Satellite communication system is expected to play a vital role for realizing various remote Internet-of-Things (IoT) applications in sixth-generation vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this article, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff-Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.

The emergency situation of COVID-19 is a very important problem for emergency decision support systems. Control of the spread of COVID-19 in emergency situations across the world is a challenge and therefore the aim of this study is to propose a q-linear Diophantine fuzzy decision-making model for the control and diagnose COVID19. Basically, the paper includes three main parts for the achievement of appropriate and accurate measures to address the situation of emergency decision-making. First, we propose a novel generalization of Pythagorean fuzzy set, q-rung orthopair fuzzy set and linear Diophantine fuzzy set, called q-linear Diophantine fuzzy set (q-LDFS) and also discussed their important properties. In addition, aggregation operators play an effective role in aggregating uncertainty in decision-making problems. Therefore, algebraic norms based on certain operating laws for q-LDFSs are established. In the second part of the paper, we propose series of averaging and geometric aggregation operators based on defined operating laws under q-LDFS. The final part of the paper consists of two ranking algorithms based on proposed aggregation operators to address the emergency situation of COVID-19 under q-linear Diophantine fuzzy information. In addition, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.

Although modern Maritime Transportation Systems (MTS) have been extensively benefited from Internet of Things (IoT) technology, but still the risks and challenges in safety and reliability have increased substantially. The involvement of different maritime parties in the marine transportation flow scheduling and management further escalates these challenges. Thus, we need an IoT-based collaborative processing system that unifies the modular structure and integrates multiple modules involved in MTS. Moreover, the need for a shared and controlled access mechanism that cannot be manipulated or tampered by unauthorized parties is also essential requirement in MTS. Blockchain, as an emerging technology, has become a key tool in data security protection because of its non-tampering and non-forgery characteristics. Keeping in view of this aspect, in this paper, an IoT-based collaborative processing system based on blockchain is proposed for marine transportation flow scheduling and management. In addition, we propose a novel consensus mechanism based on Verifiable Random Function (VRF) and reputation voting to reduce the communication cost in blockchain consensus communication process. The proposed scheme has been validated in a simulated environment and the results illustrate that the scheme has obvious effect in resisting replay attack and camouflage attack. Furthermore, the optimized consensus mechanism improves the security by 8% and the transaction processing speed by 6% on the premise that the communication cost is basically unchanged.

One of the most promising application area of Industrial Internet of Things (IIoT) is Vehicular Ad hoc NETworks (VANETs). VANETs are largely used by Intelligent Transportation Systems (ITS) to provide smart and safe road transport. To reduce the network burden, Software Defined Networks (SDNs) acts as a remote controller. Motivated by the need for greener IIoT solutions, this paper proposes an energy-efficient end-to-end security solution for Software Defined Vehicular Networks (SDVN). Besides SDN's flexible network management, network performance, and energy-efficient end-to-end security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: i) In RSU-based Group Authentication (RGA) scheme, each vehicle in the RSU range receives a group id-key pair for secure communication and ii) In private-Collaborative Intrusion Detection System (p-CIDS), SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated using NS2 & Matlab, and the simulation results provide higher energy efficiency through reduced end-to-end security communication cost and decentralized learning compared with other existing mechanisms. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDVN.

Energy harvesting (EH) is a promising and critical technology to mitigate the dilemma between the limited battery capacity and the increasing energy consumption in the Internet of everything. However, the current EH system suffers from energy-information cross threats, facing the overlapping vulnerability of energy deprivation and private information leakage. Although some existing works touch on the security of energy and information in EH, they treat these two issues independently, without collaborative and intelligent protection cross the energy side and information side. To address the aforementioned challenge, this article proposes a joint protection framework of energy security and information privacy for EH with an incentive federated learning approach. First, we design a federated-learning-based malicious energy user detection method according to energy status and behaviors to provide energy security protection. Second, a differential-privacy-empowered information preservation scheme is devised, where sensitive information is perturbed and protected by the customized demand-based noise. Third, a noncooperative-game-enabled incentive mechanism is established to encourage EH nodes to participate in the joint energy-information protection system. The proposed incentive mechanism derives the optimal energy-information security strategy for EH nodes and achieve a tradeoff between the protection of energy security and information privacy. Evaluation results have verified the effectiveness of our proposed joint protection mechanism.