Alfredo Grieco

Internet of Things in the 5G Era: Enablers, Architecture, and Business Models

The IoT paradigm holds the promise to revolutionize the way we live and work by means of a wealth of new services, based on seamless interactions between a large amount of heterogeneous devices. After decades of conceptual inception of the IoT, in recent years a large variety of communication technologies has gradually emerged, reflecting a large diversity of application domains and of communication requirements. Such heterogeneity and fragmentation of the connectivity landscape is currently hampering the full realization of the IoT vision, by posing several complex integration challenges. In this context, the advent of 5G cellular systems, with the availability of a connectivity technology, which is at once truly ubiquitous, reliable, scalable, and cost-efficient, is considered as a potentially key driver for the yet-to emerge global IoT. In the present paper, we analyze in detail the potential of 5G technologies for the IoT, by considering both the technological and standardization aspects. We review the present-day IoT connectivity landscape, as well as the main 5G enablers for the IoT. Last but not least, we illustrate the massive business shifts that a tight link between IoT and 5G may cause in the operator and vendors ecosystem.

ICN software tools: survey and cross-comparison

Research interest on Information Centric Networking (ICN) has been sharply growing. Although new architectures, algorithms, and analytical models have been proposed, their evaluation remains often isolated and not rigorously verified by the research community. This paper initially portrays the composition of open source software tools available for ICN, certifying the predominance of Content Centric Networking (CCN)/Named Data Networking (NDN) simulators. Then, inspired by similar works related to the P2P field, it surveys related research papers to qualify the ICN literature produced so far, finding that a large fraction of contributions either uses custom, proprietary, and unavailable software, or even plainly fails to mention any information in this regard. By adopting a rigorous methodology, in the second part of the paper four simulators, namely ndnSIM, ccnSim, CCNPL-Sim, and Icarus, are cross-compared under several key aspects. Our findings confirm both their accuracy with respect to reference theoretical models in simple settings, and their consistency in more complex scenario. Additionally, our analysis can both assist researchers in the choice of the tool that best fits their needs, and provide guidelines to avoid common pitfalls in the ICN performance evaluation.

End-to-end congestion control and bandwidth estimation in high speed ATM networks

We propose an end-to-end congestion control and bandwidth estimation algorithm for ABR traffic in high-speed ATM networks. The congestion control algorithm is derived using the Smith predictor and uses an estimation of the bottleneck queue level as feedback. The estimation allows the algorithm to be executed at the entry node of a high-speed ATM network without requiring any explicit feedback from the network switches.

End to end congestion control and bandwidth measurement in high speed ATM networks

We propose an end to end congestion control algorithm for ABR traffic in high-speed ATM networks. The algorithm is designed using a control theoretic approach and the Smith (1959) principle is proposed as a tool to design the feedback control law. The algorithm is executed at the entry node of ATM networks and switches do not execute either any computation or stamp any explicit feedback on RM cells. In particular, we propose that the traffic source at the entry node computes the input rate by estimating the bottleneck queue level. This estimate is done by stamping time into RM cells to measure round trip times, and by measuring available bandwidth. The available bandwidth is measured by sending probe RM cells and by measuring the rate of returning RM cells.

Enhanced graph rewriting systems for complex software domains (SoSyM abstract)

Methodologies for correct by construction reconfigurations can efficiently solve consistency issues in dynamic software architecture. Graph-based models are appropriate for designing such architectures and methods. At the same time, they may be unfit to characterize a system from a non-functional perspective. This stems from efficiency and applicability limitations in handling time-varying characteristics and their related dependencies. In order to lift these restrictions, an extension to graph rewriting systems is proposed herein. The suitability of this approach, as well as the restraints of currently available ones, is illustrated, analyzed, and experimentally evaluated with reference to a concrete example. This investigation demonstrates that the conceived solution can (i) express any kind of algebraic dependencies between evolving requirements and properties; (ii) significantly ameliorate the efficiency and scalability of system modifications with respect to classic methodologies; (iii) provide an efficient access to attribute values; (iv) be fruitfully exploited in software management systems; and (v) guarantee theoretical properties of a grammar, like its termination.

Enhanced Graph Rewriting Systems for Performance Aware Dynamic Software Architectures

Methodologies for correct by construction reconfigurations can efficiently solve consistency issues in dynamic software architecture. Graph-based models are appropriate for designing such architectures and methods. At the same time, they may be unfit to characterize a system from a non-functional perspective. This stems from efficiency and applicability limitations in handling time-varying characteristics and their related dependencies. In order to lift these restrictions, an extension to graph rewriting systems is proposed herein. The suitability of this approach, as well as the restraints of currently available ones, is illustrated, analyzed, and experimentally evaluated with reference to a concrete example. This investigation demonstrates that the conceived solution can (i) express any kind of algebraic dependencies between evolving requirements and properties; (ii) significantly ameliorate the efficiency and scalability of system modifications with respect to classic methodologies; (iii) provide an efficient access to attribute values; (iv) be fruitfully exploited in software management systems; and (v) guarantee theoretical properties of a grammar, like its termination.

Semantic-enabled Resource Discovery, Composition and Substitution in 802.11 Pervasive Environments

We present a general framework for resource discovery, composition and substitution in wireless networks, exploiting knowledge representation techniques: discovery information are reused at network layer to build a fully unified discovery and routing framework. Performances of the proposed framework have been evaluated using ns-2 simulator with reference to a disaster recovery scenario.

A new object-oriented segmentation algorithm based on CNNs - part II: performance evaluation

By using the CNN paradigm, this paper illustrates a new object-oriented segmentation algorithm that takes into account the hardware characteristics imposed by the CNNUM. In particular, this paper describes every block of the algorithm except the edge extraction one, which is described in the companion paper (Grasi et al., 2005). Additionally, by considering different video sequences, this paper illustrates some performance evaluations, showing that the approach (based on a rigorous model of the image contours) provides more accurate segmented objects than the ones obtained by other CNN-based techniques.

A new object-oriented segmentation algorithm based on CNNs - part I: edge extraction

By using the CNN paradigm, this paper and the companion one (Grasi et al., 2005) present a new object-oriented segmentation algorithm, which takes into account the hardware characteristics imposed by the CNNUM. In particular, by exploiting a rigorous model of the image contours, this paper focuses on CNN algorithms for edge extraction. Simulation results show that the approach provides more accurate edge extractions than the ones obtained by other CNN-based techniques.