Self-organizing broadband hybrid wireless networks

Milner, Stuart D.; Desai, A.; Ho, Tzung-Hsien; Llorca, Jaime; Trisno, Sugianto; Davis, Christophér C.

doi:10.1364/jon.4.000446

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…which is the product of a polynomial and an exponential function of the link distance The atmospheric attenuation coefficient ij α is defined as the attenuation in dB/km undergone by the electromagnetic radiation in going from node i to node j [8,9]. In this work, we assume uniform atmospheric conditions throughout the deployment area and thus define α α = ij for all (i,j).…”

Section: Communication Cost Modelmentioning

confidence: 99%

A convex optimization method for autonomous self-organization in dynamic wireless networks

Llorca

Milner

Davis

2008

MILCOM 2008 - 2008 IEEE Military Communications Conference

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In our attempts to model, characterize and control increasingly complex network systems we introduced a novel physics framework in which communication networks are modeled as physical systems that react to local forces exerted on network nodes. We showed that under clear atmosphere conditions the network communication energy can be modeled as the potential energy of an analogous spring system, which led to the development of distributed mobility control algorithms where nodes react to local forces exerted by neighbor nodes driving the network to energy minimizing configurations. This paper extends our previous work by including the effects of atmospheric attenuation in the channel. We show how our new formulation still results in a convex energy minimization problem. Accordingly, an updated force-driven mobility control algorithm is presented. Exponential forces are shown to appear on backbone nodes when atmospheric attenuation is present, which make them stay closer to each other to optimize backbone connectivity and reduce the network power usage. We present results in terms of power usage, network coverage and backbone connectivity and show how our updated mobility control algorithm allows the network to react to the effects of changing channel conditions. MOTIVATION

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Section: Communication Cost Modelmentioning

confidence: 99%

A convex optimization method for autonomous self-organization in dynamic wireless networks

Llorca

Milner

Davis

2008

MILCOM 2008 - 2008 IEEE Military Communications Conference

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“…Bootstrapping has provided initial network connectivity and global network information. Centralized topology optimization algorithms can now be executed in order to dynamically optimized network performance [2,3,4,5,6]. …”

Section: Computation/communication Modelmentioning

confidence: 99%

“…Our previous research on directional wireless networks involved the development of integrated hardware and software for reconfigurable, Free Space Optical (FSO) backbone networks with topology control [2,3]. In such networks, it is assumed that each node is a base station, integrating a router and optical wireless communication hardware with limited number of transceivers.…”

Section: Introductionmentioning

confidence: 99%

“…Such an assumption makes the implementation of a centralized system very suitable, as overall network information can be used to efficiently obtain close-to-optimal solutions while avoiding agreement, coordination and synchronization problems. A Designated Topology Control Node (DTCN) [2] is responsible for gathering overall network information, evaluating the current topology, deciding upon reconfiguration and computing a new optimized topology. Moreover, the available highbandwidth connectivity allows overcoming the scalability and response time limitations of centralized systems in lowbandwidth, unreliable networks.…”

Section: Introductionmentioning

confidence: 99%

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Design and implementation of a bootstrapping model for free-space optical backbone networks

2006

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Establishing and initially configuring a Free Space Optical (FSO) backbone is a challenging problem, especially when nodes only have local connectivity information and a limited number of transceivers. The problem of configuring an initial connected topology or bootstrapping a directional FSO network can be formulated as a Minimum Degree Spanning Tree (MDST) problem, which is known to be NP-Complete. Recently, we developed a distributed approximation algorithm, which constructs a spanning tree with maximal node degree at most one larger than that in the optimal solution [1]. In such a distributed approach, nodes need to coordinate their local decisions to collectively set up a connected topology. For that purpose, algorithms and protocols for local information exchange/dissemination and synchronization are required. This paper presents the design, implementation and evaluation of a complete bootstrapping process model for FSO networks. Our model integrates the algorithm presented in [1] with the required communication and synchronization mechanisms to guarantee the efficient emergence of overall network connectivity from local interactions between individual nodes. Time performance results for the overall bootstrapping process are presented. Our scheme allows the network to form a connected topology whenever one exists and shows linear time complexity.

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“…The former is related to the pointing, acquisition, and tracking (PAT) of directional laser links, and the latter to topology management. We refer to this overall process as topology control (Llorca et al 2004a;Milner et al 2005;Desai and Milner 2005).…”

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confidence: 99%

A Multiobjective Optimization Model for Dynamic Reconfiguration of Ring Topologies with Stochastic Load

et al. 2007

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Self-organizing broadband hybrid wireless networks

Cited by 18 publications

References 17 publications

A convex optimization method for autonomous self-organization in dynamic wireless networks

A convex optimization method for autonomous self-organization in dynamic wireless networks

Design and implementation of a bootstrapping model for free-space optical backbone networks

A Multiobjective Optimization Model for Dynamic Reconfiguration of Ring Topologies with Stochastic Load

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