Rao-Test Fusion Rules of Uncensored Decisions Transmitted Over a Collision Channel

Laitrakun, Seksan

doi:10.1109/wpmc.2018.8712962

Cited by 3 publications

(8 citation statements)

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“…Hence, it represents a simpler detection method for tackling composite hypothesis testing, while asymptotically yielding the same performance as the GLRT. Accordingly, several works appeared leveraging Rao test in WSN-based detection [5], [24]- [28] and, in general, score tests [29], [30] (due to analogous advantages). For example, Ciuonzo et al [5] have proposed a Rao fusion rule based on one-bit quantization of scalar measurements, whereas a corresponding generalization to multi-bit case has been devised in [24].…”

Section: B Related Workmentioning

confidence: 99%

“…The uncooperative-target case has been recently analyzed also in an online setup with a sequential version of the above fusion (one-bit) rule [27]. Furthermore, [28], [31] have applied the Rao test to collision-aware reporting for fusion design. Finally, locally most-powerful tests have been applied to decentralized detection of sparse signals in (generalized) Gaussian noise [29], [30].…”

Section: B Related Workmentioning

confidence: 99%

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Bandwidth-Constrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusion

Ciuonzo

Javadi

Mohammadi

et al. 2020

IEEE Trans. on Signal and Inf. Process. over Networks

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Decentralized detection is one of the key tasks that a wireless sensor network (WSN) is faced to accomplish. Among several decision criteria, the Rao test is able to cope with an unknown (but parametrically-specified) sensing model, while keeping computational simplicity. To this end, the Rao test is employed in this paper to fuse multivariate data measured by a set of sensor nodes, each observing the target (or the desired) event via a non-linear mapping function. In order to meet stringent energy/bandwidth requirements, sensors quantize their vector-valued observations into one or few bits and send them over error-prone (to model low-power communications) reporting channels to a fusion center (FC). Therein, a global (better) decision is taken via the proposed test. Its closed form and asymptotic (large-size WSN) performance are obtained, and the latter leveraged to optimize quantizers. The appeal of the proposed approach is confirmed via simulations.

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Bandwidth-Constrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusion

Ciuonzo

Javadi

Mohammadi

et al. 2020

IEEE Trans. on Signal and Inf. Process. over Networks

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“…Distributed detection whose FC recognizes the collision time slots, in addition to the idle time slots and successful time slots, and exploit them in final decision-making has been investigated and analyzed in many scenarios: the Neyman-Pearson framework, 28 the sequential probability ratio test, 35 and the Rao test. 36,37 In our initial work, 36,37 no channel errors have been considered and only the Rao test fusion rules for the slotted-Aloha-based distributed detection were derived. On the contrary, in this article, we comprehensively derives and extensively compares fusion rules based on the GLRT, Rao test, and Wald test for both the TDMA-based distributed detection and slotted-Aloha-based distributed detection, where channel errors exist.…”

Section: Related Workmentioning

confidence: 99%

“…where VarfEfn m jz m ; q mj0 g; q mj0 g is the variance of the MMSE estimate Efn m jz m ; q mj0 g. By substituting equations (35) and (37) into equation 14, we obtain equation (31).…”

Section: The Rao Test Fusion Rulel R Is Derived From Equationmentioning

confidence: 99%

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Decision fusion for composite hypothesis testing in wireless sensor networks over a shared and noisy collision channel

Laitrakun

2020

International Journal of Distributed Sensor Networks

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We consider the composite hypothesis testing problem of time-bandwidth-constrained distributed detection. In this scenario, the probability distribution of the observed signal when the event of interest is happening is unknown. In addition, local decisions are censored and only those uncensored local decisions will be sent to the fusion center over a shared and noisy collision channel. The fusion center also has a limited time duration to collect transmitted decisions and make a final decision. Two types of medium access control that the sensor nodes apply to send their decisions are investigated: time division multiple access and slotted-Aloha. Unlike using the time division multiple access protocol, the slotted-Aloha-based distributed detection will experience packet collisions. However, in this article, since only uncensored decisions are sent, packet collisions are informative. We derive fusion rules according to generalized likelihood ratio test, Rao test, and Wald test for both the time division multiple access–based distributed detection and the slotted-Aloha-based distributed detection. We see that the fusion rules for the slotted-Aloha-based distributed detection here also exploit packet collisions in the final decision-making. In addition, the asymptotic performances and energy consumption of both schemes are analyzed. Extensive simulation and numerical results are provided to compare the performances of these two schemes. We show that, for a given time delay, the slotted-Aloha-based distributed detection can outperform the time division multiple access–based distributed detection by increasing the number of sensor nodes which results in higher energy consumption.

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Collision-aware distributed detection with population-splitting algorithms

Laitrakun

2021

J Wireless Com Network

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We consider the design of distributed detection algorithms for single-hop, single-channel wireless sensor networks in which sensor nodes send their local decisions to a fusion center (FC) by using a random access protocol. There is also limited time to collect local decisions before a final decision must be made. We thus propose and analyze a modified random access protocol in which the FC combines slotted ALOHA with a population-splitting algorithm called population-splitting-based random access (PSRA) and collision-aware distributed detection according to an estimate-then-fuse approach. Under the PSRA, only sensor nodes whose observations fall in a particular range of reliability will send their decisions in a specific frame by using slotted ALOHA. At the end of the collection time, the FC applies the collision-aware distributed detection to make a final decision. Here, the FC will first observe the state of each time slot—idle, successful, collision—in each frame, use this information to estimate the number of sensor nodes participating in each frame, and, then, compute a final decision using a population-based fusion rule. An approximation of the optimal transmission probability of the slotted ALOHA is determined to minimize the probability of error. Numerical results show that, unlike slotted-ALOHA-based data networks, the transmission probability maximizing the number of successful time slots does not optimize the performance of the proposed distributed detection. Instead, the proposed distributed detection performs best with a transmission probability that induces many collisions.

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Rao-Test Fusion Rules of Uncensored Decisions Transmitted Over a Collision Channel

Cited by 3 publications

References 0 publications

Bandwidth-Constrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusion

Bandwidth-Constrained Decentralized Detection of an Unknown Vector Signal via Multisensor Fusion

Decision fusion for composite hypothesis testing in wireless sensor networks over a shared and noisy collision channel

Collision-aware distributed detection with population-splitting algorithms

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