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The utilization of Internet of Things (IoT)‐based networks in healthcare systems has witnessed a notable increase, particularly in services like remote patient monitoring. However, specific vulnerabilities have become apparent as more individuals connect to these networks. One pressing concern revolves around safeguarding the privacy of users' confidential information. Given the extensive reliance on sensitive data in such services, apprehensions arise regarding the security of this information within the system. Moreover, the substantial volume of real‐time data transmission poses scalability challenges for the network. This work introduces SecureCare, a novel solution for enhancing wearable IoT healthcare by proposing a blockchain‐empowered Wearable Body Area Network (WBAN) framework. Our aim to employ blockchain technology stems from its robust security capabilities, thanks to its tamperproof and decentralized structure that effectively safeguards network data. Finally, SecureCare was evaluated on a public blockchain network, where it demonstrated improvements in efficiency and reliability. This validation confirms its potential as a robust solution for enhancing security in wearable IoT healthcare systems. [ABSTRACT FROM AUTHOR]

Blockchain is undoubtedly considered one of the most innovative technologies in financial services from the past decade. Interests in blockchain technology continue to grow on a daily basis, while many promising blockchain-enabled applications and services continue to draw financial interest in the industrial sector and the broader financial services communities. Blockchain technology has the potential to streamline lending services and banking, decrease counterparty risk, and reduce settlement times and issuance. It indeed enables authenticated documentation and anti-money laundering (AML)/Know Your Client (KYC) data, minimizing operational ventures and allowing real-time/online validation of financial documents. Furthermore, as blockchain matures, it increases the value of a diversified range of industries and institutes with a better return on investment. However, such organizations must continue to adopt rigorous approaches to enhance their financial services and regulate their policies. In this article, we discuss the current status of blockchain-enabled financial services and applications, in addition to the impacts, implications, and regulations of incorporating blockchain technology in the finance industry. We further tackle the adaptation model for blockchain technology in global banking and provide a vision for the next generation of financial services based on this emerging technology. [ABSTRACT FROM AUTHOR]

This paper extends the energy function to the analysis of the stability of neural networks with asymmetric interconnections and noninvertible transfer functions. Based on the new energy function, stability theorems and convergent criteria are derived which improve the available results in the literature. A simpler proof of a previous result for complete stability is given. Theorems on complete stability of neural networks with noninvertible output functions are presented. Index Terms: Energy function, Hopfield network, neural networks, stability.

Nowadays, real‐world learning modules become vital components in computer science and engineering in general and cybersecurity in particular. However, as student enrollments have been dramatically increasing, it becomes more challenging for a university/college to keep up with the quality of education that offers hands‐on experiment training for students thoroughly. These challenges include the difficulty of providing sufficient computing resources and keep them upgraded for the increasing number of students. In order for higher education institutions to conquer such challenges, some educators introduce an alternative solution. Namely, they develop and deploy virtual lab experiments on the clouds such as Amazon AWS and the Global Environment for Network Innovations (GENI), where students can remotely access virtual resources for lab experiments. Besides, Software‐Defined Networks (SDN) are an emerging networking technology to enhance the security and performance of networked communications with simple management. In this article, we present our efforts to develop learning modules via an efficient deployment of SDN on GENI for computer networking and security education. Specifically, we first give our design methodology of the proposed learning modules, and then detail the implementations of the learning modules by starting from user account creation on the GENI testbed to advanced experimental GENI‐enabled SDN labs. It is worth pointing out that in order to accommodate students with different backgrounds and knowledge levels, we consider the varying difficulty levels of learning modules in our design. Finally, student assessment over these pedagogical efforts is discussed to demonstrate the efficiency of the proposed learning modules. [ABSTRACT FROM AUTHOR]

SYN flood attacks (half‐open attacks) have been proven a serious threat to software‐defined networking (SDN)‐enabled infrastructures. A variety of intrusion detection and prevention systems (IDPS) have been introduced for identifying and preventing such security threats, but they often result in significant performance overhead and response time. Therefore, those existing approaches are inflexible for large‐scale networks and real‐time applications. For this reason, a novel and adaptive threshold‐based kernel‐level intrusion detection and prevention system by leveraging SDN capabilities are proposed. The proposed systems to detect and mitigate the aforementioned threats within an SDN over widely used traditional IDPS technologies, Snort and Zeek, are comparatively examined. The approach is evaluated using a mixture of fundamental adverse attacks and SDN‐specific threats on a real‐world testbed. The experimental results demonstrate the efficacy of the mechanism to detect and mitigate SYN flood attacks within an SDN environment. [ABSTRACT FROM AUTHOR]

In this paper we develop and analyze real-time and accurate filters for nonlinear filtering problems based on the Gaussian distributions. We present the systematic formulation of Gaussian filters and develop efficient and accurate numerical integration of the optimal filter. We also discuss the mixed Gaussian filters in which the conditional probability density is approximated by the sum of Gaussian distributions. A new update rule of weights for Gaussian sum filters is proposed. Our numerical testings demonstrate that new filters significantly improve the extended Kalman filter with no additional cost and the new Gaussian sum filter has a nearly optimal performance. Index Terms--Author: please supply index terms. E-mail: keywords@ieee.org for information.

Abstract: Due to the low cost and capabilities of sensors, wireless sensor networks (WSNs) are promising for military and civilian surveillance of people and vehicles. One important aspect of surveillance is target localization. A location can be estimated by collecting and analyzing sensing data on signal strength, time of arrival, time difference of arrival, or angle of arrival. However, this data is subject to measurement noise and is sensitive to environmental conditions, so its location estimates can be inaccurate. In this paper, we add a novel process to further improve the localization accuracy after the initial location estimates are obtained from some existing algorithm. Our idea is to exploit the consistency of the spatial–temporal relationships of the targets we track. Spatial relationships are the relative target locations in a group and temporal relationships are the locations of a target at different times. We first develop algorithms that improve location estimates using spatial and temporal relationships of targets separately, and then together. We prove mathematically that our methods improve the localization accuracy. Furthermore, we relax the condition that targets should strictly keep their relative positions in the group and also show that perfect time synchronization is not required. Simulations were also conducted to test the algorithms. They used initial target location estimates from existing signal-strength and time-of-arrival algorithms and implemented our own algorithms. The results confirmed improved localization accuracy, especially in the combined algorithms. Since our algorithms use the features of targets and not the underlying WSNs, they can be built on any localization algorithm whose results are not satisfactory. [Copyright &y& Elsevier]

From the perspective of environmental regulation, this paper constructed a high-quality development evaluation system for the virtual reality industry (hereinafter referred to as VRI), calculated the high-quality development level of the VRI, and empirically studied the path of high-quality development of VRI based on the research methods of entropy weight method and structural equation model (hereinafter referred to as SEM), as well as the data of listed companies of VRI in China from 2011 to 2020. The results showed that (1) VRI adapted to the high-quality development level, but there was a certain gap, its high-quality development degree shows a rising trend; (2) the degree of corporate public welfare development had a positive effect on corporate profitability, while the degree of green development and coordinated development had a weak impact on corporate profitability; (3) open and innovative dimensions had a reverse effect on the profitability of enterprises. Enterprises should seek ways to reduce environmental costs and enhance competitiveness. The industrial chain, technology chain and value chain should be constructed to promote the healthy development of VRI and provide support for the formulation of high-quality development policies for VRI., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Yang and Xiong.)

In the era of digital economy, the impact of innovation resources on high-quality economic growth has become increasingly prominent. There are many researches on the influencing factors of innovation performance. The purpose of this study is to explore the factors that affect the innovation performance of high-end manufacturing clusters in China based on the dual perspectives of digital economy and innovation network. A total of 194 valid questionnaires were collected. And structural equation modeling has been used to test the proposed research models and hypotheses. The results indicated that, the higher the centrality of the innovation network, the more the cluster enterprises can play the centrality advantage, which has a significant positive impact on the innovation performance of the cluster. Similarly, both the strength and density of innovation networks also impacted on cluster innovation performance, but to a lesser extent. We also found that the digital empowerment derived from the digital economy can get rid of the limitations caused by spatial distance and lead to the improvement of resource utilization, which plays a positive moderating role between innovation network and innovation capacity. Implications for digital economy and innovation networks to improve the quality of innovation performance are provided., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Xiong, Gao and Yang.)