16,317 research outputs found
Reliable data delivery in low energy ad hoc sensor networks
Reliable delivery of data is a classical design goal for reliability-oriented collection routing protocols for ad hoc wireless sensor networks (WSNs). Guaranteed packet delivery performance can be ensured by careful selection of error free links, quick recovery from packet losses, and avoidance of overloaded relay sensor nodes. Due to limited resources of individual senor nodes, there is usually a trade-off between energy spending for packets transmissions and the appropriate level of reliability. Since link failures and packet losses are unavoidable, sensor networks may tolerate a certain level of reliability without significantly affecting packets delivery performance and data aggregation accuracy in favor of efficient energy consumption. However a certain degree of reliability is needed, especially when hop count increases between source sensor nodes and the base station as a single lost packet may result in loss of a large amount of aggregated data along longer hops. An effective solution is to jointly make a trade-off between energy, reliability, cost, and agility while improving packet delivery, maintaining low packet error ratio, minimizing unnecessary packets transmissions, and adaptively reducing control traffic in favor of high success reception ratios of representative data packets. Based on this approach, the proposed routing protocol can achieve moderate energy consumption and high packet delivery ratio even with high link failure rates. The proposed routing protocol was experimentally investigated on a testbed of Crossbow's TelosB motes and proven to be more robust and energy efficient than the current implementation of TinyOS2.x MultihopLQI
A Parallel Solver for Graph Laplacians
Problems from graph drawing, spectral clustering, network flow and graph
partitioning can all be expressed in terms of graph Laplacian matrices. There
are a variety of practical approaches to solving these problems in serial.
However, as problem sizes increase and single core speeds stagnate, parallelism
is essential to solve such problems quickly. We present an unsmoothed
aggregation multigrid method for solving graph Laplacians in a distributed
memory setting. We introduce new parallel aggregation and low degree
elimination algorithms targeted specifically at irregular degree graphs. These
algorithms are expressed in terms of sparse matrix-vector products using
generalized sum and product operations. This formulation is amenable to linear
algebra using arbitrary distributions and allows us to operate on a 2D sparse
matrix distribution, which is necessary for parallel scalability. Our solver
outperforms the natural parallel extension of the current state of the art in
an algorithmic comparison. We demonstrate scalability to 576 processes and
graphs with up to 1.7 billion edges.Comment: PASC '18, Code: https://github.com/ligmg/ligm
A Machine Learning-based Framework for Building Application Failure Prediction Models
In this paper, we present the Framework for building Failure Prediction Models (F2PM), a Machine Learning-based Framework to build models for predicting the Remaining Time to Failure (RTTF) of applications in the presence of software anomalies. F2PM uses measurements of a number of system features in order to create a knowledge base, which is then used to build prediction models. F2PM is application-independent, i.e. It solely exploits measurements of system-level features. Thus, it can be used in differentiated contexts, without the need for any manual modification or intervention to the running applications. To generate optimized models, F2PM can perform a feature selection to identify, among all the measured system features, which have a major impact in the prediction of the RTTF. This allows to produce different models, which use different set of input features. Generated models can be compared by the user by using a set of metrics produced by F2PM, which are related to the model prediction accuracy, as well as to the model building time. We also present experimental results of a successful application of F2PM, using the standard TPC-W e-commerce benchmark
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