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Efficient and resilient backbones for multihop wireless networks

delete2008-11-01
delete6
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OA
AI
S
Seungjoon Lee *
B
Bobby Bhattacharjee
A
Aravind Srinivasan
S
Samir Khuller
DOI:10.1109/TMC.2008.69delete
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摘要

摘要

En 中文
We consider the problem of finding backbones in multihop wireless networks. The backbone provides end-to-end connectivity, allowing nonbackbone nodes to save energy since they do not have to route nonlocal data or participate in the routing protocol. Ideally, such a backbone would be small, consist primarily of high capacity nodes, and remain connected even when nodes are mobile or fail. Unfortunately, it is often infeasible to construct a backbone that has all of these properties; e. g., a small optimal backbone is often too sparse to handle node failures or high mobility. We present a parameterized backbone construction algorithm that permits explicit trade-offs between backbone size, resilience to node movement and failure, energy consumption, and path lengths. We prove that our scheme can construct essentially best possible backbones (with respect to energy consumption and backbone size) when the network is relatively static. We generalize our scheme to build more robust structures better suited to networks with higher mobility. We present a distributed protocol based upon our algorithm and show that this protocol builds and maintains a connected backbone in dynamic networks. Finally, we present detailed packet-level simulation results to evaluate and compare our scheme with existing energy-saving techniques. Our results show that, depending on the network environment, our scheme increases network lifetimes by 20 percent to 220 percent without adversely affecting delivery ratio or end-to-end latency.
Keyword:
network protocols
mobile computing
an algorithm/protocol design and analysis

期刊

IEEE Transactions on Mobile Computing 封面图
IEEE Transactions on Mobile Computing
IF:
9.2
论文数:
5.8K
被引数:
1.8W

机构

University System of Maryland 封面图
University System of Maryland
学者数:
6.5W
论文数: 5.6W
被引数: 113
A
AT&T
学者数:
811
论文数: 717
被引数: 460
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