Jon Crowcroft

Bio: Jon Crowcroft is an academic researcher from University of Cambridge. The author has contributed to research in topics: The Internet & Multicast. The author has an hindex of 87, co-authored 672 publications receiving 38848 citations. Previous affiliations of Jon Crowcroft include Memorial University of Newfoundland & Information Technology University.

Editorial: Editorial for special issue on social-based routing in mobile and delay-tolerant networks

Abstract: For years, humans have been building a global communications network set eventually to bring all members of our species within range for potential communication and to support new types of networked applications. The totality of this network is growing, resulting in an increasingly connected world. While the network’s core – what we generally think of as the Internet – is highly connected and well suited for routing via conventional routing algorithms, the network’s expanding frontiers have infrastructure that suffers from intermittent connectivity and changes in topology that can be difficult or impossible to predict. Examples include the infrastructure-challenged environments found in developing countries, the interplanetary networks whose nodes are tasked with the exploration of our solar system but also the more conventional mobile networks used in developed countries. While research into routing in infrastructure-challenged environments is not new, researchers for many years assumed traffic and node movement to be random. In reality, however, mobile nodes are of course used by people, whose behaviours are better described by social models. This realization has opened up new possibilities for routing research, since the knowledge that behaviour patterns exist allows better routing decisions to be made. Because humanity’s global (and even extra-global) network is set to grow considerably along infrastructure-challenged boundaries over the next years, we expect such social-based routing to play an important and very real role in helping to interconnect our species and to support new types of networked applications that reside on the infrastructure-challenged boundaries of the network. This special issue of Ad Hoc Networks concerned with the use of knowledge about social behaviour for the routing of data on the most challenging boundaries of humanity’s growing network: mobile networks and delay-tolerant networks. The selection of papers explores an array of the many challenges related to this exciting topic. The first four papers are concerned with the absolute fundamentals: the social mobility data traces and the

Network coding with traffic engineering

Abstract: In network coding, a router in the network mixes information from different flows In the seminal work by Ahlswede et al [1], network coding is established as a technique to potentially increase the network capacity

TCP ACK Pull

Abstract: Delayed Acknowledgments (ACKs) allow reducing protocol overhead in many scenarios. However, in some cases, Delayed ACKs may significantly degrade network and device performance in terms of link utilization, latency, memory usage and/or energy consumption. This document defines the TCP ACK Pull (AKP) mechanism, which allows a sender to request the ACK for a data segment to be sent without additional delay by the receiver. AKP makes use of one of the reserved bits in the TCP header, which is defined in this specification as the AKP flag.

The implementation of stigmergy in network-assisted multi-agent system

Abstract: Multi-agent system (MAS) needs to mobilize multiple simple agents to complete complex tasks. However, it is difficult to coherently coordinate distributed agents by means of limited local information. In this demo, we propose a decentralized collaboration method named as "stigmergy" in network-assisted MAS, by exploiting digital pheromones (DP) as an indirect medium of communication and utilizing deep reinforcement learning (DRL) on top. Correspondingly, we implement an experimental platform, where KHEPERA IV robots form targeted specific shapes in a decentralized manner. Experimental results demonstrate the effectiveness and efficiency of the proposed method. Our platform could be conveniently extended to investigate the impact of network factors (e.g., latency, data rate, etc).

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Community detection in graphs

Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.