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.

TCP Usage Guidance in the Internet of Things (IoT)

Abstract: This document provides guidance on how to implement and use the Transmission Control Protocol (TCP) in Constrained-Node Networks (CNNs), which are a characterstic of the Internet of Things (IoT). Such environments require a lightweight TCP implementation and may not make use of optional functionality. This document explains a number of known and deployed techniques to simplify a TCP stack as well as corresponding tradeoffs. The objective is to help embedded developers with decisions on which TCP features to use.

Internet Multicast Tomorrow

Abstract: This article is part of a pair, the first of which looked at the state of play in IP multicast routing (Ole: IPJ Citation?) In this article, we look at the broader problems and future activities with multicast We divide the areas into routing, addressing, transport, security, operations, and research There has been quite a bit of debate about the nature of compelling applications for multicast recently[44] It is certainly the case that we do not completely understand the “market” for multicast this is at least in part because multicast does not yet provide a complete set of functions for all the applications and services we might imagine This is a typical chicken and egg situation, though: To put an extreme version of the argument, the application writers don’t see any multicast deployed; the ISPs don’t see any multicast applications; the router vendors don’t see any multicast service demand from ISPs1 As we discussed in the part I of this article, this vicious circle has been broken by streaming applications for audio and video from the classical content providers in the entertainment and news industries However we are still seeing some teething problems However, we are also seeing broader interest and development The next section presents recent work on routing and addressing After that we look at transport Subsequently, we discuss security Then we look at operations and management Finally, we take a glance at some of the research ideas that are out there

A Charging Model for Sessions on the Internet

Abstract: A chargeable session on the Internet may consist of more than one underlying chargeable service. Typically there will be two, one at the network layer and one at the session layer. Since different applications can have different demands from the Network, a generic charging scheme has to separate the service provided by the network from the service provided by an application/service provider. In this paper we propose a pricing model which is session based and we look at the impact of this on real-time multimedia conferencing over the Internet. In this model, we are trying to allow for the optional integration of charging at the network layer with charging at the session layer, while keeping the underlying technologies still cleanly apart. This paper also highlights the fact that the main problem of pricing application on the Internet is not just a simple case of analyzing the most technically feasible pricing mechanism but also making the solution acceptable to users. We take the position that session based pricing is easier for end users to accept and understand and show why this is the case in this paper.

Social-Aware Sequential Modeling of User Interests: A Deep Learning Approach

Abstract: In this paper, we propose to leverage the emerging deep learning techniques for sequential modeling of user interests based on big social data, which takes into account influence of their social circles. First, we present a preliminary analysis for two popular big datasets from Yelp and Epinions. We show statistically sequential actions of all users and their friends, and discover both temporal autocorrelation and social influence on decision making, which motivates our design. Then, we present a novel hybrid deep learning model, Social-Aware Long Short-Term Memory (SA-LSTM), for predicting the types of item/PoIs that a user will likely buy/visit next, which features stacked LSTMs for sequential modeling and an autoencoder-based deep model for social influence modeling. Moreover, we show that SA-LSTM supports end-to-end training. We conducted extensive experiments for performance evaluation using the two real datasets from Yelp and Epinions. The experimental results show that (1) the proposed deep model significantly improves prediction accuracy compared to widely used baseline methods; (2) the proposed social influence model works effectively; and (3) going deep does help improve prediction accuracy but a not-so-deep deep structure leads to the best performance.

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.