With the continuous development of IoT (Internet of Things) technology, IoT has become a typical representative of the development of new generation of information technology. The IoT allows people to use our data and computing resource anytime and everywhere. In the context of the IoT, the security of the vast amount of data generated by smart devices is one of the biggest concerns. To meet the challenge, the user authentication scheme in IoT should ensure the essential security performance protection and low computing costs. A authentication protocol preserving user anonymity was proposed by Nikooghadam et al. in 2017. In this paper, we further analyze the security of Nikooghadam et al.'s protocol and propose an improved anonymous authentication protocol for IoT. We use the timestamp mechanism and rely on CDH (Computational Diffie-Hellman) problem to improve security primarily. The security of the proposed protocol is verified using BAN logic and the performance comparison and efficiency analysis are carried out. The results show that our improved protocol has higher security with little more computation cost.
Underwater images are important carriers and forms of underwater information, playing a vital role in exploring and utilizing marine resources. However, underwater images have characteristics of low contrast and blurred details because of the absorption and scattering of light. In recent years, deep learning has been widely used in underwater image enhancement and restoration because of its powerful feature learning capabilities, but there are still shortcomings in detailed enhancement. To address the problem, this paper proposes a deep supervised residual dense network (DS_RD_Net), which is used to better learn the mapping relationship between clear in-air images and synthetic underwater degraded images. DS_RD_Net first uses residual dense blocks to extract features to enhance feature utilization; then, it adds residual path blocks between the encoder and decoder to reduce the semantic differences between the low-level features and high-level features; finally, it employs a deep supervision mechanism to guide network training to improve gradient propagation. Experiments results (PSNR was 36.2, SSIM was 96.5%, and UCIQE was 0.53) demonstrated that the proposed method can fully retain the local details of the image while performing color restoration and defogging compared with other image enhancement methods, achieving good qualitative and quantitative effects.
Wireless communications for applications of inshore fishery and large area aquatic environmental monitoring are really challenging, due to the characteristics of a long monitoring period, large coverage area, and adverse transmission conditions. Recently, LPWAN (low-power wide-area network) became the new solution to address these challenges, due to its long transmission distance and low power consumption of end-nodes. In this paper, we designed a novel network system for aquatic environmental monitoring, based on long-range 2.4G technology, which consisted of a low cost dual-channel gateway and end-nodes. A DMSF (dual-channel multiple spreading factors)–TDMA (time division multiple access) MAC (medium access control) scheme for this system was proposed, which largely reduces the channel collision probability, and improves the real-time for urgent data and the average lifetime of end-nodes. We verified the applicability of the long-range 2.4G technology in an aquatic environment, by point-to-point communication experiments over lake water. The performance evaluation and analysis of DMSF–TDMA is presented through simulations, and comparison with other existing schemes. The results demonstrated the benefit of our proposed scheme, in terms of the packet delivery rate, delay, and energy consumption.
Aiming at to avoid the security drawbacks of the authentication protocol in Long Term Evolution-Wireless Local Area Network (LTE-WLAN) heterogeneous converged network proposed by the 3rd Generation Partnership Project (3GPP), an improved protocol based on hybrid cryptosystem is proposed to achieve access authentication for WLAN user equipment(UE) with identity privacy protection. The security analysis shows that by using certificateless signcryption(CLSC) scheme without pairing calculation based on Elliptic Curve Cryptography (ECC), hash chain and identity index mechanism, the proposed authentication protocol provides the following ten kinds of security properties: anonymous protection for International Mobile Subscriber Identity (IMSI), update on shared keys, protection for master session key(MSK), resistance to impersonation attack, replay attack, man-in-the-middle attack, redirect attack and Denial of Service (DoS) attack, mutual authentication between communication entities, and without framework modification from the original protocol. The performance analysis shows that the approximate calculation time of all the communication entities is 79 ms in total and that of UE is 266 us. Thus, our proposed protocol is superior to some other related improved protocols in terms of security and efficiency.
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.