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The Internet of Things (IoT) is expected to have a positive impact on various sectors, such as manufacturing, users, and operators. Due to the capabilities of smart objects to share their contents and interact with their social networks, the combined Social Internet of Educational Things (SIoET) for smart education is expected to provide users with more efficient services. The primary objective of this paper is to provide an efficient resource management by adopting edge intelligence technique for higher education system workloads on next-generation networks. As the number of tasks handled by these interconnected devices continues to rise, there is an escalating concern regarding resource competition and power consumption within the network. To mitigate these challenges, we propose an innovative edge intelligence-based resource allocation approach to intelligently allocate resources while minimizing latency and optimizing power consumption with increasing task volumes. The problem is dissected into two core sub-problems: computation resource optimization and power optimization. Leveraging the concept of Lagrangian and Bisection methods, our proposed algorithm, implemented within a Device-to-Device (D2D) -assisted edge intelligence network, demonstrates remarkable performance improvements. Network latency and energy consumption is reduced by up to 57.9 % and 82.3 % compared to the benchmark schemes. These findings hold promising implications for the integration of this work into higher education systems driven by SIoET. Furthermore, simulations indicate that the system maintains robust performance even in dynamic urban environments characterized by random device movement.

BackgroundThe rapid expansion of telehealth services, driven by the COVID-19 pandemic, necessitates systematic evaluation to guarantee the quality, effectiveness, and cost-effectiveness of telehealth services and programs in the United States. While numerous evaluation frameworks have emerged, crafted by various stakeholders, their comprehensiveness is limited, and the overall state of telehealth evaluation remains unclear. ObjectiveThe overarching goal of this scoping review is to create a comprehensive overview of telehealth evaluation, incorporating perspectives from multiple stakeholder categories. Specifically, we aim to (1) map the existing landscape of telehealth evaluation, (2) identify key concepts for evaluation, (3) synthesize existing evaluation frameworks, and (4) identify measurements and assessments considered in the United States. MethodsWe will conduct this scoping review in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews and in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). This scoping review will consider documents, including reviews, reports, and white papers, published since January 1, 2019. It will focus on evaluation frameworks and associated measurements of telehealth services and programs in the US health care system, developed by telehealth stakeholders, professional organizations, and authoritative sources, excluding those developed by individual researchers, to collect data that reflect the collective expertise and consensus of experts within the respective professional group. ResultsThe data extracted from selected documents will be synthesized using tools such as tables and figures. Visual aids like Venn diagrams will be used to illustrate the relationships between the evaluation frameworks from various sources. A narrative summary will be crafted to further describe how the results align with the review objectives, facilitating a comprehensive overview of the findings. This scoping review is expected to conclude by August 2024. ConclusionsBy addressing critical gaps in telehealth evaluation, this scoping review protocol lays the foundation for a comprehensive and multistakeholder assessment of telehealth services and programs. Its findings will inform policy makers, health care providers, researchers, and other stakeholders in advancing the quality, effectiveness, and cost-effectiveness of telehealth in the US health care system. Trial RegistrationOSF Registries osf.io/aytus; https://osf.io/aytus International Registered Report Identifier (IRRID)DERR1-10.2196/55209

Abstract Localization in underwater wireless sensor network (UWSN) faces an imminent threat when the triangulating anchor node starts to malfunction. Traditional geometric approaches are insufficient to cope with the survivability of UWSN topology. To address these issues, this paper presents a symplectic geometry for identification of the malicious anchor node. Consequently, a geodesic search algorithm (GSA) based Target localization is proposed which reduces the positioning error by exploiting the phase-space constancy of the underwater acoustic sensor network topology to effectively triangulate the target node despite its mobility. First, a malicious anchor node model is presented. The node movement is expressed in the form of “ripple region”. GSA is then proposed which effectively frees the node metastasis from anchor node geometry, thereby making the underwater system more survivable and resilient. Simulation results evaluate the survivability of the geodesic formalism in terms of the reduced penalty incurred by node movement, as well as the reduced impact of anchor node malfunction. An improvement of 13.46% and 9.26% reveals the utility of the geodesic technique in aquamarine sensor deployments, which would be beneficial in underwater resource exploration and defense planning.

The existing literature on device-to-device (D2D) architecture suffers from a dearth of analysis under imperfect channel conditions. There is a need for rigorous analyses on the policy improvement and evaluation of network performance. Accordingly, a two-stage transmit power control approach (named QSPCA) is proposed: First, a reinforcement Q-learning based power control technique and; second, a supervised learning based support vector machine (SVM) model. This model replaces the unified communication model of the conventional D2D setup with a distributed one, thereby requiring lower resources, such as D2D throughput, transmit power, and signal-to-interference-plus-noise ratio as compared to existing algorithms. Results confirm that the QSPCA technique is better than existing models by at least 15.31% and 19.5% in terms of throughput as compared to SVM and Q-learning techniques, respectively. The customizability of the QSPCA technique opens up multiple avenues and industrial communication technologies in 5G networks, such as factory automation.

Malicious anchor node is a serious issue in underwater sensor networks. This problem gets compounded under sparse deployment scenario. To overcome this issue, the authors present a Compressed Sensing for Malicious node Compensation theorem (abbreviated as CS-MC Theorem) which would be applicable to underwater localization. To counter malicious node behavior, CS-MC Theorem is justified through three lemmas. For underwater acoustic propagation, a specific L1-norm minimization method is derived. It describes the condition for unique localization of target by discretizing the target search space. Malicious node is identified by inserting the anchor nodes into the discretized grid. An insertion vector concept is introduced to determine the extent of node metastasis. Derivations are performed to prove the mathematical basis of the proposed algorithm under acoustically stratified and unstratified scenarios. Numerical results establish the feasibility of CS-MC algorithm for sparse underwater acoustic sensor network.

Objectives:. Implement a connected network between two Tele-ICU programs to support staffing and rounding during the first wave of the coronavirus disease 2019 pandemic in the United States. Design:. Proof of Concept model. Setting:. Northwell Health; a 23 Hospital, 40 ICU (500 ICU beds) healthcare organization serving the downstate NY area. During the initial coronavirus disease 2019 pandemic, Northwell Health rapidly expanded to greater than 1,000 ICU beds. The surge in patients required redeployment of noncritical care providers to the ICU bedside. The Tele-ICU program expanded from covering 176 beds pre pandemic to assisting with care for patients in approximately 450 beds via deployment of Wi-Fi-enabled mobile telehealth carts to the newly formed ICUs. Patients:. Critically ill coronavirus disease 2019 patients hospitalized at Northwell Health, NY, at any point from March 2020 to June 2020. Interventions:. To offset the shortage of critical care physicians, Northwell Health established a collaboration with the Tele-ICU program of Providence, St. Joseph Health in the state of Washington, which enabled the critical care physicians of Providence, St. Joseph Health to participate in virtual rounding on critically ill coronavirus disease 2019 patients at Northwell Health. Main Results:. We developed an innovative hybrid model that allowed for virtual rounding on an additional 40–60 patients per day by a remote critical care physician at Providence, St. Joseph Health. This was accomplished in approximately 3 weeks and provided remote care to complex patients. Conclusions:. Our findings demonstrate the proof of concept of establishing a network of connected Tele-ICU programs as a rapidly scalable and sustainable paradigm for the provision of support from critical care physicians for noncritical care teams at the bedside.

We discuss the complete invariance property with respect to homeomorphism (CIPH) over various sets of wavelets containing all orthonormal multiwavelets, all tight frame multiwavelets, all super-wavelets of length n, and all normalized tight super frame wavelets of length n.