An Architecture of IoT Service Delegation and Resource Allocation Based on Collaboration between Fog and Cloud Computing

Despite the wide utilization of cloud computing (e.g., services, applications, and resources), some of the services, applications, and smart devices are not able to fully benefit from this attractive cloud computing paradigm due to the following issues: (1) smart devices might be lacking in their capacity (e.g., processing, memory, storage, battery, and resource allocation), (2) they might be lacking in their network resources, and (3) the high network latency to centralized server in cloud might not be efficient for delay-sensitive application, services, and resource allocations requests. Fog computing is promising paradigm that can extend cloud resources to edge of network, solving the abovementioned issue. As a result, in this work, we propose an architecture of IoT service delegation and resource allocation based on collaboration between fog and cloud computing. We provide new algorithm that is decision rules of linearized decision tree based on three conditions (services size, completion time, and VMs capacity) for managing and delegating user request in order to balance workload. Moreover, we propose algorithm to allocate resources to meet service level agreement (SLA) and quality of services (QoS) as well as optimizing big data distribution in fog and cloud computing. Our simulation result shows that our proposed approach can efficiently balance workload, improve resource allocation efficiently, optimize big data distribution, and show better performance than other existing methods

Chebyshev Polynomial-Based Fog Computing Scheme Supporting Pseudonym Revocation for 5G-Enabled Vehicular Networks

he privacy and security of the information exchanged between automobiles in 5G-enabled vehicular networks is at risk. Several academics have offered a solution to these problems in the form of an authentication technique that uses an elliptic curve or bilinear pair to sign messages and verify the signature. The problem is that these tasks are lengthy and difficult to execute effectively. Further, the needs for revoking a pseudonym in a vehicular network are not met by these approaches. Thus, this research offers a fog computing strategy for 5G-enabled automotive networks that is based on the Chebyshev polynomial and allows for the revocation of pseudonyms. Our solution eliminates the threat of an insider attack by making use of fog computing. In particular, the fog server does not renew the signature key when the validity period of a pseudonym-ID is about to end. In addition to meeting privacy and security requirements, our proposal is also resistant to a wide range of potential security breaches. Finally, the Chebyshev polynomial is used in our work to sign the message and verify the signature, resulting in a greater performance cost efficiency than would otherwise be possible if an elliptic curve or bilinear pair operation had been employed

Digital DNA Lifecycle Security and Privacy: 1 An Overview

DNA sequencing technologies have advanced significantly in the last few years leading to advancements in biomedical research which has improved personalised medicine and the discovery of new treatments for diseases. Sequencing technology advancement has also reduced the cost of DNA sequencing, which has led to the rise of Direct-To-Consumer (DTC) sequencing e.g. 23andme.com, ancestry.co.uk etc. In the meantime, concerns have emerged over privacy and security in collecting, handling, analysing, and sharing DNA and genomic data. DNA data is unique and can be used to identify individuals. Moreover, this data provides information on people’s current disease status and disposition e.g. mental health or susceptibility for developing cancer. DNA privacy violation does not only affect the owner but also affects their close consanguinity due to its hereditary nature. This paper introduces and defines the term ‘Digital DNA Lifecycle’ and presents an overview of privacy and security threats and their mitigation techniques for pre-digital DNA and throughout the digital DNA life cycle. It covers DNA sequencing hardware, software and DNA sequence pipeline in addition to common privacy attacks and their countermeasures when DNA digital data is stored, queried, or shared. Likewise, the paper examines DTC genomic sequencing privacy and security

Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification

Solar photovoltaic (PV) energy sources are rapidly gaining potential growth and popularity compared to conventional fossil fuel sources. As the merging of PV systems with existing power sources increases, reliable and accurate PV system identification is essential, to address the highly nonlinear change in PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic with a switch-mode power converter. Measured input-output data are collected from a real PV panel to be used for the identification. The data are divided into estimation and validation sets. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to best capture the PV system dynamics, and results and discussion are provided to demonstrate the accuracy of the selected model structure

PhishCatcher:Client-Side Defense Against Web Spoofing Attacks Using Machine Learning

Cyber security confronts a tremendous challenge of maintaining the confidentiality and integrity of user’s private information such as password and PIN code. Billions of users are exposed daily to fake login pages requesting secret information. There are many ways to trick a user to visit a web page such as, phishing mails, tempting advertisements, click-jacking, malware, SQL injection, session hijacking, man-in-the-middle, denial of service and cross-site scripting attacks. Web spoofing or phishing is an electronic trick in which the attacker constructs a malicious copy of a legitimate web page and request users’ private information such as password. To counter such exploits, researchers have proposed several security strategies but they face latency and accuracy issues. To overcome such issues, we propose and develop client-side defence mechanism based on machine learning techniques to detect spoofed web pages and protect users from phishing attacks. As a proof of concept, a Google Chrome extension dubbed as PhishCatcher , is developed that implements our machine learning algorithm that classifies a URL as suspicious or trustful. The algorithm takes four different types of web features as input and then random forest classifier decides whether a login web page is spoofed or not. To assess the accuracy and precision of the extension, multiple experiments were carried on real web applications. The experimental results show remarkable accuracy of 98.5% and precision as 98.5% from the trials performed on 400 classified phished and 400 legitimate URLs. Furthermore, to measure the latency of our tool, we performed experiments over forty phished URLs. The average recorded response time of PhishCatcher was just 62.5 milliseconds

Cognitive performance and convulsion risk after experimentally-induced febrile-seizures in rat

Many reports indicated that small percentage of children with febrile seizures develop epilepsy and cognitive disorders later in adulthood. In addition, the neuronal network of the hippocampus was reported to be deranged in adult animals after being exposed to hyperthermia-induced seizures in their neonatal life. The aims of this study were to investigate (1) latency and probability of seizures, (2) spatial learning and memory, in adult rats after neonatal hyperthermia-induced febrile seizures (FS). Prolonged FS were elicited in 10-day old, male Sprague Dawleys (n = 11/group) by exposure to heated air (48-52 degrees C) for 30 min; control rats were exposed to 30 degrees C air. After 1.5 months the animal's cognitive performance was assessed by 5 day trial in the Morris water maze. In another experiment the latency and probability of seizures were measured in response to pentylenetetrazole (PTZ) injections (increased doses ranged from 7 to 140 mg/kg; i.p.). In water maze, both groups showed improvements in escape latency and distance swam to reach the platform; effects were significantly greater in control versus hyperthermia-treated animals on days 3 and 4. Latency and probability of PTZ-induced seizures were shorter and higher respectively, in hyperthermia-treated animals compared to controls. We concluded that FS in neonatal rats leads to enhanced susceptibility for seizures, as well as cognitive deficits in adults. (C) 2014 ISDN. Published by Elsevier Ltd. All rights reserved

Detection of Web Cross-Site Scripting (XSS) Attacks

Most applications looking for XSS vulnerabilities have a variety of weaknesses related to the nature of constructing internet applications. Existing XSS vulnerability packages solely scan public net resources, which negatively influences the safety of internet resources. Threats may be in non-public sections of internet resources that can only be accessed by approved users. The aim of this work is to improve available internet functions for preventing XSS assaults by creating a programme that detects XSS vulnerabilities by completely mapping internet applications. The innovation of this work lies in its use of environment-friendly algorithms for locating extraordinary XSS vulnerabilities in addition to encompassing pre-approved XSS vulnerability scanning in examined internet functions to generate a complete internet resource map. Using the developed programme to discover XSS vulnerabilities increases the effectiveness of internet utility protection. This programme also simplifies the use of internet applications. Even customers unfamiliar with the fundamentals of internet security can use this programme due to its capability to generate a document with suggestions for rectifying detected XSS vulnerabilities

Are we well prepared for public health emergencies? COVID-19 pandemic effect on cancer care in Saudi Arabia: A qualitative study

Background: COVID-19 pandemic has significantly disrupted healthcare systems worldwide, raising concerns about its impact on cancer patients' access to healthcare services. This study aims to explore the experiences of cancer patients and assess effect of restrictions, delays, and changes in healthcare delivery on their health. Methods: A qualitative study was performed through individual interviews and focus group discussions (FGDs) with cancer patients and key informants (KI). Participants with different cancer types, treatment stages, and residency regions in Saudi Arabia were recruited. Thematic analysis identified four major themes: access to healthcare services; impact on appointments, diagnosis, and treatment; healthcare delivery; and cancer condition deterioration due to the pandemic. Results: Cancer patients reported variable responses to the pandemic and its effects on their healthcare seeking behavior. Several patients faced challenges in accessing healthcare services and experienced difficulties in continuing their treatment, others encountered obstacles in seeking timely diagnosis and care. Lockdown measures and travel restrictions posed barriers, affecting patients' ability to reach treatment centers. Delays in appointments, diagnosis, and treatment were also reported. In contrast, some participants did not report any negative impact but received improved care and condition prioritization. Healthcare delivery underwent a shift towards virtual appointments, online access to lab results and medication’s home delivery service. Despite these adaptations, a small group of participants experienced health deterioration due to delays in treatment and difficulties in reaching their treating physicians. Conclusion: COVID-19 pandemic has had a multifaceted impact on cancer patients. Some participants faced challenges such as care delays and disruptions in accessing healthcare services. Yet others reported positive experiences such as improved communication and utilization of new healthcare delivery modalities. These findings underscore the need for resilient and adaptable healthcare systems to safeguard the well-being of cancer patients in times of crises and public health emergencies

Integrated Approach to Achieve a Sustainable Organic Waste Management System in Saudi Arabia

Organic waste management (OWM) has always been a fundamental aspect of human populations. Approaches to OWM must be matched to the characteristics of a certain population. In this consideration, the Kingdom of Saudi Arabia (KSA) is no exception. Organizations are being aligned to focus on sustainability matters sharing significant features with universal trends, especially the integration of 3Rs (reducing waste, reusing, and recycling resources). However, the degree and nature of advancement in the direction of sustainability vary depending on the economic level of a state. High-income economies can afford to pay a higher price to integrate 3Rs technologies. Most recent endeavors have focused on achieving ‘Zero Waste’, which is costly for low-income developing countries. The expectations of OWM systems in KSA must be estimated. In this work, the situations in KSA and other countries are analyzed, and pertinent aspects are explored. Matters relating to the sustainability of OWM are conceptually assessed. This study proposes an integrated method for an organic waste management system to achieve sustainable OWM in the context of state policy and appropriate frameworks, suitable technology, institutional order, operational and monetary administration, and people consciousness and involvement. A genetic-based waste collection transportation algorithm that enhances the efficiency of waste collection truck management is presented in line with this technology. The selected routes based on the Rfs and IPv are the most efficient among those available for the examined smart bin destinations. The minimum Rfs of selected routes is less than the maximum Rfs of available routes by 2.63%. Also, the minimum IPv of selected routes is less than the maximum IPv of available routes by 27.08%. The proposed integrated approach, including the waste collection transportation algorithm, would be beneficial across a variety of country-specific layouts