Efficient task optimization algorithm for green computing in cloud.

Cloud infrastructure assets are accessed by all hooked heterogeneous network servers and applications to maintain entail reliability towards global subscribers with high performance and low cost is a tedious challenging task. Most of the extant techniques are considered limited constraints like task deadline, which leads Service Level Agreement (SLA) violation. In this manuscript, we develop Hadoop based Task Scheduling (HTS) algorithm which considers a task deadline time, completion time, migration time and future resource availability of each virtual machine. The Intelligent System (IS) enabled with adaptive neural computation method to assess all above attributes. Specifically, the result of Prophecy Resource Availability (PRA) method has been used to assess the status of each Virtual Machine (VM), which helps to streamline the resource wastage and increases the response time with low SLA violation rate

C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics.

The Medical Internet of Things (MIoT) facilitates extensive connections between cyber and physical "things" allowing for effective data fusion and remote patient diagnosis and monitoring. However, there is a risk of incorrect diagnosis when data is tampered with from the cloud or a hospital due to third-party storage services. Most of the existing systems use an owner-centric data integrity verification mechanism, which is not computationally feasible for lightweight wearable-sensor systems because of limited computing capacity and privacy leakage issues. In this regard, we design a 2-step Privacy-Preserving Multidimensional Data Stream (PPMDS) approach based on a cloudlet framework with an Uncertain Data-integrity Optimization (UDO) model and Sparse-Centric SVM (SCS) model. The UDO model enhances health data security with an adaptive cryptosystem called Cloudlet-Nonsquare Encryption Secret Transmission (C-NEST) strategy by avoiding medical disputes during data streaming based on novel signature and key generation strategies. The SCS model effectively classifies incoming queries for easy access to data by solving scalability issues. The cloudlet server measures data integrity and authentication factors to optimize third-party verification burden and computational cost. The simulation outcomes show that the proposed system optimizes average data leakage error rate by 27%, query response time and average data transmission time are reduced by 31%, and average communication-computation cost are reduced by 61% when measured against state-of-the-art approaches

An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT.

Social Edge Service (SES) is an emerging mechanism in the Social Internet of Things (SIoT) orchestration for effective user-centric reliable communication and computation. The services are affected by active and/or passive attacks such as replay attacks, message tampering because of sharing the same spectrum, as well as inadequate trust measurement methods among intelligent devices (roadside units, mobile edge devices, servers) during computing and content-sharing. These issues lead to computation and communication overhead of servers and computation nodes. To address this issue, we propose the HybridgrAph-Deep-learning (HAD) approach in two stages for secure communication and computation. First, the Adaptive Trust Weight (ATW) model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead. Second, a Quotient User-centric Coeval-Learning (QUCL) mechanism to formulate secure channel selection, and Nash equilibrium method for optimizing the communication to share data over edge devices. The simulation results confirm that our proposed approach has achieved effective communication and computation performance, and enhanced Social Edge Services (SES) reliability than state-of-the-art approaches

ASXC2 approach: a service-X cost optimization strategy based on edge orchestration for IIoT.

Most computation-intensive industry applications and servers encounter service-reliability challenges due to the limited resource capability of the edge. Achieving quality data fusion and accurate service reliability with optimized service-x execution cost is challenging. While existing systems have taken into account factors such as device service execution, residual resource ratio, and channel condition; the service execution time, cost, and utility ratios of requested services from devices and servers also have a significant impact on service execution cost. To enhance service quality and reliability, we design a 2-step adaptive service-X cost consolidation (ASXC 2) approach. This approach is based on the node-centric Lyapunov method and distributed Markov mechanism, aiming to optimize the service execution error rate during offloading. The node-centric Lyapunov method incorporates cost and utility functions and node-centric features to estimate the service cost before offloading. Additionally, the Markov mechanism-inspired service latency prediction model design assists in mitigating the ratio of offload-service execution errors by establishing a mobility-correlation matrix between devices and servers. In addition, the non-linear programming multi-tenancy heuristic method design help to predict the service preferences for improving the resource utilisation ratio. The simulations show the effectiveness of our approach. The model performance is enhanced with 0.13% service offloading efficiency, 0.82% rate of service completion when transmitting data size is 400 kb, and 0.058% average service offloading efficiency with 40 CPU Megacycles when the vehicle moves 60 Km/h speed around the server communication range. Our model simulations indicate that our approach is highly effective and suitable for lightweight, complex environments

Resource offload consolidation based on deep-reinforcement learning approach in cyber-physical systems.

In cyber-physical systems, it is advantageous to leverage cloud with edge resources to distribute the workload for processing and computing user data at the point of generation. Services offered by cloud are not flexible enough against variations in the size of underlying data, which leads to increased latency, violation of deadline and higher cost. On the other hand, resolving above-mentioned issues with edge devices with limited resources is also challenging. In this work, a novel reinforcement learning algorithm, Capacity-Cost Ratio-Reinforcement Learning (CCR-RL), is proposed which considers both resource utilization and cost for the target cyber-physical systems. In CCR-RL, the task offloading decision is made considering data arrival rate, edge device computation power, and underlying transmission capacity. Then, a deep learning model is created to allocate resources based on the underlying communication and computation rate. Moreover, new algorithms are proposed to regulate the allocation of communication and computation resources for the workload among edge devices and edge servers. The simulation results demonstrate that the proposed method can achieve a minimal latency and a reduced processing cost compared to the state-of-the-art schemes

A quantum-inspired sensor consolidation measurement approach for cyber-physical systems.

Cyber-Physical System (CPS) devices interconnect to grab data over a common platform from industrial applications. Maintaining immense data and making instant decision analysis by selecting a feasible node to meet latency constraints is challenging. To address this issue, we design a quantum-inspired online node consolidation (QONC) algorithm based on a time-sensitive measurement reinforcement system for making decisions to evaluate the feasible node, ensuring reliable service and deploying the node at the appropriate position for accurate data computation and communication. We design the Angular-based node position analysis method to localize the node through rotation and t-gate to mitigate latency and enhance system performance. We formalize the estimation and selection of the feasible node based on quantum formalization node parameters (node contiguity, node optimal knack rate, node heterogeneity, probability of fusion variance error ratio). We design a fitness function to assess the probability of node fitness before selection. The simulation results convince us that our approach achieves an effective measurement rate of performance index by reducing the average error ratio from 0.17-0.22, increasing the average coverage ratio from 29% to 42%, and the qualitative execution frequency of services. Moreover, the proposed model achieves a 74.3% offloading reduction accuracy and a 70.2% service reliability rate compared to state-of-the-art approaches. Our system is scalable and efficient under numerous simulation frameworks

A DRL-based service offloading approach using DAG for edge computational orchestration.

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption

RLIS: resource limited improved security beyond fifth generation networks using deep learning algorithms.

This study explores the feasibility of allocating finite resources beyond fifth generation networks for extended reality applications through the implementation of enhanced security measures via offloading analysis (RLIS). The quantification of resources is facilitated through the utilization of parameters, namely energy, capacity, and power, which are equipped with proximity constraints. These constraints are then integrated with activation functions in both multilayer perceptron and long short term memory models. Furthermore, the system model has been developed using vision-based computing, which involves managing data queues in terms of waiting periods to minimize congestion for data transmission with limited resources. The major significance of the proposed method is to utilize allocated spectrums for future generation networks by allocating necessary resources and therefore high usage of resources by all users can be avoided. In addition the advantage of the proposed method is secure the networks that operate beyond 5G where more number of users will try to share the allocated resources that needs to be provided with high security conditions

3D harmonic loss: towards task-consistent and time-friendly 3D object detection on edge for V2X orchestration.

The use of edge computing for 3D perception has garnered interest in intelligent transportation systems (ITS) due to its potential to enhance Vehicle-to-Everything (V2X) orchestration through real-time traffic monitoring. The ability to accurately measure depth information in the environment using LiDAR has led to a growing emphasis on 3D detection based on this technology, which has significantly advanced the field of 3D perception. However, the computationally-intensive nature of these operations has made it challenging to meet the real-time deployment requirements using existing methods. The object detection task in the pointcloud domain is hindered by a substantial inconsistency problem caused by its high sparsity, which remains unaddressed. This paper conducts an in-depth analysis of the issue, which has been brought to light by recent research on detecting inconsistency problems in image specialization. To address this problem, we propose a solution in the form of a 3D harmonic loss function, which aims to alleviate the inconsistent predictions based on pointcloud data. In addition, we showcase the viability of optimizing 3D harmonic loss mathematically. Our simulations employ the KITTI dataset and DAIR-V2X-I dataset, and our proposed approach significantly surpasses the performance of benchmark models. Additionally, we validate the efficiency of our proposed model through its deployment on an edge device (Jetson Xavier TX) in a simulated environment

Fungal diversity notes 1512-1610: taxonomic and phylogenetic contributions on genera and species of fungal taxa

This article is the 14th in the Fungal Diversity Notes series, wherein we report 98 taxa distributed in two phyla, seven classes, 26 orders and 50 families which are described and illustrated. Taxa in this study were collected from Australia, Brazil, Burkina Faso, Chile, China, Cyprus, Egypt, France, French Guiana, India, Indonesia, Italy, Laos, Mexico, Russia, Sri Lanka, Thailand, and Vietnam. There are 59 new taxa, 39 new hosts and new geographical distributions with one new combination. The 59 new species comprise Angustimassarina kunmingense, Asterina lopi, Asterina brigadeirensis, Bartalinia bidenticola, Bartalinia caryotae, Buellia pruinocalcarea, Coltricia insularis, Colletotrichum flexuosum, Colletotrichum thasutense, Coniochaeta caraganae, Coniothyrium yuccicola, Dematipyriforma aquatic, Dematipyriforma globispora, Dematipyriforma nilotica, Distoseptispora bambusicola, Fulvifomes jawadhuvensis, Fulvifomes malaiyanurensis, Fulvifomes thiruvannamalaiensis, Fusarium purpurea, Gerronema atrovirens, Gerronema flavum, Gerronema keralense, Gerronema kuruvense, Grammothele taiwanensis, Hongkongmyces changchunensis, Hypoxylon inaequale, Kirschsteiniothelia acutisporum, Kirschsteiniothelia crustaceum, Kirschsteiniothelia extensum, Kirschsteiniothelia septemseptatum, Kirschsteiniothelia spatiosum, Lecanora immersocalcarea, Lepiota subthailandica, Lindgomyces guizhouensis, Marthe asmius pallidoaurantiacus, Marasmius tangerinus, Neovaginatispora mangiferae, Pararamichloridium aquisubtropicum, Pestalotiopsis piraubensis, Phacidium chinaum, Phaeoisaria goiasensis, Phaeoseptum thailandicum, Pleurothecium aquisubtropicum, Pseudocercospora vernoniae, Pyrenophora verruculosa, Rhachomyces cruralis, Rhachomyces hyperommae, Rhachomyces magrinii, Rhachomyces platyprosophi, Rhizomarasmius cunninghamietorum, Skeletocutis cangshanensis, Skeletocutis subchrysella, Sporisorium anadelphiae-leptocomae, Tetraploa dashaoensis, Tomentella exiguelata, Tomentella fuscoaraneosa, Tricholomopsis lechatii, Vaginatispora flavispora and Wetmoreana blastidiocalcarea. The new combination is Torula sundara. The 39 new records on hosts and geographical distribution comprise Apiospora guiyangensis, Aplosporella artocarpi, Ascochyta medicaginicola, Astrocystis bambusicola, Athelia rolfsii, Bambusicola bambusae, Bipolaris luttrellii, Botryosphaeria dothidea, Chlorophyllum squamulosum, Colletotrichum aeschynomenes, Colletotrichum pandanicola, Coprinopsis cinerea, Corylicola italica, Curvularia alcornii, Curvularia senegalensis, Diaporthe foeniculina, Diaporthe longicolla, Diaporthe phaseolorum, Diatrypella quercina, Fusarium brachygibbosum, Helicoma aquaticum, Lepiota metulispora, Lepiota pongduadensis, Lepiota subvenenata, Melanconiella meridionalis, Monotosporella erecta, Nodulosphaeria digitalis, Palmiascoma gregariascomum, Periconia byssoides, Periconia cortaderiae, Pleopunctum ellipsoideum, Psilocybe keralensis, Scedosporium apiospermum, Scedosporium dehoogii, Scedosporium marina, Spegazzinia deightonii, Torula fici, Wiesneriomyces laurinus and Xylaria venosula. All these taxa are supported by morphological and multigene phylogenetic analyses. This article allows the researchers to publish fungal collections which are important for future studies. An updated, accurate and timely report of fungus-host and fungus-geography is important. We also provide an updated list of fungal taxa published in the previous fungal diversity notes. In this list, erroneous taxa and synonyms are marked and corrected accordingly