operations in a complex network, and also they are generic enough in the sense that they can be used as primitives for more sophisticated network processing applications. We evaluated the performance of SSDs and HDDs by executing these algorithms on real social network data and excluding the effects of network bandwidth which can severely bias the results. The obtained results confirmed in part earlier studies which showed that SSDs are beneficial to Hadoop. However, we also provided solid evidence that the processing pattern of the running application has a significant role, and thus future studies must not blindly add SSDs to Hadoop, but they should build components for assessing the type of processing pattern of the application and then direct the data to the appropriate storage medium.

Dynamically blocking contagions in complex networks by cutting vital connections

With the emergence of Online Social Networks (OSNs), as the most popular medium for advertisements, as source of knowledge and information, the emergence of malicious contents (viruses, false rumors, etc..) has become a critical issue that requires immediate attention. In this study we investigate on blocking the contagion of malicious things dynamically, by continuously fighting the diffusion near the source of misinformation under the Susceptible-Infectious-Recovered (SIR) model. We focus on protecting networked populations by removing key connections between nodes, and show via experimental results, that by following the infection the contagion can be controlled more efficiently and even being stopped in the earliest steps. We modify a well studied heuristic from the literature of graphs, and show that our proposed technique significantly outperforms what we believe the state-of-the-art competitors by successfully confronting the infection in real networks.

Hadoop MapReduce Performance on SSDs: The Case of Complex Network Analysis Tasks

This article investigates the relative performance of SSDs versus hard disk drives (HDDs) when they are used as underlying storage for Hadoop’s MapReduce. We examine MapReduce tasks and data suitable for performing analysis of complex networks which present different execution patterns. The obtained results confirmed in part earlier studies which showed that SSDs are beneficial to Hadoop; we also provide solid evidence that the processing pattern of the running application plays a significant role.

A Social-Based Approach for Message Dissemination in Vehicular Ad Hoc Networks

The spreading of messages in a vehicular network is an important task and finds many applications in Intelligent Transportation Systems (ITS). A common problem to this direction is to select an appropriate set of vehicles that on behalf of a sender will further rebroadcast the message and reduce redundant retransmission. Of particular interest is the use of social inspired metrics to identify potent vehicles which can set the right path for the spreading of messages and cover a wide range of a vehicular network. In this work we propose a novel approach for selecting vehicles based on the Probabilistic Control Centrality (pCoCe), which accounts for the number of directed and diverse paths emanating from each individual vehicle. We evaluated our approach and compared with the standard IETF, Optimized Link State Routing Protocol (OLSR). Our experimental results show that pCoCe outperforms its competitor in various network conditions by at least 10%.