9,702 research outputs found
Networked Computing in Wireless Sensor Networks for Structural Health Monitoring
This paper studies the problem of distributed computation over a network of
wireless sensors. While this problem applies to many emerging applications, to
keep our discussion concrete we will focus on sensor networks used for
structural health monitoring. Within this context, the heaviest computation is
to determine the singular value decomposition (SVD) to extract mode shapes
(eigenvectors) of a structure. Compared to collecting raw vibration data and
performing SVD at a central location, computing SVD within the network can
result in significantly lower energy consumption and delay. Using recent
results on decomposing SVD, a well-known centralized operation, into
components, we seek to determine a near-optimal communication structure that
enables the distribution of this computation and the reassembly of the final
results, with the objective of minimizing energy consumption subject to a
computational delay constraint. We show that this reduces to a generalized
clustering problem; a cluster forms a unit on which a component of the overall
computation is performed. We establish that this problem is NP-hard. By
relaxing the delay constraint, we derive a lower bound to this problem. We then
propose an integer linear program (ILP) to solve the constrained problem
exactly as well as an approximate algorithm with a proven approximation ratio.
We further present a distributed version of the approximate algorithm. We
present both simulation and experimentation results to demonstrate the
effectiveness of these algorithms
Hearing the clusters in a graph: A distributed algorithm
We propose a novel distributed algorithm to cluster graphs. The algorithm
recovers the solution obtained from spectral clustering without the need for
expensive eigenvalue/vector computations. We prove that, by propagating waves
through the graph, a local fast Fourier transform yields the local component of
every eigenvector of the Laplacian matrix, thus providing clustering
information. For large graphs, the proposed algorithm is orders of magnitude
faster than random walk based approaches. We prove the equivalence of the
proposed algorithm to spectral clustering and derive convergence rates. We
demonstrate the benefit of using this decentralized clustering algorithm for
community detection in social graphs, accelerating distributed estimation in
sensor networks and efficient computation of distributed multi-agent search
strategies
Rate-distortion Balanced Data Compression for Wireless Sensor Networks
This paper presents a data compression algorithm with error bound guarantee
for wireless sensor networks (WSNs) using compressing neural networks. The
proposed algorithm minimizes data congestion and reduces energy consumption by
exploring spatio-temporal correlations among data samples. The adaptive
rate-distortion feature balances the compressed data size (data rate) with the
required error bound guarantee (distortion level). This compression relieves
the strain on energy and bandwidth resources while collecting WSN data within
tolerable error margins, thereby increasing the scale of WSNs. The algorithm is
evaluated using real-world datasets and compared with conventional methods for
temporal and spatial data compression. The experimental validation reveals that
the proposed algorithm outperforms several existing WSN data compression
methods in terms of compression efficiency and signal reconstruction. Moreover,
an energy analysis shows that compressing the data can reduce the energy
expenditure, and hence expand the service lifespan by several folds.Comment: arXiv admin note: text overlap with arXiv:1408.294
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
Surface profile prediction and analysis applied to turning process
An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
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