Distributed algorithm

About: Distributed algorithm is a research topic. Over the lifetime, 20416 publications have been published within this topic receiving 548109 citations.

Distributed rate allocation for inelastic flows

Abstract: A common assumption behind most of the recent research on network rate allocation is that traffic flows are elastic, which means that their utility functions are concave and continuous and that there is no hard limit on the rate allocated to each flow. These critical assumptions lead to the tractability of the analytic models for rate allocation based on network utility maximization, but also limit the applicability of the resulting rate allocation protocols. This paper focuses on inelastic flows and removes these restrictive and often invalid assumptions. First, we consider nonconcave utility functions, which turn utility maximization into difficult, nonconvex optimization problems. We present conditions under which the standard price-based distributed algorithm can still converge to the globally optimal rate allocation despite nonconcavity of utility functions. In particular, continuity of price-based rate allocation at all the optimal prices is a sufficient condition for global convergence of rate allocation by the standard algorithm, and continuity at at least one optimal price is a necessary condition. We then show how to provision link capacity to guarantee convergence of the standard distributed algorithm. Second, we model real-time flow utilities as discontinuous functions. We show how link capacity can be provisioned to allow admission of all real-time flows, then propose a price-based admission control heuristics when such link capacity provisioning is impossible, and finally develop an optimal distributed algorithm to allocate rates between elastic and real-time flows.

A robust architecture for distributed inference in sensor networks

Abstract: Many inference problems that arise in sensor networks require the computation of a global conclusion that is consistent with local information known to each node. A large class of these problems---including probabilistic inference, regression, and control problems---can be solved by message passing on a data structure called a junction tree. In this paper, we present a distributed architecture for solving these problems that is robust to unreliable communication and node failures. In this architecture, the nodes of the sensor network assemble themselves into a junction tree and exchange messages between neighbors to solve the inference problem efficiently and exactly. A key part of the architecture is an efficient distributed algorithm for optimizing the choice of junction tree to minimize the communication and computation required by inference. We present experimental results from a prototype implementation on a 97-node Mica2 mote network, as well as simulation results for three applications: distributed sensor calibration, optimal control, and sensor field modeling. These experiments demonstrate that our distributed architecture can solve many important inference problems exactly, efficiently, and robustly.

Subcarrier allocation and bit loading algorithms for OFDMA-based wireless networks

Abstract: Orthogonal Frequency Division Multiple Access (OFDMA) is an emerging multiple access technology. In this paper, we consider OFDMA in the context of fixed wireless networks. This paper addresses the problem of assigning subcarriers and bits to point-to-point wireless links in the presence of cochannel interference and Rayleigh fading. The objective is to minimize the total transmitted power over the entire network while satisfying the data rate requirement of each link. We formulate this problem as a constrained optimization problem and present centralized algorithms. The simulation results show that our approach results in an efficient assignment of subcarriers and transmitter power levels in terms of the energy required for transmitting each bit of information. However, centralized algorithms require knowledge of the entire network topology and channel characteristics of every link. In a practical scenario, that would not be the situation and there is a need for distributed rate allocation algorithms. To address this need, we also present a distributed algorithm for allocating subcarriers and bits in order to satisfy the rate requirements of the links.

Distributed Algorithms for Constructing Approximate Minimum Spanning Trees in Wireless Sensor Networks

Abstract: While there are distributed algorithms for the MST problem, these algorithms require relatively large number of messages and time; this makes these algorithms impractical for resource-constrained networks such as ad hoc wireless sensor networks. In such networks, a sensor has very limited power, and any algorithm needs to be simple, local, and energy efficient for being practical. Motivated by these considerations, we design and analyze a class of simple and local distributed algorithms called nearest neighbor tree (NNT) algorithms for energy-efficient construction of MSTs in a wireless ad hoc setting. We assume that the nodes are uniformly distributed in a unit square and show provable bounds on the performance with respect to both the quality of the spanning tree produced and the energy needed to construct them. In particular, we show that NNT produces a close approximation to the MST, and they can be maintained dynamically with polylogarithmic number of rearrangements under node insertions/deletions. We also perform extensive simulations of our algorithms. We tested our algorithms on both uniformly random distributions of nodes, and on a realistic distributions of nodes in an urban setting. Simulations validate the theoretical results and show that the bounds are much better in practice.

Discrete-time average-consensus under switching network topologies

Abstract: This paper develops a distributed algorithm for average-consensus in a discrete-time framework based on a formal matrix limit definition of average-consensus. Using this algorithm, the average-consensus problem is solved under switching network topologies provided that the network switch between instantaneously balanced, connected-over-time networks. In other words, if at each instant the network is balanced and the union of graphs over every interval T is connected, then average-consensus can be achieved. An interesting product of this analysis is the notion of "deadbeat" consensus where a system of agents achieves consensus (average or otherwise) in finite time rather than asymptotically.