Task offloading in cognitive radio-enabled Internet of Vehicles (CIoV) networks faces critical challenges due to the coupling of vehicular mobility, spectrum dynamics induced by primary users (PUs), and delay-sensitive service constraints. Existing approaches often decouple task offloading, spectrum access, and resource allocation, resulting in poor adaptability under dynamic and dense traffic conditions. To address these limitations, this paper proposes a Robust Parallel Benders Decomposition (RPBD) framework for mobility-aware task offloading in CIoV networks with bidirectional traffic and stochastic spectrum availability. The proposed framework introduces three key innovations: (1) a parallel decomposition strategy that separates discrete offloading decisions from continuous resource allocation, enabling scalable optimization; (2) a staleness-controlled cut generation mechanism that ensures solution quality under asynchronous updates; and (3) an adaptive parameter adjustment strategy that dynamically accommodates vehicular mobility and PU-induced spectrum variability. The formulation incorporates bidirectional mobility through service time windows and models spectrum availability using Markov-modulated channel states. Theoretical analysis guarantees convergence through convex subproblem structures and martingale-bounded parameter evolution. Extensive simulation results demonstrate that RPBD reduces task latency by 62.7 % and outage probability by 41.9 % compared to conventional Benders decomposition. Moreover, it maintains latency below 300 ms under 60 km/h mobility and achieves 83.4 % spectrum utilization efficiency in urban CIoV scenarios.

In recent years, deep learning has advanced the MIDI domain, solidifying music generation as a key application of artificial intelligence. However, most research focuses on Western music, facing challenges in generating Chinese traditional melodies, particularly in capturing modal characteristics and emotional expression. To address this, we propose the Dual-Feature Modeling Module, which integrates the long-range modeling of the Mamba Block with the global structure capturing of the Transformer Block. Additionally, we introduce the Bidirectional Mamba Fusion Layer, which integrates local details and global structures through bidirectional scanning, enhancing sequence modeling. Building on this, we propose the REMI-M representation to better capture and generate modal information in melodies. To support this, we developed FolkDB, a high-quality Chinese traditional music dataset covering over 11 hours of music. Experimental results show our architecture excels in generating melodies with Chinese traditional music characteristics, offering a new solution for music generation.

Mobile opportunistic networks (MONs) have gained considerable attention in enabling impromptu communication due to the progress in communication technologies over the past decade. One of the main characteristics of MONs is their inconsistent connectivity and implicit end-to-end routing paths. This presents a challenge in making optimal routing decisions during message transmission, as we need to identify relay nodes that can reliably transport messages to their destinations quickly and cost-effectively. Here, we propose a novel multi-criteria fusion routing decision making algorithm (MFRD). Initially, we establish four metrics that measure the message transmission capability (MTC) of a node in terms of message throughput, message tolerance, geographic connectivity, and time connectivity. Next, we use the game theory mechanism to merge the DEMATEL subjective weights and the CRITIC objective weights of each metric, resulting in a more thorough integrated weight for them. Then, we integrate the benefits of two MCDM techniques, TOPSIS and GRA, to provide a new relative proximity measure for evaluating the MTC of alternative nodes. Finally, during message propagation, we select nodes with higher MTC as next-hop relays. The simulation results demonstrate that MFRD not only significantly improves the delivery rate and reduces the average latency, but it also shows excellent performance in network overhead and average hop count.

Route planning is a fundamental service for pedestrians navigating unfamiliar environments. Existing solutions often need to pay more attention to considering Quality of Service (QoS) requirements in the context of 5G cellular networks, a crucial aspect for enhancing user experiences. In this paper, we delve into the problem of QoS-aware dynamic path planning for pedestrians in 5G cellular networks, which presents itself as a multi-constrained optimization problem. To tackle this challenge, we merge the dynamic A* algorithm with Ant Colony Optimization (ACO) to determine the shortest path dynamically while ensuring QoS prerequisites. First, we employ a Markov prediction model and population statistics to forecast statechanging information. Second, we employ a retroactive priority queue to enhance the dynamism of the A* search algorithm. Then, we introduce a reference value generated by ACO to streamline the search space of A*. Finally, simulation results demonstrate our proposed method’s effectiveness and efficiency.

Viaduct structures have been built to ease traffic congestion in large-sized cities, especially in many large-medium cities in China. In Internet of Vehicles (IoV) networks, inter-level links in urban viaduct scenarios are established between two vehicles on different levels with various heights. The bridge surface of the viaduct acts as an obstacle in the propagation paths of inter-level links and may cause a degradation of the link quality. However, inter-level links may bring more commu-nication opportunities when the traffic is sparse or no intra-level link can be found. Compared with traditional three-dimensional wireless networks, inter-level links for viaduct shadowing sce-narios reveal unique characteristics which are not addressed by previous studies. This paper aims to estimate the unique link connectivity characteristics and figure out whether inter-level links can enhance opportunistic routing performance. To this end, we first build an accurate hybrid link availability and reliability model by considering the unique structure of viaducts. Second, we design a new metric over opportunistic routing combined with our link model. Simulation results show that taking advantage of inter-level links can improve the performance of opportunistic routing.

Recent research activities have shown that opportunistic routing can achieve considerable performance gains in Cognitive Radio Ad hoc Networks (CRAHNs). Most of these studies focused on designing appropriate metrics to select and prioritize the forwarding candidates. However, in multiple‐flow networks, a small number of nodes may always be with the higher priority order for different flows. Thus, some nodes may easily become overloaded with too much traffic and be severely congested. To overcome this problem, we propose a load balancing opportunistic routing (LBOR) scheme to maximize the total throughput of the whole network. We first formulate the problem of maximizing the total throughput of the network as a linear programming problem. Then, we develop heuristic load balancing candidate forwarder sorting and selection algorithms. Simulation results and comparisons demonstrate that our proposed LBOR scheme outperforms existing opportunistic routing protocols with nonload balancing methods in CRAHNs.

Cognitive Radio Vehicular Ad Hoc Networks (CRVANETs) can resolve the conflicts between limited spectrum resource and vehicular communication service needs, and thus it receives much concern. Most existing routing solutions for Cognitive Radio Ad Hoc Networks (CRAHNs) or VANETs cannot be directly applied to CR-VANETs due to the high mobility of vehicles and varying availability of spectrums. To fill this gap, with the proliferation of delay tolerant applications, this paper presents a delay tolerant routing strategy for CR-VANETs with the object of maximizing the packet delivery ratio from the source to the destination. The proposed routing scheme is composed of two parts: the routing policy and the message replication policy. In the first policy, we select a concurrent forwarding set under the restriction of CR spectrum holes, and choose an optimized routing path for the next hop. In the second policy, we propose a hybrid and adaptable method relying on Drop-Oldest and Drop-Most strategies, to reduce the delivery cost. Through extensive simulations, we demonstrate that the proposed delay tolerant routing mechanism provides with better packet delivery ratio compared to state-of-the-art protocols.

In multi-hop WLANs, it is more efficient to let some clients relay traffic for the clients whose distances are a bit far away from the AP, which increases the transmission efficiency. The existing solutions for multi-hop WLANs try to choose one or two fixed best relays to forward packets. However, the links between far-away clients and the AP are intermittently connected, and end-to-end paths may not exit. In order to fill this gap, using opportunistic forwarding, our solutions do not fix a relay; the source simply broadcasts its signal, any client who received the signal will relay the traffic. In this paper, our object is to extend the connectivity by opportunistic forwarding for far-away clients in multi-hop WLAN accesses. The original contributions made by this paper include: 1) We develop a general model to analyze the throughput of opportunistic forwarding in multi-hop WLANs. 2) We classify the clients into three groups, and propose a centralized opportunistic routing relay algorithm for each group of clients to achieve the optimal system throughput. 3) We propose a distributed opportunistic routing relay protocol for clients' disconnected and connected actions. Extensive simulations have been done in NS-2. The simulation results show that our protocol can significantly increase the service's coverage and connectivity of the entire network, compared with those methods by traditional one-hop and multi-hop relay.

In this paper, we address the problem of joint power control and scheduling for minimizing broadcast delay in wireless mesh networks. Given a set of mesh routers and a routing tree rooted from the gateway node, our task is to assign power for each relay node and compute an optimal transmission schedule such that the longest delay for a packet broadcast from the root node to all the other routers is minimized. We consider rate adaption in our scheme. This is a difficult issue. On one hand, if we increase the transmission power, the packet can be transmitted out at a higher data rate, which leads to less delay; on the other hand, a high transmission power would have larger interference range, which makes less nodes that can transmit concurrently and thus cause longer delay to deliver the packet to farther routers. We study the tradeoff between the two parameters, data rate and concurrency, and propose a balanced method for power control and transmission scheduling. We introduce a metric called standard deviation of remaining broadcast time of nodes to determine the priority of the two parameters. When this standard deviation is above a threshold, the transmitting nodes will take the data-rate-first approach to increase the data rate; otherwise the concurrency-first approach will be used to increase the number of concurrent transmissions in the system. Extensive simulations have demonstrated that our proposed method can reduce the broadcast delay significantly compared with the methods using fixed transmission power. In addition, the results also show that our balanced method performs better than both pure data-rate-first method and concurrency-first method.