Distributed algorithm

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

Distributed Clustering Using Wireless Sensor Networks

Abstract: Clustering spatially distributed data is well motivated and especially challenging when communication to a central processing unit is discouraged, e.g., due to power constraints. Distributed clustering schemes are developed in this paper for both deterministic and probabilistic approaches to unsupervised learning. The centralized problem is solved in a distributed fashion by recasting it to a set of smaller local clustering problems with consensus constraints on the cluster parameters. The resulting iterative schemes do not exchange local data among nodes, and rely only on single-hop communications. Performance of the novel algorithms is illustrated with simulated tests on synthetic and real sensor data. Surprisingly, these tests reveal that the distributed algorithms can exhibit improved robustness to initialization than their centralized counterparts.

A Distributed TDMA Slot Assignment Algorithm for Wireless Sensor Networks

Abstract: Wireless sensor networks benefit from communication protocols that reduce power requirements by avoiding frame collision. Time Division Media Access methods schedule transmission in slots to avoid collision, however these methods often lack scalability when implemented in ad hoc networks subject to node failures and dynamic topology. This paper reports a distributed algorithm for TDMA slot assignment that is self-stabilizing to transient faults and dynamic topology change. The expected local convergence time is O(1) for any size network satisfying a constant bound on the size of a node neighborhood.

Redundancy and coverage detection in sensor networks

Abstract: We study the problem of detecting and eliminating redundancy in a sensor network with a view to improving energy efficiency, while preserving the network's coverage. We also examine the impact of redundancy elimination on the related problem of coverage-boundary detection. We reduce both problems to the computation of Voronoi diagrams, prove and achieve lower bounds on the solution of these problems, and present efficient distributed algorithms for computing and maintaining solutions in cases of sensor failures or insertion of new sensors. We prove the correctness and termination properties of our distributed algorithms, and analytically characterize the time complexity and traffic generated by our algorithms. Using detailed simulations, we also quantify the impact of system parameters such as sensor density, transmission range, and failure rates on network traffic.

Demand Response Management in the Smart Grid in a Large Population Regime

Abstract: In this paper, we introduce a hierarchical system model that captures the decision making processes involved in a network of multiple providers and a large number of consumers in the smart grid, incorporating multiple processes from power generation to market activities and to power consumption. We establish a Stackelberg game between providers and end users, where the providers behave as leaders maximizing their profit and end users act as the followers maximizing their individual welfare. We obtain closed-form expressions for the Stackelberg equilibrium of the game and prove that a unique equilibrium solution exists. In the large population regime, we show that a higher number of providers help to improve profits for the providers. This is inline with the goal of facilitating multiple distributed power generation units, one of the main design considerations in the smart grid. We further prove that there exist a unique number of providers that maximize their profits, and develop an iterative and distributed algorithm to obtain it. Finally, we provide numerical examples to illustrate the solutions and to corroborate the results.

Distributed-Memory Model Checking with SPIN

Abstract: The main limiting factor of the model checker SPIN is currently the amount of available physical memory. This paper explores the possibility of exploiting a distributed-memory execution environment, such as a network of workstations interconnected by a standard LAN, to extend the size of the verification problems that can be successfully handled by SPIN. A distributed version of the algorithm used by SPIN to verify safety properties is presented, and its compatibility with the main memory and complexity reduction mechanisms of SPIN is discussed. Finally, some preliminary experimental results are presented.