Fast randomized algorithms for distributed edge coloring
PODC '92 Proceedings of the eleventh annual ACM symposium on Principles of distributed computing
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Distributed, Self-Stabilizing Placement of Replicated Resources in Emerging Networks
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
A packet scheduling approach to QoS support in multihop wireless networks
Mobile Networks and Applications
Maximizing throughput in wireless networks via gossiping
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Exploring simulated annealing and graphical models for optimization in cognitive wireless networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Joint scheduling and power control for wireless ad hoc networks
IEEE Transactions on Wireless Communications
Cross-Layer Design for Lifetime Maximization in Interference-Limited Wireless Sensor Networks
IEEE Transactions on Wireless Communications
The capacity of wireless networks
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
A tutorial on cross-layer optimization in wireless networks
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications - Part Supplement
| Hi-index | 35.68 |
This work studies the near-optimality versus the complexity of distributed configuration management for wireless networks. We first develop a global probabilistic graphical model for a network configuration which characterizes jointly the statistical spatial dependence of a physical- and a logical-configuration. The global model is a Gibbs distribution that results from the internal network properties on node positions, wireless channel and interference; and the external management constraints on physical connectivity and signal quality. A local model is a two-layer Markov Random Field (i.e., a random bond model) that approximates the global model with the local spatial dependence of neighbors. The complexity of the local model is defined through the communication range among nodes which corresponds to the number of neighbors in the two-layer Markov Random Field. The local model is near-optimal when the approximation error to the global model is within a given bound. We analyze the tradeoff between approximation error and complexity. We then derive sufficient conditions on the near-optimality of the local model. For a fast decaying wireless channel with power attenuation factor α 4, a node only needs to communicate with O(1) neighbors for a local model to be near optimal. For a slowly decaying channel with a power attenuation factor 2 ≤ α ≤ 4, a node may have to communicate with more than O(N(4-α)/4) neighbors to result in a bounded approximation error. If the communication range is kept to be O(1), a bounded approximation error can also be achieved by reducing the density of active links to O(N(α-4)/(α+4)) for α O(1) for α 4. The two-layer Markov Random Fields enable a class of randomized distributed algorithms such as the stochastic relaxation that allows a node to self-configure based on information from neighbors. We. validate the model, the analysis and the randomized distributed algorithms through simulation.