Ad Hoc Networks最新文献

The widespread deployment of Electric vehicles (EVs) leads to an increasing demand for charging piles and corresponding charging service (CS) from CS providers (CSPs). Pseudonym-based authentication mechanisms have been designed to resist the attacks which exploit the charging-authentication information to infer EV users’ identities and their driving routes. However, these existing mechanisms generated EV users' pseudonyms by relying on a trusted third entity, which affects the authentication system's resilience and EV user privacy-preservation.

To this end, this paper proposes a Traceable Anonymity-self-controllable V2X Authentication (TAVA) scheme for the multiple-CSP (forming a CSP set) scenario, where each CSP independently manages its own CPs and a CSP randomly joins or leaves the CSP set. TAVA has a series of security capabilities. (1) First, it allows the mutual authentication between an EV user and a CP, while preserving EV user privacy and also assuring forward and backward security. This capability is achieved by using the multi-party computation technique to let all CSPs join the process of generating EV-users’ credentials but each CSP knows nothing about the credentials. (2) Secondly, TAVA has the capabilities of self-controllable anonymity and unlinkability by allowing each EV user to self-generate verifiable and unlinkable one-time pseudonyms based on bilinear- mapping technique. (3) At last, each EV user under TAVA is traceable. It is achieved by applying the two-factor authentication technique in TAVA and linking the one-time pseudonym to the two factors, namely, the credential and the EV user's biometric characteristics with low entropy rates. Note that all these security capabilities are achieved with less performance degradation in terms of communication and storage overheads in the dynamic environment. We conduct the informal and formal analysis of security capabilities and also make performance evaluations. The results indicate that, compared with the latest works, the computation overhead of the mutual authentication in TAVA is reduced by up to 89 %.

The prevailing adoption of Internet of Things paradigm is giving rise to a wide range of use cases in various vertical industries including remote health, industrial automation, and smart agriculture. However, the realization of such use cases is mainly challenged due to their stringent service requirements of high reliability and low latency. This challenge grows further when the service entails processing collected data for informed decision making. In this work, we consider a field of industrial Internet of Things devices that generate computational tasks and are covered by a nearby base station equipped with an edge server. The edge server offers fast processing to the devices’ tasks to help in meeting their latency requirement. Due to statistical wireless variability, the task data may not be correctly delivered in time for processing. To this end, we utilize an unmanned aerial vehicle as a supplemental edge server that tailors its trajectory and flies closer to the IIoT devices to ensure a highly reliable task delivery based on the given task reliability constraints. We formulate the problem as a Markov Decision Process, and propose a deep reinforcement learning-based approach using proximal policy optimization to optimize the unmanned aerial vehicle trajectory and scheduling devices to offload their data for processing. We present simulation results for various system scenarios to illustrate the effectiveness of the proposed solution as compared to several baseline approaches.

Satellite networks are vital in facilitating communication services for various critical infrastructures. These networks can seamlessly integrate with a diverse array of systems. However, some of these systems are vulnerable due to the absence of effective intrusion detection systems, which can be attributed to limited research and the high costs associated with deploying, fine-tuning, monitoring, and responding to security breaches. To address these challenges, we propose a pre-trained Large Language Model for Cyber Security, for short PLLM-CS, which is a variant of pre-trained Transformers, which includes a specialized module for transforming network data into contextually suitable inputs. This transformation enables the proposed LLM to encode contextual information within the cyber data. To validate the efficacy of the proposed method, we conducted empirical experiments using two publicly available network datasets, UNSW_NB 15 and TON_IoT, both providing Internet of Things (IoT)-based traffic data. Our experiments demonstrate that proposed LLM method outperforms state-of-the-art techniques such as BiLSTM, GRU, and CNN. Notably, the PLLM-CS method achieves an outstanding accuracy level of 100% on the UNSW_NB 15 dataset, setting a new standard for benchmark performance in this domain.

In contemporary urban environments, route recommendation systems have become an indispensable tool in moving the population from large centers, serving as valuable resources for circumventing traffic congestion. Enhancing vehicular traffic flow through strategic route adjustments is a pivotal element in improving traffic mobility. However, depending exclusively on traffic-related data for route recommendations fails to meet the essential criteria for ensuring effective management and safety for drivers and passengers during travel. Thus, context awareness and traffic data are crucial for enhancing efficiency and safety in traffic management. Our study proposes a two-context-aware approach to recommend safe routes for urban traffic management, considering road safety and travel time. Experiments were carried out using the widely recognized tool — HERE Navigation. Comparatively, our approach signifies a progressive stride in balancing mobility and security when contrasted with a single focus on travel time.

Vehicular communications have advanced data exchange and real-time services in intelligent transportation systems by exploiting advanced communication between vehicles and infrastructure. The emergence of Multi-access Edge Computing (MEC) has further elevated this field by utilizing distributed edge resources near vehicles for low-latency data processing and high-reliability communication. In this dynamic environment, adequate resource allocation and task offloading are pivotal to ensure superior performance, lower latency, and efficient network resource utilization, enhancing Quality of Service (QoS) and overall driving experience and safety. This paper presents a developed vehicular network and offloading mechanism, introducing a resource management model with real-time allocation and load balancing. The proposed method integrates task prioritization, multi-agent collaboration, context-aware decision-making, and distributed learning to optimize network performance. The introduced optimized algorithm initializes Q-networks and target networks, sets up an experience replay buffer, and configures agents with local state representations. Agents use an ε-greedy policy for action selection, update Q-values through experience replay, and prioritize tasks based on urgency while sharing state information for collaborative decision-making. Evaluations through simulation demonstrate optimized performance, enhancing efficiency in vehicular MEC networks compared to baseline and the other well-known algorithms.

Compared with systems rely on wearable sensors, cameras or other devices, WiFi-based gesture recognition systems are convenient, non-contact and privacy-friendly, which have received widespread attention in recent years. In WiFi-based gesture recognition systems, the channel state information (CSI) carried by WiFi signals contains fine-grained information, which is commonly used to extract features of gesture activities. However, since the CSI patterns of the same gesture change across domains, these gesture recognition systems cannot effectively work without retraining in new domains, which will hinder the practical adoption of gesture recognition systems. Therefore, we propose a novel gesture recognition system that can address the issue of cross-domain recognition while achieving higher recognition accuracy for in-domain scenarios. Firstly, we employ CSI ratio and subcarrier selection to effectively eliminate noise from the CSI, and propose a method to reconstruct CSI sequence using low-frequency signals, which can effectively remove irrelevant noise in the high-frequency part and ensure the validity of the data. Next, we calculate the phase difference to explore the intrinsic features of gesture and convert the obtained data into RGB image. Finally, we use Dense Convolutional Network as backbone network, combined with dynamic convolution module, for RGB image recognition. Extensive experiments demonstrate that our proposed system can achieve 99.58% in-domain gesture recognition, and its performance across new person and orientations is 99.15% and 98.31%, respectively.