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.

Policy, Legal and Regulatory Implications of a Europe-Only Cloud

Abstract: There has been on-going discussion regarding the alignment of cloud computing services to a range of European policy objectives. This paper provides an outline of key legal and regulatory aspects arising from recent calls for establishing a Europe-only cloud. After covering the background to such calls, it outlines the policy objectives that may underlie the Europe-only cloud proposals. It then considers some of the legal and regulatory implications of a "Europe-only cloud" or national cloud by analysing what may be meant by "cloud", "Europe", and "only" in this connection.

On and Off-Blockchain Enforcement Of SmartContracts

Abstract: Emerging blockchain technology is a promising platform for implementing smart contracts. But there is a large class of applications, where blockchain is inadequate due to performance, scalability, and consistency requirements, and also due to language expressiveness and cost issues that are hard to solve. In this paper we explain that in some situations a centralised approach that does not rely on blockchain is a better alternative due to its simplicity, scalability, and performance. We suggest that in applications where decentralisation and transparency are essential, developers can advantageously combine the two approaches into hybrid solutions where some operations are enforced by enforcers deployed on–blockchains and the rest by enforcers deployed on trusted third parties.

System Capacity Analysis for Ultra-Dense Multi-Tier Future Cellular Networks

Abstract: Ultra-dense multi-tier cellular networks have recently drawn the attention of researchers due to their potential efficiency in dealing with high-data rate demands in upcoming 5G cellular networks. These networks consist of multi-tier base stations including micro base stations with very high-system capacity and short inter-site distances, overlooked by central macro base stations. In this way, network densification is achieved in the same area as that of traditional mobile networks, which offers much higher system capacity and bandwidth reuse. This paper utilizes a well-known analytical tool, stochastic geometry for modeling and analyzing interference in ultra-dense multi-tier cellular networks. Primarily, we have studied different factors affecting the system capacity including the network densification, cell load, and multi-tier interference. The role of the ergodic channel capacity is also discussed. Moreover, the effects of channel interference, system bandwidth, and the network densification on the spectral and energy efficiencies of the network are observed. Finally, the results show that the network densification and the cell load have a profound impact on system performance as well as spectral and energy efficiencies of the networks.

Do Bots impact Twitter activity

Abstract: The WWW has seen massive growth in population of automated programs (bots) for a variety of exploits on online social networks (OSNs). In this paper we extend on our previous work to study the affects of bots on Twitter. By setting up a bot account on Twitter and conducting analysis on a click logs dataset from our web server, we show that despite bots being in smaller numbers, they exercise a profound impact on content popularity and activity on Twitter.

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.