On the Effectiveness of Multiple Antennas in Distributed Detection over Fading MACs

Banavar, Mahesh K.; Smith, Anthony D.; Tepedelenlioğlu, Cihan; Spanias, Andreas

doi:10.1109/twc.2012.031212.110814

Cited by 58 publications

(66 citation statements)

References 22 publications

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“…is also optimal solution to problem (15). Note that the above equivalence holds for any feasible η, and hence it holds for the optimal η.…”

Section: Theorem 1 (Properties Of Optimal Solution)mentioning

confidence: 76%

“…The benefits of such technology in the context of WSNs have been recently studied for distributed inference [5], [7], [15]- [18]. For a large-scale fusion center (FC) over a Rayleigh fading channel, it has been shown in [17] that the detection/estimation performance remains asymptotically constant if the transmit power at each sensor decreases proportionally with increasing number of antennas at the FC.…”

Section: A Previous Workmentioning

confidence: 99%

“…Remark 3 (The Equivalence of Problems (15) and (SDR1)): We remark that since problem (SDR1) is a relaxed version of problem (15), in general, the solution to problem (SDR1) provides an upper bound on the optimal solution to problem (15), or equivalently, an upper bound on problem (P1) for a given v v v. Fortunately, we can show that the optimal solution to (SDR1) is also optimal to (15). To do that, let Φ η Q Q Q,W W W be the objective function of problem (15) or (SDR1) for a given feasible η, and…”

Section: Theorem 1 (Properties Of Optimal Solution)mentioning

confidence: 99%

See 2 more Smart Citations

Wireless Power Transfer for Distributed Estimation in Sensor Networks

Vien¹,

Shin²,

Ishibashi³

2017

IEEE J. Sel. Top. Signal Process.

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This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplifyand-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the non-convex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for colocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC. Index TermsAmplify-and-forwarding, best linear unbiased estimator (BLUE), distributed estimation, mean-squared error (MSE), wireless power transfer (WPT).

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“…is also optimal solution to problem (15). Note that the above equivalence holds for any feasible η, and hence it holds for the optimal η.…”

Section: Theorem 1 (Properties Of Optimal Solution)mentioning

confidence: 76%

Section: A Previous Workmentioning

confidence: 99%

Section: Theorem 1 (Properties Of Optimal Solution)mentioning

confidence: 99%

See 1 more Smart Citation

Wireless Power Transfer for Distributed Estimation in Sensor Networks

Vien¹,

Shin²,

Ishibashi³

2017

IEEE J. Sel. Top. Signal Process.

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“…Recently, the intrinsic interfering nature of the wireless medium has been exploited in the context of wireless sensor networks for distributed detection. A multiple access channel has replaced the parallel access channel for improving bandwidth efficiency [46] and the advantage of multiple antennas at the fusion center has been analytically analyzed using the error exponent [47]. Looking at the network as a "virtual" multi-input multi-output (MIMO) system, array processing techniques at the fusion center have been investigated and compared in terms of performance, complexity, and system knowledge requirements [48].…”

Section: Collection Of Indicators From Sensorsmentioning

confidence: 99%

Real-Time Data for Risk Assessment in the Offshore Oil and Gas Industry

Paltrinieri

Landucci

Rossi

2017

Volume 3B: Structures, Safety and Reliability

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Recent major accidents in the offshore oil and gas (O&G) industry have showed inadequate assessment of system risk and demonstrated the need to improve risk analysis. While direct causes often differ, the failure to update risk evaluation on the basis of system changes/modifications has been a recurring problem. Risk is traditionally defined as a measure of the accident likelihood and the magnitude of loss, usually assessed as damage to people, to the environment, and/or economic loss. Recent revisions of such definition include also aspects of uncertainty. However, Quantitative Risk Assessment (QRA) in the offshore O&G industry is based on consolidated procedures and methods, where periodic evaluation and update of risk is not commonly carried out. Several methodologies were recently developed for dynamic risk analysis of the offshore O&G industry. Dynamic fault trees, Markov chain models for the lifecycle analysis, and Weibull failure analysis may be used for dynamic frequency evaluation and risk assessment update. Moreover, dynamic risk assessment methods were developed in order to evaluate the risk by updating initial failure probabilities of events (causes) and safety barriers as new information are made available. However, the mentioned techniques are not widely applied in the common O&G offshore practice due to several reasons, among which their complexity has a primary role. More intuitive approaches focusing on a selected number of critical factors have also been suggested, such as the Risk Barometer or the TEC2O. Such techniques are based on the evaluation of technical, operational and organizational factors. The methodology allows supporting periodic update of QRA by collecting and aggregating a set of indicators. However, their effectiveness relies on continuous monitoring activity and realtime data capturing. For this reason, this contribution focuses on the coupling of such methods with sensors of different nature located in or around and offshore O&G system. The inheritance from the Centre for Integrated Operations in the Petroleum Industries represents the basis of such study. Such approach may be beneficial for several cases in which (quasi) real-time risk evaluation may support critical operations. Two representative cases have been described: i) erosion and corrosion issues due to sand production; and ii) oil production in environmental sensitive areas. In both the cases, dynamic risk analysis may employ real-time data provided by sand, corrosion and leak detectors. A simulation of dynamic risk analysis has demonstrated how the variation of such data can affect the overall risk picture. In fact, this risk assessment approach has not only the capability to continuously iterate and outline improved system risk pictures, but it can also compare its results with sensor-measured data and allow for calibration. This can potentially guarantee progressive improvement of the method reliability for appropriate support to safety-critical decisions. Direct causes of such events often differed, but...

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“…In study (Banavar et al, 2012), a distributed detection problem over fading Gaussian multiple-access channels is considered. Sensors observe a phenomenon and transmit their observations to a fusion center using the amplify and forward scheme.…”

Section: Communication Protocolmentioning

confidence: 99%

An Application Specific Windows Management Instrumentation Architecture for Improving Performance on Wireless Network

Narayanan¹

2014

Journal of Computer Science

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Wireless Communications is one of the fastest growing segments in the communications industry. Wireless Network is the network that facilitates communication among two or more computers connected through the standard network protocols, without network cabling. Generally, the Wireless Network is termed as Wireless Local Area Networks (WLAN). A WLAN is a collection of network components connected by electromagnetic (radio) waves instead of cables. Each Wireless Network consists of a Network Provider, which provides the network connection to the user. With the help of this network provider, the client (user) can able to transfer the data to and from the server. In this process, if the speed between the client and the server does not match, then there may be the chance for data loss. Due to the data loss, the network performance will get reduced. To avoid these problems, we propose architecture for wireless network communication, in order to reduce the data loss and other problems and to increase the network performance. The proposed architecture can able to perform on both the TCP layer and UDP layer.

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On the Effectiveness of Multiple Antennas in Distributed Detection over Fading MACs

Cited by 58 publications

References 22 publications

Wireless Power Transfer for Distributed Estimation in Sensor Networks

Wireless Power Transfer for Distributed Estimation in Sensor Networks

Real-Time Data for Risk Assessment in the Offshore Oil and Gas Industry

An Application Specific Windows Management Instrumentation Architecture for Improving Performance on Wireless Network

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