Situating network infrastructure with people, practices, and beyond: A community building workshop

Our world is now connected and even entangled in unprecedented ways through networked technologies. Yet pockets of unequal connectivity persist, and technical infrastructures for connectivity remain difficult to design and build even for experts. In this workshop we aim to bring together a global community of multi- and inter-disciplinary researchers and implementers working on infrastructure development and connectivity to explore the existing design challenges and opportunities for bringing technical dimensions of networked infrastructures in conversation with human-computer interaction (HCI) and the social science of infrastructure. We will share, assess and define research problems and resources for rethinking networked infrastructures from human-, community-, and society-centered perspectives, understanding them to be embedded with human values and biases. We particularly intend our collaborative work to support real-world connectivity initiatives, which have grown in critical importance over the pandemic years—especially projects in support of Global South communities. Concrete deliverables from the workshop will include: (1) an initial shared bibliography to help formalize the state of knowledge in our area, (2) an agenda of shared goals, challenges, and intentions in our field, (3) a compilation of resources to support future work, and (4) social and organizing infrastructures for continued communication and academic collaboration

Characterization and performance analysis of a cognitive routing scheme for a metropolitan-area sensor network

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 47-48).This MEng thesis is an exploration of the notion of cognitive methods for routing in a network, and the resulting potential for improvements in network performance. In cognitive routing, individual network nodes gain information about the state of the network in a distributed fashion, by measuring observable data such as packet arrival counts and timing. The nodes then use inference and estimation methods on the network traffic to modify the parameters of their routing protocols and/or routing tables, in order to improve some performance metric such as packet delay or network throughput. In this project we provide an example of the performance improvements possible through cognitive routing, by demonstrating a simple but nontrivial use of network measurement and inference to minimize the maximum average packet delay, and increase the max load that the network can handle. With more information-rich metrics that are available to be passively gathered by a routing protocol, such as source-destination IDs, the sizes of packets passing through a node, and packet loss rates, cognitive routing protocols may be able to predict congestion or link failures, potentially leading to much greater efficiency gains than are described in this project.by Esther Jang.M. Eng