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WLAN-based Indoor Path Tracking Using Compressive RSS Measurements WLAN-based Indoor Path Tracking Using Compressive RSS Measurements

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Date added: 02/21/2014
Date modified: 02/21/2014
Filesize: 382.89 kB
Downloads: 843

In this paper, a hybrid path-tracking system is introduced, which exploits the power of compressive sensing (CS) to recover accurately sparse signals, in conjunction with the efficiency of a Kalman filter to update the states of a dynamical system. The proposed method first employs a hierarchical region-based approach to constrain the area of interest, by modeling the signal-strength values received from a set of wireless access points using the statistics of multivariate Gaussian models. Then, based on the inherent spatial sparsity of indoor localization, CS is applied as a refinement of the estimated position by recovering an appropriate sparse position-indicator vector. The experimental evaluation with real data reveals that the proposed approach achieves increased localization accuracy when compared with previous methods, while maintaining a low computational complexity, thus, satisfying the constraints of mobile devices with limited resources.

Wireless Sensor Network for Spectrum Cartography Based on Kriging Interpolation Wireless Sensor Network for Spectrum Cartography Based on Kriging Interpolation

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Date added: 08/30/2013
Date modified: 08/30/2013
Filesize: 286 Bytes
Downloads: 1476

Dynamic spectrum access with Cognitive Radio (CR) network is a promising approach to increase the efficiency of spectrum usage. To allow the optimization of resource allocation and transmission adaptation techniques, each CR terminal needs to acquire awareness of the state of the time-frequency-location varying radio spectrum. In this paper we present a Spectrum Cartography (SC) approach where CR terminals are supported by a fixed wireless sensor network (WSN) to estimate and update the Power Spectral Density (PSD) over the area of interest. The wireless sensors collaborate to estimate the spatial distribution of the received power at a given frequency using either a centralized or a distributed Kriging (DK) algorithm. We present an analysis of the semivariogram models used to estimate the spatial statistics of wireless PSD distributions. The performance of the centralized and DK algorithms are evaluated by simulating different realizations of the PSD and the results are compared with classical interpolating schemes varying the density of nodes in the area and the number of nodes used for local estimation.

Weighted Sum-Rate Maximization for MISO Downlink Cellular Networks via Branch and Bound Weighted Sum-Rate Maximization for MISO Downlink Cellular Networks via Branch and Bound

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Date added: 12/24/2013
Date modified: 12/28/2013
Filesize: 286 Bytes
Downloads: 1353

The problem of weighted sum-rate maximization (WSRMax) in multicell downlink multiple-input single-output (MISO) systems is considered. The problem is known to be NP-hard. We propose a method, based on branch and bound technique, which solves globally the nonconvex WSRMax problem with an optimality certificate. Specifically, the algorithm computes a sequence of asymptotically tight upper and lower bounds and it terminates when the difference between them falls below a pre-specified tolerance. Novel bounding techniques via conic optimization are introduced and their efficiency is demonstrated by numerical simulations. The proposed method can be used to provide performance benchmarks by back-substituting it into many existing network design problems which relies on WSRMax problem. The method proposed here can be easily extended to maximize any system performance metric that can be expressed as a Lipschitz continuous and increasing function of signal-to-interference-plus-noise ratio.

Visual and inertial multi-rate data fusion for motion estimation via Pareto-optimization Visual and inertial multi-rate data fusion for motion estimation via Pareto-optimization

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Date added: 02/11/2015
Date modified: 02/11/2015
Filesize: 286 Bytes
Downloads: 1065

Motion estimation is an open research field in control and robotic applications. Sensor fusion algorithms are generally used to achieve an accurate estimation of the vehicle motion by combining heterogeneous sensors measurements with different statistical characteristics. In this paper, a new method that combines measurements provided by an inertial sensor and a vision system is presented. Compared to classical model-based techniques, the method relies on a Pareto optimization that trades off the statistical properties of the measurements. The proposed technique is evaluated with simulations in terms of computational requirements and estimation accuracy with respect to a classical Kalman filter approach. It is shown that the proposed method gives an improved estimation accuracy at the cost of a slightly increased computational complexity.

Topology optimization for a trade off between energy cost and network lifetime in average consensus Topology optimization for a trade off between energy cost and network lifetime in average consensus

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Date added: 12/30/2013
Date modified: 12/30/2013
Filesize: 286 Bytes
Downloads: 1329

Consensus algorithms are simple processes that involve repeated communications between the nodes of the network until a consensus is reached with certain accuracy. In this setting, the lifetime of the network and the total required energy not only depend on the number of iterations needed to achieve consensus, but also on the power consumption per node and iteration. In this work, we propose a method to optimize the network topology in order to reduce the total energy required to achieve consensus while increasing the network lifetime. Our solution is based on an optimization technique that performs a tradeoff between these two concepts. Simulation results, under different types of networks, are presented to show clearly the efficiency and validity of our approach.

Stream Correlation Monitoring for Uncertainty-Aware Data Processing Systems Stream Correlation Monitoring for Uncertainty-Aware Data Processing Systems

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1267

In several industrial applications, monitoring large-scale infrastructures in order to provide notifications for abnormal behavior is of high significance. For this purpose, the deployment of large-scale sensor networks is the current trend. However, this results in handling vast amounts of low-level, and often unreliable, data, while an efficient and real-time data manipulation is a strong demand. In this paper, we propose an uncertainty-aware data management system capable of monitoring pairwise correlations of large sensor data streams in real-time. An efficient similarity function based on the truncated DFT is employed instead of the typical correlation coefficient to monitor dynamic phenomena for timely alerting notifications, and to guarantee the validity of detected extreme events. Experimental evaluation with a set of real data recorded by distinct sensors in an industrial water desalination plant reveals a high performance of our proposed approach in terms of achieving significantly reduced execution times, along with increased accuracy in detecting highly correlated pairs of sensor data streams.

Reducing the observation error in a WSN through a consensus-based subspace projection Reducing the observation error in a WSN through a consensus-based subspace projection

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Date added: 12/30/2013
Date modified: 12/30/2013
Filesize: 286 Bytes
Downloads: 1359

An essential process in a Wireless Sensor Network is the noise mitigation of the measured data, by exploiting their spatial correlation. A widely used technique to achieve this reduction is to project the measured data into a proper subspace. We present a low complexity and distributed algorithm to perform this projection. Unlike other algorithms existing in the literature, which require the number of connections at every node to be larger than the dimension of the involved subspace, our algorithm does not require such dense network topologies for its applicability, making it suitable for a larger number of scenarios. Our proposed algorithm is based on the execution of several consensus processes, and therefore the mixing weights that drive the iterative process can be much more easily computed by using information local to each particular node. These two main advantages makes our approach more suitable for large networks composed by simple and power limited nodes. Simulations results are presented to show that our algorithm performs the projection faster and, in several scenarios, consuming less energy than other existing works in the related literature.

Real-time scheduling in LTE for smart grids Real-time scheduling in LTE for smart grids

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Date added: 02/06/2013
Date modified: 12/28/2013
Filesize: 286 Bytes
Downloads: 1826

 

The latest wireless network, 3GPP Long Term Evolution (LTE), is considered to be a promising solution for smart grids because it provides both low latency and large bandwidth. However, LTE was not originally intended for smart grids applications, where data generated by the grid have specific delay requirements that are different from traditional data or voice communications. In this paper, the specific requirements imposed by a smart grids on the LTE communication infrastructure is first determined. The latency offered by the LTE network to smart grids components is investigated and an empirical mathematical model of the distribution of the latency is established. It is shown by experimental results that with the current LTE up-link scheduler, smart grid latency requirements are not always satisfied and that only a limited number of components can be accommodated. To overcome such a deficiency, a new scheduler of the LTE medium access control is proposed for smart grids. The scheduler is based on a mathematical linear optimization problem that considers simultaneously both the smart grid components and common user equipments. An algorithm for the solution to such a problem is derived based on a theoretical analysis. Simulation results based on this new scheduler illustrate the analysis. It is concluded that LTE can be effectively used in smart grids if new schedulers are employed for improving latency.

Reaction-Diffusion on Dynamic Inhibition Areas: A Bio-Inspired Link Scheduling Algorithm Reaction-Diffusion on Dynamic Inhibition Areas: A Bio-Inspired Link Scheduling Algorithm

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Date added: 02/11/2015
Date modified: 02/11/2015
Filesize: 286 Bytes
Downloads: 1088

We present the Dynamic Inhibition Areas Reaction-Diffusion (DIA-RD) algorithm, a distributed medium access control protocol that globally maximizes the spatial reusability (number of simultaneous transmissions per unit area) of wireless sensor networks. This algorithm is able, in consequence, to minimize the number of time slots needed to schedule the set of demanded links, making it very efficient to solve the Shortest Link Schedule problem. DIA-RD combines accurate interference management, provided by the use of dynamic inhibition areas based on the physical interference model; and global intelligent behavior, provided by the bio-inspired technique known as Reaction-Diffusion. This technique ensures global convergence to dense feasible transmission patterns (no active link inside the inhibition area of other active link) in a decentralized way. Experimental results show that our DIA-RD algorithm provides superior performance, in terms of spatial reusability, than the best state-of-the-art approaches, namely the DIA-LS, RD-MAC, GOW* and ML2 S algorithms.

Rate Allocation for Quantized Control Over Binary Symmetric Channels Rate Allocation for Quantized Control Over Binary Symmetric Channels

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Date added: 01/29/2013
Date modified: 08/03/2013
Filesize: 286 Bytes
Downloads: 1714

 

Utility maximization in networked control systems (NCSs) is difficult in the presence of limited sensing and communication resources. In this paper, a new communication rate optimization method for state feedback control over a noisy channel is proposed. Linear dynamic systems with quantization errors, limited transmission rate, and noisy communication channels are considered. The most challenging part of the optimization is that no closed-form expressions are available for assessing the performance and the optimization problem is nonconvex. The proposed method consists of two steps: (i) the overall NCS performance measure is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize a weighted quadratic objective function is solved. The proposed method is applied to the problem of state feedback control and the problem of state estimation. Monte Carlo simulations illustrate the performance of the proposed rate allocation. It is shown numerically that the proposed method has better performance when compared to arbitrarily selected rate allocations. Also, it is shown that in certain cases nonuniform rate allocation can outperform the uniform rate allocation, which is commonly considered in quantized control systems, for feedback control over noisy channels.