Computer Networks最新文献

Secure multi-party computation (SMPC) is a crucial technology that supports privacy preservation, enabling multiple users to perform computations on any function without disclosing their private inputs and outputs in a distrustful environment. Existing secure multi-party computation models typically rely on obfuscation circuits and cryptographic protocols to facilitate collaborative computation of tasks. However, the efficiency and privacy leakage of users have not been paid much attention during the computation process. To address these problems, this article proposes a privacy-preserving approach Blockchain-assisted Verifiable Secure Multi-Party Data Computing (BVS-MPDC). Specifically, to prevent privacy leakage when users and multiple participants share data, BVS-MPDC uses additive homomorphic encryption to encrypt data shares; and verifies the generated Pedersen commitment of all the data. BVS-MPDC utilizes an improved Schnorr aggregation signature to improve computation efficiency between computing nodes and smart contracts by submitting an aggregation signature to the blockchain. Moreover, we design and implement a smart contract for verifying aggregation signature results on Ethereum. The security proof is presented under the UC framework. Finally, simulation experiments of performance evaluations demonstrate that our scheme outperforms existing schemes in computation overhead and verification.

To discover threats to a network system, investigating the behaviors of attackers after successful exploitation is an important phase, called post-exploitation. Although various efficient tools support post-exploitation implementation, the crucial factor in completing this process remains experienced human experts, known as penetration testers or pen-testers. This study proposes the Raiju framework, a Reinforcement Learning (RL)-driven automation approach, which automatically implements steps of the post-exploitation phase for security-level evaluation. We implement two well-known RL algorithms, Advantage Actor–Critic (A2C) and Proximal Policy Optimization (PPO), to evaluate specialized agents capable of making intelligent actions. With the support of Metasploit, modules corresponding to selected actions of the agent automatically launch real attacks of privileges escalation (PE), gathering hashdump (GH), and lateral movement (LM) on multiple platforms. Through leveraging RL, our objective is to empower agents that can autonomously select suitable actions to exploit vulnerabilities within target systems. This approach enables the automation of specific components within the penetration testing (PT) workflow, thereby enhancing its efficiency and adaptability to evolving threats and vulnerabilities. The experiments are performed in four real environments with agents trained in thousands of episodes. The agents can automatically launch exploits on the four environments and achieve a success ratio of over 84% across the three attack types. Furthermore, our experiments demonstrate the remarkable effectiveness of the A2C algorithm in the realm of post-exploitation automation.

Nowadays, quantum computing has reached the engineering phase, with fully-functional quantum processors integrating hundreds of noisy qubits. Yet – to fully unveil the potential of quantum computing out of the labs into the business reality – the challenge ahead is to substantially scale the qubit number, reaching orders of magnitude exceeding thousands of fault-tolerant qubits. To this aim, the distributed quantum computing paradigm is recognized as the key solution for scaling the number of qubits. Indeed, accordingly to such a paradigm, multiple small-to-moderate-scale quantum processors communicate and cooperate for executing computational tasks exceeding the computational power of single processing devices. The aim of this survey is to provide the reader with an overview about the main challenges and open problems arising with distributed quantum computing from a computer and communications engineering perspective. Furthermore, this survey provides an easy access and guide towards the relevant literature and the prominent results in the field.

In the interconnected world, where wearable devices and implantable sensors have become integral to healthcare, Wireless Body Area Networks (WBANs) play a very pivotal role. These networks enable continuous monitoring of vital signs, early detection of health issues, and personalized patient care. However, ensuring the longevity and reliability of WBANs remains a multifaceted challenge. This comprehensive survey addresses this challenge by providing key insights and practical solutions. We investigate the intricacies of indoor communication links within WBANs, characterizing wireless channels thereby revealing factors that influence the quality of the received signal. By understanding multipath propagation, small-scale fading, and interference, we empower engineers to optimize communication protocols for reliable data transmission. In particular, we describe practical path loss models that have been successfully implemented in real-world WBANs. These models account for indoor scenarios, considering floor attenuation, wall partitions, and other environmental factors.

Furthermore, we explore diverse energy harvesting (EH) techniques, from converting body movements to electrical energy to harnessing ambient RF signals. By addressing energy efficiency challenges, we extend the lifespan of WBAN devices, benefiting both patients and healthcare providers. We have also demonstrated the feasibility of incorporating EH into WBANs, via three case studies showcasing real-world validation. Finally, as the field evolves, we highlight open research problems, inviting further investigation into efficient wireless communication, seamless integration of EH techniques, and novel protocols for WBANs. In summary, this survey bridges theory and practice, fostering advancements in WBAN technology. By addressing wireless indoor channel modeling, energy efficiency, and their practical implementation in BAN, we contribute to the effectiveness of WBANs in indoor scenarios.

The hybrid vehicle to everything (V2X) network based on visible light communication (VLC) and radio frequency (RF) is an excellent choice for future vehicle communication. In this paper, the (user behavior)-based longitudinal-separation (LS) prediction model and the reverse-leadership strategy in the hybrid V2X network are firstly proposed. By user behavior, the LS is divided into active separation (ASP) and passive separation (PSP). By characteristics of ASP and PSP, the longitudinal-separation prediction model is constructed. By this model, ASP and PSP can be predicted respectively before they occur. By the prediction result of the model, a reverse-leadership strategy for the platoon is presented. By this strategy, communication links can be self-adaptively changed to avoid outages caused by LS. In this scheme (model＋strategy), LS can be predicted timely and communication links can be handed over in advance. Finally, the validity of the model and the effectiveness of the scheme are demonstrated by simulations based on data from NGSIM data set and PTV-Vissim simulation software. Besides, the proposed scheme not only has its comparative advantages of fewer outages and handovers, but also can decrease the path loss, average outage time and handover delay significantly.

Wireless mesh networks are critical in enabling key networking scenarios in beyond-5G (B5G) and 6G networks, including integrated access and backhaul (IAB), multi-hop sidelinks, and V2X. However, it still poses a challenge to deliver scalable per-node throughput via mesh networking. As shown in Gupta and Kumar’s seminal research (Gupta and Kumar, 2000), multi-hop transmission results in a per-node throughput of $\Theta (1/\sqrt{nlogn})$ in a wireless network with $n$ nodes, significantly limiting the potential of large-scale deployment of wireless mesh networks. Follow-up research has achieved $O\left(1\right)$ per-node throughput in a dense network, but how to achieve scalability remains an unresolved issue for an extended wireless network where the network size increases with a constant node density. This issue prevents a wireless mesh network from large-scale deployment. To this end, this paper aims to develop a theoretical approach to achieving scalable per-node throughput in wireless mesh networks. First, the key factors that limit the per-node throughput of wireless mesh networks are analyzed, through which two major ones are identified, i.e., link sharing and interference. Next, a multi-tier hierarchical architecture is proposed to overcome the link-sharing issue. The inter-tier interference under this architecture is then mitigated by utilizing orthogonal frequency allocation between adjacent tiers, while the intra-tier interference is reduced by considering two specific transmission schemes, one is MIMO spatial multiplexing with time-division, the other is MIMO beamforming. Theoretical analysis shows that the multi-tier mesh networking architecture can achieve a per-node throughput of $\Theta \left(1\right)$ in both schemes, as long as certain conditions on network parameters including bandwidth, the number of antennas, and the number of nodes of each tier are satisfied. A case study on a realistic deployment of 10,000 nodes is then carried out, which demonstrates that a scalable throughput of $\Theta \left(1\right)$ is achievable with a reasonable assumption on bandwidth and the number of antennas.

In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize multiple settings of the Contention Window (CW) in IEEE 802.11 wireless networks. Unlike traditional DRL methods that adjust only a single CW parameter, our innovative approach simultaneously optimizes both the CW minimum ($C{W}_{min}$) and CW Threshold ($C{W}_{Threshold}$), significantly improving network traffic control. We utilize a Double Deep Q-learning Network (DDQN) for dynamic updates of these CW settings, broadcasted across the dense Wi-Fi networks. This dual adjustment method, coupled with dynamic, data-driven updates, not only enhances throughput, but also reduces collision rates, and ensures fairness access across both static and dynamic wireless environments. Enhanced-SETL achieves a throughput improvement ranging from 3.55% up to 43.73% and from 3.98% up to 30.15% in static and dynamic scenarios over standard protocols and state-of-the-art DRL models, while maintaining a fairness index near 99% across diverse stations, showcasing its effectiveness and adaptability in various network conditions.

Cooperative edge caching has emerged as a promising solution to alleviate the traffic burden of backhaul and improve the Quality of Service of 5G applications in the 5G era. Several cooperative edge caching methods alleviate the traffic burden of backhaul by transmitting contents between edge nodes cooperatively, thereby reducing the delay of content transmission. However, these methods have a limited ability to handle complex information in multi-edge scenarios, and they mainly focus on cooperation in content transmission while scarcely considering cooperation in cache replacement. As a result, they cannot effectively utilize the cache space of collaborative edges, leading to suboptimal system utility. In this paper, we propose a cache replacement strategy for cooperative edge caching based on a novel multi-agent deep reinforcement learning network. Firstly, we present a cooperative edge caching model aimed at maximizing the system throughput. Then, we formulate the cache replacement process in the cooperative edge caching system as a Markov Game (MG) model. Finally, we design a Discrete MADDPG algorithm based on a discrete multi-agent actor-critic network to derive the cache replacement strategy and effectively manage content redundancy within the system. Simulation results demonstrate that our proposed algorithm achieves higher system throughput while effectively controlling content redundancy.

Drones and other forms of Unmanned Aerial Vehicles (UAVs) usages are increasing with time from military operations like surveillance, reconnaissance to commercial operations such as transportation, agriculture. The Drone market will be grown to around 43 billion USD by the 2025. With the increase in the usages, there is increase in cyber-attacks. So, Drone security and privacy are of major concern as they are used to perform the critical operations. The rapid adoption of UAVs in various sectors has prompted the need for robust and comprehensive cyber security measures. By implementing robust countermeasures, such as authentication, encryption etc. the risks posed by cyber threats can be significantly mitigated. The study investigates cyber security vulnerabilities and countermeasures in UAV systems within the scope of Authentication techniques, Physical Layer Security, Covert Communication. Relaying and Trajectory Optimization techniques are also discussed so that the flight trajectory can be optimized. It also discusses the communication modes used in UAV communication. An analysis of communication protocols is also carried out in this survey. The goal of the survey is to get the idea of cyber security threats involved in UAV communication. Our analysis emphasizes the importance of a holistic approach to UAV cyber security, leveraging the synergies among these domains to enhance overall resilience.

The increasing popularity of the Internet of Everything and small-cell devices has enormously accelerated traffic loads. Consequently, increased bandwidth and high data rate requirements stimulate the operation at the millimeter wave and the Tera-Hertz spectrum bands in the fifth generation (5G) and beyond 5G (B5G) wireless networks. Furthermore, efficient spectrum allocation, maximizing the spectrum utilization, achieving efficient spectrum sharing (SS), and managing the spectrum to enhance the system performance remain challenging. To this end, recent studies have implemented artificial intelligence and machine learning techniques, enabling intelligent and efficient spectrum leveraging. However, despite many recent research advances focused on maximizing utilization of the spectrum bands, achieving efficient sharing, allocation, and management of the enormous available spectrum remains challenging. Therefore, the current article acquaints a comprehensive survey on intelligent SS methodologies for 5G and B5G wireless networks, considering the applications of artificial intelligence for efficient SS. Specifically, a thorough overview of SS methodologies is conferred, following which the various spectrum utilization opportunities arising from the existing SS methodologies in intelligent wireless networks are discussed. Subsequently, to highlight critical limitations of the existing methodologies, recent literature on existing SS methodologies is reviewed in detail, classifying them based on the implemented technology, i.e., cognitive radio, machine learning, blockchain, and multiple other techniques. Moreover, the related SS techniques are reviewed to highlight significant challenges in the B5G intelligent wireless network. Finally, to provide an insight into the prospective research avenues, the article is concluded by presenting several potential research directions and proposed solutions.