High Dynamic Network Research Articles

Adaptive and low-cost resource synchronization based on data distribution service in high dynamic networks

In disaster-stricken areas monitoring, management, search, and rescue operations, unmanned aerial vehicles (UAVs) play a crucial role in disaster management and emergency communication due to their high mobility. To efficiently coordinate and plan UAVs and their carried sensor and base station resources, synchronization is essential to establish consistency, laying the foundation for high-level demands. In such scenarios, synchronization relies on request–response (RR) or publish–subscribe (PS) forms of information exchange. Existing research in the field typically focuses on higher-level applications and selects either RR or PS synchronization, thereby overlooking the potential advantages that could be gained from combining both methods to meet synchronization requirements. We propose a resource synchronization method based on the Data Distribution Service (DDS) and a linear time complexity subscription mechanism tailored to specific query demands, which considers the pros and cons of the above two information exchange forms and the bottom-layer network topology. Experimental results using open-source simulation tools demonstrate that the proposed method adapts to scene requirements and decreases bandwidth by at least 21.2% and packet rate by at least 3.7% compared to different baseline methods across three topologies, while satisfying delay and query success rate requirements. Furthermore, the method maintains robust performance in the face of dynamic changes in network topology, showcasing its robustness.

Facile supramolecular processing of recyclable adaptive polymer composites for highly reproducible sensory materials

Dynamic polymer networks (DPNs), when cross-linked, can display chemical and physical adaptiveness due to their dynamic connections and associated phase transitions. However, the reliable preparation of these conductive composites and achieving consistent performance through recycling remain challenging, primarily due to a lack of systematic approaches. Herein, we describe synergistic association of supramolecular gel and high density dynamic crosslinking using vinylogous urethane (VU) bond that enable highly reliable sensitivity and reproducibility after recycling to high performance adaptive dynamic polymer networks containing conductive fillers. We introduce a straightforward method to create DPNs-single-walled carbon nanotube (SWCNT) composites. This involves facile grinding a random copolymer in a supramolecular eutectic liquid, resulting in a supramolecular gel. Subsequently, dynamic cross-linking is applied. The resulting dynamic polymer-SWCNT (DPC) composites, incorporating a supramolecular gel, demonstrate adjustable electrical conductivity and high sensitivities to various mechanical motions and irradiations, including specific light and heat. Significantly, the DPC exhibits excellent recyclability, along with enhanced reproducibility and sensitivity as a strain sensor compared to other recyclable sensors. Furthermore, we establish a strong correlation between the sensitivity and stiffness of the DPC composite with dynamic cross-linking density. These findings highlight the promising potential of adaptive gel-based processing of DPNs for highly sensitive, recyclable sensory materials with excellent reproducibility.

Machine Learning-assisted Distance Based Residual Energy Aware Clustering Algorithm for Energy Efficient Information Dissemination in Urban VANETs

A Vehicular Ad-hoc Network (VANET) is an essential component of intelligent transportation systems in the building of smart cities. A VANET is a self-configure high mobile and dynamic potential wireless ad-hoc network that joins all vehicle nodes in a smart city to provide in-vehicle infotainment services to city administrators and residents. In the smart city, the On-board Unit (OBU) of each vehicle has multiple onboard sensors that are used for data collection from the surrounding environment. One of the main issues in VANET is energy efficiency and balance because the small onboard sensors can’t be quickly recharged once installed on On-board Units (OBUs). Moreover, conserving energy stands out as a crucial challenge in VANET which is primarily contingent on the selection of Cluster Heads (CH) and the adopted packet routing strategy. To address this issue, this paper proposes distance and energy-aware clustering algorithms named SOMNNDP, which use a Self-Organizing Map Neural Network (SOMNN) machine learning technique to perform faster multi-hop data dissemination. Individual Euclidean distances and residual node energy are considered as mobility parameters throughout the cluster routing process to improve and balance the energy consumption among the participating vehicle nodes. This maximizes the lifetime of VANET by ensuring that all intermediate vehicle nodes use energy at approximately the same rate. Simulation findings demonstrate that SOMNNDP improves Quality of Service (QoS) better and consumes 17% and 14% less energy during cluster routing than distance and energy-aware variation of K-Means (KM) and Fuzzy C-Means (FCM) called KMDP and FCMDP respectively.

Supramolecular Hydrogel with Ultra-Rapid Cell-Mediated Network Adaptation for Enhancing Cellular Metabolic Energetics and Tissue Regeneration.

Cellular energetics plays an important role in tissue regeneration, and the enhanced metabolic activity of delivered stem cells can accelerate tissue repair and regeneration. However, conventional hydrogels with limited network cell adaptability restrict cell-cell interactions and cell metabolic activities. In this work, it is shown that a cell-adaptable hydrogel with high network dynamics enhances the glucose uptake and fatty acid β-oxidation of encapsulated human mesenchymal stem cells (hMSCs) compared with a hydrogel with low network dynamics. It is further shown that the hMSCs encapsulated in the high dynamic hydrogels exhibit increased tricarboxylic acid (TCA)cycle activity, oxidative phosphorylation (OXPHOS), and adenosine triphosphate (ATP) biosynthesis via an E-cadherin- and AMP-activated protein kinase (AMPK)-dependent mechanism. The in vivo evaluation further showed that the delivery of MSCs by the dynamic hydrogel enhanced in situ bone regeneration in an animal model. It is believed that the findings provide critical insights into the impact of stem cell-biomaterial interactions on cellular metabolic energetics and the underlying mechanisms.

Adaptive protocol of raft in wireless network

Original distributed consensus algorithms are typically engineered with wired communication networks in mind. However, when these algorithms are applied in wireless networks, the unique characteristics of wireless communication among nodes, including high error rates, variable latency, and dynamic network topology, present novel challenges that necessitate the development of adaptive protocols. This article aims to address these challenges by designing an adaptive protocol specifically for the implementation of the Raft consensus algorithm within wireless environments. We first outline the key stages of this adaptive protocol, focusing on enhancing robustness and efficiency in the state synchronization and routing processes, which are demonstrated through extensive simulations. The proposed adaptive protocol intends to ensure consistent states across nodes in wireless networks, which promote reliable and effective distributed systems for a variety of applications in autonomous wireless networks, including Vehicle-to-everything (V2X), Internet of Things (IoT), and edge computing.

A Study on Enhancing Channel Estimation in Vehicular Communications: Metamaterial-Enhanced Microstrip Antennas

Vehicular communication systems play a pivotal role in modern transportation, offering solutions for traffic management, road safety, and infotainment services. Effective optimization of channel estimation in these systems is imperative to overcome inherent challenges. This paper has explored the issues that hinder reliable and efficient channel estimation in vehicular communications. High mobility and dynamic environments, multipath fading, heterogeneous networks, scalability, and security and privacy concerns were identified as key challenges. Addressing these challenges is crucial for ensuring the robustness and effectiveness of vehicular communication systems. By advancing channel estimation techniques, we can enhance data throughput, reduce latency, and improve overall network performance, ultimately contributing to safer and more efficient transportation systems.

Decentralized piggybacking-based dissemination of Cooperative Awareness Messages in vehicular ad-hoc networks

Vehicle-to-Vehicle (V2V) communication has shown great promise to improve road safety and traffic efficiency via the periodic broadcasting of Cooperative Awareness Messages (CAMs), in which traffic information could be exchanged among adjacent vehicles. Since each CAM contains local-observed traffic information such as geolocation, speed, and safety alerts, frequent CAM losses will decrease the reliability on avoiding crashes and injuries on the road. Existing solutions tend to recover the lost CAMs through either source-based re-transmission or neighbor-based forwarding, which may suffer from significant performance degradation due to the fast movement of vehicles and high dynamic network topology. In this paper, we develop a decentralized cooperative broadcasting protocol for CAM dissemination, where each vehicle can piggyback some received CAMs in its routine broadcasting to help others recover their lost CAMs. To promote cooperation of CAM piggybacking, a new data structure called bitmap is introduced to record the packet receiving status at each vehicle, based on which time slots could be allocated to vehicles with low data collisions, lost CAMs could be detected only using each vehicle’s local information, and the qualities of wireless links could be estimated dynamically. Then, we propose two piggybacking schemes, Benefit-based Piggybacking (BP) and Suggestion-based Piggybacking (SP), aiming to maximize the average CAM delivery ratio. We evaluate the proposed schemes in simulations with realistic vehicle mobility traces and channel models. Simulation results show that our bitmap-based lost CAM detection has superior performance than traditional request-based solutions. In comparison with forwarding-based schemes, our schemes can achieve much higher CAM broadcast reliability with short time delay and small communication overhead.

A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues

SummaryThe Internet of Vehicles (IoV) integrates various technologies to enable real‐time data exchange between vehicles, pedestrians, roadside units, unmanned aerial vehicles, road‐added things, and so forth. IoV is an improved internet‐enabled vehicular ad hoc network (VANET), which is similarly affected by data traffic aggravated by time‐variant network density and the network's topological and informational changes. A high density of simultaneous vehicular communication leads to channel saturation and data congestion, which leads to rapid increases in data loss, packet latency, and lower service quality. Due to the importance of real‐time data communications in high dynamic networks, we used the systematic literature review (SLR) approach to identify 54 articles up to May 2023 to discuss the ways to deal with data traffic congestion in VANETs and IoV networks as well as existing and upcoming issues in this area.

DESLR: Energy-efficient and secure layered routing based on channel-aware trust model for UASNs

Underwater acoustic sensor networks (UASNs) have garnered significant interest and have found widespread applications in smart ocean applications and auxiliary navigation. Nevertheless, UASNs encounter a multitude of challenges due to the constraints of the underwater environment, such as high energy consumption, secure transmission, and dynamic network topology in uncertain conditions. In this paper, we propose an energy-efficient and secure layered routing method (DESLR) for UASNs to enhance energy efficiency and improve transmission reliability. The DESLR establishes a channel-based trust model which considers factors of direct trust and indirect trust to describe the behaviors of nodes. In order to effectively identify malicious nodes, great consideration is given to the state of the acoustic channel’s reliability, as varying channel conditions can significantly impact packet transmission. An improved Ant Colony Optimization Algorithm (ACOA) is introduced to discover energy-efficient and low-latency routing paths by balancing energy consumption and routing distance. Additionally, DESLR utilizes a Layered Clustering method to group nodes into clusters and a two-layer fuzzy logic approach to select cluster heads (CH) that avoid hotspots and ensure uniform energy distribution. Simulation results demonstrate that DESLR can effectively detect defective nodes by analyzing anomalous behaviors and outperforms other related work in terms of energy consumption, average end-to-end delay, and packet loss.

Artificial intelligence support for 5G/6G-enabled Internet of Vehicles networks: An overview

Improving transportation efficiency and on-road safety using Intelligent Transportation Systems (ITSs) has become crucial as road congestion and vehicle complexity increase coupled with ongoing rapid development and deployment of electric vehicles across the globe. Recent advances in computer systems and wireless communications have ushered in more possibilities for smart solutions to road traffic safety, congestion reduction, convenience, and overall efficiency. The evolution and deployment of 5G have opened up new technologies and features that can provide the much needed high-mobility wireless networks for the emerging Internet of Vehicles (IoV). The application of AI consisting of Deep Learning (DL), Machine Learning (ML) and Swarm Intelligence (SI) techniques have emerged in both conventional and vehicular wireless networks with strong promises towards enhancing traditional data-centric methods. Particularly, in the application domains of IoV, big data is frequently generated from various sources within the vehicular communication environment. The collected big data is usually processed and used for both safety and infotainment services including routing, broadening drivers' awareness, traffic mobility prediction for hazardous situation avoidance to improve overall safety and passenger comfort, and general quality of road experience. Applying data-driven methods enables AI to address high mobility and dynamic vehicular communications and network issues facing traditional solutions and approaches like network optimization techniques and conventional control loop design. This study provides a concise review of DL, ML and SI techniques and applications that are currently being explored by different research efforts within the application area of vehicular networks. The paper further discusses the strengths and weaknesses of the proposed AI-based solutions for the IoV networks.

STALB: A Spatio-Temporal Domain Autonomous Load Balancing Routing Protocol

Due to vehicle mobility, the topology of Vehicle Ad-hoc Networks (VANETs) may change dynamically. High mobility, limited bandwidth, and dynamic network topology pose challenges for communication in the Internet of Vehicles (IoVs). Literature works have attempted to promote efficient (e.g., lower end-to-end latency) message forwarding. However, due to the uncertain direction of message forwarding and vehicle mobility, they suffer from unreachable destinations and unstable connections. This paper explores the efficient method of message forwarding to alleviate network congestion in IoVs. We propose a Spatio-Temporal domain Autonomous Load Balancing (STALB) routing protocol. Specifically, STALB is a trajectory-based method for controlling the direction of message forwarding. STALB can significantly reduce the end-to-end latency and overload ratio, since it considers the local status of network relay devices (i.e., buffer score, congestion status) from the spatio-temporal domain. Then, we present a path reconstruction mechanism, which ensures that messages are forwarded to destinations within limited Time-To-live (TTL). Extensive simulation results show that STALB significantly outperforms other baseline methods (BSaW, TDOR, and TBHGR) regarding overhead ratio, average delivery latency, and average buffer time. Especially, the delivery rate of STALB can reach 99.9% under the sparse network scenario (4,500 messages), at least 0.7% higher than other baseline methods. Similarly, the average delivery delay of STALB is at least 84.31% lower than that of other baseline methods under the dense network scenario (18,000 messages).

Decentralized Edge Intelligence-Driven Network Resource Orchestration Mechanism

With the development of artificial intelligence of things (AIoT), multi-access edge computing (MEC) becomes a key enabler to migrate cloud services to edge clients. In comparison to traditional cloud computing techniques, MEC is characterized with low transmission latency, good flexibility, adaptability and robustness. Nevertheless, traditional resource allocation methods are difficult to meet the requirements of achieving a ubiquitous, pervasive, and intelligent computation offloading strategy in highdynamic network environments. In this paper, we construct an edge intelligence-enabled cloud-edge-client collaborative network structure, and conceive a model-aided multi-agent deep deterministic policy gradient (MA2DDPG) computation offloading framework relying on both centralized training and distributed execution. Simulation results corroborate that our proposed decentralized resource orchestration platform significantly reduces the energy consumption and the transmission latency against state-of-the-art methods. Finally, we highlight open challenges and potential solutions.

Data‐centric approaches in the Internet of Vehicles: A systematic review on techniques, open issues, and future directions

SummaryThe Internet of Vehicles (IoV) is an emerging network of connected vehicles as a branch of dynamic objects in the Internet of Things (IoT) ecosystem. With the rapid development of IoV, real‐time data‐centric applications would be a significant concern in academia and industry to promote efficiency and realize modern services in such high dynamic networks. In this paper, we aim to present a systematic literature review (SLR) for the IoV networks to investigate the different attitudes in the field of data‐centric approaches. This paper systematically categorizes the 48 recent articles on data‐driven techniques in the IoV field published from 2017 to March 2022. A complete technical taxonomy is presented for the data‐centric approaches in IoV according to the content of current studies. Collected methods are chosen with the SLR process, and they are investigated considering some technical classifications including IoV security, data traffic, vehicular social network, data propagation, energy, and multimedia categories. The achievements, drawbacks, and new findings of studies are carefully investigated for addressing the deficiencies, as well as emphasizing future research direction and open issues of data‐driven approaches in IoV.

CDMR: Effective Computing-Dependent Multi-Path Routing Strategies in Satellite and Terrestrial Integrated Networks

With the development of science and technology, Satellite and Terrestrial Integrated Network (STIN) can effectively make up for the defects in the terrestrial network coverage and provide robust end-to-end transmission and computing supports for users in many communication scenarios such as emergency rescue, space exploration, and so on. However, many researchers only studied transmission or computing, ignoring that the task can be computed in task transmission. And limited by the satellite's battery resources and high dynamics, it is challenging to realize the fusion of communication and computing with the constraint of energy consumption in the high dynamic satellite network. Therefore, this paper proposes a Computing-Dependent Multi-path Routing (CDMR) paradigm to address such a challenge. In CDMR, the task is mapped to the multi-path route in STIN, and the energy consumption is taken as the optimization goal. Furthermore, the processing latency is the constraint to ensure that the optimal CDMR strategy is under latency constraint. Finally, the simulation results show that the CDMR proposed in this paper can effectively compute the task during the end-to-end transmission. Compared with other computing models, CDMR has more evident advantages in energy saving.

ARTIFICIAL INTELLIGENCE - BASED MULTIOPATH TRANSMISSION MODEL FOR WSN ENERGY EFFICIENCY

This paper presents a network-coding-based transmission strategy called alternative multipath to boost the transmission reliability and energy efficiency of wireless sensor networks (AMNC). Encoded data packets are transmitted to the sink through alternative multipath from the cluster head. MATLAB 7.0 simulates WSNs energy utilization and reliability. Simulation findings show AMNC outperforms parallel multipath based on network coding. The two techniques show this. A well-defined discussion of the research subject, specific technologies, strengths and weaknesses, scopes, and problem formulation is required for any study. The ocean covers approximately two-thirds of the earths surface, therefore its hard for any economy to avoid it (i.e., country). The ocean serves as a main method of transportation, supports international trade, and is crucial for defense. In recent years, it has come to light how crucial ocean or water-routes are for enabling rapid and cost-effective transportation for the expansion of businesses. In order to realize transcontinental commercial practices and discover the true meaning of socioeconomic globalization, the role that oceanic waterways have played has been essential. On the other hand, maintaining efficient real-time environmental or auditory conditioning (also known as condition awareness) is required in order to make optimum mobility possible. Because of its exceptionally high dynamic network topology, an audio channel experiences a significant amount of network deviation as a result, it calls for a communication paradigm that is more effective in order to support the aforementioned application environment.

GaDQN-IDS: A Novel Self-Adaptive IDS for VANETs Based on Bayesian Game Theory and Deep Reinforcement Learning

Due to the nature of high mobility and dynamic network topology, Intrusion Detection Systems (IDSs) in Vehicular Ad-hoc Networks (VANETs) face lots of challenges, especially in balancing the accuracy and efficiency of detection. Current researches about the deployment of IDSs in VANETs mainly focus on a tradeoff between the effectiveness and efficiency, but few efforts have been done about the adaptability of the tradeoff in the changeable networks. Thus, we address two crucial problems: 1) how to perceive the environmental change in the perspective of an IDS? 2) how to make the IDS adaptive in different scenarios? In this paper, a Bayesian Game theory and Deep Q-learning Network-based IDS is proposed for VANETs, called GaDQN-IDS. The interactions between an IDS and attackers are formulated as a dynamic intrusion detection game, in which the IDS decides either to just adjust the tradeoff between the accuracy and efficiency or to be retrained completely when its detection capacity has declined. The Nash Equilibria (NE) of the game is derived to reveal how the optimal decision of the IDS depends on the detection performance and road conditions. Moreover, a Deep Q-learning Network (DQN)-Adjustment is proposed to realize the self-adaptation of the IDS in the dynamic game, while an Error Priority Learning (EPL) is further designed for IDS retraining in changing VANETs. Simulation results show that the GaDQN-IDS has better performance than other existing IDSs with higher detection rate as well as lower detection time and overhead.

Efficient Wireless Traffic Prediction at the Edge: A Federated Meta-Learning Approach

Wireless traffic prediction plays a vital role in managing high dynamic and low latency communication networks, especially in 6G wireless networks. Regarding data and computing resources constraints in edge devices, federated wireless traffic prediction has attracted considerable interest. However, federated learning is limited to dealing with heterogeneous scenarios and unbalanced data availability. Along this line, we propose an efficient federated meta-learning approach to learn a sensitive global model with knowledge collected from different regions. The global model can efficiently adapt to the heterogeneous local scenarios by processing only one or a few steps of fine-tuning on the local data sets. Additionally, distance-based weighted model aggregation is designed to capture the dependencies among different regions for better spatial-temporal prediction. We evaluate the performance of the proposed scheme by comparing it with the conventional federated learning approaches and other commonly used benchmarks for traffic prediction. The extensive simulation results reveal that the proposed scheme outperforms the benchmarks.

A Technique of the Distributed Information Systems Control Method Choice under the High Network Dynamics Conditions

Purpose of research. The purpose of this study is to select a method for managing a distributed system, which, based on known parameters, would reduce the consumption of resources of computing devices. The resource of an information system is understood as the probability of failure-free operation (reliability function), which degrades over time for each node, the faster, the higher its workload.Methods. Considering that with high dynamics of the edge layer of the network, the frequency of system reconfigurations becomes relatively high, and the need for reconfigurations is unpredictable, reducing the total time spent on reconfigurations makes it possible to increase the time spent on solving functional computational problems of the system and thereby reduce the load of nodes. The reconfiguration time can be reduced both by reducing the time for detecting a failure in a distributed system, and by reducing the new configuration forming time. In this paper, a method for reducing the time of the failure detection is considered. The analysis of the applicability of system control methods (centralized, with a distributed leader, decentralized) is based on the obtained analytical estimates of the time the system detects a failure under control conditions using one method or another. A numerical experiment makes it possible to identify areas of system parameters, where it is preferable to use the method with a distributed leader.Results. The main result of this work is a methodology for choosing a method for managing distributed information systems in conditions of high dynamics of the network infrastructure, focused on reducing the consumption of resources of computing devices.Conclusion. System reconfiguration time can be shortened by choosing the most appropriate control method. Thus, the time allotted for solving the functional tasks of the application increases, the workload of the computational nodes decreases, and, therefore, the FBG values increase over the planning horizon.

An Escalated Performance Aware Approach for Cumulative Effectiveness in Mobile Ad-Hoc Networks

In order to provide secure communication between mobile nodes in a hostile environment, security has become a top priority. There are a variety of nontrivial difficulties to security design in mobile ad hoc networks because of its unique properties, such as open peer to peer network architecture and shared wireless medium. Building multi-layered security systems that provide broad coverage while still delivering optimal network performance makes perfect sense in light of these problems. Specifically, in this study, we address the basic issue of maintaining the multi-hop network connectivity between mobile nodes in a Mobile Ad Hoc Networks (MANET). There are a number of security concerns that need to be addressed in order to safeguard the multihop wireless channel's MANET connection and network layer activities when delivering packets. We go through these difficulties and possible solutions in this paper. Complete security solutions should cover both layers and include the prevention, detection and response aspects of the three components of the security system. Providing secure communication between mobile nodes in a hostile environment has become a top priority. For example, the open peer-to-peer architecture, shared wireless medium, resource limits, and high dynamic network topology of mobile ad hoc networks provide a variety of nontrivial security design difficulties. The work integrates the effectual approach with the integrity and performance aware implementation for the Mobile Adhoc Networks.

Optimal UAV Formation Tracking Control with Dynamic Leading Velocity and Network-Induced Delays.

With the rapid development of UAV technology, the research of optimal UAV formation tracking has been extensively studied. However, the high maneuverability and dynamic network topology of UAVs make formation tracking control much more difficult. In this paper, considering the highly dynamic features of uncertain time-varying leader velocity and network-induced delays, the optimal formation control algorithms for both near-equilibrium and general dynamic control cases are developed. First, the discrete-time error dynamics of UAV leader–follower models are analyzed. Next, a linear quadratic optimization problem is formulated with the objective of minimizing the errors between the desired and actual states consisting of velocity and position information of the follower. The optimal formation tracking problem of near-equilibrium cases is addressed by using a backward recursion method, and then the results are further extended to the general dynamic case where the leader moves at an uncertain time-varying velocity. Additionally, angle deviations are investigated, and it is proved that the similar state dynamics to the general case can be derived and the principle of control strategy design can be maintained. By using actual real-world data, numerical experiments verify the effectiveness of the proposed optimal UAV formation-tracking algorithm in both near-equilibrium and dynamic control cases in the presence of network-induced delays.