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Owing to the influence of the injection molding process, warpage and volume shrinkage are two common quality defects for products manufactured by glass fiber-reinforced plastic (GFRP) injection molding. To minimize these two defects, an extreme learning machine optimized with a genetic algorithm (GA-ELM), multi-objective firefly algorithm (MOFA), and a multi-objective decision-making method (GRA-TOPSIS) were implemented in this study. All of the experiments, based on Latin hypercubic sampling (LHS), were conducted using Moldflow software to obtain the results for warpage and volume shrinkage. The prediction accuracy of the defect-prediction models based on the extreme learning machine (ELM) and GA-ELM algorithms were compared. The results show that the GA-ELM models can better predict the defect values. Finally, MOFA was used to find the Pareto optimal front, and the GRA-TOPSIS method was used to find the optimum solution from the Pareto optimal front. According to the results of the simulation verification, the warpage and volume shrinkage were effectively reduced by 12.25% and 6.11%, respectively, compared with before optimization, which indicates the effectiveness and reliability of the optimization method.

In Vehicular Networks (VANETs), the rapid movement of vehicles leads to highly time-varying network topology and brings the problem of blind spots in signal coverage. To address these issues, drones are employed to assist data dissemination in VANETs attribute to the flexible development of drones. As random data transmission will result in decreased network performance and low efficiency in data retrieval service, it is urgent to develop efficient data scheduling schemes that meet the quality of service (QoS) of different applications in VANETs. In this context, to fulfill the request of real-time and reliable data transmission, we formulate a data scheduling problem that considers the factors such as priority of data transmission, link quality, link connection time and network fairness. Our goal is to reduce random data transmission, therefore maximizing network transmission utility. We utilize graph theory to describe network topology, in which vehicles and drones are represented as vertices and links between the nodes are represented as edges. The weight of edges indicates the utility obtained when data transmits between the corresponding nodes. We reduce the data scheduling problem to the maximum weighted matching problem and then propose a data scheduling scheme that can satisfy data requests of different vehicles to the maximum extent. The theoretical analysis derives the scheduling algorithm’s time complexity and the number of scheduling stages required to satisfy the data requests. Finally, simulation verifies the proposed scheme’s effectiveness in terms of service rate, service delay, fairness and throughput.

In Internet of Vehicles (IoV), frequent link switching and channel interference will increase transmission delay and decrease network throughput. Unmanned Aerial Vehicles (UAVs) can avoid shadowing effect and provide line-of-sight communication to enhance IoV performance, which can help reduce communication handovers. This study introduces UAVs to assist ground vehicular network for collaborative communication and focuses on relay selection to achieve effective data dissemination. The relay selection problem is formulated as a multi-objective optimization problem related to link transmission rate, communication handovers, and node transmit power. To solve the problem, we propose an interference-aware relay selection mechanism, which is a distributed strategy to make decisions at the sender. The mechanism applies the dual decomposition method to decompose the problem into two convex optimization subproblems. The first subproblem jointly optimizes communication handovers and link transmission rate while the second subproblem optimizes node transmit power under the premise of ensuring link reliability. Simulations verify the effectiveness of the proposed relay selection mechanism in terms of data delivery ratio, average end-to-end delay and network throughput.

Drastic changes in network topology of Flying Ad Hoc Networks (FANETs) result in the instability of the single-hop delay and link status accordingly. Therefore, it is difficult to implement the congestion control with delay-sensitive traffic according to the instantaneous link status. To solve the above difficulty effectively, we formulate the delay-aware congestion control as a network utility maximization, which considers the link capacity and end-to-end delay as constraints. Next, we combine the Lagrange dual method and delay auxiliary variable to decouple the link capacity and delay threshold constraints, as well as to update single-hop delay bound with the delay-outage mode. Built on the methods above, a distributed optimization algorithm is proposed in this work by considering the estimated single-hop delay bound for each transmission, which only uses the local channel information to limit the end-to-end delay. Finally, we deduce the relationship between the primal and dual solutions to underpin the advantages of the proposed algorithm. Simulation results demonstrate that the proposed algorithm effectively can improve network performances in terms of packet time-out rate and network throughput.

In urban roads, the safety service of vehicular ad hoc networks is particularly important because of the frequent traffic accidents. In order to minimise redundancy and delay of network and ensure the high reception rate of vehicles in the target area when spreading the emergency message. In this study, the authors propose a reliable broadcast mechanism based on the layout information of the road. Different from existing mechanisms, in the straight, the proposed mechanism regards effective communication distance based on prediction rather than fix distance as one of the parameters of waiting time calculation. Considering the obstacles such as buildings around the intersection in the actual urban scenario, the vehicle in the intersection area (IA) of the target area will have an additional broadcast, if there are no vehicles in the IA, the vehicle in the pre-selected area for intersections (IPA) has chance to broadcast when it enters IA, which avoids the failure of emergency message transmission in some direction because of the obstacles around the intersection and ensures the coverage and reliability of emergency message. The simulation results verify that the novel scheme has better performance than the compared schemes in terms of reliability and delay.

Due to the dynamically changing topology of Internet of Vehicles (IoV), it is a challenging issue to achieve efficient data dissemination in IoV. This paper considers strongly connected IoV with a number of heterogenous vehicular nodes to disseminate information and studies distributed replication-based data dissemination algorithms to improve the performance of data dissemination. Accordingly, two data replication algorithms, a deterministic algorithm and a distributed randomised algorithm, are proposed. In the proposed algorithms, the number of message copies spread in the network is limited and the network will be balanced after a series of average operations among the nodes. The number of communication stages needed for network balance shows the complexity of network convergence as well as network convergence speed. It is proved that the network can achieve a balanced status after a finite number of communication stages. Meanwhile, the upper and lower bounds of the time complexity are derived when the distributed randomised algorithm is applied. Detailed mathematical results show that the network can be balanced quickly in complete graph; thus highly efficient data dissemination can be guaranteed in dense IoV. Simulation results present that the proposed randomised algorithm outperforms the present schemes in terms of transmissions and dissemination delay.

To provide data transmission with high quality of service, we aim to adaptively allocate network resources in vehicular networks. Considering the temporal property of the dynamic networks, we propose the concept of delay-constrained temporal varying graphs based on the concept of time-varying graphs. By adding the delay factor to the original graph model, it is more general and can be applied to a wide range of delay-tolerant applications. On this basis, we formulate the resource allocation problem, then apply the technique of Lagrange duality to decompose the problem and obtain the optimal transmission rate and transmit power. Accordingly, an efficient resource allocation scheme is proposed, which can obtain the exact solutions. We evaluate the proposed algorithm using realistic vehicular mobility traces. Simulation results show the effectiveness of the proposed scheme in terms of data delivery ratio and network throughput.

Cloud storage provides elastic storage services for enterprises and individuals remotely. However, security problems such as data integrity are becoming a major obstacle. Recently, blockchain-based verification approaches have been extensively studied to get rid of a centralized third-party auditor. Most of these schemes suffer from poor scalability and low search efficiency and even fail to support data dynamic update operations on blockchain, which limits their large-scale and practical applications. In this work, we propose a blockchain-based dynamic data integrity verification scheme for cloud storage with T-Merkle hash tree. A decentralized scheme is proposed to eliminate the restrictions of previous centralized schemes. The data tags are generated by the technique of ZSS short signature and stored on blockchain. An improved verification method is designed to check the integrity of cloud data by transferring computation from a verifier to cloud server and blockchain. Furthermore, a storage structure called T-Merkle hash tree which is built based on T-tree and Merkle hash tree is designed to improve storage utilization of blockchain and support binary search on chain. Moreover, we achieve efficient and secure dynamic update operations on blockchain by an append-only manner. Besides, we extend our scheme to support batch verification to handle massive tasks simultaneously; thus, the efficiency is improved and communication cost is reduced. Finally, we implemented a prototype system based on Hyperledger Fabric to validate our scheme. Security analysis and performance studies show that the proposed scheme is secure and efficient.

The problem of large-scale development of renewable energy has become increasingly serious, and the technology of virtual power plants has gradually become the key technology of this problem. This paper studies the multi-objective optimization problem of gas-electric interconnected virtual power plants. First of all, this article integrates wind turbines, photovoltaic generators, gas turbines, electric gas conversion equipment, gas storage devices, etc. into a virtual power plant, and simultaneously links public networks and natural gas networks. The load side includes electric power load, hydrogen fuel cell vehicle load, and natural gas load, and the demand response is introduced at the terminal load for adjustment. Then, taking the operational economic benefits, peak-cutting and valley-filling effects, and carbon dioxide emissions as optimization goals, combined with power balance and other constraints, a multi-objective model of a gas-electric interconnected virtual power plant is constructed, and the multi-objective optimization model is converted by weighting each objective function Solve for a single objective model. Finally, in order to verify the validity and feasibility of the established model, a virtual power plant in a certain area was selected as an example to analyze and compare the operating results under four scenarios.

Unmanned aerial vehicles (UAVs) are gaining increasing importance in military and civilian applications. A challenging problem is the efficient communication between multiple UAVs and terrestrial base station (BS), where a wireless link has to cover hundreds of miles. These long-distance wireless networks suffer from heterogeneous and varying propagation delays, making conventional medium access control (MAC) protocols inappropriate. In this paper, a propagation delay-aware access scheme, LDMAC, is designed for long-distance UAV networks. It is a joint design of random access and collision-free time slot allocation. UAVs contend for time slots based on improved Irregular Repetition Slotted Aloha (IRSA), and subsequently the BS makes a collision-free, fair and optimized time slot allocation with temporal reuse. Both of the processes take full advantage of propagation delay to efficiently improve system throughput. Simulation results show that LDMAC achieves up to 30% improvement over IRSA with respect to successful decoding rate (SDR), and outperforms competing protocols that include IRSA and dynamic time division multiple access (D-TDMA) in terms of throughput in mobile wireless networks.

Vehicular Ad Hoc Networks (VANETs) provide safety management and entertainment services in smart city scenarios. However, due to the highly time-varying network topology caused by vehicles’ movement and non-line-of-sight (NLOS) areas in complex urban environment, the connection time between vehicles might be very short, leading to limited transmission capacity between vehicles. As a result, large file transmission such as online video sharing might be unavailable. To alleviate this issue, we study high-integrity based file transmission using pure vehicle-to-vehicle (V2V) communication at urban intersections. First, we develop the analytical models to evaluate file transmission at intersections, including vehicle mobility model, connection time prediction model, and V2V communication model. Then, we derive the relationship between transmission capacity and vehicles’ mobility. Based upon this analysis, we propose a cooperative file transmission strategy for the transmitting vehicle, which helps transmit the target file to the receiving vehicle. The proposed strategy includes three phases: transmission capacity evaluation, cluster establishment, and cooperative file transmission. By taking advantage of the cluster characteristics, the strategy can achieve file transmission with high integrity. Through extensive simulations, we demonstrate the accuracy of the proposed system model which matches our analysis well, and show the effectiveness of the proposed strategy by comparing with other file transmission strategies in aspects of transmission success rate and data transmission volume.

Significance Wavefront shaping with spatial light modulators has recently motivated many studies in the field of light manipulation in diffusive media. Here, we extend this concept to acoustic waves by designing and building a binary phase-modulating spatial sound modulator (SSM). The SSM is an acoustic metasurface consisting of unit cells with two states, switchable through programmed electronics. We demonstrate in audible frequencies, and in a reverberating environment, the capability of controlling and reshaping any complex sound field. Our work will not only open avenues to study wave propagation in complex and chaotic media but also inspire applications in acoustic engineering., A reverberating environment is a common complex medium for airborne sound, with familiar examples such as music halls and lecture theaters. The complexity of reverberating sound fields has hindered their meaningful control. Here, by combining acoustic metasurface and adaptive wavefield shaping, we demonstrate the versatile control of reverberating sound fields in a room. This is achieved through the design and the realization of a binary phase-modulating spatial sound modulator that is based on an actively reconfigurable acoustic metasurface. We demonstrate useful functionalities including the creation of quiet zones and hotspots in a typical reverberating environment.

Due to the high mobility of vehicles, the frequent path failures caused by dynamic network topology, and a variety of obstructions, efficient data dissemination with delay constraint in vehicular ad hoc networks (VANETs) is a challenging issue. To address these problems, a novel mobile relaying technique by employing unmanned aerial vehicles (UAVs) is considered to facilitate data dissemination in vehicular environments where the communication infrastructures are not available or the network connectivity is poor. This paper studies and formulates the throughput maximization problem in UAV-assisted VANETs, which aims to achieve high throughput while guarantee the delay constraint of data flows to the vehicles in the area. To maximize the network throughput, the maximization problem tries to find an optimal delivery strategy for data dissemination by optimizing the transmission rate. To solve the problem, the knapsack problem can be reduced to the maximization problem, which is proved NP-hard. A polynomial time approximation scheme is proposed to achieve an approximate solution. Detailed theoretical analysis including time complexity and approximation ratio of the proposed algorithm is presented. Simulation results demonstrate the effectiveness of the proposed algorithm.

Efficient data dissemination in vehicular ad hoc networks (VANETs) is a challenging issue due to the high mobility of vehicles. We consider a novel mobile relaying technique by employing unmanned aerial vehicles (UAVs) to assist VANETs when the communication infrastructure is not available or network connectivity is poor. A throughput maximization problem with delay constraint is formulated to achieve high network throughput and guarantee real-time data dissemination. We reduce the graph knapsack problem to the throughput maximization problem, which is proved NP-hard. A polynomial time approximation scheme is proposed to solve the problem. Theoretical analysis that includes time complexity and approximation ratio of the algorithm is presented. The evaluation demonstrates the effectiveness of the proposed algorithm.

In Vehicular Ad-Hoc Network (VANET), fast and reliable emergency message dissemination among vehicles on the road has positive significance. Besides, the topology of VANET is dynamic and we need to consider the adaptability of message dissemination scheme to different scenarios. By studying the relation between reliable broadcast scheme and different effect factors, we improve the equation of contention window. To meet the needs of multidimensional scenarios, we propose a novel topology-aware broadcast scheme. Each node monitors its neighbors’ state according to a FSM (finite-state machine) and transmits the information to proper nodes. The advantages of our scheme are that it enhances the transmission reliability in multidimensional scenarios and improves self-adaptability in dynamic VANET with low propagation latency. By evaluating the proposed solution, our scheme is implemented and simulation results are provided.

Motivated by message delivery in vehicular ad hoc networks, we study distributed data replication algorithms for information delivery in a special completely connected network. To improve the efficiency of data dissemination, the number of message copies that can be spread is controlled and a distributed randomized data replication algorithm is proposed. The key idea is to let the data carrier distribute the data dissemination tasks to multiple nodes to speed up the dissemination process. We show how the network converges and prove that the network can enter into a balanced status in a small number of stages. Most of the theoretical results described in this paper are to study the complexity of network convergence. Simulation results show that the proposed algorithm can disseminate data to a specific area with low delay.

Software Defined Networking (SDN) has emerged as a new paradigm that offers the programmability required to dynamically configure and control a network. In this paper, we present a path assignment method based on load balancing(PA-LB) for a centralized controller to calculate optimal end-to-end virtual paths over the underlying network infrastructure, considering multiple requests and load balance simultaneously. In the paper, theoretical analysis shows that the path selection is NP-hard, and strongly polynomial time inapproximable under the assumption P not equal to NP. We also give a constant-factor polynomial time approximation algorithm for this problem under some bounded parameters. Extensive simulation results, over a wide range of underlying network topologies and input parameters, demonstrate that the proposed method outperforms traditional shortest path first (SPF) approaches not only in paths mapping number, but also in network load balance. Under the condition of limited resources, such as bandwidth, the proposed method has a better performance with the increase of the number of nodes. Moreover, optimal load balance can be kept in the network, in order to avoid network congestion and minimize the average packet transmission latency.

With the development of vehicular ad hoc network (VANET), data dissemination has been widely studied and its performance is expected to be greatly improved. Research on data dissemination generally focuses on how to utilize road infrastructures, such as roadside units (RSUs), to facilitate the dissemination. In this paper, we consider a VANET where a given number of RSUs are available for disseminating information in an urban area. We formulate the problem as to minimize the number of RSUs selected from the available RSUs to cover a specific region. The paper proposes a c-street model and a city model. Then we develop a greedy polynomial time covering algorithm under the c-street model and a polynomial time approximation scheme is proposed under the city model based on shifting strategy. By evaluating the proposed solution in realistic urban environment, our simple greedy algorithm is implemented and some simulation results are provided.

Efficient data dissemination in vehicular ad hoc networks (VANETs) is a challenging issue due to the dynamic nature of the network. To improve the performance of data dissemination, we study distributed data replication algorithms in VANETs for exchanging information and computing in an arbitrarily-connected network of vehicle nodes. To achieve low dissemination delay and improve the network performance, we control the number of message copies that can be disseminated in the network and then propose an efficient distributed data replication algorithm (EDDA). The key idea is to let the data carrier distribute the data dissemination tasks to multiple nodes to speed up the dissemination process. We calculate the number of communication stages for the network to enter into a balanced status and show that the proposed distributed algorithm can converge to a consensus in a small number of communication stages. Most of the theoretical results described in this paper are to study the complexity of network convergence. The lower bound and upper bound are also provided in the analysis of the algorithm. Simulation results show that the proposed EDDA can efficiently disseminate messages to vehicles in a specific area with low dissemination delay and system overhead.