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Nowadays, the increasingly complex and changeable marine environment makes the signals received by the underwater sensing equipment not only contain the weak signals radiated by underwater targets but also accompanied by marine solid background noise, which leads to the degradation and distortion of underwater acoustic signals and the decline of underwater communication quality. Under the severe influence of ocean noise, the underwater acoustic sensing and acquisition system will have the problems of high SNR ratio threshold, minimal sensing bandwidth, and unable to sense the signal with unknown frequency effectively. The Lévy noise model has been selected to describe the marine noise environment and explain its scientificity in this paper. A parameter estimation method for Lévy noise is proposed. Under the condition of characteristic index α=1.5 and noise intensity D=0.1 of the Lévy noise model, the estimated mean values of parameters are 1.5026 and 1.1664. The estimated variances are 0.0034 and 0.0046, which proves the effectiveness and applicability of the estimation method. Then, an improved dual-coupled Duffing oscillator sensing system is proposed to sense the weak signals with unknown frequency under Lévy noise. Under the background of Lévy with characteristic index α=1.5, deflection parameter β=0 and noise intensity D=0.1, the sensing error rate of our system with unknown frequency is 0.054%, the lowest sensing signal amplitude is A=0.010, the lowest sensing SNR ratio is − 23.9254 dB, and the frequency of multi-frequency weak signals to be measured can be obtained. The estimation error of frequency sensing is 0.33%.

Enhancing the task offload performance of UAV-assisted Vehicular Edge Computing Networks (VECNs) is complex, especially in vehicle-to-everything (V2X) applications. These networks rely on UAVs and roadside units (RSUs) to offload heavy computational tasks and reduce the load on the on-board systems. However, UAV-assisted VECNs face severe challenges from heterogeneous offload node resources and dynamic edge network environments in providing low-latency and high-response task offloading, especially during traffic congestion or infrastructure failures. In this paper, we propose a digital twin (DT)-driven task offloading framework for UAV-assisted VECNs. The aim of the proposed framework is to improve the global performance of VECN task offloading under limited computational and communication resource constraints. Firstly, we construct a decentralized offloading decision-centralized evaluation task offloading framework for UAV-assisted VECNs based on the asynchronous advantage actor-critic (A3C) algorithm. Secondly, we integrate the graph attention networks (GAT) into the framework to incorporate the dynamically changing DT network topology information into the state evaluation of VECNs. By simulating a DT-driven multi-UAV cooperative system and comprehensive evaluation of real-world task request datasets. The framework has a better task throughput rate and stability when performing task offloading in local resource overload and dynamic edge environment scenarios.

In offshore wind power projects, large-diameter monopiles are subjected to scour resulting in reduced horizontal bearing capacity. Analytical p-y models are widely used to calculate the horizontal bearing capacity of monopiles. However, traditional p-y models for soft clays do not consider the effect of pile diameter on the horizontal bearing capacity of large-diameter monopiles subjected to local scour. In this study, well-calibrated three-dimensional finite element numerical models are used to modify the traditional p-y model for soft clays considering local scour. A p-y model suitable for analyzing the horizontally loaded large-diameter monopile foundations subjected to local scour in soft clays is proposed. The proposed p-y model and the traditional p-y model are compared with the finite element calculation results for horizontally loaded large-diameter monopile foundations in soft clays. The results show that the proposed p-y model performs better than the traditional p-y model in predicting the horizontal bearing capacity of large-diameter monopile foundations in soft clays under local scour conditions.

Generative artificial intelligence (GenAI) has brought about profound transformations across the diverse domains of the Internet of Things such as manufacturing, marketing, medicine, education and work assistance. However, the proliferation of computationally intensive and highly complex GenAI models poses substantial challenges to servers and central network capacities. To effectively permeate various facets of our lives, GenAI heavily relies on mobile edge computing. In this Perspective article, we first introduce GenAI applications on edge devices highlighting its potential capacity to revolutionize our everyday life. We then outline the challenges associated with deploying GenAI on edge devices and present possible solutions to effectively address these obstacles. Finally, we introduce an intelligent mobile edge computing paradigm able to reduce response latency, improve efficiency, strengthen security and privacy preservation and conserve energy, opening the way to a sustainable and efficient application of the different GenAI models.The application of generative artificial intelligence to mobile devices has the potential to enable integrated, personalized, contextually aware experiences. However, the computational energy demand is challenging. This Perspective article introduces an intelligent mobile edge computing paradigm for the implementation of generative artificial intelligence on the Internet of Things system.

Purpose This paper aims to provide a secure and efficient pairing protocol for two devices. Due to the large amount of data involving sensitive information transmitted in Internet of Things (IoT) devices, generating a secure shared key between smart devices for secure data sharing becomes essential. However, existing smart devices pairing schemes require longer pairing time and are difficult to resist attacks caused by context, as the secure channel is established based on restricted entropy from physical context. Design/methodology/approach This paper proposes a fuzzy smart IoT device pairing protocol via speak to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the environment around the devices, which is easily implemented in the vast majority of Internet products. Specifically, to protect the privacy of secret keys and improve efficiency, this paper presents a single-round pairing protocol by adopting a recently published asymmetric fuzzy encapsulation mechanism (AFEM), which allows devices with similar environmental fingerprints to successfully negotiate the shared key. To instantiate AFEM, this paper presents a construction algorithm, the AFEM-ECC, based on elliptic curve cryptography. Findings This paper analyzes the security of the FS2M and its pairing efficiency with extensive experiments. The results show that the proposed protocol can achieve a secure device pairing between two IoT devices with high efficiency. Originality/value In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT device pairing by using a new context-environment (i.e., human voice) . The experimental results show that FS2M has a good performance in both communication cost (i.e., 130 KB) and running time (i.e., 10 S).

Key agreement is one the most essential steps when applying cryptographic techniques to secure device-to-device (D2D) communications. Recently, several PHY-based solutions have been proposed by leveraging the channel gains as a common randomness source for key extraction in wireless networks. However, these schemes usually suffer a low rate of key generation and low entropy of generated key and rely on the mobility of devices. In this paper, a novel secret key extraction protocol is proposed by using interference in wireless D2D fading channel. It establishes symmetrical keys for two wireless devices by measuring channel gains and utilizing artificial jamming sent by the third party to change the measured value of channel gains. We give a theoretically reachable key rate of the proposed scheme from the viewpoint of the information theory. It shows that the proposed scheme can make hundred times performance gain than the existing approaches theoretically. Experimental results also demonstrate that the proposed scheme can achieve a secure key distribution with a higher key rate and key entropy compared with the existing schemes.

In the modern cloud environment, considering the cost of hardware and software resources, applications are often co-located on a platform and share such resources. However, co-located execution and resource sharing bring memory access conflict, especially in the Last Level Cache (LLC). In this paper, a lightweight method is proposed for partition LLC named by Classification-and-Allocation (C&A). Specifically, Support Vector Machine (SVM) is used in the proposed method to classify applications into the triple classes based on the performance change characteristic (PCC), and the Bayesian Optimizer (BO) is leveraged to schedule LLC to guarantee applications with the same PCC sharing the same part of LLC. Since the near-optimal partition can be found efficiently by leveraging BO-based scheduling with a few sampling steps, C&A can handle unseen and versatile workloads with low overhead. We evaluate the proposed method in several workloads. Experimental results show that C&A can outperform the state-of-art method KPart (El-Sayed et al in Proceedings of 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA) 104−117, 2018) by 7.45% and 22.50% respectively in overall system throughput and fairness, and reduces 20.60% allocation overhead.

To address these challenges, various emerging technologies have been introduced in vehicular communication networks, such as software defined space-air-ground integrated vehicular network [4], fog computing in vehicular networks [5], drone-assisted vehicular networks [6], and machine learning for data delivery [7]. The fifth article, “Routing protocol in VANETs Equipped with Directional Antennas: Topology-Based Neighbor Discovery and Routing Analysis” by H. Li and Z. Xu, proposes a novel neighbor discovery algorithm which makes vehicles sense the topology changes around them and arrange their directional antennas accordingly. [...]a routing protocol is proposed for vehicular networks, which is based on the conventional epidemic routing protocol, whereby the vehicles make their routing decisions according to the information collected during the neighbor discovery process. Ning Zhang Ning Lu Tao Han Yi Zhou Dajiang Chen [1] A. Lei, H. Cruickshank, Y. Cao, P. Asuquo, C. P. A. Ogah, Z. Sun, \"Blockchain-Based Dynamic Key Management for Heterogeneous Intelligent Transportation Systems,\" IEEE Internet of Things Journal, vol. 4 no. 6, pp. 1832-1843, DOI: 10.1109/JIOT.2017.2740569, 2017.

Convoy driving has great potential in the development of autonomous driving industry, which can bring great improvement to the utilization rate of roads and the travel experience of passengers. However, the emergence of convoy driving also brings some new challenges to the location privacy of autonomous vehicles. In this paper, a novel dynamic mixed zone establishment scheme is proposed to protect the location privacy of autonomous vehicles in convoy driving context. As the convoy is a closed group, the request for the establishment of the mix zone will be broadcast first within the convoy, followed by outside the convoy. In order to prevent pseudonym syntactic join attacks within the convoy, the proposed scheme allows autonomous vehicles to use multiple valid pseudonyms if only itself change the pseudonym in the convoy. In order to trace the real identity of the offending vehicle, the proposed scheme specifies the distribution method of the pseudonym, the legitimate vehicle can trace the real identity of the offending vehicle by submitting the pseudonym of the offending vehicle. Compared with the scheme to protect location privacy in the traditional vehicular network, the proposed scheme has less overhead and a higher level of security.

To this end, Xiang Li et al. propose a secure and compliant continuous assessment framework (SCCAF), to facilitate cloud service customers to select an optimal cloud service provider (CSP) which satisfies their desired security requirements. [...]it also enables cloud service customers to evaluate the compliance of the selected CSP in the process of using cloud services. An energy-efficient transmission scheme (EET) is proposed, which can be suitable for the resource-constrained devices and applications in IoT communication. [...]the secrecy outage probability (SOP) and secure energy efficiency (SEE) of different transmission strategies are derived, which contributes to the design of energy-efficient secure transmission. Unidirectional Visible Light Communication (VLC) is applied to the over-the-air reprogramming and commercial off-the-shelf devices such as smartphone and sensor node are used to improve applicability. [...]a reprogramming approach named ReVLC is proposed.