With the advancement of the Internet of Vehicles (IoV), delay-sensitive vehicular applications have flourished. Among them, the autonomous driving technology is a focal point. For autonomous driving vehicles, efficien...
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With the advancement of the Internet of Vehicles (IoV), delay-sensitive vehicular applications have flourished. Among them, the autonomous driving technology is a focal point. For autonomous driving vehicles, efficiently and timely processing the ever-increasing data is critical. In real traffic scenes, the task-processing efficiency is closely related to the traffic flows. However, the traffic flow modeling is always ignored or considered roughly in the most existing studies. For this issue, a traffic model based on a stochastic geometry framework is proposed to simulate a real traffic environment of autonomous driving vehicles. To reduce the cost of processing tasks, a distributed computation offloading scheme based on mobile edge computing (MEC) is proposed by soliciting nearby vehicles and roadside units (RSUs) with rich computing resources. For the average cost minimization optimization problem, we divide the NP-hard problem into several sub-problems and take advantage of the Lagrange multiplier with KKT constraints to solve by optimizing task splitting ratios. We compare the proposed traffic model with some common ones and also consider the pros and cons of different computation offloading strategies. Simulation results show that the proposed strategy outperforms other benchmarks and the proposed modeling method is rational.
Sparsity has been extensively employed in multimedia sensing and computing in consumer electronics, signal and image processing, depth video codec, adaptive sparse-type equalizer, blind speech separation, and machine ...
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Sparsity has been extensively employed in multimedia sensing and computing in consumer electronics, signal and image processing, depth video codec, adaptive sparse-type equalizer, blind speech separation, and machine learning. Throughout this paper, we propose a novel distributed projection neurodynamic approach for solving the Basis Pursuit (BP) with flexible partition methods in a distributed manner. The proposed neurodynamic approach requires only that the network is undirected and connected, and no node can access the entire matrix simultaneously. First, we equivalently formulate the BP into a standard distributed optimization problem with a flexible partition-by-blocks method to obtain global information, and discuss the equivalence of their optimality conditions. Then, we propose a distributed continuous-time neurodynamic approach on the basis of primal-dual dynamical systems and projection operators, and also study its global convergence property. Finally, numerical experiments on sparse signals and image recovery further verify the effectiveness and superiority of our proposed neurodynamic approach.
We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets. These subsets are only accessed locally by individual workers and...
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We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets. These subsets are only accessed locally by individual workers and no data is shared between the workers. We approximate the model evidence for the full data set through Monte Carlo sampling from the posterior on every subset generating a model evidence per subset. The results are combined using a novel approach which corrects for the splitting using summary statistics of the generated samples. Our divide-and-conquer approach enables Bayesian model choice in the large data setting, exploiting all available information but limiting communication between workers. We derive theoretical error bounds that quantify the resulting trade-off between computational gain and loss in precision. The embarrassingly parallel nature yields important speed-ups when used on massive data sets as illustrated by our real world experiments. In addition, we show how the suggested approach can be extended to model choice within a reversible jump setting that explores multiple feature combinations within one run.
This paper presents a novel strategy to decentralize the soft detection procedure in an uplink cell-free massive multiple-input-multiple-output network. We propose efficient approaches to compute the a posteriori prob...
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ISBN:
(纸本)9789082797060
This paper presents a novel strategy to decentralize the soft detection procedure in an uplink cell-free massive multiple-input-multiple-output network. We propose efficient approaches to compute the a posteriori probability-per-bit, exactly or approximately, when having a sequential fronthaul. More precisely, each access point (AP) in the network computes partial sufficient statistics locally, fuses it with received partial statistics from another AP, and then forward the result to the next AP. Once the sufficient statistics reach the central processing unit, it performs the soft demodulation by computing the log-likelihood ratio (LLR) per bit, and then a channel decoding algorithm (e.g., a Turbo decoder) is utilized to decode the bits. We derive the distributed computation of LLR analytically.
Our paper presents a MapReduce-based wireless distributed computing framework designed to handle data-intensive computing on edge devices with limited storage. The framework involves three stages: Map, Shuffle, and Re...
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ISBN:
(纸本)9781665464833
Our paper presents a MapReduce-based wireless distributed computing framework designed to handle data-intensive computing on edge devices with limited storage. The framework involves three stages: Map, Shuffle, and Reduce. However, shuffling large data during the second stage can lead to performance degradation over wireless interference networks with limited spectrum bandwidth. To address these issues, we propose using over-the-air computation (AirComp) technology, which leverages interference in the multiple-access channel to compute multiple target functions reliably. This approach achieves higher computation efficiency than traditional orthogonal multi-access schemes and is more effective in combating interference. Furthermore, we employ cell-free massive MIMO technology to improve coverage and reduce the system power overhead. This technology is essential for the upcoming sixth-generation (6G) networks. We optimize the transmitting-receiving (Tx-Rx) policy to minimize the averaged computation mean squared error (MSE) while adhering to each device's power constraint. Our simulation results demonstrate that our proposed algorithm is effective and our computation framework has advantages over state-of-the-art baselines.
Many of the computational problems people face are difficult to solve under the limited time and cognitive resources available to them. Overcoming these limitations through social interaction is one of the most distin...
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Many of the computational problems people face are difficult to solve under the limited time and cognitive resources available to them. Overcoming these limitations through social interaction is one of the most distinctive features of human intelligence. In this paper, we show that information accumulation in multigenerational social networks can be produced by a form of distributed Bayesian inference that allows individuals to benefit from the experience of previous generations while expending little cognitive effort. In doing so, we provide a criterion for assessing the rationality of a population that extends traditional analyses of the rationality of individuals. We tested the predictions of this analysis in two highly controlled behavioral experiments where the social transmission structure closely matched the assumptions of our model. Participants made decisions on simple categorization tasks that relied on and contributed to accumulated knowledge. Success required these microsocieties to accumulate information distributed across people and time. Our findings illustrate how in certain settings, distributed computation at the group level can pool information and resources, allowing limited individuals to perform effectively on complex tasks.
In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to compute an approximation of the multiplication of two massive matrices. The objective is to reduce the recovery threshold, defined ...
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In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to compute an approximation of the multiplication of two massive matrices. The objective is to reduce the recovery threshold, defined as the total number of worker nodes that the master node needs to wait for to be able to recover the final result. To exploit the fact that only an approximated result is required, in reducing the recovery threshold, some sorts of pre-compression are required. However, compression inherently involves some randomness that would lose the structure of the matrices. On the other hand, considering the structure of the matrices is crucial to reduce the recovery threshold. In CodedSketch, we use count-sketch, as a hash-based compression scheme, on the rows of the first and columns of the second matrix, and a structured polynomial code on the columns of the first and rows of the second matrix. This arrangement allows us to exploit the gain of both in reducing the recovery threshold. To increase the accuracy of computation, multiple independent count-sketches are needed. This independency allows us to theoretically characterize the accuracy of the result and establish the recovery threshold achieved by the proposed scheme. To guarantee the independency of resulting count-sketches in the output, while keeping its cost on the recovery threshold minimum, we use another layer of structured codes. The proposed scheme provides an upper-bound on the recovery threshold as a function of the required accuracy of computation and the probability that the required accuracy can be violated. In addition, it provides an upper-bound on the recovery threshold for the case that the result of the multiplication is sparse, and the exact result is required.
The low earth satellite networks are envisioned to be an indispensable part of next-generation network due to the seamless Internet access. Deploying distributed computation into LEO satellite networks can decrease th...
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ISBN:
(纸本)9781665454681
The low earth satellite networks are envisioned to be an indispensable part of next-generation network due to the seamless Internet access. Deploying distributed computation into LEO satellite networks can decrease the latency of transmitting satellite-terrestrial computing jobs, which is crucial to expanding the service capability. distributed computation depends on the exchange of data flows between worker nodes, as a type of concurrent and interrelated flows called coflow. However, the mesh-shaped topologies of LEO satellite networks make coflows prone to bandwidth competition on multi-hop links, which impedes the efficiency of distributed computation. In this paper, we formulated the multi-hop coflow scheduling process in LEO satellite networks as a routing and bandwidth allocation problem. Then, we simplified the problem to a coflow routing problem, and proposed the coflow routing greedy scheduling (CRGS) algorithm on the basis of the characteristics of multi-hop networks. Finally, we simulated in an SDN environment, where CRGS was deployed in an SDN controller. Compared with several existing algorithms, the CRGS algorithm is proved to reduce the coflow completion time (CCT) more effectively.
Social networking has been growing rapidly in Vietnam. The sharing information is diverse and circulates in many forms. It requires user-friendly solutions such as topic sorting and perspectives analysis in analyzing ...
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Social networking has been growing rapidly in Vietnam. The sharing information is diverse and circulates in many forms. It requires user-friendly solutions such as topic sorting and perspectives analysis in analyzing community trends, advertisements or anticipating and monitoring the spread of bad news. Unfortunately, Vietnamese is highly different from other languages and little research has been conducted in the literature on messages classification. The implementation of machine learning models on Vietnamese has not been thoroughly investigated and these models' performance is unknown when applying in a different language. Vietnamese text is a serialization of syllables, hence, word boundary identification is not trivial. This research portrays our endeavor to construct an effective distributed framework for addressing the task of classification of short Vietnamese texts on social networks using the idea of probability categorization. The authors argue that addressing the task sharps the successful combination of machine learning, natural language processing, and ambient intelligence. The proposed framework is effective and enables fast calculation, suitable for implementation in Apache Spark, meeting the demand for dealing with large amounts of textual data on the current social networks. Our data has been collected from several online text sources of 12412 short messages classified into five different topics. The evaluation shows that our approach has achieved an average of 82.73% classification accuracy. Thoughtfully learning the literature, we could state that this is the first attempt to classify short Vietnamese messages under a distributed computation framework.
We consider a status update system in which the update packets need to be processed to extract the embedded useful information. The source node sends the acquired information to a computation unit (CU) which consists ...
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We consider a status update system in which the update packets need to be processed to extract the embedded useful information. The source node sends the acquired information to a computation unit (CU) which consists of a master node and $n$ worker nodes. The master node distributes the received computation task to the worker nodes. Upon computation, the master node aggregates the results and sends them back to the source node to keep it updated. We investigate the age performance of uncoded and coded (repetition coded, MDS coded, and multi-message MDS (MM-MDS) coded) schemes in the presence of stragglers under i.i.d. exponential transmission delays and i.i.d shifted exponential computation times. We show that asymptotically MM-MDS coded scheme outperforms the other schemes. Furthermore, we characterize the optimal codes such that the average age is minimized.
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