Ad Hoc Networks最新文献

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

Unmanned aerial vehicles (UAVs) have opened new communication possibilities by being able to access remote areas. Their ability to serve a large number of users based on demand and adaptability is a key strength. In Sixth Generation (6G) networks, UAVs are highly valued for their cost-efficiency and versatile deployment. However, the mobility of UAVs introduces different types of interference issues, resulting in a decrease in network performance and quality of service (QoS) for edge users. To address these challenges, this paper introduces a clustering-based solution involving three main steps. Firstly, UAVs are deployed in three-dimension (3D) space based on user requests using mini-batch K-mean clustering Subsequently, re-clustering is explored to tackle load balancing within clusters. Finally, outliers and boundary users are classified to enhance QoS for edge users. This model effectively reduces interference and boosts UAV reliability in terrestrial networks. Also, a case study is presented to show how UAVs can mitigate interference in maritime communication within terrestrial networks. Numerical results demonstrated that the proposed scheme increases throughput by 33.06% and reduces energy consumption and time delay by 73.15% and 9.15%, respectively, as compared to the existing baseline schemes.

Internet applications such as video gaming virtual/ augmented reality necessitate efficient fifth-generation (5G) millimeter-wave (mmWave) cellular networks. The Transmission Control Protocol (TCP) is an essential protocol for network connectivity. However, TCP faces challenges in efficiently utilizing the available bandwidth of 5G mmWave cellular networks while maintaining low latency, mainly due to constraints like Non-Line of Sight (NLoS) conditions. This paper introduces Round-Trip-Time Variations-TCP (RTTV-TCP), enhancing TCP performance in 5G mmWave cellular networks. Simulation scenarios for a 5G mmWave cellular network have been conducted to evaluate RTTV-TCP’s performance, comparing it to legacy TCP variants such as NewReno, HighSpeed, CUBIC, Bottleneck Bandwidth and Round-trip propagation time (BBR), FB-TCP (Fuzzy Based-TCP). The results demonstrate that RTTV-TCP achieves higher average throughput than these TCP variants while maintaining the same level of delay in 5G mmWave cellular networks. RTTV-TCP outperforms NewReno and CUBIC by a very significant margin, demonstrating a 208% improvement compared to HighSpeed and a 6% increase compared to BBR protocol in the worst Packet Error Rate (PER) scenario and when the buffer size matches the Bandwidth Delay Product (BDP).

The phenomenon of urbanization, characterized by the migration of people from rural to urban areas, has led to an expansion of existing urban challenges while introducing new ones. Among these, mobility is a primary concern due to its far-reaching impacts on personal health, safety, social, economic, and environmental aspects. Information and communication technologies (ICT) have been identified as effective solutions to address these issues, leveraging the Internet of Things (IoT) and smart city infrastructure. However, the mainstream approach in smart cities is characterized by a vertical-siloed pattern, addressing individual problems (mobility, pollution, energy management, healthcare, safety, and security) in isolation, without actively engaging citizens, people, and communities as stakeholders.

This paper proposes a paradigm shift towards a holistic, multilateral approach to address mobility, incorporating diverse perspectives, stakeholder needs, and problem-solving strategies. By integrating smart city infrastructure, smart vehicles, and personal devices, an all-encompassing solution is implemented through direct interaction and cooperation between these entities. The resulting City-Vehicle Participatory-Opportunistic Cooperative Route Navigation system (CV POp-CoRN) enables the enforcement of mobility policy trade-offs, reconciling city, vehicle, and people requirements across various domains, including safety, emergency response, traffic management, travel time optimization, vehicle maintenance, pollution mitigation, and special event management. The paper presents the CV POp-CoRN framework, comprising route navigation policies, a heuristics for trading them off, the system design and architecture, and a model for assessing and demonstrating the effectiveness of the proposed approach and the feasibility of the solution.

The introduction of fifth-generation (5G) technology marks a significant milestone in next-generation networks, offering higher data rates and new services. Achieving optimal performance in 5G and beyond 5G (B5G) systems requires addressing key requirements like increased capacity, high efficiency, improved performance, low latency, support for many connections, and quality of service. It is well-known that suboptimal network configuration, hardware impairments, or malfunctioning components can degrade system performance. The physical layer of the radio access network, particularly channel estimation and synchronization, plays a crucial role. Hence, this paper offers an in-depth evaluation of the 5G Physical Downlink Shared Channel (PDSCH), along with its related channel models such as the Clustered Delay Line (CDL) and the Tapped Delay Line (TDL). This work assesses 5G network performance through practical and IA-based channel estimation and synchronization techniques, and anticipates numerologies for B5G networks. Extensive simulations leveraging the Matlab 5G New Radio (NR) toolbox assess standardized channel scenarios in both macro-urban and indoor environments, following configurations set by the 3rd Generation Partnership Project (3GPP). The numerical results offer valuable insights into achieving the maximum achievable throughput across various channel environments, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The throughput comparisons are performed under assumptions of ideal, realistic, and convolutional neural networks (CNN)-based channel estimation with both perfect and realistic synchronization conditions. Importantly, the study pinpoints certain physical layer elements that have a pronounced impact on system performance, providing essential insights for devising effective strategies or refining CNN-based methods for forthcoming mobile B5G networks.

The new radio unlicensed (NR-U) technology is proposed by 3GPP to extend NR to the unlicensed spectrum because of the shortage of the licensed spectrum. Different from the ground and fixed communication equipment-based unlicensed spectrum access scheme, the unmanned aerial vehicle (UAV) mobile platform-based unlicensed spectrum access scheme is not only related to incumbent users but also its trajectory and resource allocation. Therefore, this paper proposes a hybrid unlicensed spectrum access scheme for the UAV-assisted unlicensed mobile edge computing (MEC) communication (UAUM) system, where each flight time slot of the UAV is divided into two parts: power free (PF) and power controlled (PC) stages. In the PF stage, the transmit power is only restrained by the unlicensed spectrum regulations, and thus the UAV can provide high-rate services for real-time downlink users (RDUs) and uplink computing users (UCUs). In the PC stage, the transmit power of the UAV is mainly restrained by the interference to WiFi devices, and thus UAV can be allowed to provide low-rate services for non-realtime downlink users (NDUs) without affecting WiFi users. Based on the proposed scheme, a multi-variable optimization problem regarding trajectory, bandwidth, transmit power, and duty cycle is formulated to maximize the total offloaded computing bits on the premise of ensuring the quality of experience of RDUs, NDUs, and WiFi users under the maximum energy budget. To solve this problem efficiently, we propose an iterative algorithm based on the block coordinate descent method and successive convex approximation technique to decompose the original problem into four optimization subproblems of trajectory, bandwidth, transmit power and duty cycle, which are then solved alternatively in an iterative manner. A large number of simulation results demonstrate that in terms of spectrum efficiency and total offloaded computing bits, the proposed algorithm outperforms other unlicensed spectrum access schemes and optimization algorithms. The other performances of the proposed algorithm are deeply evaluated to prove its effectiveness and feasibility.

In emergency rescue, target search and other mission scenarios with Unmanned Aerial Vehicles (UAVs), the Relay UAVs (RUs) and Mission UAVs (MUs) can collaborate to accomplish tasks in unknown environments. In this paper, we investigate the problem of trajectory planning and power control for the MU and RU collaboration. Firstly, considering the characteristics of multi-hop data transmission between the MU and Ground Control Station, a multi-UAV collaborative coverage model is designed. Meanwhile, a UAV control algorithm named MUTTO is proposed based on multi-agent reinforcement learning. In order to solve the problem of the unknown information about the number and locations of targets, the geographic coverage rate is used to replace the target coverage rate for decision making. Then, the reward functions of two types of UAVs are designed separately for the purpose of better cooperation. By simultaneously planning the trajectory and transmission power of the RU and MU, the mission target coverage rate and network transmission rate are maximized while the energy consumption of the UAV is minimized. Finally, numerical simulations results show that MUTTO can solve the UAV network control problem in an efficient way and has better performance than the benchmark method.

Nanotechnology has recently emerged as a pivotal field with wide-ranging implications. Its integration into the 6G-enabled Internet of Things (IoT) has given rise to the 6G-enabled IoNT (Internet of Nano Things) paradigm, impacting sectors such as healthcare, industries, smart homes, aerospace, and defense. This technology offers opportunities to revolutionize existing methodologies and enhance efficiency. Research efforts are now focusing on developing secure, scalable network infrastructures tailored for the healthcare sector at the nanoscale, leading to the concept of the Internet of Nano Medical Things (IoNMT). However, the unique characteristics of nanotechnology pose security challenges, particularly concerning privacy, confidentiality, dependability, latency, and the expensive consequences of blockchain-based storage. Authentication and transparency are vital for ensuring secure data handling in IoNMT networks, necessitating a secure access mechanism resistant to unauthorized interference. To tackle these challenges, this study proposes a smart contract-based authentication protocol developed specifically for 6G-IoNMT networks. The framework aims to manage real-time information with minimal latency through decentralized peer-to-peer cloud servers while addressing security and privacy concerns. Thorough security and privacy assessments, including ROR model evaluations, Scyther tool analysis, and informal security evaluations, validate the protocol’s effectiveness. Moreover, the simulation highlights that this protocol offers superior security and efficiency as well as energy consumption compared to existing protocols.

Wireless power transfer (WPT) provides a promising technology for energy replenishment of wireless rechargeable sensor networks (WRSNs), where wireless chargers can be deployed at fixed locations for charging nodes simultaneously within their effective charging range. Optimal charger placement (OCP) for sustainable operations of WRSN with cheaper charging cost is a challenging and difficult problem due to its NP-completeness in nature. This paper proposes a novel reinforcement learning (RL) based approach for OCP, where the problem is firstly formulated as a charging cluster determination problem with a fixed clustering radius and then tackled by the reinforcement learning-based charging cluster determination (RL-CCD) algorithm. Specifically, nodes are coarsely clustered by the K-Means++ algorithm, with chargers placed at the cluster center. Meanwhile, RL is applied to explore the potential locations of the cluster centers to adjust the center locations and reduce the number of clusters, using the number of nodes in the cluster and the summation of distances between the cluster center and nodes as the reward. Moreover, an experience-strengthening mechanism is introduced to learn the current optimal charging experience. Extensive simulations show that RL-CCD significantly outperforms existing algorithms.

The fast advancement of quantum computing poses a substantial challenge to the privacy and security of critical scientific research data. This is because the standard cryptography methods, which have been proven effective in classical computers, are rendered less secure in the face of quantum computing approaches. Previously, numerous endeavors have been made to safeguard confidential information through the utilization of different standards and quantum cryptographic methods. However, there remains a research void with several challenges and limitations, including excessive computational burden, vulnerability to various attacks, and limited hardware compatibility for implementation. We propose a modern hybrid cryptographical approach to secure sensitive data from various attacks and vulnerabilities to address the existing limitations. The suggested standard integrates traditional cryptographic standards with quantum-resistant standards to boost sensitive scientific data privacy and security and address various classical cyber-attacks and critical quantum attacks. For the context of scientific data privacy and security, our work depicts a hybrid standard structure by performing a systematic exploration of current encipherment model challenges and issues such as the investigation of various susceptibilities of mathematical cryptographic models. In this work, we apply lattice-based coding as the outer layer and Advanced Encryption Standard (AES) as the inner layer to improve security and efficacy. The proposed security theorem launches the operational veracity of lattice-based coding in the face of quantum attacks, while a complete investigation of the proposed algorithm efficacy vitrines the enhanced security and scalability of the anticipated hybrid standard transversely diverse input sensitive data volumes. Furthermore, this proposed work offers the security confidence score of the hybrid model by the amalgamation of AES and lattice-based cryptography (LBC), hence guaranteeing strength next to both quantum and traditional computing weaknesses. The investigational results prove the improved efficiency of the proposed hybrid model in contrast to traditional and past quantum-resistant models.