Due to its ability to supply reliable, robust and scalable computational power, cloud computing is becoming increasingly popular in industry, government, and academia. High-speed networks connect both virtual and real machines in cloud computing data centres. The system’s dynamic provisioning environment depends on the requirements of end-user computer resources. Hence, the operational costs of a particular data center are relatively high. To meet service level agreements (SLAs), it is essential to assign an appropriate maximum number of resources. Virtualization is a fundamental technology used in cloud computing. It assists cloud providers to manage data centre resources effectively, and, hence, improves resource usage by creating several virtualmachine (VM) instances. Furthermore, VMs can be dynamically integrated into a few physical nodes based on current resource requirements using live migration, while meeting SLAs. As a result, unoptimised and inefficient VM consolidation can reduce performance when an application is exposed to varying workloads. This paper introduces a new machine-learning-based approach for dynamically integrating VMs based on adaptive predictions of usage thresholds to achieve acceptable service level agreement (SLAs) standards. Dynamic data was generated during runtime to validate the efficiency of the proposed technique compared with other machine learning algorithms.
In today’s world, the use of computer networks is everywhere, and to access the home network we use the Internet. IoT networks are the new range of these networks in which we try to connect different home appliances and try to give commands from a remote place. Access to any device over an insecure network invites various types of attacks. User authentication can be performed using some password or biometric technique. However, when it comes to authenticating a device, it becomes challenging to maintain data security over a secure network such as the Internet. Many encryptions and decryption algorithms assert confidentiality, and hash code or message authentication code MAC is used for authentication. Traditional cryptographic security methods are expensive in terms of computational resources such as memory, processing capacity, and power consumption. They are incompatible with the Internet of Things devices that have limited resources. Although automatic Device-to-Device communication enables new potential applications, the limited resources of the networks’ machines and devices impose various constraints. This paper proposes a home device authentication scheme when these are accessed from a remote place. An authentication device is used for the home network and controller device to control home appliances. Our scheme can prevent various attacks such as replay attacks, server spoofing, and man-in-the-middle attack. The proposed scheme maintains the confidentiality and authenticity of the user and devices in the network. At the same time, we check the system in a simulated environment, and the results show that the network’s performance does not degrade much in terms of delay, throughput, and energy consumed.
Recently, there have been exploratory growth in the research of wireless sensor network due to wide applications like health monitoring, environment monitoring, and urban traffic management. Sensor network applications have been used in habitat monitoring, border monitoring, health care, and military surveillance. In some applications, the security of these networks is very essential and need robust support. For a network, it is very important that node in the network trust each other and malicious node should be discarded. Cryptography techniques are normally used to secure the networks. Key plays a very important role in network security. Other aspects of security such as integrity, authentication, and confidentiality also depend on keys. In wireless sensor network, it is very difficult to manage the keys as this includes distribution of key, generation of new session key as per requirements, and renewal or revoke the keys in case of attacks. In this paper, we proposed a scalable and storage efficient key management scheme (SSEKMS) for wireless sensor networks that establish the three types of keys for the network: a network key that is shared by all the nodes in the network, a cluster key shared for a cluster, and pairwise key for each pair of nodes. We analysed the resiliency of the scheme (that is the probability of key compromise against the node capture) and compared it with other existing schemes. SSEKMS is a dynamic key management system that also supports the inclusion of the new node and refreshes the keys as per requirements.
A disease mainly known as diabetes is causing several side effects on the human body. The most affected part of the human body is the human eye retina. This disease of the retina that can result in loss of vision. This phenomenon is known as Diabetic Retinopathy (DR.)Basically Daibetis reduces the capability of the human body to store and regulate the blood sugar , as a result of this, the retina of human eye gets effected. In the early stages DR is showing the typical result of damaging the human eye retina and further it leads to the Sight-threatening diabetic retinopathy (SDTR) i.e. complete loss of vision.Computer assisted diagnosis is one of the tool which helps to diagnose the diabetic patient by using the results of medical investigations. i.e assisting images, reports, etc. for initializing the treatment. In the screening of diabetes sight-threatening DR plays a vital role. These are some major techniques available till date for machine-driven analysis of STDR, to detect hemorrhages and microaneurysms. With limitations for analysis of STDR, the survey mainly focuses on both the qualitative as well as a quantitative comparison of the existing study.While considering the future work , it is a try to encapsulate the convenient algorithms for easy understanding and informative to the experimenters working in this field so that to understand the previous algorithms in a better way and try to develop the most effective one .
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.