Novel Processing and Transmission Techniques Leveraging Edge Computing for Smart Health Systems

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User-centric Networks Selection with Adaptive Data Compression for Smart Health

The increasing demand for intelligent and sustainable healthcare services has prompted the development of smart health systems. Rapid advances in wireless access technologies and in-network data reduction techniques can significantly assist in implementing such smart systems through providing seamless integration of heterogeneous wireless networks, medical devices, and ubiquitous access to data. Utilization of the spectrum across diverse radio technologies is expected to significantly enhance network capacity and quality of service (QoS) for emerging applications such as remote monitoring over mobile-health (m-health) systems. However, this imposes an essential need to develop innovative networks selection mechanisms that account for energy efficiency while meeting application quality requirements. In this context, this paper proposes an efficient networks selection mechanism with adaptive compression for improving medical data delivery over heterogeneous m-health systems. We consider different performance aspects, as well as networks characteristics and application requirements, so as to obtain an efficient solution that grasps the conflicting nature of the various users’ objectives and addresses their inherent tradeoffs. The proposed methodology advocates a user-centric approach towards leveraging heterogeneous wireless networks to enhance the performance of m-health systems. Simulation results show that our solution significantly outperforms state-of-the-art techniques

Biofunctional molecules from Citrullus colocynthis: An HPLC/MS analysis in correlation to antimicrobial and anticancer activities

Background: Citrullus colocynthis belongs to Family Cucurbitaceae. It grows widely in Egypt and Sudan and it has been used in folk medicine of Sudan and many other African countries as anti inflammatory, anti diabetic, and antioxidant agent. Objectives: To evaluate the antibacterial, antifungal, antiviral, anticancer activities of ethanolic crude extracts of the fruits, leaves, seeds and roots of this plant, as well as identifying them HPLC/MS. Materials and Methods: Dried fruits, seeds, leaves and roots of C. colocynthis were powdered and passed through a 40- mesh, then, the powders were extracted with 95% ethanol in a soxhlet apparatus. The residues were dried under reduced pressure in rotary evaporator. Crude extract from different plant parts were evaluated biologically and phytochemically. Results: All extracts showed good antifungal activities with inhibition zone ranges between 15.1 ± 0.32 to 25.6 ± 0.16 mm. In terms of plant organ, fruits were the most active. In term of fungal strain Aspergillus fumigatus and Geotricum candidum were the most sensitive. Against tested Gram +ve, all extracts showed good activities except roots, while antibacterial activity against Gram –ve showed that the fruits extract have good activity as it was the sole extract with activity against Pseudomonas aeruginosa. Test for antiviral activities showed moderate to weak inhibitions of cytopathic effect (CPE). Anticancer activities of different crude extracts showed that fruits had significant antitumor activities against all tested cell lines, the IC50 values were 24.6, 16, 18.5 and 19.7 µg /ml for HCT-116, MCF-7, Hep-G2 and Caco-2 respectively. Seeds extract was only active on HCT-116 and Hep G2 with IC50 =21.2 µg/ml for HCT-116 and 22.4 µg/ml for Hep G2. Leaves extract was only active against Hep G2 cancer cell line with IC50 19.7 µg/ml. Roots extract show weak antitumor activity on tested cell lines (IC50 values > 30µg/ml). HPLC/MS qualitative and quantitative analysis of different organs extracts revealed the presence of 21 compounds identified as fourteen cucurbitacins, three flavonoids, three tannins, and one sterol. The presence of cucurbitacins can explain most of the biological activities. Conclusion: The biological activities of colocynth different parts are due to the presence of secondary metabolites mainly cucurbitacins in addition to flavonoids and tannins.  These activities prove the use of this plant in folk medicine and deserve much more future exploration targeting their discovery in unexplored sources and their derivatives for improving their anticancer and antimicrobial abilities. Keywords: Citrullus colocynthis, crude extracts, antifungal, antibacterial, antiviral, anticancer, HPLC/M

In-Network Data Reduction Approach Based On Smart Sensing

The rapid advances in wireless communication and sensor technologies facilitate the development of viable mobile-Health applications that boost opportunity for ubiquitous real- time healthcare monitoring without constraining patients' activities. However, remote healthcare monitoring requires continuous sensing for different analog signals which results in generating large volumes of data that needs to be processed, recorded, and transmitted. Thus, developing efficient in-network data reduction techniques is substantial in such applications. In this paper, we propose an in-network approach for data reduction, which is based on fuzzy formal concept analysis. The goal is to reduce the amount of data that is transmitted, by keeping the minimal-representative data for each class of patients. Using such an approach, the sender can effectively reconfigure its transmission settings by varying the target precision level while maintaining the required application classification accuracy. Our results show the excellent performance of the proposed scheme in terms of data reduction gain and classification accuracy, and the advantages that it exhibits with respect to state-of-the-art techniques.Scopu

Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges

Improving the efficiency of healthcare systems is a top national interest worldwide. However, the need to deliver scalable healthcare services to patients while reducing costs is a challenging issue. Among the most promising approaches for enabling smart healthcare (s-health) are edge-computing capabilities and next-generation wireless networking technologies that can provide real-time and cost-effective patient remote monitoring. In this article, we present our vision of exploiting MEC for s-health applications. We envision a MEC-based architecture and discuss the benefits that it can bring to realize in-network and context-aware processing so that the s-health requirements are met. We then present two main functionalities that can be implemented leveraging such an architecture to provide efficient data delivery, namely, multimodal data compression and edge-based feature extraction for event detection. The former allows efficient and low distortion compression, while the latter ensures high-reliability and fast response in case of emergency applications. Finally, we discuss the main challenges and opportunities that edge computing could provide and possible directions for future research

Green nanotechnology: Anticancer Activity of Silver Nanoparticles using Citrullus colocynthis aqueous extracts

Green synthesis of metal nanoparticles is a growing research area because of their potential applications in nanomedicines. The green synthesis of silver nanoparticles (SNPs) is a convenient, cheap and environmentally safe approach compared to chemical synthesis. In the present study, we synthesized SNPs from AgNO3 using aqueous extracts (AEs) of fruits, leaves, roots and seeds of Citrullus colocynthis as reducing and capping agents. The SNPs were early detected in the aqueous extracts by color change to the reddish brown, and further were confirmed by Transmission Electron Microscope (TEM) analysis. The TEM analysis of SNPs showed spherical nanoparticles with mean size between 7 to 19nm. The anticancer activity of SNPs has been assessed invitro.  MTT assay on human cancer cell lines of colon (HCT-116), breast (MCF-7), liver (Hep-G2) and intestine (Caco-2) showed good anticancer activity which was negligible for the aqueous plant extracts. Regarding to the tested cell lines the Hep-G2 cell line and HCT-116 were the most sensitive cell line towards the cytotoxic activities of the tested SNPs, while the Caco-2 was the most resistant cell line towards the cytotoxic activities. Keywords: green synthesis, silver nanoparticles, Citrullus colocynthis, anticancer

Active Learning with Noisy Labelers for Improving Classification Accuracy of Connected Vehicles

Machine learning has emerged as a promising paradigm for enabling connected, automated vehicles to autonomously cruise the streets and react to unexpected situations. Reacting to such situations requires accurate classification for uncommon events, which in turn depends on the selection of large, diverse, and high-quality training data. In fact, the data available at a vehicle (e.g., photos of road signs) may be affected by errors or have different levels of resolution and freshness. To tackle this challenge, we propose an active learning framework that, leveraging the information collected through onboard sensors as well as received from other vehicles, effectively deals with scarce and noisy data. Given the information received from neighboring vehicles, our solution: (i) selects which vehicles can reliably generate high-quality training data, and (ii) obtains a reliable subset of data to add to the training set by trading off between two essential features, i.e., quality and diversity. The results, obtained with different real-world datasets, demonstrate that our framework significantly outperforms state-of-the-art solutions, providing high classification accuracy with a limited bandwidth requirement for the data exchange between vehicles

Edge-based Compression and Classification for Smart Healthcare Systems: Concept, Implementation and Evaluation

Smart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the net- work edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart health- care systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make au- tonomous decisions for smart health applications. In particular, we present and im- plement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low com- putational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient’s conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%

COVID-19 Data Warehouse: A Systematic Literature Review

The coronavirus disease (COVID-19) affects the whole world and led clinicians to use the available knowledge to diagnose or predict the infection. Data Warehouse is one of the most crucial tools that may enhance decision-making (DW).In this paper, three main questions will be investigated according to using DW in the COVID-19 pandemic. The effect of using DW in the field of diagnosing and prediction will be investigated, besides, the most used architecture of DW will be explored. The sectors that faced a lot of researchers' attention such as diagnosing, predicting, and finding the correlations among features will be examined. The selected studies are explored where the papers that have been published between 2019-2022 in the digital libraries (ACM, IEEE, Springer, Science Direct, and Elsevier) in the field of DW that handle the COVID-19 are selected. During the research, many limitations have been detected, while some future works are presented. Enterprise DW is the most used architecture for COVID-19 DW while finding correlation among features and prediction are the sectors that had taken the researchers' attentio