IPv6 over Low Power Wireless Personal Area Network (6LoWPAN) provides IP connectivity to the highly constrained nodes in the Internet of Things (IoTs). 6LoWPAN allows nodes with limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard, thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network. The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES (Advanced Encryption Standard), but the 6LoWPAN standard lacks and has omitted the security and privacy requirements at higher layers. The sensor nodes in 6LoWPAN can join the network without requiring the authentication procedure. Therefore, from security perspectives, 6LoWPAN is vulnerable to many attacks such as replay attack, Man-in-the-Middle attack, Impersonation attack, and Modification attack. This paper proposes a secure and efficient cluster-based authentication scheme (CBAS) for highly constrained sensor nodes in 6LoWPAN. In this approach, sensor nodes are organized into a cluster and communicate with the central network through a dedicated sensor node. The main objective of CBAS is to provide efficient and authentic communication among the 6LoWPAN nodes. To ensure the low signaling overhead during the registration, authentication, and handover procedures, we also introduce lightweight and efficient registration, de-registration, initial authentication, and handover procedures, when a sensor node or group of sensor nodes join or leave a cluster. Our security analysis shows that the proposed CBAS approach protects against various security attacks, including Identity Confidentiality attack, Modification attack, Replay attack, Man-in-the-middle attack, and Impersonation attack. Our simulation experiments show that CBAS has reduced the registration delay by 11%, handoff authentication delay by 32%, and signaling cost
The number of wireless services and devices have remarkably increased, especially since the introduction of smart phones. The population of mobile nodes (MNs) is now exceeding the traditional non-mobile nodes. Mobility is a key factor in mobile core networks as it is responsible for providing continuous communication when a MN is on the move. Currently, a centralized mobile core network architecture is implemented, which has certain limitations. Distributed mobility management (DMM) is often seen as a solution to the problems associated with centralized mobility management (CMM). Address and tunneling management are big challenges for current DMM-based mobility protocols. Keeping in mind the current advancement of mobile network architecture, this paper proposes a novel tunnel-free distributed mobility management support protocol intended for such an evolution. In addition, the performance of the existing DMM IPv6 mobility management protocols in the context of handover latency, handover blocking probability, and data packet loss is analyzed and compared to the proposed framework. The performance analyses show that the proposed tunnel-free method can reduce about 12% of handover latency, 71% of handover blocking probability, and 82% of data packet loss.
<div>To date, the novel Corona virus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent COVID-19 forecasting, diagnosing, and monitoring systems have been proposed to tackle the COVID-19 pandemic. In this article based on our extensive literature review, we provide a taxonomy based on the intelligent COVID-19 forecasting, diagnosing, and monitoring systems. We review the available literature extensively under the proposed taxonomy and have analyzed a significantly wide range of machine learning algorithms and IoTs which can be used in predicting the spread of COVID-19 and in diagnosing and monitoring the infected individuals. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.</div>
<div>To date, the novel Corona virus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent COVID-19 forecasting, diagnosing, and monitoring systems have been proposed to tackle the COVID-19 pandemic. In this article based on our extensive literature review, we provide a taxonomy based on the intelligent COVID-19 forecasting, diagnosing, and monitoring systems. We review the available literature extensively under the proposed taxonomy and have analyzed a significantly wide range of machine learning algorithms and IoTs which can be used in predicting the spread of COVID-19 and in diagnosing and monitoring the infected individuals. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.</div>
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.