Ben Leong

Bio: Ben Leong is an academic researcher from National University of Singapore. The author has contributed to research in topics: Linear network coding & Multicast. The author has an hindex of 20, co-authored 48 publications receiving 4648 citations. Previous affiliations of Ben Leong include Massachusetts Institute of Technology & Vassar College.

A Random Linear Network Coding Approach to Multicast

Abstract: We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks

Byzantine modification detection in multicast networks using randomized network coding

Abstract: Distributed randomized network coding, a robust approach to multicasting in distributed network settings, can be extended to provide Byzantine modification detection without the use of cryptographic functions is presented in this paper.

Geographic routing without planarization

Abstract: We present a new geographic routing algorithm, Greedy Distributed Spanning Tree Routing (GDSTR), that finds shorter routes and generates less maintenance traffic than previous algorithms. While geographic routing potentially scales well, it faces the problem of what to do at local dead ends where greedy forwarding fails. Existing geographic routing algorithms handle dead ends by planarizing the node connectivity graph and then using the right-hand rule to route around the resulting faces. GDSTR handles this situation differently by switching instead to routing on a spanning tree until it reaches a point where greedy forwarding can again make progress. In order to choose a direction on the tree that is most likely to make progress towards the destination, each GDSTR node maintains a summary of the area covered by the subtree below each of its tree neighbors. While GDSTR requires only one tree for correctness, it uses two for robustness and to give it an additional forwarding choice. Our simulations show that GDSTR finds shorter routes than geographic face routing algorithms: GDSTR's stretch is up to 20% less than the best existing algorithm in situations where dead ends are common. In addition, we show that GDSTR requires an order of magnitude less bandwidth to maintain its trees than CLDP, the only distributed planarization algorithm that is known to work with practical radio networks.

Path vector face routing: geographic routing with local face information

Abstract: Existing geographic routing algorithms depend on the planarization of the network connectivity graph for correctness, and the planarization process gives rise to a well-defined notion of "faces". In this paper, we demonstrate that we can improve routing performance by storing a small amount of local face information at each node. We present a protocol, path vector exchange (PVEX), that maintains local face information at each node efficiently, and a new geographic routing algorithm, greedy path vector face routing (GPVFR), that achieves better routing performance in terms of both path stretch and hop stretch than existing geographic routing algorithms by exploiting available local face information. Our simulations demonstrate that GPVFR/PVEX achieves significantly reduced path and hop stretch than greedy perimeter stateless routing (GPSR) and somewhat better performance than greedy other adaptive face routing (GOAFR+) over a wide range of network topologies. The cost of this improved performance is a small amount of additional storage, and the bandwidth required for our algorithm is comparable to GPSR and GOAFR+ in quasi-static networks.

Byzantine Modification Detection in Multicast Networks With Random Network Coding

Abstract: An information-theoretic approach for detecting Byzantine or adversarial modifications in networks employing random linear network coding is described. Each exogenous source packet is augmented with a flexible number of hash symbols that are obtained as a polynomial function of the data symbols. This approach depends only on the adversary not knowing the random coding coefficients of all other packets received by the sink nodes when designing its adversarial packets. We show how the detection probability varies with the overhead (ratio of hash to data symbols), coding field size, and the amount of information unknown to the adversary about the random code.

A Random Linear Network Coding Approach to Multicast

Abstract: We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks

Network Information Theory

Abstract: This comprehensive treatment of network information theory and its applications provides the first unified coverage of both classical and recent results. With an approach that balances the introduction of new models and new coding techniques, readers are guided through Shannon's point-to-point information theory, single-hop networks, multihop networks, and extensions to distributed computing, secrecy, wireless communication, and networking. Elementary mathematical tools and techniques are used throughout, requiring only basic knowledge of probability, whilst unified proofs of coding theorems are based on a few simple lemmas, making the text accessible to newcomers. Key topics covered include successive cancellation and superposition coding, MIMO wireless communication, network coding, and cooperative relaying. Also covered are feedback and interactive communication, capacity approximations and scaling laws, and asynchronous and random access channels. This book is ideal for use in the classroom, for self-study, and as a reference for researchers and engineers in industry and academia.

XORs in the air: practical wireless network coding

Abstract: This paper proposes COPE, a new architecture for wireless mesh networks. In addition to forwarding packets, routers mix (i.e., code) packets from different sources to increase the information content of each transmission. We show that intelligently mixing packets increases network throughput. Our design is rooted in the theory of network coding. Prior work on network coding is mainly theoretical and focuses on multicast traffic. This paper aims to bridge theory with practice; it addresses the common case of unicast traffic, dynamic and potentially bursty flows, and practical issues facing the integration of network coding in the current network stack. We evaluate our design on a 20-node wireless network, and discuss the results of the first testbed deployment of wireless network coding. The results show that using COPE at the forwarding layer, without modifying routing and higher layers, increases network throughput. The gains vary from a few percent to several folds depending on the traffic pattern, congestion level, and transport protocol.

Network Coding for Distributed Storage Systems

Abstract: Distributed storage systems provide reliable access to data through redundancy spread over individually unreliable nodes. Application scenarios include data centers, peer-to-peer storage systems, and storage in wireless networks. Storing data using an erasure code, in fragments spread across nodes, requires less redundancy than simple replication for the same level of reliability. However, since fragments must be periodically replaced as nodes fail, a key question is how to generate encoded fragments in a distributed way while transferring as little data as possible across the network. For an erasure coded system, a common practice to repair from a single node failure is for a new node to reconstruct the whole encoded data object to generate just one encoded block. We show that this procedure is sub-optimal. We introduce the notion of regenerating codes, which allow a new node to communicate functions of the stored data from the surviving nodes. We show that regenerating codes can significantly reduce the repair bandwidth. Further, we show that there is a fundamental tradeoff between storage and repair bandwidth which we theoretically characterize using flow arguments on an appropriately constructed graph. By invoking constructive results in network coding, we introduce regenerating codes that can achieve any point in this optimal tradeoff.