Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G

By caching content at network edges close to the users, the content-centric networking (CCN) has been considered to enforce efficient content retrieval and distribution in the fifth generation (5G) networks. Due to the volume, velocity, and variety of data generated by various 5G users, an urgent and strategic issue is how to elevate the cognitive ability of the CCN to realize context-awareness, timely response, and traffic offloading for 5G applications. In this article, we envision that the fundamental work of designing a cognitive CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to associatively learn and control the states of edge devices (such as phones, vehicles, and base stations) and in-network resources (computing, networking, and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework for C-CCN in 5G, which can aggregate the idle computing resources of the neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive learning tasks. By leveraging artificial intelligence (AI) to jointly processing sensed environmental data, dealing with the massive content statistics, and enforcing the mobility control at network edges, the FEL makes it possible for mobile users to cognitively share their data over the C-CCN in 5G. To validate the feasibility of proposed framework, we design two FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network acceleration, 2) enhanced mobility management. Simultaneously, we present the simulations to show the FEL's efficiency on serving for the mobile users' delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201

Tactical Communications for Cooperative SAR Robot Missions

This chapter describes how the ICARUS communications (COM) team defined, developed and implemented an integrated wireless communication system to ensure an interoperable and dependable networking capability for both human and robotic search and rescue field teams and crisis managers. It starts explaining the analysis of the requirements and the context of the project, the existing solutions and the design of the ICARUS communication system to fulfil all the project needs. Next, it addresses the implementation process of the required networking capabilities, and finally, it explains how the ICARUS communication system and associated tools have been integrated in the overall mission systems and have been validated to provide reliable communications for real‐time information sharing during search and rescue operations in hostile conditions

Chapter Tactical Communications for Cooperative SAR Robot Missions

This chapter describes how the ICARUS communications (COM) team defined, developed and implemented an integrated wireless communication system to ensure an interoperable and dependable networking capability for both human and robotic search and rescue field teams and crisis managers. It starts explaining the analysis of the requirements and the context of the project, the existing solutions and the design of the ICARUS communication system to fulfil all the project needs. Next, it addresses the implementation process of the required networking capabilities, and finally, it explains how the ICARUS communication system and associated tools have been integrated in the overall mission systems and have been validated to provide reliable communications for real‐time information sharing during search and rescue operations in hostile conditions

Reduction of the Delays within an Intrusion Detection System (IDS) based on Software Defined Networking (SDN)

Software Defined Networking (SDN) is a very useful tool not only to manage networks but also to increase network security, in particular by implementing Intrusion Detection Systems (IDS) directly into the SDN architecture. The implementation of IDS within the SDN paradigm can simplify the implementation, speed up incident responses, and, in general, allow to promptly react to cyber attacks through proper countermeasures. Nevertheless, embedding IDS within SDN also introduces delays that cannot be tolerated in specific network environments, like industrial control systems. This paper focuses on the implementation of an IDS based on Machine Learning (ML) algorithms into an SDN architecture and proposes a very practical approach to reduce the delay by using the sequential implementation of prototypes of increasing software and hardware complexity so allowing quick tests to highlight the main problems, solve them and pass to the next operative step. A fully validated performance evaluation is then shown by exploiting all the presented solutions and by using further improved hardware features. The overall performance is very good and compliant with most, even if not yet all, industrial control systems constraints. Results show how the proposed solutions provide a significant improvement of the latency so opening the door to a real implementation in the field

Report of the 2014 NSF Cybersecurity Summit for Large Facilities and Cyberinfrastructure

This event was supported in part by the National Science Foundation under Grant Number 1234408. Any opinions, findings, and conclusions or recommendations expressed at the event or in this report are those of the authors and do not necessarily reflect the views of the National Science Foundation