Topic
Distributed algorithm
About: Distributed algorithm is a research topic. Over the lifetime, 20416 publications have been published within this topic receiving 548109 citations.
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Papers
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21 Jun 2010TL;DR: This work considers the problem of gathering correlated sensor data by a sink node in a wireless sensor network, and proposes an efficient heuristic algorithm, JRPRA, to solve the general problem.
Abstract: We consider the problem of gathering correlated sensor data by a sink node in a wireless sensor network. We design efficient distributed protocols to maximize the network lifetime subject to nodal energy constraints. Many existing approaches address the routing layer only, but the routing often interacts with physical-layer power control and MAC-layer link access. We present a first effort to maximize the network lifetime by jointly considering the three layers. We first solve the joint power control and routing problem, by assuming that the link access probabilities are known. We show that the problem is convex and propose a distributed algorithm, JRPA, as solution. When the link access probabilities are unknown, we then generalize the problem to encompass all three layers of routing, power control, and link random access. The general problem is non-convex; a duality gap exists when the Lagrangian dual method is employed. We propose an efficient heuristic algorithm, JRPRA, to solve the general problem. Numerical results show that JRPRA is highly effective; particularly, even without the best link access probabilities pre-determined for JRPA, JRPRA achieves extremely competitive performance. Our results also show the convergence of the algorithms and their advantages over existing solutions.
119 citations
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TL;DR: This paper proposes two distributed algorithms for cluster-head election and provides distributed clustering algorithms for mobile sensor nodes which minimize the energy dissipation for data-gathering in a wireless mobile sensor network.
119 citations
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20 Sep 2004TL;DR: A scalable density-based distributed clustering algorithm which allows a user-defined trade-off between clustering quality and the number of transmitted objects from the different local sites to a global server site.
Abstract: Clustering has become an increasingly important task in analysing huge amounts of data. Traditional applications require that all data has to be located at the site where it is scrutinized. Nowadays, large amounts of heterogeneous, complex data reside on different, independently working computers which are connected to each other via local or wide area networks. In this paper, we propose a scalable density-based distributed clustering algorithm which allows a user-defined trade-off between clustering quality and the number of transmitted objects from the different local sites to a global server site. Our approach consists of the following steps: First, we order all objects located at a local site according to a quality criterion reflecting their suitability to serve as local representatives. Then we send the best of these representatives to a server site where they are clustered with a slightly enhanced density-based clustering algorithm. This approach is very efficient, because the local detemination of suitable representatives can be carried out quickly and independently from each other. Furthermore, based on the scalable number of the most suitable local representatives, the global clustering can be done very effectively and efficiently. In our experimental evaluation, we will show that our new scalable density-based distributed clustering approach results in high quality clusterings with scalable transmission cost.
119 citations
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TL;DR: This paper proposes a multihop cooperative and distributed computation offloading algorithm that considers the data processing tasks and the mining tasks together for blockchain-empowered IIoT and designs an efficient distributed algorithm based on exchanging messages betweenIIoT devices to achieve the NE with low computational complexity.
Abstract: Offloading computation-intensive blockchain mining tasks to the edge servers (ESs) is a promising solution for blockchain-empowered Industrial Internet of Things (IIoT) because the computing capabilities in IIoT are usually limited, whereas the blockchain mining tasks are computationally intensive. However, the computation offloading solutions for data processing tasks and for blockchain mining tasks have been studied separately. Moreover, most of the existing solutions for offloading assume that all IIoT devices can directly connect to the ESs or cloud data centers. To address these issues, in this paper, we propose a multihop cooperative and distributed computation offloading algorithm that considers the data processing tasks and the mining tasks together for blockchain-empowered IIoT. First, we study the multihop computation offloading problem for both the data processing tasks and the mining tasks to minimize the economic cost of IIoT devices. Second, we formulate the offloading problem as a potential game in which the IIoT devices can make their decisions autonomously and prove the existence of Nash equilibrium (NE) for the game. Third, we design an efficient distributed algorithm based on exchanging messages between IIoT devices to achieve the NE with low computational complexity. Lastly, our experimental results demonstrate that our distributed algorithm scales well as the number of IIoT devices increases and has the minimum system cost compared with other approaches.
119 citations
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TL;DR: Numerical results, obtained by simulating several scenarios, show that the algorithm can reach a good level of convergence even when the number of communications is limited.
Abstract: In this paper we propose a distributed algorithm for solving the positioning problem in ad-hoc wireless networks. The method is based on the capability of the nodes to measure the angle of arrival (AOA) of the signals they produce. The main features of the distributed algorithm are simplicity, asynchronous operations (i.e. no global coordination among nodes is required), ability to operate in disconnected networks. Moreover each node can join the computation at any time. Numerical results, obtained by simulating several scenarios, show that the algorithm can reach a good level of convergence even when the number of communications is limited.
118 citations