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Distributed algorithm

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


Papers
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Journal ArticleDOI
TL;DR: In this article, a consensus-based distributed primal-dual perturbation (PDP) algorithm was proposed to solve the distributed demand response control problem in a smart grid, where each agent has no global knowledge and can access only its local mapping and constraint functions.
Abstract: Various distributed optimization methods have been developed for solving problems which have simple local constraint sets and whose objective function is the sum of local cost functions of distributed agents in a network. Motivated by emerging applications in smart grid and distributed sparse regression, this paper studies distributed optimization methods for solving general problems which have a coupled global cost function and have inequality constraints. We consider a network scenario where each agent has no global knowledge and can access only its local mapping and constraint functions. To solve this problem in a distributed manner, we propose a consensus-based distributed primal-dual perturbation (PDP) algorithm. In the algorithm, agents employ the average consensus technique to estimate the global cost and constraint functions via exchanging messages with neighbors, and meanwhile use a local primal-dual perturbed subgradient method to approach a global optimum. The proposed PDP method not only can handle smooth inequality constraints but also non-smooth constraints such as some sparsity promoting constraints arising in sparse optimization. We prove that the proposed PDP algorithm converges to an optimal primal-dual solution of the original problem, under standard problem and network assumptions. Numerical results illustrating the performance of the proposed algorithm for a distributed demand response control problem in smart grid are also presented.

340 citations

Journal ArticleDOI
TL;DR: The algorithm is implemented in TinyOS and shown to be effective in adapting to local topology changes without incurring global overhead in the scheduling, and the effect of the time-varying nature of wireless links on the conflict-free property of DRAND-assigned time slots is evaluated.
Abstract: This paper presents a distributed implementation of RAND, a randomized time slot scheduling algorithm, called DRAND. DRAND runs in O(delta) time and message complexity where delta is the maximum size of a two-hop neighborhood in a wireless network while message complexity remains O(delta), assuming that message delays can be bounded by an unknown constant. DRAND is the first fully distributed version of RAND. The algorithm is suitable for a wireless network where most nodes do not move, such as wireless mesh networks and wireless sensor networks. We implement the algorithm in TinyOS and demonstrate its performance in a real testbed of Mica2 nodes. The algorithm does not require any time synchronization and is shown to be effective in adapting to local topology changes without incurring global overhead in the scheduling. Because of these features, it can also be used even for other scheduling problems such as frequency or code scheduling (for FDMA or CDMA) or local identifier assignment for wireless networks where time synchronization is not enforced. We further evaluate the effect of the time-varying nature of wireless links on the conflict-free property of DRAND-assigned time slots. This experiment is conducted on a 55-node testbed consisting of the more recent MicaZ sensor nodes.

339 citations

Proceedings ArticleDOI
20 Mar 2003
TL;DR: Two algorithms for dynamically adjusting transmission power level on a per-node basis are proposed and it is shown that these local algorithms outperform fixed power level assignment and that the lifetime achieved by them is usually within a factor of two of globally computed solution while being scalable.
Abstract: In a wireless, multi-hop sensor network, choosing transmission power levels has an important impact on energy efficiency and network lifetime. Two algorithms for dynamically adjusting transmission power level on a per-node basis are proposed here. Network lifetime, convergence speed as well as resulting network connectivity are used as figures of merit for these two algorithms. They have been evaluated in an indoor sensor environment. The network lifetime metrics of these two local algorithms are also benchmarked against power control algorithms using global information. We show that these local algorithms outperform fixed power level assignment and that the lifetime achieved by them is usually within a factor of two of globally computed solution while being scalable.

338 citations

Journal ArticleDOI
TL;DR: To reduce the complexity of optimal binary power allocation for large networks, simple algorithms achieving 99% of the capacity promised by exhaustive binary search are provided.
Abstract: We consider allocating the transmit powers for a wireless multi-link (N-link) system, in order to maximize the total system throughput under interference and noise impairments, and short term power constraints. Employing dynamic spectral reuse, we allow for centralized control. In the two-link case, the optimal power allocation then has a remarkably simple nature termed binary power control: depending on the noise and channel gains, assign full power to one link and minimum to the other, or full power on both. Binary power control (BPC) has the advantage of leading towards simpler or even distributed power control algorithms. For N>2 we propose a strategy based on checking the corners of the domain resulting from the power constraints to perform BPC. We identify scenarios in which binary power allocation can be proven optimal also for arbitrary N. Furthermore, in the general setting for N>2, simulations demonstrate that a throughput performance with negligible loss, compared to the best non-binary scheme found by geometric programming, can be obtained by BPC. Finally, to reduce the complexity of optimal binary power allocation for large networks, we provide simple algorithms achieving 99% of the capacity promised by exhaustive binary search.

337 citations

Journal ArticleDOI
TL;DR: The main focus is on studies characterized by distributed control, simplicity of individual robots and locality of sensing and communication, and distributed algorithms are shown to bring cooperation between agents.

337 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202381
2022135
2021583
2020759
2019876
2018845