Description:
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Improved performance in terms of throughput, delay and energy
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Robust and distributed
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Does not require any prior knowledge of dynamic network load conditions, node locations, or node mobility
Abstract
In backpressure scheduling and routing the packets are preferentially transmitted over links with high queue differentials which offers the guarantee of throughput-optimal operation for a wide range of communication networks. However, when the traffic load is low, due to the corresponding low queue occupancy, backpressure scheduling/routing experiences long delays. This is of concern in intermittent encounter-based mobile networks which are already delay-limited due to the sparse and highly dynamic network connectivity. While traditional mechanisms for such networks have proposed the use of redundant transmissions to improve delay, they do not work well when the traffic load is high.
Benefit
The researchers at USC have developed a novel hybrid approach, an adaptive redundancy technique for backpressure routing that yields the benefits of replication to reduce delay under low load conditions, while at the same time preserving the performance and benefits of traditional backpressure routing under high traffic conditions. This technique, which is referred to as backpressure with adaptive redundancy (BWAR), essentially creates copies of packets in a new duplicate buffer upon an encounter, when the transmitter’s queue occupancy is low. These duplicate packets are transmitted only when the original queue is empty. This mechanism dramatically improves delay of backpressure during low load conditions.
Market Application
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Device-to-device communication
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Vehicle-to-Vehicle communication
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Mobile peer-to-peer content sharing
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Ad-hoc disaster recovery network
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Wildlife monitoring system
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Distributed crowd sensing
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Tactical network for military/security
Publications
Backpressure with Adaptive Redundancy (BWAR).” Backpressure with Adaptive Redundancy (BWAR) - IEEE Conference Publication, IEEE Xplore, May 2012
Other
Stage of Development: Tested on real mobile vehicular data