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.

Towards Low Cost Prototyping of Mobile Opportunistic Disconnection Tolerant Networks and Systems

Abstract: Fast emerging mobile edge computing, mobile clouds, Internet of Things, and cyber physical systems require many novel realistic real-time multi-layer algorithms for a wide range of domains, such as intelligent content provision and processing, smart transport, smart manufacturing systems, and mobile end-user applications. This paper proposes a low-cost open source platform, MODiToNeS, which uses commodity hardware to support prototyping and testing of fully distributed multi-layer complex algorithms over real-world (or pseudoreal) traces. MODiToNeS platform is generic and comprises multiple interfaces that allow real-time topology and mobility control, deployment and analysis of different self-organized and self-adaptive routing algorithms, real-time content processing, and real-time environment sensing with predictive analytics. Our platform also allows rich interactivity with the user. We show deployment and analysis of two vastly different complex networking systems: a fault and disconnection-aware smart manufacturing sensor network and cognitive privacy for personal clouds. We show that our platform design can integrate both contexts transparently and organically and allows a wide range of analysis.

Energy-Efficient MAC for Cellular IoT: State-of-the-Art, Challenges, and Standardization.

Abstract: In the modern world, the connectivity-as-we-go model is gaining popularity. Internet-of-Things (IoT) envisions a future in which human beings communicate with each other and with devices that have identities and virtual personalities, as well as sensing, processing, and networking capabilities, which will allow the developing of smart environments that operate with little or no human intervention. In such IoT environments, that will have battery-operated sensors and devices, energy efficiency becomes a fundamental concern. Thus, energy-efficient (EE) connectivity is gaining significant attention from the industrial and academic communities. This work aims to provide a comprehensive state-of-the-art survey on the energy efficiency of medium access control (MAC) protocols for cellular IoT. we provide a detailed discussion on the sources of energy dissipation at the MAC layer and then propose solutions. In addition to reviewing the proposed MAC designs, we also provide insights and suggestions that can guide practitioners and researchers in designing EE MAC protocols that extend the battery life of IoT devices. Finally, we identify a range of challenging open problems that should be solved for providing EE MAC services for IoT devices, along with corresponding opportunities and future research ideas to address these challenges.

CRAM: Cell Re-labeling At Merge-points for ATM multicast

Abstract: Many distributed multimedia applications involve data delivery from a source to multiple destinations, the participating nodes forming a multicast group. In the naive solution, separate connections can be established from each source to other group members. However, a tree can be established for each source with the participants as the leaf nodes or just have one tree spanning all the participants. In this paper, we introduce a data forwarding model to support such shared multicast trees over the ATM networks called CRAM (Cell Re-labeling At Merge-points for ATM multicast). Our work allows VC merging in the MPLS architecture and supports recently proposed wide area multicast protocols (like CBT and PIM) in ATM networks.

I(FIB)F: Iterated Bloom Filters for Routing in Named Data Networks

Abstract: Named Data Networks provide a clean-slate redesign of the Future Internet for efficient content distribution. Because Internet of Things are expected to compose a significant part of Future Internet, most content will be managed by constrained devices. Such devices are often equipped with limited CPU, memory, bandwidth, and energy supply. However, the current Named Data Networks design neglects the specific requirements of Internet of Things scenarios and many data structures need to be further optimised. The purpose of this research is to provide an efficient strategy to route in Named Data Networks by constructing a Forwarding Information Base using Iterated Bloom Filters defined as I(FIB)F. We propose the use of content names based on iterative hashes. This strategy leads to reduce the overhead of packets. Moreover, the memory and the complexity required in the forwarding strategy are lower than in current solutions. We compare our proposal with solutions based on hierarchical names and Standard Bloom Filters. We show how to further optimise I(FIB)F by exploiting the structure information contained in hierarchical content names. Finally, two strategies may be followed to reduce: (i) the overall memory for routing or (ii) the probability of false positives.

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.