Mr. Mohammad Fanaei Profile

Mohammad Fanaei

at West Virginia Univ

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Proceedings Article | 9 May 2012 Paper

Distributed parameter estimation in wireless sensor networks using fused local observations

Mohammad Fanaei, Matthew Valenti, Natalia Schmid, Marwan Alkhweldi

Proceedings Volume 8404, 840404 (2012) https://doi.org/10.1117/12.919664

KEYWORDS: Sensors, Signal to noise ratio, Analog electronics, Expectation maximization algorithms, Quantization, Sensor networks, Error analysis, Statistical analysis, Sensor fusion, Environmental sensing

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The goal of this paper is to reliably estimate a vector of unknown deterministic parameters associated with an underlying function at a fusion center of a wireless sensor network based on its noisy samples made at distributed local sensors. A set of noisy samples of a deterministic function characterized by a nite set of unknown param- eters to be estimated is observed by distributed sensors. The parameters to be estimated can be some attributes associated with the underlying function, such as its height, its center, its variances in dierent directions, or even the weights of its specic components over a predened basis set. Each local sensor processes its observation and sends its processed sample to a fusion center through parallel impaired communication channels. Two local processing schemes, namely analog and digital, are considered. In the analog local processing scheme, each sensor transmits an amplied version of its local analog noisy observation to the fusion center, acting like a relay in a wireless network. In the digital local processing scheme, each sensor quantizes its noisy observation before trans- mitting it to the fusion center. A at-fading channel model is considered between the local sensors and fusion center. The fusion center combines all of the received locally-processed observations and estimates the vector of unknown parameters of the underlying function. Two dierent well-known estimation techniques, namely maximum-likelihood (ML), for both analog and digital local processing schemes, and expectation maximization (EM), for digital local processing scheme, are considered at the fusion center. The performance of the proposed distributed parameter estimation system is investigated through simulation of practical scenarios for a sample underlying function.

Proceedings Article | 5 May 2011 Paper

Channel-aware distributed classification in wireless sensor networks using binary local decisions

Mohammad Fanaei, Matthew Valenti, Natalia Schmid, Vinodkrishnan Kulathumani

Proceedings Volume 8050, 80501L (2011) https://doi.org/10.1117/12.884397

KEYWORDS: Sensors, Binary data, Sensor networks, Signal to noise ratio, Classification systems, Sensor fusion, Environmental sensing, Magnetic sensors, Magnetism, Solids

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This paper considers the problem of distributed multi-hypothesis classification in the context of wireless sensor networks. The goal is to reliably classify an underlying hypothesis at a fusion center using simple localized decisions at individual sensors. The fusion-center classification must be performed despite the presence of faults in both local sensor decisions and transmission channels between the sensors and fusion center. Local sensor nodes make binary classifications based on their noisy observations and send their decisions to the fusion center through parallel additive white Gaussian noise channels. The fusion center then uses these noisy versions of local decisions to perform a global classification. In contrast with other similar approaches for multi-hypothesis classification based on combined binary decisions, our approach exploits the relationship between the influence fields of different hypotheses and the accumulated noisy versions of local binary decisions as received by the fusion center, where the influence field of a hypothesis is defined to be the spatial region in its surrounding in which it can be sensed using some specific modality. The main contribution of this paper is the formulation of local and fusion decision rules that maximize the probability of correct global classification at the fusion center, along with an algorithm for channel-aware global optimization of the local and fusion center decision thresholds. The performance of the proposed classification system is investigated through practical scenarios. Performance analysis results show that the proposed approach could simplify decision making at local sensors while achieving acceptable performance in terms of the global probability of correct classification at the fusion center.

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