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NeTS:Small:Collaborative Research: Effective control of wireless networks via topology adaptation and randomization

NeTS:Small:Collaborative Research: Effective control of wireless networks via topology adaptation and randomization
NeTS:Small:协作研究:通过拓扑自适应和随机化有效控制无线网络
批准号:
0915988
负责人:
Eytan Modiano
金额:
$25.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-08-01 至 2013-07-31

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中文摘要
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英文摘要
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Wireless Mesh Networks have emerged as a solution for providing last-mile Internet access. By exploiting advanced communication technologies, they can achieve very high rates. However, effectively controlling these networks, especially in the context of advanced physical layer technologies, realistic models for channel interference, and distributed operation, remains a major challenge. Hence, the project focuses on developing effective and practical network control algorithms that make efficient use of wireless resources through joint topology adaptation, network layer routing, MAC layer scheduling, and physical layer power, channel, and rate control. The design of the algorithms leverages recent developments in the control of dynamical systems and randomized algorithms, and takes into account realistic channel models. This includes: (i) topology adaptation algorithms that take advantage of channel allocation, power control, and the controlled mobility capabilities of some of the nodes to dynamically decompose the network into sub-networks in which low-complexity distributed scheduling and routing algorithms are guaranteed to achieve high throughput, (ii) randomized distributed algorithms that solve the scheduling and routing problems in a computationally efficient manner using only local topological and queue size information, and (iii) evaluation of the algorithms? performance in terms of throughput, delay, and complexity. The developed algorithms will enable highly efficient operation of wireless networks. The project incorporates training of graduate and undergraduate students, outreach activities to local high-school teachers, and technology transfer to industry and government laboratories.
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RINGS: Enabling Wireless Edge-cloud Services via Autonomous Resource Allocation and Robust Physical Layer Technologies
Collaborative Research: CNS Core: Medium: Inference and Control in Overlay Networks
CNS Core: Small: Wireless Network Control in Uncooperative and Adversarial Environments
CRISP Type 2/Collaborative Research: Understanding the Benefits and Mitigating the Risks of Interdependence in Critical Infrastructure Systems
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