Due to technical bottlenecks and errors caused by artificial operation,the problem of incomplete data always exists in big data *** data imputation algorithms incur high complexity and the accuracy cannot reach the de...
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ISBN:
(纸本)9781509012572
Due to technical bottlenecks and errors caused by artificial operation,the problem of incomplete data always exists in big data *** data imputation algorithms incur high complexity and the accuracy cannot reach the desired *** the same time,analysis and computation involved in mass data makes limitation of traditional algorithms and computing platform more *** this paper,we propose a data imputation method based on Apriori algorithm,and implement the corresponding algorithm on the distributed computing system built with *** experimental results show that the proposed algorithm outperforms a traditional data imputation algorithm in terms of efficiency and accuracy.
In this paper we introduce the Cluster-Based Beacon Dissemination Process (CB-BDP) that aims to provide vehicles with a local vehicle proximity map of their vicinity. Based on this map, safety applications can be used...
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In this paper we introduce the Cluster-Based Beacon Dissemination Process (CB-BDP) that aims to provide vehicles with a local vehicle proximity map of their vicinity. Based on this map, safety applications can be used for accident prevention by informing drivers about evolving hazardous situations. The CB-BDP is designed under the two following objectives. First, since it is used for safety applications, we want the map to be detailed and accurate as much as possible. Second, we want the map to be coordinated with nearby vehicles, thereby allowing synchronized and coordinated reactions of nearby vehicles to evolving hazardous situations. We design a cluster based aggregation-dissemination beaconing process that uses an optimized topology to distribute the vehicle proximity map. The topology is adaptive and robust in order to meet the challenging VANET conditions. An accurate and detailed map results in a heavy load of beacon messages. Our proposed scheme deals with this problem by integrating a contention-free medium access control (MAC) strategy for reliable communication. (C) 2015 Elsevier Inc. All rights reserved.
distributed systems offer many features such as resource sharing, scalability, fault tolerance and reliability. Several distributed algorithms have been proposed in literature to solve fundamental problems such as mut...
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ISBN:
(纸本)9781479976836
distributed systems offer many features such as resource sharing, scalability, fault tolerance and reliability. Several distributed algorithms have been proposed in literature to solve fundamental problems such as mutual exclusion and leader election in distributed systems. When more than one algorithm is invented to solve the same problem particularly in asynchronous distributed systems, their performance is compared mostly based on the message complexity. This paper reviews the concept of message complexity and offers more clarity by studying the performance of the two most popular distributed algorithms - Ricart-Agrawala's algorithm and Raymond algorithm designed to solve the mutual exclusion problem. The paper has four main contributions (i) observes how the message complexity is understood and computed in the asynchronous distributed system so far and exposes its elusiveness;(ii) offers a more suitable definition of message complexity;(iii) briefly presents the simulator designed to study the performance of the distributed algorithms using the refined metric;and finally (iv) discusses about the simulation study to illustrate the significance and usefulness of the proposed metric.
In this work, we focus on minimizing the overall network energy consumption problem under delay-constrained: N different packets from N time varying channels must be transmitted by a hard deadline of T slots. Each tra...
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ISBN:
(纸本)9781479950799
In this work, we focus on minimizing the overall network energy consumption problem under delay-constrained: N different packets from N time varying channels must be transmitted by a hard deadline of T slots. Each transmitter determines how much power to transmit with, during each time slot based on the current channel quality and the number of un transmitted bits, with the objective of minimizing overall network energy consumption. We transform the non-convex optimization problem into a geometric programming problem which has convex form and propose a distributed approximate optimization algorithm. Moreover, a lazy updating distributed algorithm is also presented for infrequent message passing. Experimental results show that the proposed distributed algorithm converges fast and the results of the distributed algorithm and results of centralized algorithm are very close.
We study the following problem: B-0 and B-1 each has a sparse input vector V-0 and V-1;for each j we need to decide whether B-0[j] + B-1[j] > t. We give a privacy-preserving algorithm, in which B-0 and B-1 do not n...
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We study the following problem: B-0 and B-1 each has a sparse input vector V-0 and V-1;for each j we need to decide whether B-0[j] + B-1[j] > t. We give a privacy-preserving algorithm, in which B-0 and B-1 do not need to reveal any information about their input vectors to each other, except the output of algorithm. Our algorithm is highly efficient.
Sensor networks are getting much more complex these days. The mixture of various low-cost sensors together with increasing computational power enables for whole new systems running a lot of different analysis and cont...
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ISBN:
(纸本)9783319250670;9783319250663
Sensor networks are getting much more complex these days. The mixture of various low-cost sensors together with increasing computational power enables for whole new systems running a lot of different analysis and control algorithms concurrently. It is impossible to anticipate their composition and data flows a priori. Although the actual data flows are hardly predictable during design-time, we present a lightweight and self-organizing approach on how shared data stores are used to optimize the storage allocation of data during run-time. While mostly using the existing traffic to disseminate routing information, we show that our distributed algorithm significantly reduces query latencies by placing data according to the access-centric storage paradigm.
The minimum connected cover problem is one of the most fundamental issues in wireless sensor networks, which directly affects the capability and efficiency of the wireless sensor network (WSN). Constructing a minimum ...
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The minimum connected cover problem is one of the most fundamental issues in wireless sensor networks, which directly affects the capability and efficiency of the wireless sensor network (WSN). Constructing a minimum connected coverage has a great relevance for network services, such as prolonged system lifetime by utilizing redundant deployment of sensor nodes where the main challenge in the design of sensor networks is the limited battery power of the sensors and the difficulty of replacing and/or recharging these batteries. Thus, it is necessary that the sensors be densely deployed and energy-efficient protocols be designed to maximize the network lifetime while meeting the specific application requirements in terms of coverage and connectivity. In this paper, we propose a new distributed algorithm to find the minimum connected cover of the queried region by discovering the redundant sensors for heterogeneous sensors, each with arbitrary sensing range and is not aware of its location or relative direction of its neighbor. We provide performance metrics to analyze the performance of our approach and the simulation results show that our approach clearly improves the network lifetime over existing algorithms. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
This paper presents a novel demand side management with integration of renewable energy sources and storage devices. The presented work includes both traditional consumers and smart consumers (equipped with energy con...
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ISBN:
(纸本)9781467365246
This paper presents a novel demand side management with integration of renewable energy sources and storage devices. The presented work includes both traditional consumers and smart consumers (equipped with energy consumption schedulers, energy generation and storage devices). The users are intended to reduce their energy usage and prices by using renewable energy sources rather purchasing from the grid which can be regulated by the independent central unit by means of day ahead optimization process. The resulting grid optimization problem is formulated as a non-cooperative game. It is assumed that the independent central unit can adopt adequate pricing tariffs that differentiate the energy usage in time and level. Furthermore, we present a distributed algorithm to be run on the users' smart meters, which provide the optimal production and/or storage strategies, while preserving the privacy of the users. Finally, Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges. Moreover, the block pricing method is proved to be more beneficiary for the users compared to hourly pricing strategy
Clustering algorithm is applied to all kinds of fields, especially in the field of data mining. Due to the increasing number of the data, it's too hard for the clustering algorithm to afford the computation time i...
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ISBN:
(纸本)9781467391054
Clustering algorithm is applied to all kinds of fields, especially in the field of data mining. Due to the increasing number of the data, it's too hard for the clustering algorithm to afford the computation time in traditional computing model. When handling with big data, the corresponding algorithms of data mining have been transformed from the original single-core or single ported into the parallel and distributed processing. Parallel processing becomes the most popular way to improve the execution performace. This paper established a Hadoop distributed cluster based on the CloudStack and implemented the optimal distributed K-Means clustering algorithm based on MapReduce. The proposed optimal distributed K-Means clustering can obtain good quality of the results and the efficiency of the execution time. The experiment results show that the optimal distributed K-Means cluster algorithm can have better performance for dealing with large-scale data set.
In this paper, a robust optimization problem is formulated for decode-and-forward relay network via game theory. The uncertainty of channel gain is considered to overcome the performance degradation in practical envir...
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ISBN:
(纸本)9789881563897
In this paper, a robust optimization problem is formulated for decode-and-forward relay network via game theory. The uncertainty of channel gain is considered to overcome the performance degradation in practical environment. In this study, a probability threshold method is used to suppress the channel fluctuation. Besides, a seller-buyer game is formulated to jointly consider the maximum utility of source node and relay node. To reduce the overhead information exchanging, a distributed iteration algorithm is applied. Numerical results show the effectiveness of the proposed schemes, and also present the robustness of the relay network. Moreover, the proposed game is proved to converge to the unique equilibrium point.
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