Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Data networks (2nd ed.)
A new approach to channel access scheduling for Ad Hoc networks
Proceedings of the 7th annual international conference on Mobile computing and networking
Introduction to Linear Optimization
Introduction to Linear Optimization
Energy-efficient broadcast and multicast trees in wireless networks
Mobile Networks and Applications
Energy-efficient broadcast and multicast trees in wireless networks
Mobile Networks and Applications
A note on greedy algorithms for the maximum weighted independent set problem
Discrete Applied Mathematics
Opportunistic routing in multi-hop wireless networks
ACM SIGCOMM Computer Communication Review
Inducing multiscale clustering using multistage MAC contention in CDMA ad hoc networks
IEEE/ACM Transactions on Networking (TON)
Efficient broadcasting using network coding
IEEE/ACM Transactions on Networking (TON)
Dynamic algorithms for multicast with intra-session network coding
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Minimum-cost multicast over coded packet networks
IEEE Transactions on Information Theory
A Random Linear Network Coding Approach to Multicast
IEEE Transactions on Information Theory
Network planning in wireless ad hoc networks: a cross-Layer approach
IEEE Journal on Selected Areas in Communications
Distributed utility maximization for network coding based multicasting: a shortest path approach
IEEE Journal on Selected Areas in Communications
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We consider multicasting using random linear network coding over a multihop wireless network in the bandwidth limited regime. We address the associated medium access problem and propose a scheduling technique that activates hyperarcs rather than links, as in classical scheduling approaches. We encapsulate the constraints on valid network configurations in a conflict graph model and formulate a joint optimization problem taking into account both the network coding subgraph and the schedule. Next, using Lagrangian relaxation, we decompose the overall problem into two subproblems, a multiple-shortest-paths problem and a maximum weighted stable set (MWSS) problem. We show that if we use a greedy heuristic for the MWSS part of the problem, the overall algorithm is completely distributed. We provide extensive simulation results for both the centralized optimal and the decentralized algorithms. The optimal algorithm improves performance by up to a factor of two over widely used techniques such as orthogonal or two-hop-constrained scheduling. The decentralized algorithm is shown to buy its distributed operation with some throughput losses. Experimental results on randomly generated networks suggest that these losses are not large. Finally, we study the power consumption of our scheme and quantify the tradeoff between power and bandwidth efficiency.