Distributed algorithm

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

Physarum Optimization: A Biology-Inspired Algorithm for the Steiner Tree Problem in Networks

Abstract: Using insights from biological processes could help to design new optimization techniques for long-standing computational problems. This paper exploits a cellular computing model in the slime mold physarum polycephalum to solve the Steiner tree problem which is an important NP-hard problem in various applications, especially in network design. Inspired by the path-finding and network formation capability of physarum, we develop a new optimization algorithm, named as the physarum optimization, with low complexity and high parallelism. To validate and evaluate our proposed models and algorithm, we further apply the physarum optimization to the minimal exposure problem which is a fundamental problem corresponding to the worst-case coverage in wireless sensor networks. Complexity analysis and simulation results show that our proposed algorithm could achieve good performance with low complexity. Moreover, the core mechanism of our physarum optimization also may provide a useful starting point to develop some practical distributed algorithms for network design.

A New Distributed Time Synchronization Protocol for Multihop Wireless Networks

Abstract: A distributed algorithm to achieve accurate time synchronization in large multihop wireless networks is presented. The central idea is to exploit the large number of global constraints that have to be satisfied by a common notion of time in a multihop network. If, at a certain instant, Oij is the clock offset between two neighboring nodes i and j, then for any loop i1, i2, i3 , ..., in, in + 1 - i1 in the multihop network, these offsets must satisfy the global constraint Sigma k = 1 nOik, ik + 1 = 0. Noisy estimates Ocirc ij of Oij are usually arrived at by bilateral exchanges of timestamped messages or local broadcasts. By imposing the large number of global constraints for all the loops in the multihop network, these estimates can be smoothed and made more accurate. A fully distributed and asynchronous algorithm which functions by simple local broadcasts is designed. Changing the time reference node for synchronization is also easy, consisting simply of one node switching on adaptation, and another switching it off. Implementation results on a forty node network, and comparative evaluation against a leading algorithm, are presented

A Delay-Efficient Algorithm for Data Aggregation in Multihop Wireless Sensor Networks

Abstract: Data aggregation is a key functionality in wireless sensor networks (WSNs). This paper focuses on data aggregation scheduling problem to minimize the delay (or latency). We propose an efficient distributed algorithm that produces a collision-free schedule for data aggregation in WSNs. We theoretically prove that the delay of the aggregation schedule generated by our algorithm is at most 16R + Δ - 14 time slots. Here, R is the network radius and Δ is the maximum node degree in the communication graph of the original network. Our algorithm significantly improves the previously known best data aggregation algorithm with an upper bound of delay of 24D + 6Δ + 16 time slots, where D is the network diameter (note that D can be as large as 2R). We conduct extensive simulations to study the practical performances of our proposed data aggregation algorithm. Our simulation results corroborate our theoretical results and show that our algorithms perform better in practice. We prove that the overall lower bound of delay for data aggregation under any interference model is max{log n,R}, where n is the network size. We provide an example to show that the lower bound is (approximately) tight under the protocol interference model when rI = r, where rI is the interference range and r is the transmission range. We also derive the lower bound of delay under the protocol interference model when r <; rI <; 3r and rI ≥ 3r.

Decentralized Learning for Multiplayer Multiarmed Bandits

Abstract: We consider the problem of distributed online learning with multiple players in multiarmed bandit (MAB) models. Each player can pick among multiple arms. When a player picks an arm, it gets a reward. We consider both independent identically distributed (i.i.d.) reward model and Markovian reward model. In the i.i.d. model, each arm is modeled as an i.i.d. process with an unknown distribution with an unknown mean. In the Markovian model, each arm is modeled as a finite, irreducible, aperiodic and reversible Markov chain with an unknown probability transition matrix and stationary distribution. The arms give different rewards to different players. If two players pick the same arm, there is a collision, and neither of them get any reward. There is no dedicated control channel for coordination or communication among the players. Any other communication between the users is costly and will add to the regret. We propose an online index-based distributed learning policy called dUCB4 algorithm that trades off exploration versus exploitation in the right way, and achieves expected regret that grows at most as near- O(log2T). The motivation comes from opportunistic spectrum access by multiple secondary users in cognitive radio networks wherein they must pick among various wireless channels that look different to different users. This is the first distributed learning algorithm for multiplayer MABs with heterogeneous players (that have player-dependent rewards) to the best of our knowledge.

Distributed Downlink Beamforming With Cooperative Base Stations

Abstract: In this paper, we consider multicell processing on the downlink of a cellular network to accomplish ldquomacrodiversityrdquo transmit beamforming. The particular downlink beamformer structure we consider allows a recasting of the downlink beamforming problem as a virtual linear mean square error (LMMSE) estimation problem. We exploit the structure of the channel and develop distributed beamforming algorithms using local message passing between neighboring base stations. For 1-D networks, we use the Kalman smoothing framework to obtain a forward-backward beamforming algorithm. We also propose a limited extent version of this algorithm that shows that the delay need not grow with the size of the network in practice. For 2-D cellular networks, we remodel the network as a factor graph and present a distributed beamforming algorithm based on the sum-product algorithm. Despite the presence of loops in the factor graph, the algorithm produces optimal results if convergence occurs.