Distributed Detection and Estimation in Wireless Sensor Networks

Wireless sensor networks (WSNs) are typically formed by a large number of densely deployed, spatially distributed sensors with limited sensing, computing, and communication capabilities that cooperate with each other to achieve a common goal. In this dissertation, we investigate the problem of distributed detection, classification, estimation, and localization in WSNs. In this context, the sensors observe the conditions of their surrounding environment, locally process their noisy observations, and send the processed data to a central entity, known as the fusion center (FC), through parallel communication channels corrupted by fading and additive noise. The FC will then combine the received information from the sensors to make a global inference about the underlying phenomenon, which can be either the detection or classification of a discrete variable or the estimation of a continuous one.;In the domain of distributed detection and classification, we propose a novel scheme that enables the FC to make a multi-hypothesis classification of an underlying hypothesis using only binary detections of spatially distributed sensors. This goal is achieved by exploiting the relationship between the influence fields characterizing different hypotheses and the accumulated noisy versions of local binary decisions as received by the FC, where the influence field of a hypothesis is defined as the spatial region in its surrounding in which it can be sensed using some sensing modality. In the realm of distributed estimation and localization, we make four main contributions: (a) We first formulate a general framework that estimates a vector of parameters associated with a deterministic function using spatially distributed noisy samples of the function for both analog and digital local processing schemes. ( b) We consider the estimation of a scalar, random signal at the FC and derive an optimal power-allocation scheme that assigns the optimal local amplification gains to the sensors performing analog local processing. The objective of this optimized power allocation is to minimize the L 2-norm of the vector of local transmission powers, given a maximum estimation distortion at the FC. We also propose a variant of this scheme that uses a limited-feedback strategy to eliminate the requirement of perfect feedback of the instantaneous channel fading coefficients from the FC to local sensors through infinite-rate, error-free links. ( c) We propose a linear spatial collaboration scheme in which sensors collaborate with each other by sharing their local noisy observations. We derive the optimal set of coefficients used to form linear combinations of the shared noisy observations at local sensors to minimize the total estimation distortion at the FC, given a constraint on the maximum average cumulative transmission power in the entire network. (d) Using a novel performance measure called the estimation outage, we analyze the effects of the spatial randomness of the location of the sensors on the quality and performance of localization algorithms by considering an energy-based source-localization scheme under the assumption that the sensors are positioned according to a uniform clustering process

Limited-Feedback-Based Channel-Aware Power Allocation for Linear Distributed Estimation

This paper investigates the problem of distributed best linear unbiased estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless sensor network (WSN). In particular, the application of limited-feedback strategies for the optimal power allocation in distributed estimation is studied. In order to find the BLUE estimator of the unknown parameter, the FC combines spatially distributed, linearly processed, noisy observations of local sensors received through orthogonal channels corrupted by fading and additive Gaussian noise. Most optimal power-allocation schemes proposed in the literature require the feedback of the exact instantaneous channel state information from the FC to local sensors. This paper proposes a limited-feedback strategy in which the FC designs an optimal codebook containing the optimal power-allocation vectors, in an iterative offline process, based on the generalized Lloyd algorithm with modified distortion functions. Upon observing a realization of the channel vector, the FC finds the closest codeword to its corresponding optimal power-allocation vector and broadcasts the index of the codeword. Each sensor will then transmit its analog observations using its optimal quantized amplification gain. This approach eliminates the requirement for infinite-rate digital feedback links and is scalable, especially in large WSNs.Comment: 5 Pages, 3 Figures, 1 Algorithm, Forty Seventh Annual Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2013

Effects of Spatial Randomness on Locating a Point Source with Distributed Sensors

Most studies that consider the problem of estimating the location of a point source in wireless sensor networks assume that the source location is estimated by a set of spatially distributed sensors, whose locations are fixed. Motivated by the fact that the observation quality and performance of the localization algorithm depend on the location of the sensors, which could be randomly distributed, this paper investigates the performance of a recently proposed energy-based source-localization algorithm under the assumption that the sensors are positioned according to a uniform clustering process. Practical considerations such as the existence and size of the exclusion zones around each sensor and the source will be studied. By introducing a novel performance measure called the estimation outage, it will be shown how parameters related to the network geometry such as the distance between the source and the closest sensor to it as well as the number of sensors within a region surrounding the source affect the localization performance.Comment: 7 Pages, 5 Figures, To appear at the 2014 IEEE International Conference on Communications (ICC'14) Workshop on Advances in Network Localization and Navigation (ANLN), Invited Pape

Power Allocation for Distributed BLUE Estimation with Full and Limited Feedback of CSI

This paper investigates the problem of adaptive power allocation for distributed best linear unbiased estimation (BLUE) of a random parameter at the fusion center (FC) of a wireless sensor network (WSN). An optimal power-allocation scheme is proposed that minimizes the $L^2$-norm of the vector of local transmit powers, given a maximum variance for the BLUE estimator. This scheme results in the increased lifetime of the WSN compared to similar approaches that are based on the minimization of the sum of the local transmit powers. The limitation of the proposed optimal power-allocation scheme is that it requires the feedback of the instantaneous channel state information (CSI) from the FC to local sensors, which is not practical in most applications of large-scale WSNs. In this paper, a limited-feedback strategy is proposed that eliminates this requirement by designing an optimal codebook for the FC using the generalized Lloyd algorithm with modified distortion metrics. Each sensor amplifies its analog noisy observation using a quantized version of its optimal amplification gain, which is received by the FC and used to estimate the unknown parameter.Comment: 6 pages, 3 figures, to appear at the IEEE Military Communications Conference (MILCOM) 201

Dynamic Simulation and Control of a Continuous Bioreactor Based on Cell Population Balance

ABSTRACT: Saccharomyces cerevisiae (baker's yeast) can exhibit sustained oscillations during the operation in a continuou

Artificial intelligence ethics and challenges in healthcare applications: a comprehensive review in the context of the European GDPR mandate

This study examines the ethical issues surrounding the use of Artificial Intelligence (AI) in healthcare, specifically nursing, under the European General Data Protection Regulation (GDPR). The analysis delves into how GDPR applies to healthcare AI projects, encompassing data collection and decision-making stages, to reveal the ethical implications at each step. A comprehensive review of the literature categorizes research investigations into three main categories: Ethical Considerations in AI; Practical Challenges and Solutions in AI Integration; and Legal and Policy Implications in AI. The analysis uncovers a significant research deficit in this field, with a particular focus on data owner rights and AI ethics within GDPR compliance. To address this gap, the study proposes new case studies that emphasize the importance of comprehending data owner rights and establishing ethical norms for AI use in medical applications, especially in nursing. This review makes a valuable contribution to the AI ethics debate and assists nursing and healthcare professionals in developing ethical AI practices. The insights provided help stakeholders navigate the intricate terrain of data protection, ethical considerations, and regulatory compliance in AI-driven healthcare. Lastly, the study introduces a case study of a real AI health-tech project named SENSOMATT, spotlighting GDPR and privacy issues.info:eu-repo/semantics/publishedVersio

Formulation and Optimization of Oral Mucoadhesive Patches of Myrtus Communis by Box Behnken Design

Purpose: Recurrent aphthous stomatitis (RAS) is the most common painful ulcerative disease of oral mucosa happening in ~20% of people. Aimed to develop Myrtus communis L. (Myrtle) containing oral patches, we applied box-behnken design to evaluate the effect of polymers such as Polyvinyl pyrrolidone (PVP), Gelatin, Methylcellulose (MC) and Pectin. Methods: The patches properties such as tensile strength, folding endurance, swelling index, thickness, mucoadhesive strength and the pattern of myrtle release were evaluated as dependent variables. Then, the model was adjusted according to the best fitted equation with box behnken design. Results: The results indicated that preparation of myrtle patch with hydrophilic polymers showed the disintegration time up to 24h and more. Using of polyvinyl pyrrolidone as a water soluble polymer and a pore-former polymer led to faster release of soluble materials from the patch to 29 (min-1). Also it decreases swelling index by increasing the patch disintegration. Gelatin and Pectin, with rigid matrix and water interaction properties, decreased the swelling ratio. Pectin increased the tensile strength, but gelatin produced an opposite effect. Thinner Myrtle patch (about 28μm) was obtained by formulation of methyl cellulose with equal ratio with polyvinyl pyrrolidone or gelatin. Conclusion: Altogether, the analysis showed that the optimal formulation was achieved with of 35.04 mg of Gelatin, 7.22 mg of Pectin, 7.20 mg of polyvinyl pyrrolidone, 50.52 mg of methyl cellulose and 20 mg of Myrtle extract

Frequency of oral and maxillofacial giant cell lesions in Iran in a period of 22-year (1991-2012)

BACKGROUND AND AIM: Giant cell lesions as a group of the oral and maxillofacial lesions are common and potentially destructive. The aim of this study was to assess the frequency of oral lesions containing giant cells in a 22-year period in Isfahan Dental School, Iran. METHODS: In this epidemiological, cross-sectional, retrospective study the archive information in the Department of Oral Pathology, School of Dentistry between 1991 and 2012 was used. All information obtained from the patients records with giant cell lesions [peripheral giant cell granuloma (PGCG), central giant cell granuloma (CGCG), aneurysmal bone cyst, and Cherubism and Brown tumor] were analyzed using SPSS, chi-square test and Fisher (P < 0.050). RESULTS: Of the 8217 cases with pathology records, 591 cases (7.1%) were giant cell lesions. The most common lesion was PGCG (68.5%). The prevalence of lesions in the mandible was more than the maxilla (P = 0.039), and also the prevalence of these lesions in woman was slightly more than men (P = 0.078). CONCLUSION: The giant cell lesions were more common in women and in the mandible. They were seen more frequently in the second decade of life. Regards the results of this study, we can prevent PGCG using methods such as improvement of oral hygiene. KEYWORDS: Epidemiology; Giant Cells; Granulom

A novel elastic sensor sheet for pressure injury monitoring: design, integration, and performance analysis

This study presents the SENSOMATT sensor sheet, a novel, non-invasive pressure monitoring technology intended for placement beneath a mattress. The development and design process of the sheet, which includes a novel sensor arrangement, material selection, and incorporation of an elastic rubber sheet, is investigated in depth. Highlighted features include the ability to adjust to varied mattress sizes and the incorporation of AI technology for pressure mapping. A comparison with conventional piezoelectric contact sensor sheets demonstrates the better accuracy of the SENSOMATT sensor for monitoring pressures beneath a mattress. The report highlights the sensor network’s cost-effectiveness, durability, and enhanced data measurement, alongside the problems experienced in its design. Evaluations of performance under diverse settings contribute to a full understanding of its potential pressure injury prediction and patient care applications. Proposed future paths for the SENSOMATT sensor sheet include clinical validation, more cost and performance improvement, wireless connection possibilities, and improved long-term monitoring data analysis. The study concludes that the SENSOMATT sensor sheet has the potential to transform pressure injury prevention techniques in healthcare.This work was carried out under the SensoMatt project, grant agreement no. CENTRO-01-0247-FEDER-070107, co-financed by European Funds (FEDER) by CENTRO2020.info:eu-repo/semantics/publishedVersio