The Experts below are selected from a list of 217878 Experts worldwide ranked by ideXlab platform
Ian F. Akyildiz - One of the best experts on this subject based on the ideXlab platform.
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Spatial Correlation and mobility aware traffic modeling for wireless sensor networks
IEEE ACM Transactions on Networking, 2011Co-Authors: Pu Wang, Ian F. AkyildizAbstract:Recently, there has been a great deal of research on using mobility in wireless sensor networks (WSNs) to facilitate surveillance and reconnaissance in a wide deployment area. Besides providing an extended sensing coverage, node mobility along with Spatial Correlation introduces new network dynamics, which could lead to the traffic patterns fundamentally different from the traditional (Markovian) models. In this paper, a novel traffic modeling scheme for capturing these dynamics is proposed that takes into account the statistical patterns of node mobility and Spatial Correlation. The contributions made in this paper are twofold. First, it is shown that the joint effects of mobility and Spatial Correlation can lead to bursty traffic. More specifically, a high mobility variance and small Spatial Correlation can give rise to pseudo-long-range-dependent (LRD) traffic (high bursty traffic), whose autoCorrelation function decays slowly and hyperbolically up to a certain cutoff time lag. Second, due to the ad hoc nature of WSNs, certain relay nodes may have several routes passing through them, necessitating local traffic aggregations. At these relay nodes, our model predicts that the aggregated traffic also exhibits the bursty behavior characterized by a scaled power-law decayed autocovariance function. According to these findings, a novel traffic shaping protocol using movement coordination is proposed to facilitate effective and efficient resource provisioning strategy. Finally, simulation results reveal a close agreement between the traffic pattern predicted by our theoretical model and the simulated transmissions from multiple independent sources, under specific bounds of the observation intervals.
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Spatial Correlation and mobility aware traffic modeling for wireless sensor networks
Global Communications Conference, 2009Co-Authors: Pu Wang, Ian F. AkyildizAbstract:Recently there has been a great deal of research on using mobility in wireless sensor networks to facilitate surveillance and reconnaissance in a wide deployment area. Besides providing an extended sensing coverage, the node mobility along with the Spatial Correlation of the monitored phenomenon introduces new dynamics to the network traffic. These dynamics could lead to long range dependent (LRD) traffic, which necessitates network protocols fundamentally different from what we have employed in the traditional (Markovian) traffic. Therefore, characterizing the effects of mobility and Spatial Correlation on the dynamic behavior of the network traffic is particularly important in the effective design of network protocols. In this paper, a novel traffic modeling scheme for capturing these dynamics is proposed that takes into account the statistical patterns of human mobility and Spatial Correlation. The contributions made in this paper are twofold: first, it is shown that the mobility variability and the Spatial Correlation can lead to the pseudo-LRD traffic, whose autoCorrelation function follows a power law form with the Hurst parameter up to a certain cutoff time lag. Second, it is shown that the degree of traffic burstiness, which is characterized by the Hurst parameter, has an intimate connection with the mobility variability and the degree of Spatial Correlation. Furthermore, we show that this connection can be utilized to design the mobility-aware traffic smoothing schemes, which point out a new direction for traffic control protocols. Finally, simulation results reveal a close agreement between the traffic pattern predicted by our theoretical model and the simulated transmissions from multiple independent sources, under specific bounds of the observation intervals.
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a Spatial Correlation model for visual information in wireless multimedia sensor networks
IEEE Transactions on Multimedia, 2009Co-Authors: Rui Dai, Ian F. AkyildizAbstract:Wireless multimedia sensor networks (WMSNs) are interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists Correlation among the visual information observed by cameras with overlapped field of views. This paper proposes a novel Spatial Correlation model for visual information in WMSNs. By studying the sensing model and deployments of cameras, a Spatial Correlation function is derived to describe the Correlation characteristics of visual information observed by cameras with overlapped field of views. The joint effect of multiple correlated cameras is also studied. An entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. Furthermore, according to the proposed Correlation function and entropy-based framework, a Correlation-based camera selection algorithm is designed. Experimental results show that the proposed Spatial Correlation function can model the Correlation characteristics of visual information in WMSNs through low computation and communication costs. Further simulations show that, given a distortion bound at the sink, the Correlation-based camera selection algorithm requires fewer cameras to report to the sink than the random selection algorithm.
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a Spatial Correlation model for visual information in wireless multimedia sensor networks
IEEE Transactions on Multimedia, 2009Co-Authors: Ian F. AkyildizAbstract:Wireless multimedia sensor networks (WMSNs) are interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists Correlation among the visual information observed by cameras with overlapped field of views. This paper proposes a novel Spatial Correlation model for visual information in WMSNs. By studying the sensing model and deployments of cameras, a Spatial Correlation function is derived to describe the Correlation characteristics of visual information observed by cameras with overlapped field of views. The joint effect of multiple correlated cameras is also studied. An entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. Furthermore, according to the proposed Correlation function and entropy-based framework, a Correlation-based camera selection algorithm is designed. Experimental results show that the proposed Spatial Correlation function can model the Correlation characteristics of visual information in WMSNs through low computation and communication costs. Further simulations show that, given a distortion bound at the sink, the Correlation-based camera selection algorithm requires fewer cameras to report to the sink than the random selection algorithm.
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Spatial Correlation-based collaborative medium access control in wireless sensor networks
IEEE ACM Transactions on Networking, 2006Co-Authors: Mehmet Can Vuran, Ian F. AkyildizAbstract:Wireless Sensor Networks (WSN) are mainly characterized by dense deployment of sensor nodes which collectively transmit information about sensed events to the sink. Due to the Spatial Correlation between sensor nodes subject to observed events, it may not be necessary for every sensor node to transmit its data. This paper shows how the Spatial Correlation can be exploited on the Medium Access Control (MAC) layer. To the best of our knowledge, this is the first effort which exploits Spatial Correlation in WSN on the MAC layer. A theoretical framework is developed for transmission regulation of sensor nodes under a distortion constraint. It is shown that a sensor node can act as a representative node for several other sensor nodes observing the correlated data. Based on the theoretical framework, a distributed, Spatial Correlation-based Collaborative Medium Access Control (CC-MAC) protocol is then designed which has two components: Event MAC (E-MAC) and Network MAC (N-MAC). E-MAC filters out the Correlation in sensor records while N-MAC prioritizes the transmission of route-thru packets. Simulation results show that CC-MAC achieves high performance in terms energy, packet drop rate, and latency.
Badi H. Baltagi - One of the best experts on this subject based on the ideXlab platform.
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prediction in the panel data model with Spatial Correlation the case of liquor
Spatial Economic Analysis, 2006Co-Authors: Badi H. BaltagiAbstract:Abstract This paper considers the problem of prediction in a panel data regression model with Spatial autoCorrelation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965–1994. The Spatial autoCorrelation due to neighbouring states and the individual heterogeneity across states is taken explicitly into account. We compare the performance of several predictors of the states’ demand for liquor for 1 year and 5 years ahead. The estimators whose predictions are compared include OLS, fixed effects ignoring Spatial Correlation, fixed effects with Spatial Correlation, random-effects GLS estimator ignoring Spatial Correlation and random-effects estimator accounting for the Spatial Correlation. Based on RMSE forecast performance, estimators that take into account Spatial Correlation and heterogeneity across the states perform the best for forecasts 1 year ahead. However, for forecasts 2–5 years ahead, estimators that take into account the heterogeneity a...
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prediction in the panel data model with Spatial Correlation the case of liquor
Social Science Research Network, 2006Co-Authors: Badi H. BaltagiAbstract:This paper considers the problem of prediction in a panel data regression model with Spatial auto-Correlation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965-1994. The Spatial auto-Correlation due to neighboring states and the individual heterogeneity across states is taken explicitly into account. We compare the performance of several predictors of the states demand for liquor for one year and five years ahead. The estimators whose predictions are compared include OLS, fixed effects ignoring Spatial Correlation, fixed effects with Spatial Correlation, random effects GLS estimator ignoring Spatial Correlation and random effects estimator accounting for the Spatial Correlation. Based on RMSE forecast performance, estimators that take into account Spatial Correlation and heterogeneity across the states perform the best for one year ahead forecasts. However, for two to five years ahead forecasts, estimators that take into account the heterogeneity across the states yield the best forecasts.
George C Alexandropoulos - One of the best experts on this subject based on the ideXlab platform.
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hybrid processing design for multipair massive mimo relaying with channel Spatial Correlation
IEEE Transactions on Communications, 2019Co-Authors: Milad Fozooni, Hien Quoc Ngo, Michail Matthaiou, Shi Jin, George C AlexandropoulosAbstract:Massive multiple-input multiple-output (MIMO) avails of simple transceiver design which can tackle many drawbacks of relay systems in terms of complicated signal processing, latency, and noise amplification. However, the cost and circuit complexity of having one radio frequency (RF) chain dedicated to each antenna element are prohibitive in practice. In this paper, we address this critical issue in amplify-and-forward (AF) relay systems using a hybrid analog and digital (A/D) transceiver structure. More specifically, leveraging the channel long-term properties, we design the analog beamformer which aims to minimize the channel estimation error and remain invariant over a long timescale. Then, the beamforming is completed by simple digital signal processing, i.e., maximum ratio combining/maximum ratio transmission (MRC/MRT) or zero forcing (ZF) in the baseband domain. We present analytical bounds on the achievable spectral efficiency taking into account the Spatial Correlation and imperfect channel state information at the relay station. Our analytical results reveal that the hybrid A/D structure with ZF digital processor exploits Spatial Correlation and offers a higher spectral efficiency compared to the hybrid A/D structure with MRC/MRT scheme. Our numerical results show that the hybrid A/D beamforming design captures nearly 95% of the spectral efficiency of a fully digital AF relaying topology even by removing half of the RF chains. It is also shown that the hybrid A/D structure is robust to coarse quantization, and even with 2-bit resolution, the system can achieve more than 93% of the spectral efficiency offered by the same hybrid A/D topology with infinite resolution phase shifters.
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hybrid processing design for multipair massive mimo relaying with channel Spatial Correlation
arXiv: Information Theory, 2018Co-Authors: Milad Fozooni, Hien Quoc Ngo, Michail Matthaiou, Shi Jin, George C AlexandropoulosAbstract:Massive multiple-input multiple-output (MIMO) avails of simple transceiver design which can tackle many drawbacks of relay systems in terms of complicated signal processing, latency, and noise amplification. However, the cost and circuit complexity of having one radio frequency (RF) chain dedicated to each antenna element are prohibitive in practice. In this paper, we address this critical issue in amplify-and-forward (AF) relay systems using a hybrid analog and digital (A/D) transceiver structure. More specifically, leveraging the channel long-term properties, we design the analog beamformer which aims to minimize the channel estimation error and remain invariant over a long timescale. Then, the beamforming is completed by simple digital signal processing, i.e., maximum ratio combining/maximum ratio transmission (MRC/MRT) or zero-forcing (ZF) in the baseband domain. We present analytical bounds on the achievable spectral efficiency taking into account the Spatial Correlation and imperfect channel state information at the relay station. Our analytical results reveal that the hybrid A/D structure with ZF digital processor exploits Spatial Correlation and offers a higher spectral efficiency compared to the hybrid A/D structure with MRC/MRT scheme. Our numerical results showcase that the hybrid A/D beamforming design captures nearly 95% of the spectral efficiency of a fully digital AF relaying topology even by removing half of the RF chains. It is also shown that the hybrid A/D structure is robust to coarse quantization, and even with 2-bit resolution, the system can achieve more than 93% of the spectral efficiency offered by the same hybrid A/D topology with infinite resolution phase shifters.
Sudip Barua - One of the best experts on this subject based on the ideXlab platform.
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effects of Spatial Correlation in random parameters collision count data models
Analytic Methods in Accident Research, 2015Co-Authors: Sudip Barua, Karim Elbasyouny, Md Tazul IslamAbstract:This study investigated the inclusion of Spatial Correlation in random parameters collision count-data models. Three different modeling formulations were applied to measure the effects of Spatial Correlation in random parameters models using three years of collision data collected from two cities, Richmond and Vancouver (British Columbia, Canada). The proposed models were estimated in a Full Bayesian (FB) context using a Markov Chain Monte Carlo (MCMC) simulation. The Deviance Information Criteria (DIC) values and chi-square statistics indicated that all the models were comparable to one another. According to the parameter estimates, a variety of traffic and road geometric covariates were found to significantly influence collision frequencies. For the Richmond dataset, only 38.3% of the total variability was explained by Spatial Correlation under model with both heterogeneous effects and Spatial Correlation (Model C), as most of the variations were likely captured by heterogeneous effects and site variation. For the Vancouver dataset, the effects of Spatial Correlation were much clearer, with a high percentage of the total variability (83.8%) explained by Spatial Correlation under Model C. Moreover, model estimation results showed that the precision of parameter estimates slightly improved by inclusion of Spatial Correlation when the sample size was small. However, parameter estimations did not change significantly and goodness of fit did not improve which indicate that it cannot be substantiated with the current datasets that the random parameters model with both heterogeneous effects and Spatial Correlation is better than other models investigated. Therefore, further studies with different datasets are needed to get more clear understanding of the added benefits of incorporating Spatial Correlation in random parameters model.
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a full bayesian multivariate count data model of collision severity with Spatial Correlation
Analytic Methods in Accident Research, 2014Co-Authors: Sudip Barua, Karim Elbasyouny, Tazul IslamAbstract:This study investigated the inclusion of Spatial Correlation in multivariate count data models of collision severity. The models were developed for severe (injury and fatal) and no-injury collisions using three years of collision data from the city of Richmond and the city of Vancouver. The proposed models were estimated in a Full Bayesian (FB) context via Markov Chain Monte Carlo (MCMC) simulation. The multivariate model with both heterogeneous effects and Spatial Correlation provided the best fit according to the Deviance Information Criteria (DIC). The results showed significant positive Correlation between various road attributes and collision severities. For the Richmond dataset, the proportion of variance for Spatial Correlation was smaller than the proportion of variance for heterogeneous effects. Conversely, the Spatial variance was greater than the heterogeneous variance for the Vancouver dataset. The Correlation between severe and no-injury collisions for the total random effects (heterogeneous and Spatial) was significant and quite high (0.905 for Richmond and 0.945 for Vancouver), indicating that a higher number of no-injury collisions is associated with a higher number of severe collisions. Furthermore, the multivariate Spatial models were compared with two independent univariate Poisson lognormal (PLN) Spatial models, with respect to model inference and goodness-of-fit. Multivariate Spatial models provide a superior fit over the two univariate PLN Spatial models with a significant drop in the DIC value (35.3 for Richmond and 116 for Vancouver). These results advocate the use of multivariate models with both heterogeneous effects and Spatial Correlation over univariate PLN Spatial models.
Milad Fozooni - One of the best experts on this subject based on the ideXlab platform.
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hybrid processing design for multipair massive mimo relaying with channel Spatial Correlation
IEEE Transactions on Communications, 2019Co-Authors: Milad Fozooni, Hien Quoc Ngo, Michail Matthaiou, Shi Jin, George C AlexandropoulosAbstract:Massive multiple-input multiple-output (MIMO) avails of simple transceiver design which can tackle many drawbacks of relay systems in terms of complicated signal processing, latency, and noise amplification. However, the cost and circuit complexity of having one radio frequency (RF) chain dedicated to each antenna element are prohibitive in practice. In this paper, we address this critical issue in amplify-and-forward (AF) relay systems using a hybrid analog and digital (A/D) transceiver structure. More specifically, leveraging the channel long-term properties, we design the analog beamformer which aims to minimize the channel estimation error and remain invariant over a long timescale. Then, the beamforming is completed by simple digital signal processing, i.e., maximum ratio combining/maximum ratio transmission (MRC/MRT) or zero forcing (ZF) in the baseband domain. We present analytical bounds on the achievable spectral efficiency taking into account the Spatial Correlation and imperfect channel state information at the relay station. Our analytical results reveal that the hybrid A/D structure with ZF digital processor exploits Spatial Correlation and offers a higher spectral efficiency compared to the hybrid A/D structure with MRC/MRT scheme. Our numerical results show that the hybrid A/D beamforming design captures nearly 95% of the spectral efficiency of a fully digital AF relaying topology even by removing half of the RF chains. It is also shown that the hybrid A/D structure is robust to coarse quantization, and even with 2-bit resolution, the system can achieve more than 93% of the spectral efficiency offered by the same hybrid A/D topology with infinite resolution phase shifters.
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hybrid processing design for multipair massive mimo relaying with channel Spatial Correlation
arXiv: Information Theory, 2018Co-Authors: Milad Fozooni, Hien Quoc Ngo, Michail Matthaiou, Shi Jin, George C AlexandropoulosAbstract:Massive multiple-input multiple-output (MIMO) avails of simple transceiver design which can tackle many drawbacks of relay systems in terms of complicated signal processing, latency, and noise amplification. However, the cost and circuit complexity of having one radio frequency (RF) chain dedicated to each antenna element are prohibitive in practice. In this paper, we address this critical issue in amplify-and-forward (AF) relay systems using a hybrid analog and digital (A/D) transceiver structure. More specifically, leveraging the channel long-term properties, we design the analog beamformer which aims to minimize the channel estimation error and remain invariant over a long timescale. Then, the beamforming is completed by simple digital signal processing, i.e., maximum ratio combining/maximum ratio transmission (MRC/MRT) or zero-forcing (ZF) in the baseband domain. We present analytical bounds on the achievable spectral efficiency taking into account the Spatial Correlation and imperfect channel state information at the relay station. Our analytical results reveal that the hybrid A/D structure with ZF digital processor exploits Spatial Correlation and offers a higher spectral efficiency compared to the hybrid A/D structure with MRC/MRT scheme. Our numerical results showcase that the hybrid A/D beamforming design captures nearly 95% of the spectral efficiency of a fully digital AF relaying topology even by removing half of the RF chains. It is also shown that the hybrid A/D structure is robust to coarse quantization, and even with 2-bit resolution, the system can achieve more than 93% of the spectral efficiency offered by the same hybrid A/D topology with infinite resolution phase shifters.
