Why Information Matters: A Foundation for Resilience

Embracing Change: The Critical Role of Information, a research project by the Internews' Center for Innovation & Learning, supported by the Rockefeller Foundation, combines Internews' longstanding effort to highlight the important role ofinformation with Rockefeller's groundbreaking work on resilience. The project focuses on three major aspects:- Building knowledge around the role of information in empowering communities to understand and adapt to different types of change: slow onset, long-term, and rapid onset / disruptive;- Identifying strategies and techniques for strengthening information ecosystems to support behavioral adaptation to disruptive change; and- Disseminating knowledge and principles to individuals, communities, the private sector, policymakers, and other partners so that they can incorporate healthy information ecosystems as a core element of their social resilience strategies

Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks

Mobile cellular network operators spend nearly a quarter of their revenue on network maintenance and management. A significant portion of that budget is spent on resolving faults diagnosed in the system that disrupt or degrade cellular services. Historically, the operations to detect, diagnose and resolve issues were carried out by human experts. However, with diversifying cell types, increased complexity and growing cell density, this methodology is becoming less viable, both technically and financially. To cope with this problem, in recent years, research on self-healing solutions has gained significant momentum. One of the most desirable features of the self-healing paradigm is automated fault diagnosis. While several fault detection and diagnosis machine learning models have been proposed recently, these schemes have one common tenancy of relying on human expert contribution for fault diagnosis and prediction in one way or another. In this paper, we propose an AI-based fault diagnosis solution that offers a key step towards a completely automated self-healing system without requiring human expert input. The proposed solution leverages Random Forests classifier, Convolutional Neural Network and neuromorphic based deep learning model which uses RSRP map images of faults generated. We compare the performance of the proposed solution against state-of-the-art solution in literature that mostly use Naive Bayes models, while considering seven different fault types. Results show that neuromorphic computing model achieves high classification accuracy as compared to the other models even with relatively small training dat

Optimizing the internal reuse of wireless network equipment

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Global Operations Program at MIT, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 46).Reusing high-value wireless network equipment can allow telecommunications providers to achieve both financial and environmental benefits. However, without standardized processes and tools to reuse equipment, the reuse process can be highly inefficient and a significant amount of reusable equipment may remain in inventory. This thesis examines the reuse of wireless network equipment at Verizon Wireless (VzW) and presents tools and processes to increase the amount and efficiency of network equipment reuse within VzW. An analytical model is presented to differentiate between items that can be reused and items that should be immediately resold or scrapped. Once a pool of reusable items is identified, incentives to promote equipment sharing across internal VzW regions are discussed. A web-based tool and process to increase the ease and speed of identifying and requesting equipment is then examined. Finally, a framework by which reuse metrics can be evaluated is presented.by Jessica Lin.M.B.A.S.M

The future of Cybersecurity in Italy: Strategic focus area

This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management

Fog computing : enabling the management and orchestration of smart city applications in 5G networks

Fog computing extends the cloud computing paradigm by placing resources close to the edges of the network to deal with the upcoming growth of connected devices. Smart city applications, such as health monitoring and predictive maintenance, will introduce a new set of stringent requirements, such as low latency, since resources can be requested on-demand simultaneously by multiple devices at different locations. It is then necessary to adapt existing network technologies to future needs and design new architectural concepts to help meet these strict requirements. This article proposes a fog computing framework enabling autonomous management and orchestration functionalities in 5G-enabled smart cities. Our approach follows the guidelines of the European Telecommunications Standards Institute (ETSI) NFV MANO architecture extending it with additional software components. The contribution of our work is its fully-integrated fog node management system alongside the foreseen application layer Peer-to-Peer (P2P) fog protocol based on the Open Shortest Path First (OSPF) routing protocol for the exchange of application service provisioning information between fog nodes. Evaluations of an anomaly detection use case based on an air monitoring application are presented. Our results show that the proposed framework achieves a substantial reduction in network bandwidth usage and in latency when compared to centralized cloud solutions