Uncrewed aerial vehicle (UAV) has emerged as a promising solution for automating railway inspections due to its high mobility, flexible deployment, and reduced labor cost. In this paper, we investigate UAV-assisted railway inspections, which include object recognition, humidity monitoring, and critical infrastructure modeling, each with distinct data volumes and computational requirements. Particularly, we introduce a UAV-assisted railway inspection framework. Different types of sensors are divided into several clusters. The UAV departs from the hive, flies over each cluster to collect their computational requirements, and performs task offloading before returning to the hive. This process is formulated as a joint optimization problem of trajectory planning and task offloading to minimize the weighted sum of latency and energy consumption. Considering the constrained computing and storage capabilities of UAVs, it is crucial but challenging to develop a lightweight yet high-performing solution for the multi-objective optimization problems. As such, a novelArtificial General Intelligence (AGI)-orientedTransformer (AoT) algorithm is proposed to solve the optimization problem. It uses an encoder-only architecture to process either sensor location or task features, and then directs the encoded outputs to different output heads to make decisions on UAV trajectory and task offloading. Simulation results demonstrate that the proposed AoT algorithm outperforms benchmark algorithms in terms of trajectory length and average offloading cost.

Multipath transmission offers bandwidth aggregation capabilities for mobile networks. However, path heterogeneity and user mobility often lead to increased packet out-oforder (OFO) rate, causing buffer blocking, reduced throughput, and degraded transmission quality. To mitigate the OFO effect in multipath transmission, this paper proposes LooM, a learning-based multipath scheduler. LooM is designed to optimize throughput and OFO rate, employing a learning-based scheduling strategy to achieve the optimal packet scheduling under fluctuating paths. Particularly, LooM employs singleround scheduling as its basic unit, calculating the number of OFO packets across scheduling units to dynamically set path blocking delay, thereby adjusting packet transmission order to ensure in-order delivery. Simulation results show that, compared to traditional scheduling algorithms, LooM reduces the OFO rate by 16% while maintaining high throughput and achieving a lower packet loss rate.

The surge in mobile application services has led to diversified traffic and increased demands on network resources. Traditional multipath algorithms, designed for resource integration through subflow scheduling across paths, struggle with disharmonious transmission caused by terminal mobility and differentiated path resources. Especially when differentiated services are transmitted concurrently, disharmonious transmission can give rise to resource contention, causing a large number of subflows to congest a single path and leading to performance degradation. To mitigate these challenges, this paper introduces HarmonyPath, a fine-grained flexible multipath transmission mechanism that can ensure harmonious resource occupation. Specifically, HarmonyPath firstly employs an in-band telemetry protocol to gather path resource information, generating a network resource distribution map. Based on this map, it flexibly allocates path resources according to the network resource distribution and service requirements. Then, HarmonyPath establishes a collaborative matching model for service demands and path resources. Through matrix transformation and calculation, it rapidly generates and deploys the scheduling strategy. To further alleviate service contention, HarmonyPath employs heuristic algorithms to optimize the scheduling strategy and achieve precise multipath transmission. Experiments demonstrate that HarmonyPath surpasses traditional algorithms in the multipath transmission of differentiated services, offering flexible service resource guarantees and enhancing network resource utilization efficiency.

Delay-sensitive applications like telemedicine and VR/AR intensify competition for network resources and elevate congestion risks, particularly in mobile networks with highly dynamic link conditions. Traditional end-to-end congestion control methods suffer from prolonged response times, rendering them ineffective for Delay-sensitive applications. To this end, this paper proposes a novel In-Network Congestion Control (INCC) mechanism that accelerates congestion control by enabling network nodes to proactively identify bottlenecks and promptly notify end-hosts. Unlike traditional end-host-centric approaches, INCC facilitates collaborative congestion decision-making between end-hosts and in-network unit. INCC classifies congestion into two phases: “yellow” and “red” based on the local queue length bottleneck awareness and global congestion flow bottleneck statistics. For the “yellow” local congestion phrase, we design an in-network local control algorithm that performs proactive packet dropping and rate adjustment to mitigate emerging congestion. For the “red” global congestion phrase, we design an end-host and network cooperative global congestion control algorithm to make precise sending rate adaptation by proactive bottleneck awareness. We implement INCC via Linux kernel modifications and design three experiments to compare with Cubic, NewReno, and BBR. Experimental results demonstrate INCC has good performance on round-trip time and throughput, achieving 99.03% scheduling fairness in flow contention scenarios. Additionally, INCC has low execution overhead on CPU utilization and realize microsecond computational latency.

UAVs can significantly improve the access networks of next-generation mobile networks during the building of smart cities. Drones equipped with base stations can expand the coverage of communication networks and assist more users’ devices to access the 5G/6G network, in which reliable authentication for drones becomes essential. However, traditional authentication methods still utilize the overt channel to transmit identity and key information, which are vulnerable and very easy to be eavesdropped, hijacked, and forged by malicious third parties. Therefore, this paper proposes an authentication scheme (CCRA). It includes 1) covert channels to assist authentication and key negotiation, and 2) a covert channel algorithm (EIDOP). Specifically, CCRA transmits fake identity information, part of the key information, and unimportant data in the overt channel, while using the covert channel to transmit important data and another part of the key for authentication and key negotiation to enhance the reliability of authentication. In addition, this paper proposes an algorithm called EIDOP based on the order of packet delay intervals to establish the covert channel for embedding and hiding important information. Finally, we conduct experiments on physical machines and compare EIDOP with other covert timing mechanisms to conclude that our algorithm has better concealment and latency overhead and still guarantees a very low BER under such circumstances.

Unmanned Aerial Vehicles (UAVs) provide a flexible working paradigm for device-cloud communication. Besides working as a relay between devices and clouds, UAVs can also provide mobile edge computing (MEC) services. In this paper, we investigate a computation offloading problem for UAV-assisted MEC networks in which local devices, UAVs, and clouds collaboratively process computing tasks to achieve energy-saving and latency reduction. In such green UAV-assisted MEC networks, we treat the same energy consumption of task processing differently due to processing location and assign different weights to the energy consumption of devices, UAVs, and clouds. Specifically, we propose a two-stage computation offloading framework including 1) the device clustering stage to determine the device cluster connected to certain UAVs and 2) the network operation stage to conduct computation offloading. We formulate the offloading process as a stochastic optimization problem to minimize the offloading cost. Furthermore, we decouple the optimization problem into a UAV selection subproblem and an offloading decision subproblem. Particularly, for the former subproblem, we employ a simulated annealing-based algorithm to minimize the total transmit energy of devices and UAVs. For the latter, we utilize a proximal policy optimization-based offloading algorithm to ascertain the processing locations of computing tasks. Simulation results show that the proposed algorithm outperforms in terms of energy reservation and latency reduction.

Integrated Sensing and Communication (ISAC) introduces greater challenges to network transmission in terms of delay, bandwidth, and reliability. Achieving stable and efficient congestion control is a critical issue in emerging ISAC scenarios. However, many traditional congestion control algorithms primarily focus on transmission efficiency but perform poorly in ensuring fairness between different data flows. To address this limitation, this paper proposes a queuing-aware fair congestion control (Q-FCC) solution for ISAC. In particular, Q-FCC incorporates a queue status monitoring module which can provide real-time feedback on the queuing delays at targeted network switches. Additionally, this paper analyzes the traditional BBR algorithm and identifies an inherent flaw: longer RTT flows have a higher bandwidth gain coefficient compared to shorter RTT flows, leading to unfairness. Based on this insight, Q-FCC introduces bandwidth gain factor. Q-FCC uses the queue status monitoring module to categorize data flows into three types and interacts with bursty flow endpoints via ACK packets to assist them in calculating the bandwidth gain factor, which enables the control of the transmission rate. Finally, the algorithm was implemented in the Linux kernel. The results of the semi-physical simulation show that Q-FCC outperforms the traditional BBR and CUBIC algorithms in terms of bandwidth fairness and transmission stability, respectively.

The convergence of Blockchain and Internet of Things (BIoT) is fully considered as a paradigm for mitigating threats related to the trust, security, and privacy of Internet of Things (IoT) data. However, because the bandwidth across nodes and time varies in practical IoT networks, it is difficult for existing BIoT mechanisms guarantee blockchain consensus performances. The consensus time could become long owing to low-bandwidth nodes taking longer to download blocks than high-bandwidth nodes. Conventional wisdom holds that removing low-bandwidth nodes can decrease the consensus time, but the nodes could have high-bandwidth at another time owing to bandwidth variability; thus, kicking which nodes out of the consensus is a great challenge. In this article, a novel lightweight BIoT convergence (namely, E-Chain) is proposed to overcome bandwidth variability. The E-Chain first decouples the blockchain into on-chain validating and off-chain voting components. In the off-chain voting part, each node incurs a one-bit communication overhead for voting on a block based on a reputation index. This voting component does not need to download the full content of the block, and is therefore not affected by bandwidth variability. The reputation index was formulated using a rating algorithm with multidimensional IoT network metrics. In addition, the voting mechanism is secure and can still reach the correct consensus when suffering from byzantine attacks. By contrast, a block is validated and stored in a dispersed manner in the on-chain validating part. The E-Chain performances were then evaluated and compared with state-of-the-art mechanisms. Experimental results show that the E-Chain mechanism can significantly decrease both the consensus time and memory resources, and incur an acceptable memory overhead for resource-constrained IoT nodes.

Multipath transmission brings strong bandwidth aggregation capability for services in wireless networks. Nonetheless, the heterogeneous nature of paths and the motion of terminals results in varying transmission delays, leading to out-of-order (OFO) delivery and transmission quality decrease. Traditional algorithms, limited in their scope, fail to strike a balance between high bandwidth and low OFO extent. Recent studies have focused on utilizing learning algorithms to find a multi-performance joint optimal transmission strategy. In light of this, this paper proposes a framework called DRL-based OFO-Friendly Multipath Scheduling (DOFMS) to ensure high bandwidth and low OFO extent transmission in mobile heterogeneous networks. In particular, the framework introduces a novel OFO evaluation index to assess the degree of OFO more accurately. To achieve elastic scheduling, the framework employs the Double Deep Q Network (DDQN) to dynamically regulate the scheduling ratio. Recognizing the dynamic and unpredictable nature of path delays, an asynchronous module is introduced to enhance learning accuracy. Experimental results demonstrate that the framework reduces the OFO rate by 25% compared to traditional bandwidth aggregation algorithms, while maintaining low bandwidth and packet loss rates. Furthermore, compared to conventional OFO avoidance algorithms, the framework improves bandwidth by 4% and reduces fluctuation by 90%.

Low Earth Orbit (LEO) satellite networks are gaining more interest as a crucial component of future space-air-ground integrated networks. However, the traditional IP-based communication mode is not well-suited for supporting low-cost and highly reliable routing in inter-satellite packet transmission. On one hand, the centralized IP address allocation model increases server resource consumption and also leads to excessive communication between satellites. On the other hand, the single-path routing feature of IP cannot guarantee timely recovery of the path in the event of a satellite node failure. Therefore, this paper proposes a mechanism called Relative Position-based Efficient Routing (RP-ER) for LEO satellite networks. RP-ER can achieve distributed address allocation at a low cost and enable redundant routing in the event of a path failure. In particular, RP-ER first establishes the relative position model based on the laws of satellite motion. Then, the central satellite broadcasts the address allocation instructions, and each satellite reacts and disperses packets. Finally, these satellites allocate independent addresses and generate primary and backup routes simultaneously. Compared to other routing mechanisms, RP-ER utilizes fewer satellite resources during the network addressing phase. Additionally, it can establish redundant high-quality paths during the communication phase with a concise routing table.

Multipath transmission can effectively utilize multiple paths and provide high Quality of Service (QoS) performance for video streaming services. However, when multiple video streaming services are transmitted simultaneously, the network is prone to the preemption of path resources by these services, which can reduce QoS. This is because the traditional multipath scheduling algorithm aims to achieve high QoS performance for all services. Therefore, this paper proposes a Friendliness Balancing based Multipath Scheduling algorithm (FBMS) to maximize the utilization of path resources and achieve a friendly and balanced consumption of network resources. First, FBMS obtain the path resources and service requirements to build adaptation matrices. Then, FBMS considers the friendliness balancing value as the optimization objective and utilizes a two-stage evaluation-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) algorithm to assess each path. Finally, FBMS preferentially selects a single transmission path that meets the service requirements in order to avoid resource competition. If there is no qualified path, FBMS will balance the demands of each service, integrate them friendly, and schedule multiple paths for transmission. Experiments show that, compared to traditional scheduling algorithms, FBMS improves path resource utilization, reduces competition among services, and ensures high QoS performance for each service.

With the continuous development of autonomous vehicles, telemedicine, digital media and other time-sensitive applications, a soaring number of network services have high demand for the quality of service (QoS) with extra low delay and jitter. Traditional network architecture only offers best-effort services which cannot meet the stringent delay and jitter requirements. In this paper, we propose a resilient-priority queue scheduling algorithm (RPQ) for delay-sensitive services. RPQ can guarantee stable delay in a fine-grained manner. Particularly, on the premise of meeting the delay requirements of high priority streams, RPQ can give consideration to the delay requirements of lower priority streams depending on its resilient scheduling mechanism. We implement RPQ on programmable switch. The experimental results show that RPQ not only guarantees QoS with low delay and low jitter for delay-sensitive streams but also improves network throughput by comparing with the existing solutions, i.e., SP-PIFO and WRR.

Space/aerial-assisted internet of things (IoT) is promising to provide extensive coverage and heterogeneous network services. However, it also faces the risk of eavesdropping attacks due to the peculiarity of highly open transport. In this paper, we propose a combating eavesdropping solution with resilient multipath (CERM) for space/aerial-assisted IoT. Firstly, we analyze the dynamics of space/aerial-assisted IoT and build a betweenness centrality based eavesdropping probability model. Furthermore, we formulate multipath selection problem as an integer optimization by minimizing eavesdropping probability. Based on this, we propose a programmable CERM solution to flexibly schedule multipath traffic to reduce eavesdropping risk. Extensive experimental results verify the proposed CERM solution decreases eavesdropping probability as well as increases transmission throughput compared with the traditional single-path and Round-Robin multipath solutions.

Efficient transmission control is a challenging issue in vehicular networks due to the highly dynamic network environment. In this paper, we propose a Deep reinforcement learning based adaptive Transmission Scheduling Mechanism (DTSM), which is able to adaptively select different transmission control policies based on the current network status and the history data learning. In particular, we first introduce the adaptive transmission scheduling units (ATSU) in both Software-Defined Vehicular Networking (SDVN) controllers and the corresponding base stations. Based on this architecture, we formulate a mathematical model for optimal decision-making in SDVN controllers. Besides, in ATSUs, we proposed a deep Q-learning based transmission control method to dynamically adapt to the time-varying vehicular network scenarios. Simulation results verify that the proposed DTSM solution outperforms the single transmission control method of four existing benchmarks (e.g., TcpVegas, TcpBic, TcpWestwood, TcpVeno) in terms of average throughput and round-trip time.

Software-defined networking (SDN) enables routing control to program in the logically centralized controllers. It is expected to improve the routing efficiency even in highly dynamic situations. In this article, we make an in-depth observation of practical Internet datasets and investigate the relationship between betweenness centrality and network throughput . Furthermore, we propose a new routing observation factor, differential ratio of betweenness centrality (DRBC), to denote the varying amplitude of betweenness centrality to node degree. We reveal an interesting phenomenon that DRBC is proportional to the routing efficiency when the maximum betweenness centrality varies in a small range. Based on this, a DRBC-based routing scheme is proposed to improve routing efficiency. The experimental results verify that DRBC-based routing can improve the network throughput and accelerate the routing optimization.