In this study, we consider fake message attacks in sparse mobile ad hoc networks, in which nodes are chronically isolated. In these networks, messages are delivered to their destination nodes using store-carry-forward routing, where they are relayed by some nodes. Therefore, when a node has messages in its buffer, it can falsify the messages easily. When malicious nodes exist in the network, they alter messages to create fake messages, and then they launch fake message attacks, that is, the fake messages are spread over the network. To analyze the negative effects of a fake message attack, we model the system dynamics without attack countermeasures using a Markov chain, and then formalize some performance metrics (i.e., the delivery probability, mean delivery delay, and mean number of forwarded messages). This analysis is useful for designing countermeasures. Moreover, we consider a hash-based countermeasure against fake message attacks using a hash of the message. Whenever a node that has a message and its hash encounters another node, it probabilistically forwards only one of them to the encountered node. By doing this, the message and the hash value can be delivered to the destination node via different relay nodes. Therefore, even if the destination node receives a fake message, it can verify the legitimacy of the received message. Through simulation experiments, we evaluate the effectiveness of the hash-based countermeasure.

With the increasing densification of 5G and future 6G networks high-capacity backhaul links to connect the numerous base stations become an issue. Since not all base stations can be connected via fibre links for either technical or economic reasons wireless connections at 300GHz, which may provide data rates comparable to fibre links, are an alternative. This paper deals with the planning of 300GHz backhaul links and describes two novel automatic planning approaches for backhaul links arranged in ring and star topology. The two planning approaches are applied to various scenarios and the corresponding planning results are evaluated by comparing signal to interference plus noise ratio under various simulation conditions including weather impacts showing the feasibility of wireless backhaul links.

As the basis of fine breeding management and animal husbandry insurance, individual recognition of dairy cattle is an important issue in the animal husbandry management field. Due to the limitations of the traditional method of cow identification, such as being easy to drop and falsify, it can no longer meet the needs of modern intelligent pasture management. In recent years, with the rise of computer vision technology, deep learning has developed rapidly in the field of face recognition. The recognition accuracy has surpassed the level of human face recognition and has been widely used in the production environment. However, research on the facial recognition of large livestock, such as dairy cattle, needs to be developed and improved. According to the idea of a residual network, an improved convolutional neural network (Res_5_2Net) method for individual dairy cow recognition is proposed based on dairy cow facial images in this letter. The recognition accuracy on our self-built cow face database (3012 training sets, 1536 test sets) can reach 94.53%. The experimental results show that the efficiency of identification of dairy cows is effectively improved.

In recent years, the development of visual tracking is getting better and better, but some methods cannot overcome the problem of low accuracy and success rate of tracking. Although there are some trackers will be more accurate, they will cost more time. In order to solve the problem, we propose a reinforced tracker based on Hierarchical Convolutional Features (HCF for short). HOG, color-naming and grayscale features are used with different weights to supplement the convolution features, which can enhance the tracking robustness. At the same time, we improved the model update strategy to save the time costs. This tracker is called RHCF and the code is published on https://github.com/z15846/RHCF. Experiments on the OTB2013 dataset show that our tracker can validly achieve the promotion of the accuracy and success rate.

Basic characteristics for relating design and base station layout design in land mobile communications are provided through a propagation model for path loss prediction. Owing to the rapid annual increase in traffic data, the number of base stations has increased accordingly. Therefore, propagation models for various scenarios and frequency bands are necessitated. To solve problems optimization and creation methods using the propagation model, a path loss prediction method that merges multiple models in machine learning is proposed herein. The method is discussed based on measurement values from Kitakyushu-shi. In machine learning, the selection of input parameters and suppression of overlearning are important for achieving highly accurate predictions. Therefore, the acquisition of conventional models based on the propagation environment and the use of input parameters of high importance are proposed. The prediction accuracy for Kitakyushu-shi using the proposed method indicates a root mean square error (RMSE) of 3.68dB. In addition, predictions are performed in Narashino-shi to confirm the effectiveness of the method in other urban scenarios. Results confirm the effectiveness of the proposed method for the urban scenario in Narashino-shi, and an RMSE of 4.39dB is obtained for the accuracy.

In the state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection (FDI) attack. In this letter, to enforce the security of power networks, we propose a method of detecting an FDI attack. In the proposed method, an FDI attack is detected by randomly choosing sensors used in the state estimation. The effectiveness of the proposed method is presented by two examples including the IEEE 14-bus system.

We propose an augmented-reality-based method for arranging furniture using natural markers extracted from the edges of the walls of rooms. The proposed method extracts natural markers and estimates the camera parameters from single images of rooms using deep neural networks. Experimental results show that in all the measurements, the superimposition error of the proposed method was lower than that of general marker-based methods that use practical-sized markers.

With the high development of computation requirements in Internet of Things, resource-limited edge servers usually require to cooperate to perform the tasks. Most related studies usually assume a static cooperation approach which might not suit the dynamic environment of edge computing. In this paper, we consider a dynamic cooperation approach by guiding edge servers to form coalitions dynamically. It raises two issues: 1) how to guide them to optimally form coalitions and 2) how to cope with the dynamic feature where server statuses dynamically change as the tasks are performed. The coalitional Markov decision process (CMDP) model proposed in our previous work can handle these issues well. However, its basic solution, coalitional Q-learning, cannot handle the large scale problem when the task number is large in edge computing. Our response is to propose a novel algorithm called deep coalitional Q-learning (DCQL) to solve it. To sum up, we first formulate the dynamic cooperation problem of edge servers as a CMDP: each edge server is regarded as an agent and the dynamic process is modeled as a MDP where the agents observe the current state to formulate several coalitions. Each coalition takes an action to impact the environment which correspondingly transfers to the next state to repeat the above process. Then, we propose DCQL which includes a deep neural network and so can well cope with large scale problem. DCQL can guide the edge servers to form coalitions dynamically with the target of optimizing some goal. Furthermore, we run experiments to verify our proposed algorithm's effectiveness in different settings.

A variety of smart services are being provided on multiple virtual networks embedded into a common inter-cloud substrate network. The substrate network operator deploys critical substrate nodes so that multiple service providers can achieve enhanced services due to the secure sharing of their service data. Even if one of the critical substrate nodes incurs damage, resiliency of the enhanced services can be assured due to reallocation of the workload and periodic backup of the service data to the other normal critical substrate nodes. However, the connectivity of the embedded virtual networks must be maintained so that the enhanced services can be continuously provided to all clients on the virtual networks. This paper considers resilient virtual network embedding (VNE) that ensures the connectivity of the embedded virtual networks after critical substrate node failures have occurred. The resilient VNE problem is formulated using an integer linear programming model and a distance-based method is proposed to solve the large-scale resilient VNE problem efficiently. Simulation results demonstrate that the distance-based method can derive a sub-optimum VNE solution with a small computational effort. The method derived a VNE solution with an approximation ratio of less than 1.2 within ten seconds in all the simulation experiments.

One of the features of a software-defined network (SDN) is a logically centralized control plane hosting one or more SDN controllers. As SDN controller placement can impact network performance, it is widely studied as the controller placement problem (CPP). For a cost-effective network design, network providers need to minimize the number of SDN controllers used in the network since each SDN controller incurs installation and maintenance costs. Moreover, the network providers need to deal with the failure of SDN controllers. Existing studies that consider SDN controller failures use the scheme of connecting each SDN switch to one Master controller and one or more Slave controllers. The problem with this scheme is that the computing capacity of each SDN controller cannot be used efficiently since one SDN controller handles the load of all SDN switches connected to it. The number of SDN controllers required can be reduced by distributing the load of each SDN switch among multiple SDN controllers. This paper proposes a controller placement model that allows the distribution against SDN controller failures. The proposed model determines the ratios of computing capacity demanded by each SDN switch on the SDN controllers connected to it. The proposed model also determines the number and placement of SDN controllers and the assignment of each SDN switch to SDN controllers. Controller placement is determined so that a network provider can continue to manage all SDN switches if no more than a certain number of SDN controller failures occur. We develop two load distribution methods: split and even-split. We formulate the proposed model with each method as integer linear programming problems. Numerical results show that the proposed model reduces the number of SDN controllers compared to a benchmark model; the maximum reduction ratio is 38.8% when the system latency requirement between an SDN switch and an SDN controller is 100[ms], the computing capacity of each SDN controller is 6 × 106[packets/s], and the maximum number of SDN controllers that can fail at the same time is one.

This paper presents a physical layer wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. This work introduces classification and detection tasks in one process. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, a technology to identify wireless devices by using unique variations in analog signals. We propose a quadrant IQ image technique to reduce the size of CNN while maintaining accuracy. The CNN utilizes the IQ transition image, which image processing cut out into four-part. An over-the-air experiment is performed on six Zigbee wireless devices to confirm the proposed identification method's validity. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 weight parameters in serial use and 146,000 in parallel use. Furthermore, the proposed threshold algorithm can verify the authenticity using one classifier and achieved 80% accuracy for further secured wireless communication. This work also introduces the identification of expanded signals with SNR between 10 to 30dB. As a result, at SNR values above 20dB, the proposals achieve classification and detection accuracies of 87% and 80%, respectively.

Although deep neural networks (DNNs) have achieved high performance across a variety of applications, they can often be deceived by adversarial examples that are generated by adding small perturbations to the original images. Adversaries may generate adversarial examples using the property of transferability, in which adversarial examples that deceive one model can also deceive other models because adversaries do not obtain any information on the DNNs deployed in real scenarios. Recent studies show that adversarial examples with feature space perturbations are more transferable than others. Adversarial training is an effective method to defend against adversarial attacks. However, it results in a decrease in the classification accuracy for natural images, and it is not sufficiently robust against transferable adversarial examples because it does not consider adversarial examples with feature space perturbations. We propose a novel adversarial training method to train DNNs to be robust against transferable adversarial examples and maximize their classification accuracy for natural images. The proposed method trains DNNs to correctly classify natural images and adversarial examples and also minimize the feature differences between them. The robustness of the proposed method was similar to those of the previous adversarial training methods for MNIST dataset and was up to average 6.13% and 9.24% more robust against transfer adversarial examples for CIFAR-10 and CIFAR-100 datasets, respectively. In addition, the proposed method yielded an average classification accuracy that was approximately 0.53%, 6.82%, and 10.60% greater than some state-of-the-art adversarial training methods for all datasets, respectively. The proposed method is robust against a variety of transferable adversarial examples, which enables its implementation in security applications that may benefit from high-performance classification but are at high risk of attack.

Packet processing on commodity hardware is a cost-efficient and flexible alternative to specialized networking hardware. However, virtualizing dedicated networking hardware as a virtual machine (VM) or a container on a commodity server results in performance problems, such as longer latency and lower throughput. This paper focuses on obtaining a low-latency networking system in a VM and a container. We reveal mechanisms that cause millisecond-scale networking delays in a VM through a series of experiments. To eliminate such delays, we design and implement a low-latency networking system, kernel busy poll (KBP), which achieves three goals: (1) microsecond-scale tail delays and higher throughput than conventional solutions are achieved in a VM and a container; (2) application customization is not required, so applications can use the POSIX sockets application program interface; and (3) KBP software does not need to be developed for every Linux kernel security update. KBP can be applied to both a VM configuration and a container configuration. Evaluation results indicate that KBP achieves microsecond-scale tail delays in both a VM and a container. In the VM configuration, KBP reduces maximum round-trip latency by more than 98% and increases the throughput by up to three times compared with existing NAPI and Open vSwitch with the Data Plane Development Kit (OvS-DPDK). In the container configuration, KBP reduces maximum round-trip latency by 21% to 96% and increases the throughput by up to 1.28 times compared with NAPI.

In this letter, we propose a deep neural network and semi-supervised learning based dehazing algorithm. The dehazing network uses a pyramidal architecture to recover the haze-free scene from a single hazy image in a coarse-to-fine order. To faithfully restore the objects with different scales, we incorporate cascaded multi-scale convolutional blocks into each level of the pyramid. Feature fusion and transfer in the network are achieved using the paths constructed by interleaved residual connections. For better generalization to the complicated haze in real-world environments, we also devise a discriminator that enables semi-supervised adversarial training. Experimental results demonstrate that the proposed work outperforms comparative ones with higher quantitative metrics and more visually pleasant outputs. It can also enhance the robustness of object detection under haze.

Aiming for accurate data-driven predictions for the passenger walking time, this study proposes a novel neuron-network-based mixture probability (NNBMP) model with repetition learning (RL) to estimate the probability density distribution of passenger walking time (PWT) in the metro station. Our conducted experiments for Fuzhou metro stations demonstrate that the proposed NNBMP-RL model achieved the mean absolute error, mean square error, and mean absolute percentage error of 0.0078, 1.33 × 10-4, and 19.41%, respectively, and it outperformed all the seven compared models. The developed NNBMP model fitting accurately the PWT distribution in the metro station is readily applicable to the microscopic analyses of passenger flow.

Convolutional Neural Network (CNN) has made extraordinary progress in image classification tasks. However, it is less effective to use CNN directly to detect image manipulation. To address this problem, we propose an image filtering layer and a multi-scale feature fusion module which can guide the model more accurately and effectively to perform image manipulation detection. Through a series of experiments, it is shown that our model achieves improvements on image manipulation detection compared with the previous researches.

The haze situation will seriously affect the quality of license plate recognition and reduce the performance of the visual processing algorithm. In order to improve the quality of haze pictures, a license plate recognition algorithm based on haze weather is proposed in this paper. The algorithm in this paper mainly consists of two parts: The first part is MPGAN image dehazing, which uses a generative adversarial network to dehaze the image, and combines multi-scale convolution and perceptual loss. Multi-scale convolution is conducive to better feature extraction. The perceptual loss makes up for the shortcoming that the mean square error (MSE) is greatly affected by outliers; the second part is to recognize the license plate, first we use YOLOv3 to locate the license plate, the STN network corrects the license plate, and finally enters the improved LPRNet network to get license plate information. Experimental results show that the dehazing model proposed in this paper achieves good results, and the evaluation indicators PSNR and SSIM are better than other representative algorithms. After comparing the license plate recognition algorithm with the LPRNet algorithm, the average accuracy rate can reach 93.9%.

Energy efficiency is one of the critical issues for Wireless Sensor Networks (WSN). IEEE 802.15.4 beacon-enabled MAC protocol achieves low energy consumption by having periodical inactive portions, where nodes run in low power. However, IEEE 802.15.4 beacon-enabled protocol cannot respond to dynamic changes in the number of sensor nodes and data rates in WSN because its duty cycle is fixed and immutable. In this paper, we propose a dynamic superframe duration adaptation scheme based on the Markov chain-based analysis methods for IEEE 802.15.4 beacon-enabled protocol. The proposed methods are flexible enough to accommodate changes in the number of sensor nodes and differences in data rates in WSNs while maintaining low latency and low energy consumption despite slight degradation in packet delivery ratio.

Machine reading comprehension with multi-hop reasoning always suffers from reasoning path breaking due to the lack of world knowledge, which always results in wrong answer detection. In this paper, we analyze what knowledge the previous work lacks, e.g., dependency relations and commonsense. Based on our analysis, we propose a Multi-dimensional Knowledge enhanced Graph Network, named MKGN, which exploits specific knowledge to repair the knowledge gap in reasoning process. Specifically, our approach incorporates not only entities and dependency relations through various graph neural networks, but also commonsense knowledge by a bidirectional attention mechanism, which aims to enhance representations of both question and contexts. Besides, to make the most of multi-dimensional knowledge, we investigate two kinds of fusion architectures, i.e., in the sequential and parallel manner. Experimental results on HotpotQA dataset demonstrate the effectiveness of our approach and verify that using multi-dimensional knowledge, especially dependency relations and commonsense, can indeed improve the reasoning process and contribute to correct answer detection.

In this paper, a novel method of resource abstraction and an abstracted-resource model for dynamic resource control in optical access networks are proposed. Based on this proposal, an implementation assuming application to 5G mobile fronthaul and backhaul is presented. Finally, an evaluation of the processing time for resource allocation using this method is performed using a software prototype of the control function. From the results of the evaluation, it is confirmed that the proposed method offers better characteristics than former approaches, and is suitable for dynamic resource control in 5G applications.