AI-driven Service Broker for Simple and Composite Cloud SaaS Selection

University of Technology Sydney. Faculty of Engineering and Information Technology.Cloud Software as a Service (SaaS) is one of the three types of services offered in cloud computing. Cloud SaaS is a software application that runs on top of Platform as a Service (PaaS), which in turn works on top of Infrastructure as a Service (IaaS). Due to the numerous advantages offered by cloud SaaS to service consumers, such as reducing the cost of IT expenditures, security capabilities and disaster recovery offered by cloud SaaS service providers, Cloud SaaS is becoming a leading and growing type of cloud service among other cloud services (i.e., IaaS and PaaS). Therefore, Cloud SaaS service consumers may face a difficult task when searching for the most suitable service based on their preferences. Service selection is based on matching the service requirements of functional and non-functional quality attributes. However, selecting a Cloud SaaS service provider with a high number of non-functional quality attributes that fulfils consumer requirements within a large number of similar functional services is a key factor for a Cloud SaaS service selection. In addition, considering that a cloud SaaS service can involve a long-term contract, Cloud SaaS providers frequently offer a free trial period to test and evaluate services before the consumers make the decision of whether they will use that service. Furthermore, selecting multiple Cloud SaaS service providers in order to create a new business value, known as a service composition in the service-oriented architecture (SOA) model, is very important, since Cloud SaaS services are the first option for deploying IT services for many new enterprises. Therefore, this research aims to propose intelligent methods for a simple and composite service selection framework based on consumer preferences. By simple, we mean a singular service whereas by composite, we mean an aggregated service. This work seeks to find the services with a high number of non-functional quality attributes that meet the consumer requirements. To achieve the objectives of this research, a design science research methodology will be adopted. Fuzzy logic will be proposed to address the uncertainty of consumer preferences. A ranking service system, evaluation system and composite decision maker system are proposed in this thesis to help a Cloud SaaS service consumer select the optimal service required. Multiple approaches of decision-makers will be developed in order to achieve our research objectives. It is expected that this research work will enhance the selection mechanism of Cloud SaaS, either simple or composite based on service consumer’s preferences

Sensor-cloud architecture: a taxonomy of security issues in cloud-assisted sensor networks

© 2021 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://ieeexplore.ieee.org/document/9451213The orchestration of cloud computing with wireless sensor network (WSN), termed as sensor-cloud, has recently gained remarkable attention from both academia and industry. It enhances the processing and storage capabilities of the resources-constrained sensor networks in various applications such as healthcare, habitat monitoring, battlefield surveillance, disaster management, etc. The diverse nature of sensor network applications processing and storage limitations on the sensor networks, which can be overcome through integrating them with the cloud paradigm. Sensor-cloud offers numerous benefits such as flexibility, scalability, collaboration, automation, virtualization with enhanced processing and storage capabilities. However, these networks suffer from limited bandwidth, resource optimization, reliability, load balancing, latency, and security threats. Therefore, it is essential to secure the sensor-cloud architecture from various security attacks to preserve its integrity. The main components of the sensor-cloud architecture which can be attacked are: (i) the sensor nodes; (ii) the communication medium; and (iii) the remote cloud architecture. Although security issues of these components are extensively studied in the existing literature; however, a detailed analysis of various security attacks on the sensor-cloud architecture is still required. The main objective of this research is to present state-of-the-art literature in the context of security issues of the sensor-cloud architecture along with their preventive measures. Moreover, several taxonomies of the security attacks from the sensor-cloud’s architectural perspective and their innovative solutions are also provided.This work was supported by the Taif University, Taif, Saudi Arabia, through the Taif University Researchers Supporting Project under Grant TURSP-2020/126.Published versio

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Multilabel CNN-Based Hybrid Learning Metric for Pedestrian Reidentification

Pedestrian reidentification has recently emerged as a hot topic that attains considerable attention since it can be applied to many potential applications in the surveillance system. However, high-accuracy pedestrian reidentification is a stimulating research problem because of variations in viewpoints, color, light, and other reasons. This work addresses the interferences and improves pedestrian reidentification accuracy by proposing two novel algorithms, pedestrian multilabel learning, and investigating hybrid learning metrics. First, unlike the existing models, we construct the identification framework using two subnetworks, namely, part detection subnetwork and feature extraction subnetwork, to obtain pedestrian attributes and low-level feature scores, respectively. Then, a hybrid learning metric that combines pedestrian attributes and low-level feature scores is proposed. Both low-level features and pedestrian attributes are utilized, thus enhancing the identification rate. Our simulation results on both datasets, i.e., CUHK03 and VIPeR, reveal that the identification rate is improved compared to the existing pedestrian reidentification methods

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks (CRNs) have become a successful platform in recent years for a diverse range of future systems, in particularly, industrial internet of things (IIoT) applications. In order to provide an efficient connection among IIoT devices, CRNs enhance spectrum utilization by using licensed spectrum. However, the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection. Specifically, the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User (PU) activity and create a robust routing path. This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain. Thus, a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method, namely, Channel Availability Probability. Moreover, a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique. This protocol combines lower layer (Physical layer and Data Link layer) sensing that is derived from the channel estimation model. Also, it periodically updates and stores the routing table for optimal route decision-making. Moreover, in order to achieve higher throughput and lower delay, a new routing metric is presented. To evaluate the performance of the proposed protocol, network simulations have been conducted and also compared to the widely used routing protocols, as a benchmark. The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio (with an improved margin of around 5–20% approximately) under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks (MCRNs). Moreover, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting the high channel stability, and reducing the probability of PU interference for continued communication

RSSI-Controlled Long-Range Communication in Secured IoT-Enabled Unmanned Aerial Vehicles

Unmanned aerial vehicle (UAV) has recently gained significant attention due to their efficient structures, cost-effectiveness, easy availability, and tendency to form an ad hoc wireless mobile network. IoT-enabled UAV is a new research domain that uses location tracking with the advancement of aerial technology. In this context, the importance of 3D aerial networks is attracting a lot of attention recently. It has various applications related to information processing, communication, and location-based services. Location identification of wireless nodes is a challenging job and of extreme importance. In this study, we introduced a novel technique for finding indoor and open-air three-dimensional (3D) areas of nodes by measuring the signal strength. The mathematical formulation is based on a path loss model and decision tree machine learning classifier. We constructed 2D and 3D models to gather more accurate information on the nodes. Simulation findings demonstrate that the proposed machine learning-based model excels in nodes location estimation, the actual and estimated distance of different nodes, and calculation of received signal strength in aerial ad hoc networks. In addition, the decision tree constructs an offline phase control in the flying vehicle’s location to enhance the time complexity along with experimental accuracy