Improving Catheter Segmentation&Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN

Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image quality of US requires extra training for sonographers to localize the catheter. In this paper, we propose a catheter detection method based on a pre-trained VGG network, which exploits 3D information through re-organized cross-sections to segment the catheter by a shared fully convolutional network (FCN), which is called a Direction-Fused FCN (DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized by model fitting. Our experiments show that the proposed method can successfully detect an ablation catheter in a challenging ex-vivo 3D US dataset, which was collected on the porcine heart. Extensive analysis shows that the proposed method achieves a Dice score of 57.7%, which offers at least an 11.8 % improvement when compared to state-of-the-art instrument detection methods. Due to the improved segmentation performance by the DF-FCN, the catheter can be localized with an error of only 1.4 mm.

INTRODUCTION
Cardiac catheterization has been extensively applied during interventional therapy for heart diseases, which offers a short recovery period and low risk for the patients. Because a direct view on the organ is occluded by skin or other tissues during the operation, medical imaging methods, such as fluoroscopic (X-ray) or ultrasound (US) imaging, are used to guide the intervention. The maturity of 3D cardiac US imaging, e.g. Transesophageal Echography (TEE), makes 3D USguided operations an attractive option because of its lower radiation exposure for both patient and surgeon. Besides this, 3D US also provides richer spatial information of the tissue than the traditional 2D X-ray imaging. Unfortunately, low contrast 3D US with anatomical structure makes it difficult for the physician or sonographer to timely localize the catheter during intervention. Therefore, computer-aided catheter detection (CAD) in 3D US can be helpful for the physician and make the operation more efficient.
Techniques like active sensing [1] or robotics [2], have introduced extra cost for equipment and training. Alternatively, direct image-based catheter detection will lead to a lower cost for hospitals. Catheter detection in US has therefore been studied in literature. A template matching approach was proposed by Cao [3]. Alternatively, the Frangi feature was introduced with supervised learning methods and shown to give successful detection for instruments [4]. A recent study on instrument detection combined Gabor features with Frangi features for catheter detection in phantom heart data, which showed promising results in Phantom [5]. More recently, more discriminative features were introduced by [6] and showed a clear improvement in catheter detection using ex-vivo data.
In recent years, deep learning methods, like convolutional neural networks (CNN), have shown a performance improvement in medical instruments detection, such as catheter/needle detection in 3D US [7] [8]. Nevertheless, the local voxel-wise processing in [7][8] requires the CNN to label the 3D image in a voxel-by-voxel manner, which gives 3D contextual information loss and more computation time. Additionally, the continuous work of Poutaherian et al. [9] demonstrates a needle segmentation using a shared 2D fully convolutional network, ShareFCN [9][10], together with (image) plane-based segmentation. However, this approach with plane-based processing cannot fully exploit the 3D information by simply fusing the probability estimation through averaging along two axes. To better exploit the 3D information while preserving the advantages of the 2D pretrained model, we propose a catheter segmentation method in 3D US by extracting its learned 2D features along the principal axes, based on which 2D features are fused to boost the performance. Meanwhile, we introduce the U-Net structure into the FCN network to exploit intra-plane information ranging from local textures up to semantic contextual information [11]. With extensive validation on the challenging ex-vivo dataset, our system obtains a promising Dice score at 57.7%, which offers at least an 11.8% improvement when compared to [6][7] [8]. Based on the segmented catheter in 3D US, its skeleton can be correctly localized with an average error of only 1.4 mm, which is less than catheter diameter.   . 1 illustrates the framework for catheter segmentation in 3D US. The full volume US is divided into smaller patches V in . The patch is first sliced into neighborhood pseudo-RGB images by using a spatial gap d in each direction. Then, the images are processed as in Fig. 1 (b), which is an FCN with U-Net like structure, partly based on a pre-trained VGG-16 network [12]. Based on the prediction from stacked features, the predicted patches are stitched back into the 3D volume to provide a final result of the catheter segmentation. More details of the semantic segmentation in the 3D patch and VGG16based FCN are discussed below.

Catheter segmentation using Direction-Fused FCN
The 3D US volume is divided into several smaller patches, i.e. V in , where each V in is sliced into several 2D (RGB) images along each axis. For example, a patch V in with size 48 × 48 × 48 is decomposed into 48 planes along each axis. For each plane, a 3-channel image is constructed based on the adjacent images of the actual plane with spatial gap d voxels along the axis, which is shown in Fig. 1 (b). As a result, the patch V in leads to the generation of 48 different 3-channel images in each direction (padding is applied at the boundary plane). Then, each image is processed by the 2D FCN. As shown in Fig. 1 (a), the images are processed by the FCN and its output features for each image (from the red block) are stacked based on the plane's original position to construct feature maps in three axes (a high dimensional transpose operation is applied). Further, feature maps from different axes are accumulated to form a fused feature map. Finally, the final prediction is obtained by applying a 3D convolution and a softmax layer. This approach can exploit 3D information in both inter-slice and intra-slice by the 2.5D manner. Because this structure fused the 2D feature maps into 2.5D maps through three different directions, we denote it as Direction-Fused FCN (DF-FCN). As a comparison to show the ability of DF-FCN, we also consider a simpler case as baseline, just FCN. Then, the FCN predicts each 2D image directly along the axis and stacks the 2D predictions back to the 3D patch (the predictions are applied along one of the three axes, and randomly chosen). This approach only makes use of 3D information by introducing the voxel gap d. Fig. 1 (b) shows the VGG16-based FCN architecture of our work, which consists of a convolution and a deconvolution part. The convolution layers with 5 maxpooling layers are the same as with VGG-16 [12]. Then, the output of the last maxpooling layer is filtered by 3 convolutional layers with filter size 1024, 1024 and 2. The deconvolution part includes 4 deconvolution layers with masks of 2 × 2, 2 × 2, 2 × 2 and 4 × 4 pixels. Furthermore, after each deconvolution layer, an extra convolution operation is included to improve the performance [11]. To reduce the usage of the GPU memory, we employ summation instead of concatenation in the deconvolution part. The output of the VGG16-based FCN is the probability prediction after the softmax layer or 2D features from ReLU layer [13], which is indicated by the red square in Fig. 1 (b).

VGG16-based FCN and training
During the training, the parameters of the convolution part in the FCN are initialized using the pre-trained VGG16 model. The remaining parameters in the deconvolution part are randomly initialized. In order to generalize the network, data augmentations like rotation, mirroring, contrast and brightness transformations are performed randomly on-thefly on each input patch. To augment the training images, the training patches are extracted based on annotated catheter voxels in 3D US, i.e. a patch is extracted from 3D US based on an annotated catheter voxel using it as the patch center. Meanwhile, non-catheter voxels are randomly downsampled to generate the same amount of non-catheter patches, which may not include the catheter or only partly include the catheter. All parameters of the FCN are fine-tuned or learned by minimizing the cross-entropy using the Adam optimizer [14] with an initial learning rate of 10 −5 , based on the 3D patch V in and its prediction. Furthermore, to avoid overfitting, dropout layers [15] are introduced with an empirical probability 0.85 after convolution layer 14 and 15, see [12].

Catheter localization by model fitting
The segmented volume includes some false positives due to complex anatomical structures in the data volume. To accurately localize the catheter, we adapt the catheter modelfitting algorithm according to the Sparse-Plus-Dense (SPD) RANSAC algorithm [8] [16]. In the segmented binary image, named dense volume, the voxels are clustered through connectivity analysis. The skeleton of each cluster is extracted along the dominant direction of that cluster. These skeletons generate a sparse volume. When fitting the catheter model, three control points are randomly selected and re-ranked by direction analysis from the sparse volume. Then a cubic spline is applied to generate the catheter model. The model of the dense volume that includes the highest number of dense voxels is chosen as the catheter. In this paper, the threshold controlling the ratio of inliers and outliers is chosen as three voxels. The SPD algorithm avoids redundancy in the selection of boundary points during RANSAC iterations [16].

Testing and evaluation metrics
During the prediction, the observed volume is divided into adjacent patches with size N × N × N voxels. Then, these patches are enlarged by including their neighborhood voxels around them in the original volume, which finally leads to size M ×M ×M voxels with N ≤ M . Based on the prediction of patch V out , its center volume with size N ×N ×N is extracted for 3D US reconstruction.
To evaluate the segmentation performance on the whole volume with imbalanced classes distribution, the metrics of Recall, Precision, Dice coefficient (Dice), and Average Hausdorff Distance (AHD) are employed [17]. The catheter localization accuracy is measured by the skeleton error (SE) and the position errors of the two endpoints (which is averaged as 'endpoints' error, termed EE). The SE is the average distance between 5 equally-sampled points on the fitted skeleton and the annotation skeleton. The EE is the average distance between the endpoints on the detected catheter and the corresponding points on the annotation.

EXPERIMENTS
The proposed method was evaluated on a 3D US dataset of isolated porcine heart (approved by review board), which was collected by a 2-7 MHz phase array transducer (TEE) when inserting an ablation catheter (Boston Scientific, diameter 2.3 mm) into the left ventricle or right ventricle. The dataset includes 25 images, individually extracted from 25 separate 3second videos. The original voxels were re-sampled to obtain an isotropic voxel size in each direction (volume size: 128 × 128 × 128 voxels, space unit: 0.54 mm/direction). The volumes were manually annotated and partitioned into two classes: catheter and non-catheter (confirmed by experts). During the experiment, we empirically used a 3-fold crossvalidation to separate training and testing data. Due to the spatial resolution and trade-off of the GPU memory, we have empirically chosen M = 48 and N = 32 in our experiment to fit the GPU memory. Meanwhile, to fully exploit the influence of voxel gap d, we have also performed validation on it with different integer values from 0 to 5. All the experiments were performed on a GeForce GTX 1080Ti.

Spatial representation of fine-tuned DF-FCN
First, our proposed systems are compared based on different values of d in FCN and DF-FCN. The methods using 2D FCN prediction with/without pre-training is abbreviated to "2D w(/o) pre-train", while the DF-FCN using 2D feature fusion is denoted by "2.5D w(/o) pre-train". Their performances with respect to the voxel gap d are depicted in Fig. 2 using the Dice score. From the figure, several conclusions can be made. First, with a pre-trained model, the network achieves better performance. Meanwhile, the 2.5D approach without pre-training has the worst performance due to the training of a much more complex network, when compared to the 2D without pre-training approach. Second, with increasing d, the performances are first increasing, but finally degrading with a large distance. This is caused by a too large gap which make the catheter invisible for the network, so that the spatial correlation is decreased. Third, the performance based on 2.5D features has a better and more robust performance when compared with a single direction method. This is evident and due to the increased use of 3D information, which re-organizes the segmentation of the catheter in 3D US. From all cases, the 2.5D with pre-trained model performs the highest Dice when d = 3.

Segmentation and localization performance
Based on DF-FCN with a pre-trained model, our proposed method is compared to a method using multi-scale handcrafted features with an AdaBoost classifier [6] and methods for medical instrument segmentation based on voxelwise tri-planar CNN classification (ShareConv and LateFusion) [7] [8]. With respect to the handcrafted system [6], the performances were obtained by tuning the threshold to achieve the highest Dice score in each fold. Besides, we do not consider the weighted loss function from [8] to show fairness in voxel distribution. The catheter segmentation performances are shown in Table 1. Furthermore, the catheter localization performances are shown in the table expressed in millimeters/voxels using the earlier proposed localization metrics.
Considering Table 1, DF-FCN achieves the best performance when compared with handcrafted features and voxel-wise CNNs with respect to all segmentation metrics (p-value<0.05). Our proposed method is able to achieve better catheter localization results, which rely on a better segmentation using more contextual information. Furthermore, due to semantic segmentation, the patch-wise segmentation requires less inference time when compared to the voxel-level classification (from several min. to less than one min.). Fig. 3 depicts some segmentation results. From the comparison, it is found that with the limited contextual information in Late-Fusion CNN and FCN, these methods cannot recognize the boundary voxels of the catheter in challenging US images. Although the handcrafted method is able to classify more voxels as part of the catheter, too much false positives lead to more difficulties in catheter localization.

CONCLUSIONS
We have proposed a catheter detection method for 3D US images with improved detection accuracy. This method is based on learning a fine-tuned FCN with feature maps fused along principal directions, which is combined with catheter segmentation and model-based localization. The proposed method was evaluated on an ex-vivo dataset, which showed significant improvement when compared to the state-of-the-art methods (achieved Dice score of 57.7% with 11.8% improvement). The results show that our method can localize a catheter with an error less than the catheter diameter in a complex anatomical 3D US image. Therefore, the proposed catheter detection method can facilitate surgeons to interpret the catheter during interventions, which reduces the operation time and provides a better outcome for the patient.


Design of remote security control system based on 4G

With the rise of the concept of “smart earth”, the new generation of information technology has changed the way information interacts. The combination of things, intelligence and interconnection has made people’s daily life more convenient. However, while enjoying convenience, information security has become a bottleneck restricting comprehensive interconnection. A variety of devices can be remotely controlled through public networks and sensor networks, but a large number of facts show that in an open network environment, information is easily stolen and tampered, and the resulting security problems are becoming more serious. The 4G (the 4 Generation mobile communication technology) remote security control system implemented in this paper consists of a main controller, a cloud server and an Android client. The information of the main controller and the client is encrypted by the national secret algorithms SM2 and SM4, the information exchange is performed by the cloud server, and the main controller establishes a connection with the terminal device through the short-range wireless network. Compared with the remote control system that usually uses international algorithms, this system has advantages in terms of ensuring the security and control on the basis of satisfying convenience and intelligence.

Introduction
Application scenarios such as smart home, intelligent transportation, and smart city all involve remote security control issues. At the end of 2016, the Mari botnet attacked the domain name service provider Dyn with a maximum of 620G of DDos traffic, resulting in a large number of websites being inaccessible. At the end of 2017, the "small droplets" video live event caused people to worry about the security of the terminal equipment. Configuration and security vulnerabilities are hidden dangers of information security problems, but fundamentally, because the information is not cipher protected during transmission, the attacker obtains remote control rights of the device and this has led to a series of security threats and privacy leaks.
The development of communication technology has brought more convenient and high-quality network access experience. Compared with traditional networks, 4G adapts to mobile data communication, its speed is faster, network spectrum is wider, and communication billing More economical, able to meet high-quality multimedia communication needs. The wide application of 4G technology makes the communication terminal equipment more intelligent, which is characterized by higher speed computing power, lower power consumption chip integration and more friendly humanmachine interface. The high-speed and stable mobile network and intelligent terminal enable users to use a abundant application for data interaction. At the same time, applications with dedicated functions can be developed based on different operating systems.
The cloud is a concentrated expression of information technology development and service model innovation. In the era of the Internet of Everything, with the cloud platform, data sharing between enterprises, users and governments has become more convenient. The cloud can meet the computing power requirements and flexibility of digital services. Through dynamic resource allocation, it saves a lot of repetitive construction costs and avoids the risk of data loss. But the cloud is also facing the security problems and the resulting risk of privacy breaches. According to Gartner, by 2022, at least 95% of cloud security failures will become an important issue for enterprises and organizations to consider. [1] In order to solve the security problem on the cloud, a feasible solution is to protect the cloud data through cryptography. In the context of autonomous and controllable information security, the use of international algorithms for data protection increases the risk of data breaches and information systems being attacked. With the national secret algorithm combined with the Internet of Things cloud platform, a relatively complete data security mechanism can be established to effectively prevent users from leaking private data. In this paper, we use remote control for wireless sensor networks, and propose a new remote control system design that uses national secret algorithm to protect communication security. The system can provide an effective solution to the security problems that constrain the development of the Internet of Things.

System structure Figure1
Schematic diagram of the system architecture The 4G-based remote security control system consists of a client, a cloud server and a main controller on the mobile phone. The information exchange between the mobile terminal and the terminal device needs to be performed by the 4G network and the main controller. The cloud server is equivalent to the transfer of data transfer. The data is separated from the mobile terminal and the host controller, but the encryption is only for control signaling or Device status data does not encrypt the device name, which makes it easier to query data from the database. The main controller sends the device status to the cloud server every 300 ms. If the device is abnormal, the alarm is sent through the short message module while updating the status. The main controller sends an encrypted control command to the cloud server user on the client, and transmits the data to the cloud server through the 4G network. The cloud server interacts with the main controller to perform remote security control on the device, and at the same time, different terminal devices pass the wireless sensor network. The device status is sent back to the main controller, and the main controller encrypts and uploads it to the cloud server to implement remote and secure information interaction. In the functional layer, the design selects four functions of control command, status transfer, encryption and decryption service and device alarm. The information exchange between the main controller and the cloud needs to be performed through the encryption and decryption service, so that the data is transmitted in a dense state in the common channel and the cloud server. On the basis of the encryption and decryption function, the local data communication method is not changed. The control command function receives the setting state of the cloud and controls the local terminal device. The status transfer function displays the current working status of the device and encrypts the status information and transmits it to the cloud. The alarm function is used to send an alarm message to the user through SMS or dial-up when the device is abnormal.
The main controller mainly includes two types of modules, one of which is a 4G module, which can be connected and communicated with a public network, and further includes a short message module for sending an alarm message to a preset number when a device abnormal condition occurs; It is a wireless sensor module, including but not limited to Bluetooth, infrared, ZigBee modules, for implementing data communication in a wireless sensor network.

Cryptographic module design
In the design of the remote security control system, the security of the data is mainly guaranteed by the security of the key. The security of the key is reflected in the generation, distribution and storage of the key. Destroy various stages of the life cycle. The distribution of the key is to transfer the key to the authorized entity in a secure manner, which can be done in a digital envelope. Digital envelope technology combines asymmetric encryption with symmetric encryption to provide secure delivery of session keys. However, the cryptographic algorithm used is generally an international cryptographic algorithm, which increases the risk of information leakage and system attack. Therefore, a national secret algorithm module is designed for the security protection of communication data. The process of transmitting information using the national secret algorithm is as follows: Figure3 Flow chart of secure information transmission using national secret algorithm The design of the cryptographic module in Figure3 includes the design of the asymmetric algorithm SM2 [2] and the design of the symmetric algorithm SM4. The message is encrypted with the session 4 key using the SM4 algorithm, and the session key is encrypted with the recipient public key using the SM2 algorithm, and the encrypted message is sent together with the session key. After receiving the information, the receiver uses the SM2 algorithm to decrypt the session key with its own private key, and then decrypts the received message with the session key.

Introduction to the Android platform
Android is an open source operating system based on Linux. It is mainly used for mobile devices and is continuously developed and led by Google and the Open Handheld Alliance [3]. It uses a software stack (aka software stack) architecture, which is divided into three parts. The underlying layer is based on the work of the Linux kernel and provides only basic functionality. Other applications are developed by each company and are part of the programming process in Java. The kernel of the Android system belongs to a branch of the Linux system kernel, with typical Linux features and functions. In addition, Google has modified and customized Android to make it work well on mobile devices. The advantage of Android is that the system is open source, so that more hardware manufacturers and developers can invest in it and continue to promote its growth. On the other hand, the ecosystem disorder caused by open source has also caused the system to be mixed, resulting in many problems in compatibility.
Its (2) Application interoperability. Applications installed on Android devices can access the system features of the device using the interfaces provided by the system. Applications can use configuration files to indicate that their functionality can be opened to other applications. (3) Unrestricted developers. Due to the compatibility of the Android system, the selectivity of the hardware platform is also a lot richer. In addition, based on the open principle of the Android operating platform, many hardware manufacturers are gradually getting rid of the shackles of hardware devices, and also made a lot of breakthroughs in the innovation research of many developers, and then designed a lot of innovative application software.

Functional module design
Figure4 Android client software system composition The remote control system software not only enables secure remote control but also the current and historical status of the device. In order to facilitate the user's use, the intuitive and convenient operation interface is designed, and the status check and log query are realized through timely data update. Figure4 is the components of the Android terminal of the remote control system. It can be seen from Fig. 4 that the remote control system consists of five parts: information storage module, data (1) Data storage module The various device operating parameters obtained need to be stored in the database for querying and for other functions.
(2) Data encryption and decryption module When the user logs in successfully, the control terminal starts to receive the data transmitted from the server, and saves it to the corresponding file buffer, and then the data encryption and decryption module decrypts the data in the buffer.
(3) Data display module Users can view and control devices in real time through the cloud platform, and the system is based.
(4) Human machine interface module The Android client uses different interfaces for data transfer and command requests to facilitate database storage and query. Users do not need to know the underlying communication commands, just click the button to complete the acquisition of device status and the transmission of control signaling.
(5) Device Management Module The appliances and sensors in the user's home are constantly changing in actual operation. The system should support this dynamic change and make modifications according to the content of different rights.

Cloud Server Design
Cloud storage is a new type of storage service. Service providers provide storage capacity and data storage services to customers through the network. At the same time, customers do not know the specific implementation details and underlying mechanisms. Compared with traditional storage devices, cloud storage is more than just a piece of hardware, but a complex system consisting of multiple parts such as network devices, storage devices, servers, application software, public access interfaces, access networks, and client programs. Each part takes the storage device as the core and provides data storage and service access services through application software [4]. All devices in the cloud storage system are completely transparent to the user, and the user only needs to connect to the cloud through the network to access the data. Users who need storage services no longer need to set up their own data centers, only need to apply for storage services to SSP (storage service provider), which saves expensive software and hardware infrastructure investment. Compared with the traditional data centralized storage solution, the efficient clustered cloud storage system has the advantages of easy expansion (including bandwidth), lower cost, more secure data, and uninterrupted service . The cloud server of this system is designed with PHP and MYSQL language. It mainly handles connection requests initiated by the Android terminal and the main controller, and extracts information and stores data.

Main controller design
The wireless aggregation-4G main controller is designed as follows: the main controller ARM is connected to the 4G module, and the DC power supply provides the 9V-1A power output, which is respectively connected to the PWR and GND of the UNV-SIM7600CE module, and the ARM serial port and the UNV-SIM7600CE module. The TTL level serial port is connected, and the controller sends an AT command to the UNV-SIM7600CE module through the serial port to perform operations such as sending short messages and network data transmission, and the main controller waits for the 4G module to respond, and when receiving the short message or receiving the network data, the completion is completed. The corresponding control operation. The system hardware connections are as shown.

Introduction to ARM
The embedded system is a tailored real-time Linux system, which has the advantages of small space, reliability, low power consumption, strong specificity and high real-time performance [5], which makes it have a wide range of vitality. By estimating the utilization of the solution resources, the AM335x-based development board is selected. The AM335x is based on the ARM Cortex-A8 processor and its speed ranges from 600MHz to over 1GHz, meeting the requirements of most lowpower devices.
The AM335x used in this article is the core development board. The hardware size is much smaller than that of the PC. By cutting and modifying the kernel, many unnecessary modules are removed, and some necessary driver support is retained. The operating system is changed from a general-purpose Linux system to a real-time operating system, which saves space, improves convenience, and greatly improves system performance.

Sensor Network
Wireless sensor networks offer a high degree of flexibility, including self-organizing, fast cabling, and intelligent processing, while dramatically reducing wiring costs. Wireless sensor network nodes are generally embedded in the device to convert device data and wireless data. The wireless sensor network node has a wireless relay function and constitutes a multi-hop Mesh network. The wireless access point AP on the primary controller implements aggregation of data of the wireless sensor network node.

Introduction to 4G Module
UNV-SIM7600CE V1.2 is a high-performance industrial 4G development board launched by Global Eagle. Support mobile, China Unicom, Telecom 4G, 3G and 2G. Rich in function interface, especially suitable for SMS, MMS, 4G wireless transmission of data in various fields. This module can be controlled by AT commands. In the system, you first need to send the AT to initialize the 4G module, then send AT+CREG=1 to register the network, and use AT+COPS to confirm the registration information. In the short message module processing, use AT+CMGF to select the short message format, use AT+CSCS to select the character set, use AT+CSCA SMS to set the SMS service center address, use AT+CSMP to set the text mode parameter, AT+CNMI will set the new message. Pass to TE. At the same time, the judgment is made in the code, and if the return prompt does not match the normal prompt, an error message is returned.

Function module call
The main controller has the same wireless communication module as the wireless sensor network node device, and also connects the 4G module through the serial port, which is the aggregation node of the wireless sensor network, and also the gateway node between the wireless sensor network and the 4G network. The wireless aggregation-4G main controller uses the ARM processor and the wireless communication module in the 2.4 GHz band. In addition, the UNV-SIM7600CE module and the user card (USIM) support 4G access. The node device sensing information in the wireless sensor network is transmitted to the wireless convergence-4G main controller through the wireless ad hoc network. The main controller encrypts the sensing data of the wireless sensor network node device and sends it to the cloud server, and forwards commands for querying, controlling, and managing the sensing node to the wireless sensor network.
The main controller needs to support the three services of listening, requesting and configuring concurrently [6], so three modules are designed to satisfy different function calls. The listening service is a periodic event. The listening module sends a broadcast request every 300ms, and all the devices that receive the request reply to its working state; the requesting service is an emergency, and the main controller makes a sudden request to a device, and the device responds to it. The detailed working status of the user; the configuration service is also an emergency, the main controller bursts the configuration command to a certain device, and the device modifies the configuration and replies to the main controller.

Conclusion
For remote security control systems, the constant integration of various wireless communication technologies and network structures makes the system face greater network security threats. At present, there are relatively few studies on the security of remote control systems. Generally, they are commonly used in the smart home field. The devices that receive control commands are relatively single. The device status and control commands are usually transmitted through the public network. It is easy for hackers to obtain device rights and use them. Illegal purpose. In remote control systems that use cryptographic techniques for protection, their security generally relies on international algorithms for protection, which increases the risk of information systems being attacked.
This paper studies Android platform software development, embedded system development and cloud server design, and analyzes the existing remote control system problems. A security scheme for 4G access is proposed, and a 4G-based remote security control system is designed. Using the security architecture for 4G access proposed in this paper, the integrated sensor network technologies such as mobile vehicle network, RFID system and ZigBee networking, on the basis of this, the remote security control across the terminal platform is studied. Because wireless sensor networks sense nodes with simple functions and low carrying capacity, they cannot have complex security protection capabilities [7]. In the next step, research on the security protection mechanism of the wireless sensor network is also needed.


Concurrent Transmission Scheduling for Perceptual Data Sharing in mmWave Vehicular Networks

Sharing perceptual data with other vehicles enhances the traffic safety of autonomous vehicles because it helps vehicles locate other vehicles and pedestrians in their blind spots. Such safety applications require high throughput and short delay, which cannot be achieved by conventional microwave vehicular communication systems. Therefore, millimeter-wave (mmWave) communications are considered to be a key technology for sharing perceptual data because of their wide bandwidth. One of the challenges of data sharing in mmWave communications is broadcasting because narrow-beam directional antennas are used to obtain high gain. Because many vehicles should share their perceptual data to others within a short time frame in order to enlarge the areas that can be perceived based on shared perceptual data, an efficient scheduling for concurrent transmission that improves spatial reuse is required for perceptual data sharing. This paper proposes a data sharing algorithm that employs a graph-based concurrent transmission scheduling. The proposed algorithm realizes concurrent transmission to improve spatial reuse by designing a rule that is utilized to determine if the two pairs of transmitters and receivers interfere with each other by considering the radio propagation characteristics of narrow-beam antennas. A prioritization method that considers the geographical information in perceptual data is also designed to enlarge perceivable areas in situations where data sharing time is limited and not all data can be shared. Simulation results demonstrate that the proposed algorithm doubles the area of the cooperatively perceivable region compared with a conventional algorithm that does not consider mmWave communications because the proposed algorithm achieves high-throughput transmission by improving spatial reuse. The prioritization also enlarges the perceivable region by a maximum of 20%.

Introduction
Millimeter-wave (mmWave) vehicular adhoc networks (VANETs) are expected to be an enabler of numerous safety applications for autonomous vehicles that require high-throughput transmission capability [1][2][3][4][5]. As vehicles become increasingly automated, the number of sensors equipped on vehicles increases and an increasingly massive amount of data are generated while driving. Sharing these sensor data, such as camera and LiDAR data, would help extend a vehicle's perceptual range to cover its blind spots or locate hidden objects. However, a sufficient data rate for sharing sensor data cannot be provided by currently standardized vehicular communication systems (e.g., IEEE Manuscript received January 1, 20xx. Manuscript revised January 1, 20xx. † The author is with the Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501 Japan. * A part of this paper was presented at IEEE CAVS 2018. a) E-mail: nishio@i.kyoto-u.ac.jp DOI: 10.1587/transinf.E0.D.1 802.11p/dedicated short range communications (DSRC) and cellular vehicle-to-everything (C-V2X), standardized in the third generation partnership project (3GPP) Release 14 [6]) because of their limited bandwidth. Therefore, mmWave communications, which provide high-throughput communication by leveraging huge bandwidth and efficient spatial reuse, have been attracting much attention for vehicular communications.
One of the most important traffic safety applications facilitated by mmWave communications is cooperative perception, which enables autonomous vehicles to perceive their blind spots by sharing perceptual data, such as camera, Li-DAR, and radar data, with other vehicles. For example, seethrough systems provide following vehicles with front views of the leader of platooning vehicles and bird's-eye-view systems generate top views of surrounding areas by aggregating perceptual data of multiple vehicles [7,8]. Such techniques are particularly important at intersections with poor visibility to avoid car crash. By sharing information regarding their surroundings, the region that autonomous vehicles can perceive is enlarged based on the shared information. Computer vision systems enable vehicles to recognize other vehicles, pedestrians, and traffic signs, even if they cannot be seen directly because buildings or other obstacles block the line of sight. To cover the entire area surrounding an intersection, vehicles near the intersection should send their massive data to the other vehicles within a short period, in particular 100 ms for safety applications [9]. For example, assume 20 vehicles attempt to share compressed camera images within 100 ms. The image sizes range from 1-9 Mbit because they are generated at rates of 10-90 Mbit/s [10]. Therefore, 20-180 Mbit of data must be transmitted within 100 ms by 20 vehicles, meaning each datum must be transmitted at a rate of 0.2-1.8 Gbit/s. Such a high-throughput system is difficult to be realized by DSRC or C-V2X owing to their limited bandwidth.
Although mmWave communications enable highthroughput transmission, it is difficult to broadcast data to all vehicles compared with microwave communications because few vehicles can receive transmitted signals because of narrow-beam directional antennas and severe attenuation by the blockage effect. Therefore, an efficient mechanism to share perceptual data in mmWave multihop networks should be developed. As mentioned above, vehicles are required to share perceptual data and obtain data of as wide region as possible within 100 ms. To meet these requirements, concur- rent transmission and routing with cached data are promising approaches. Concurrent transmission, where many transmitters send data to different receivers at the same time, promotes efficient spatial reuse, which is realized by leveraging antenna directionality and high attenuation. On the other hand, routing using cached data reduces redundant transmissions for data sharing in multihop networks because each datum is requested to be sent to many different vehicles. In multihop networks, if relay vehicles store the forwarded data, the source vehicles do not need to transmit the same data many times. Leveraging the geographical information in perceptual data also helps to enlarge perceivable regions.
There have been a few studies on concurrent transmissions in mmWave VANET. For example, [4] proposed a beam-width-controlling scheme to reduce beam-alignment delay by considering signal-to-interference plus noise power ratio (SINR). Most concurrent transmission protocols for mmWave communications are found not in VANETs, but in wireless sensor networks (WSNs) [11][12][13]. However, such protocols do not adopt data caching because their objectives are not data sharing. In data sharing, the same data are sent from the source vehicles to different vehicles and thus, the same data might be transmitted redundantly without data caching. Additionally, their algorithms do not consider the geographical information in transmitted data. There have been many studies on data dissemination methods for DSRC-based VANETs, some of which utilize the geographical information in disseminated data. [14,15] proposed the data aggregation of the geographical information to suppress redundant data broadcasts. [16] proposed controlling the frequency of broadcasting. However, these studies did not discuss concurrent transmission or multihop routing with directional antennas.
Concurrent dissemination with data caching was proposed in [17], where the authors presented a system to realize a road-side-unit (RSU)-controlled concurrent dissemination by two communication mode: vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. [17] proposed a graph-based algorithm, where potential transmissions (from which, to which, and which data should be transmitted) and their conflicts (e.g. half duplex and interference constraints) are represented as a graph, i.e., two transmissions are connected when they cannot be operated at the same time. Each vertex has weight that represents the priority of receiver vehicles. Then, the optimal concurrent transmission schedule for multihop dissemination can be obtained by solving the maximum weighted independent set (MWIS) problem on the graph. Although the MWIS problem is one of the NP-hard problems, a greedy algorithm with a performance guarantee to maximize the total vertex weights can be utilized. However, [17] does not assume mmWave communications and thus, it cannot be used directly for mmWave communications. There is also room to utilize the geographical information in the transmitted data for cooperative perceptions.
In this paper, we propose a mmWave data sharing algorithm where vehicles share perceptual data with each other and enlarge the perceivable regions. The proposed algorithm is based on [17] to realize concurrent transmission with data caching. Because the algorithm in [17] optimizes concurrent transmissions considering not only pair selection of the transmitter and receiver but also which data to transmit among the currently cached data, it effectively reduces redundant transmission in data sharing, where the same data are transmitted to different vehicles. However, the original algorithm is based on microwave communications, meaning it must be modified for mmWave communications. [17] designed a conflict rule, which is utilized to decide which pair of transmission vertices of the graph should be connected, considering radio interference among omnidirectional antennas. We design a new rule for mmWave communications by estimating interference among narrow-beam directional antennas, which are utilized for mmWave communications to obtain high gain. Because the conflict rule should be defined between two transmissions, we develop an interference approximation scheme that can calculate the interference between two transmissions without summing all possible interferences. By using the newly designed rule, near-optimal concurrent transmission in mmWave networks can be realized.
We also design a prioritization method in order to enlarge the perceivable region for situations where data sharing time is limited. Although [17] gave high priority to receiver vehicles that soon run out of the service area of the RSU, such a prioritization does not fit for cooperative perceptions at an intersection. We give high priority to data corresponding to regions far from an intersection to enlarge the perceivable area based on shared data because regions near the center of the intersection are covered by many vehicles, meaning it is desirable to transmit data far from the intersection. Such a prioritization scheme can be realized by customizing the weight function of the MWIS problem.
The main contributions of this paper are summarized as follows: (1) We propose a data sharing algorithm for cooperative perception, which improves spatial reuse by considering interference among narrow-beam directional antennas and increases the perceivable region by prioritizing the data to be forwarded based on geographical information, even if not all data can be collected. In order to realize concurrent transmission, we employ the algorithm presented in [17]. (2) We prove that if the data sharing time is sufficiently long and the vehicular network is represented as a connected graph, the proposed data sharing algorithm guarantees that all data are shared with all vehicles.

Related Works
Data sharing for cooperative perceptions should achieve a large perceivable area within a short period, in particular 100 ms for safety applications. Key techniques to meet this requirement are concurrent transmission with directional antennas for improving system throughput, efficient routing with cached data for reducing redundant transmission, and leveraging geographical information in transmitted data.
Dissemination algorithms with directional antennas for VANETs have been studied by many researchers. [18] presented theoretical analysis of content dissemination time in vehicular networks with directional antennas and demonstrated that directional antennas accelerate content propagation. [19] proposed a broadcast protocol for directional antennas in VANETs. In this protocol, the furthest receiver forwards data packets along road segments and a directional repeater forwards the data in multiple directions at intersections. In contrast to the protocol in [19], which considers the positions of transmitters, our algorithm considers the positions where data are obtained to achieve a large perceivable region.
Dissemination algorithms for local information were proposed in [14][15][16]. In [14], a scalable dissemination protocol, called segment-oriented data abstraction and dissemination (SODAD), and its application, self-organizing trafficinformation system (SOTIS), were proposed. SOTIS is a mechanism for gathering traffic information sensed by vehicles. It aggregates the received traffic information from road segments and sends only up-to-date information to vehicles. In [15], Zone Flooding and Zone Diffusion were proposed to suppress redundant data broadcasting. In Zone Flooding, only vehicles in a flooding zone forward received packets. Zone Diffusion is a data aggregation method considering geographical information, where vehicles merge road environment data as it is received and broadcast only merged data. [16] proposed controlling the frequency of information broadcasting and selecting the data to send to reduce communication traffic. Although these studies considered the geographical information in each datum, they did not focus on concurrent transmission.
The authors of [4] proposed vehicle pairing and beamwidth controlling for mmWave VANETs. In the protocol in [4], pairs of transmitters and receivers are selected based on matching theory and beam widths are determined via particle swarm optimization. This protocol successfully improves throughput and reduces delay by considering SINR. Other concurrent transmission methods for mmWave communications have been proposed for WSN, rather than VANETs [11][12][13]. The authors of [11] formulated the concurrent transmission scheduling problem as an optimization problem to maximize the number of flows to satisfy the quality-of-service requirements of each flow. In [12], relay selection and spatial reuse were jointly optimized to improve network throughput and a blockage robust algorithm was proposed. The authors of [13] minimized transmission time by solving an optimization problem. Although these algorithms for concurrent transmissions presented in [4,[11][12][13] achieved efficient spatial reuse, redundant data were transmitted because their primary objective was not data sharing and thus, they did not consider situations where the same data are sent to different receivers. Additionally, they did not consider geographical information.
The authors of [17] proposed an RSU-controlled scheduling that maximizes system throughput in hybrid V2I/V2V communications. This algorithm realizes concur- rent dissemination based on the graph theory. It also adopts a data caching mechanism. The algorithm proposed in [17] generates graphs for dissemination scheduling, where the set of vertices represents potential transmissions consisting of a transmitter, receiver, and data, and the set of edges represents pairs of transmissions that cannot be performed at the same time. The authors of [17] proved that optimal scheduling can be obtained by solving the MWIS problem for a generated graph. However, because the algorithm in [17] assumes omnidirectional antennas, interference calculations must be extended for mmWave communications, where narrow-beam directional antennas are utilized. Additionally, there is still room to improve the efficiency of data transmissions for cooperative perception by leveraging the geographical information in perceptual data. Thus, a data sharing algorithm in mmWave vehicular networks that increases perceivable regions should be developed for traffic safety, especially when data sharing time is limited. Figure 1 shows our system model. At an intersection, there are vehicles equipped with mmWave communication devices for data transmission via V2V channels and microwave communication devices for control signal transmission via V2I channels. Vehicles participating in cooperative perception are selected among vehicles within tens of meters from the center of the intersection considering stopping distance. The number of participants is also limited to N v vehicles because it is difficult to complete data sharing owing to the time limit when the number of participants is large. The vehicles perceive the surrounding environment utilizing their sensors, such as LiDARs or cameras. We assume that the vehicle sensors cover a surrounding rectangular region (on road segments) or cross-shaped region (at the intersection), bounded by the buildings along the roads and their sensor range r s . The data generated by vehicle v i is denoted as d i . We assume the sizes of d i are approximately the same among vehicles for simplicity. The vehicles share the data with each other to obtain information regarding the intersection and then perform cooperative perception.

System Model
Data are transmitted through mmWave V2V channels to reduce the pressure on V2I channels. However, control signals, which must be broadcasted to all vehicles, are transmitted through microwave V2I channels. We assume there is an RSU (or an eNodeB) that covers all vehicles near the intersection on the microwave channel and performs scheduling based on vehicle positions and mmWave V2V link topology. While a large amount of sensor data are transmitted over the mmWave V2V channels, control signals and position information, which are relatively small, can be broadcasted by the RSU utilizing DSRC or C-V2X. Figure 2 shows the time frame for data sharing. The vehicles perform sensing at an interval of T and generate d i . The data update interval consists of the scheduling period and data sharing period. In the scheduling period, data sharing scheduling is determined by the RSU. Vehicle position information obtained from global positioning system (GPS) is sent to the RSU, which then estimates the mmWave connectivity between vehicles and determines the preferred data to be shared.
In the data sharing period, vehicles share their data through mmWave V2V channels. The data sharing period consists of τ max time slots, each of which is sufficiently long to transmit one datum. Let τ ∈ {0, 1, . . . , τ max } denote the index of a time slot. τ max is limited by the transmitted data volume and data rate.
By sharing data d i , the perceivable region is enlarged. Let d i,τ and R i,τ denote the dataset possessed by vehicle v i and the perceivable region of the dataset d i,τ , respectively.
where R(d) denotes the perceivable region of d (i.e., the region covered by the sensor of a single vehicle). At the beginning of the data sharing period, the datasets are initialized as d i,0 ← {d i }. When vehicle v i transmits d k ∈ d i,τ to vehicle v j , v j updates its dataset d j,τ as follows: Subsequently, the area of region R j,τ is enlarged. We evaluate system performance based on the normalized perceivable area, which is defined as follows: where S(R) and R all denote the area of region R and the area covered by all data, defined as R all At the end of the data sharing period, the vehicles regenerate d i by sensing. Then, the RSU collects vehicle position information and determines scheduling for sharing new perceptual data in the following scheduling period.

Data Sharing Algorithm
In the scheduling period, the RSU selects transmitters, receivers, and data to be transmitted during each time slot. First, the RSU constructs a vehicular network graph that represents the network topology of the mmWave vehicular network by estimating the connectivity between vehicles based on their positions and a propagation loss model. Second, a graph that is utilized to determine concurrent transmission behavior is constructed from the vehicular network graph for each time slot. Because the vertices of the graph represent transmissions, each of which consists of a transmitter, receiver, and data to be transmitted, and the edges of the graph represent conflicts between two transmissions, independent sets in the graph represent sets of transmissions that do not conflict with each other. Therefore, by solving the MWIS problem for the graph, which we call a scheduling graph, the optimal concurrent transmission can be found for each time slot.
Although our algorithm is based on that proposed in [17], our system model is quite different from that in [17]. First, we consider a short period (i.e., 100 ms), while longspan dissemination was discussed in [17]. Because [17] designed a prioritization based on vehicle mobility over a long period, we modify the prioritization design to enlarge perceivable regions within a short period. Second, our objective is to share data generated by vehicles with each other, while [17] assumed that each vehicle requests data that is stored in the RSU. We prove that data sharing can be completed by our algorithm if the vehicular network graph is connected and there are enough time slots. Third, we utilize mmWave communications for data transmission and thus, we redesign how to construct the scheduling graph. Especially, conflict rules between two potential transmissions are modified to reflect the mmWave propagation characteristics. Finally, data are transmitted through V2V channels in our system to reduce the pressure on V2I channels, while [17] utilized both V2I and V2V channels for data transmission. The following subsections describe the details of constructing a vehicular network graph and scheduling graph.

Vehicular Network Graphs
The RSU estimates the connectivity between each pair of vehicles and defines the vehicular network graph G v as follows:

Algorithm 1 Constructing a scheduling graph
end if 8: end for 9: end for 10: end for 11: for all t i j k , t i j k ∈ T τ do 12: if t i j k and t i j k conflict with each other then 13: where V, L, LOSS(v i , v j ), and θ denote the set of vehicles, set of vehicle connections, mmWave propagation loss between v i and v j , and a threshold that indicates that mmWave communications are possible, respectively. The mmWave propagation loss can be obtained from the path loss models proposed in [3]. The authors of [3] measured the propagation loss of 60-GHz mmWave channels when there were one, two, or three vehicles between the transmitter and receiver. For scenarios with more than three blockers, [1] provided an extension to the path loss model. Another approach for predicting mmWave propagation loss was proposed in [20], where the authors predicted received signal power based on perceptual data. The threshold θ is calculated based on the Shannon capacity as follows: where B, P t , G t , G r , N, and Rate denote the bandwidth, transmission power, transmitter and receiver antenna gain, thermal noise power spectral density, and rate requirements, respectively. When calculating the vehicle connectivity, the antenna directions of the transmitter and receiver point at each other. We also assume that G v does not change within the data update interval because the interval is very short (less than 100 ms), meaning the mobility of the vehicles is negligible.

Scheduling Graph and Data Sharing Scheduling
For every time slot τ, the RSU selects transmitter and receiver vehicles from V, as well as data d k ∈ d i,τ to send for each transmitter vehicle v i . This selection is calculated by solving

Algorithm 2 Data sharing scheduling
Perform t τ and update d i, τ+1 7: Obtain G s, τ+1 from Algorithm 1 with G v , d i, τ+1 8: the MWIS problem for the scheduling graphs, which are constructed as follows: Algorithm 1 is utilized to construct scheduling graphs for each time slot G s,τ (T τ , C τ , W) from G v , where T τ , C τ , and W denote the set of vertices, set of edges, and vertex weighting function such that W : T τ → R + , respectively. R + is the set of positive real numbers. A transmission t i jk ∈ T τ represents a set containing transmitter v i , receiver v j , and data d k , meaning v i transmits d k to v j . Each element in C τ represents a conflict between two transmissions, meaning they cannot be performed concurrently. Further details are explained in Section 4.4. The weight of vertex W(t i jk ) represents the priority of each transmission, and its definition is described in Section 4.3. Figure 3b presents an example of a scheduling graph constructed from the vehicular network graph shown in Fig. 3a. From lines 2-10 in Algorithm 1, a set of transmissions T τ is obtained by listing all directly connected pairs of vehicles (i.e., neighbors in G v ) and data not possessed by receivers. Next, the conflict between each pair of transmissions is calculated in lines 11-15. Algorithm 2 is a scheduling algorithm utilizing G s,τ . In each time slot, a set of transmissions t τ ⊂ T τ is selected. After the transmissions are performed, the datasets d i,τ+1 are updated utilizing (1), and G s is recalculated based on the updated d i,τ+1 . We describe our method for selecting transmissions in the following subsection. After the scheduling for all time slots in the data update interval is completed, each vehicle follows the determined schedule during the data sharing period.

Priority of Transmissions
To perform scheduling, the controller calculates the MWIS of G s,τ to increase the number of transmissions performed in each time slot. Independent sets of G s,τ represent sets of nonconflicting transmissions and thus, maximum transmissions that can be performed concurrently can be obtained by solving the maximum independent set (MIS) problem. The MIS problem is a special case of MWIS, where the weight function W is a constant function (i.e., W(t i jk ) = 1, ∀t i jk ∈ T τ ). We refer to the data sharing algorithm with MIS as max transmission scheduling. Although max transmission scheduling maximizes the number of transmissions, it does not consider the perceivable region represented by the perceptual data. When τ max is small due to the limit of the data sharing period, the algorithm stops before all data are shared with all vehicles. In such cases, vehicles perceive their environments based on limited information that covers only a limited area of the intersection.
To increase the perceivable area in such cases, prioritization for transmitted data can be implemented. Data from near the intersection tend to overlap with each other, because the vehicle density near intersections is higher than that far from intersections. Therefore, it is inefficient to forward data representing areas near intersections. We propose assigning a high priority to data that represent areas far from the intersection and lower priority to data that represent areas near the intersection. We refer to the algorithm with a priority function as max distance scheduling. Distance priority can be represented as a weight function W of transmissions t i jk as follows: where p k , p c , and dist(a, b) denote the position of v k , center of the intersection, and distance between positions a and b, respectively. Although the MIS and MWIS problem are NP-hard problems, it has been proven that a simple greedy algorithm can approximately solve these problems with a guaranteed performance ratio of greater than or equal to 1/∆, where ∆ denotes the maximum degree of any vertex in the graph [21]. Thus, we adopt a greedy approach in our proposed scheduling algorithm.

Conflict Rule of Interference
When constructing G s,τ , conflicts between transmissions t i jk ∈ T τ must be determined to obtain the set of edges C τ . The rules used to determine the conflicts are referred to as conflict rules. The basic conflict rules are defined as follows: Because the original algorithm proposed in [17] assumed a DSRC channel and omnidirectional antennas, the following conflict rule was added to the basic rules: (d) A receiver near a transmitter cannot receive data from other transmitters: However, this conflict rule does not match our problem because we assume mmWave V2V communications. Considering the narrow beam width and high attenuation of mmWave communications, it seems that the radio interference between two transmissions is negligible. In this case, the conflict set C τ is defined only by the basic rules: (a), (b), and (c). However, interference sometimes occurs when an interferer is near a receiver or the transmission direction of the desired and interfering signal are nearly parallel.
In order to overcome interference, we design a conflict rule that reflects mmWave radio characteristics. However, it is difficult to estimate SINR during scheduling because interference cannot be calculated before all the transmitters and their antenna directions are determined. We propose an approximation method that can be adopted for our conflict rules, which are defined for only two transmissions and utilized when constructing the scheduling graphs.
Considering the narrow beam width and high attenuation of mmWave radio signals, we assume that the largest interference signal is the main factor of SINR. Therefore, SINR can be approximated as follows: where SINR i, j , P (i, j) r , and I k denote the SINR at vehicle v i , whose desired signal comes from v j , received signal strength of desired signals from v j at vehicle v i , and interference power from vehicle v k , respectively. Although knowledge regarding all interference signals seems to be required when calculating max k i, j I k , a conflict rule can be designed between pairs of transmissions by assuming that the currently considered interferer is the largest one. The conflict rule reflecting interference is designed as follows: Consider the interfering signals from v j t and v k t to v i r , where I j t < I k t . v j t and v k t attempt to transmit signals to v j r and v k r , respectively, and v i r receives signals from v i t . If sinr(t i t i r a , t k t k r b ) ≤ Θ, then the concurrent transmission of t i t i r a and t k t k r b cannot be scheduled by the proposed algorithm. Therefore, when calculating the interference from v j t to v i r , we do not need to consider interference from v k t , and the interference from v j t is assumed to be the largest. This assumption can be extended inductively for more than three transmitters.

Required Time Slot for Complete Data Sharing
In this section, we discuss the situation where sufficient time slots are available to complete data sharing. First, we prove that the proposed scheduling algorithm terminates in finite time and that all data are shared with all vehicles if the data sharing time is not limited and the vehicular network graph G v is connected. We then discuss the bounds for the required number of time slots.

Theorem 1:
If G v is connected, the proposed data sharing algorithm terminates in finite time and all the data initially possessed by vehicles are shared with all vehicles when the algorithm terminates, which can be expressed as follows: where τ end denotes the step count at the end of Algorithm 2.
Proof 1: First, we prove that the proposed algorithm terminates in finite time and then, we prove that all vehicles possess all data at the end of the algorithm. Let n τ denote the total size of the dataset d i,τ , defined as n τ N v i=1 |d i,τ |, where | · | represents the cardinality of a set. When t i jk is performed, meaning vehicle v j receives data d k , n τ is updated as n τ+1 ← n τ + 1 because T τ is constructed from all elements in t i jk that satisfy d k d j,τ (lines 5-7 in Algorithm 1). Let m τ |t τ | denote the number of transmissions selected by the RSU. Then, n τ is updated as n τ+1 ← n τ + m τ in each time slot. Meanwhile, the maximum value of n τ is bounded by N 2 v . Therefore, the algorithm terminates in finite time if at least one transmission is selected in each time slot.
Next, we prove that if there exists a vehicle that does not possess all data, at least one transmission can be performed. Assume v i does not possess d j , which means d j d i . Then, there exists a connected pair {v α , v β } ∈ L on the paths between v i and v j that satisfies d j ∈ d α ∧ d j d β because at least v j possesses d j . Note that the paths between v i and v j exist because G v is connected. Now, we have t αβ j ∈ T τ because v α possesses d j and v β does not possess d j , meaning T τ ∅. An independent set of a graph is not ∅ if a vertex set of the graph is not ∅. Therefore, G s,τ has an independent set whose size is greater than zero.
If not all vehicles possess all data, a transmission can be performed and thus, the algorithm does not terminate. When the algorithm terminates, all vehicles possess all data. Because it is guaranteed that the algorithm always terminates in finite time, all data can be shared with all vehicles in finite time.
Next, we reveal the bounds of τ end .
where · represents the floor function.

Proof 2:
At the beginning of the algorithm, we have n 0 = N v . From (10), we have n τ end = N 2 v . Meanwhile, n τ end is also written as n τ end = n 0 + τ end −1 τ=0 m τ . Because vehicles cannot transmit and receive data simultaneously, m τ satisfies m τ ≤ N v /2 . Additionally, m τ also satisfies m τ ≥ 1 because at least one transmission is performed in each time slot. Therefore, we have On one hand, τ end is equal to the upper bound if only one vehicle transmits data in every time slot. On the other hand, τ end achieves the lower bound if half of the vehicles send data in every time slot, which is the optimal case under the constraint that each vehicle cannot send and receive data simultaneously. In Section 5, simulation results demonstrate that τ end is near the lower bound in many cases.

Simulation Results
We evaluated our algorithm through simulations. In our simulations, the distribution of inter-vehicle distance followed an exponential distribution. This assumption was confirmed in [22], where the authors demonstrated that the distribution of inter-vehicle distance follows an exponential distribution based on empirical data collected in a real environment. We assumed the average distance between vehicles l avg in each lane was approximately the same as the stopping distance for traffic safety. We evaluated the situations where l avg = 20 m and 40 m, which are slightly larger than the stopping distances when the velocity is 40 km/h and 60 km/h, respectively [23]. We evaluated an intersection with two roads and four buildings assuming an urban area. Each road had four lanes and sidewalks on both sides. Each vehicle was modeled as a rectangle with a size of 1. approximately 25 m of the center of the intersection participate in data sharing. When the number of vehicles is doubled, perceivable region covers (50 m + r s /2) from the center of the intersection. The achievable coverage is sufficient for some applications, e.g., accident or congestion detection system with which a driver or self-driving system gets alerts and stops or slows down the vehicle when an accident or congestion is detected at the intersection. We drew lines from the transmitter to the receiver vehicles and counted the number of blocking vehicles on the lines. The number of blockers was used to calculate the pass loss based on the model proposed in [3] and construct G v from (3) and (4). We also assumed that vehicles could not communicate with each other if the buildings blocked their line-of-sight path. The antenna gain was calculated from the model in [24]. The other parameters are listed in Table 1. In our simulations, the RSU first determined the scheduling. Next, vehicles transmitted data based on the scheduling. When interference occurred, a receiver failed to receive data. If a transmitter was scheduled to transmit data that it did not possess, it did not transmit any data during that time slot. Figure 4 presents the normalized perceivable areaŜ τ defined in Section 3 as a function of the number of time slots τ when l avg , N v = (40 m, 20). When the proposed data sharing algorithm was used, the perceivable areasŜ τ were enlarged by data sharing at first. Then,Ŝ τ saturated when τ ≥ 40 because the entire region R all was covered by the shared perceptual data. In other words, transmitted data after τ ≥ 40 did not contribute to enlarging the perceivable area because of overlap. Finally, the algorithm terminated at τ = 61 when all scheduled transmissions were completed. The proposed algorithm achieved approximately twice the perceivable area compared with the conventional algorithm at τ = 40. This is because the conventional algorithm assumed microwave communications and thus, few vehicles could transmit data concurrently because of the conflict rule (d), which was designed for microwave communications. In contrast, the proposed method achieved efficient concurrent transmission because its conflict rules reflect mmWave radio characteristics. Additionally, adopting the conflict rule (d') enlarged the perceivable area because when this rule is not adopted, certain interferences cannot be avoided and data sharing cannot be completed owing to transmission failure. Figure 5 shows the empirical cumulative distribution function (CDF) of the normalized perceivable areaŜ τ . When utilizing the conflict rule (d'), nearly all vehicles achieved 90% of the normalized perceivable area at τ = 40, whereas only 86% of the vehicles achieved 90% of the normalized perceivable area where interference was not considered when determining the scheduling.
The normalized perceivable areas with different intervehicle distances and number of vehicles are shown in Fig. 6. The beam width was 15 • . When the vehicle average inter- vehicle distance was the same, the smaller the number of vehicles, the faster the data sharing terminated. This is confirmed by (11). It is also shown that the superiority of adopting conflict rule (d') does not depend on l avg and N v . The performance gain of adopting conflict rule (d') is larger when the number of participants is large or the inter-vehicle distance is small because interference is more likely to occur in these cases. Figure 7 presents the normalized perceivable areas with beam widths of 15 • and 30 • , when l avg , N v = (40 m, 20). The differences between the beam widths of 15 • and 30 • were larger when the conflict rule for mmWave interference was not adopted compared with when the rule (d') is adopted. This is because interferences occur more frequently with a wider beam width, but the proposed conflict rule (d') can successfully avoid interference. Figure 8 presents differences between the two priority designs when l avg , N v = (40 m, 20). The max distance scheduling design achieved a larger perceivable area than the max transmission scheduling design when τ ≤ 36.Ŝ τ of the max distance design was 20% larger than that of the max transmission design when τ = 8. Based on the max distance scheduling, data far from the center of the intersection were transmitted at first and thus, the overlapping regions tended to be smaller than those in the algorithm without such priority control. Therefore,Ŝ τ became larger than that in the max transmission scheduling design. When the number of time slot was limited, such that τ max ≤ 36, the max distance scheduling design provided a larger perceivable area during every data update interval. Figure 9 shows empirical CDF of the number of time slots required to share all data, denoted τ end , when τ max is much larger than τ end . Inter-vehicle distance was assumed to be 40 m. From (11), the lower bounds of τ end were calculated as 18, 30, and 38, for N v = 10, 15, 20. The lower bounds are depicted as black vertical lines in Fig. 9. In most cases, τ end were closer to the lower bounds than the upper bounds of 90, 210, and 380. In very few cases, as shown  in Fig. 9, the data sharing algorithm terminated after fewer iterations than the lower bounds. This is because the vehicular network graphs G v were disconnected in such cases and thus, the data sharing algorithm terminated before all data were shared. The differences between the protocols with and without prioritization can be observed when N v = 20. The max transmission scheduling design achieved efficient data sharing because it maximized the number of transmitted data at each time slot, meaning the algorithm terminated faster than the max distance scheduling design when data sharing time was not limited.

Conclusion
We proposed a data sharing scheduling method with concurrent transmission for mmWave VANETs for cooperative perception. We modified the algorithm in [17] by designing a conflict rule that represents mmWave communication characteristics and a weight function that prioritizes data to be forwarded to enlarge the perceivable area. Simulation results demonstrated that the proposed conflict rule for scheduling graphs achieved a larger perceivable area compared with the original rules, which did not consider directional antennas. Priority control methods also enlarged the perceivable region by sharing data that covered areas far from an intersection at first. The priority control worked efficiently in situations where the number of time slots was limited. We also proved that the proposed algorithms terminate in finite time and all data can be shared with all vehicles if a vehicular network is represented as a connected graph and there are sufficient time slots. For future work, we will develop a data-aggregation and vehicle-selection method for reducing redundant data transmissions. The algorithm proposed in this paper transmits data without considering overlapping regions covered by multiple data. To suppress the transmission of data representing overlapping regions can reduce data traffic without reducing the perceivable area. The data-aggregation method that aggregates some overlapping data to a single datum also reduces the amount of data to be transmitted. When the vehicles densely located, to select vehicles generating data considering their sensor coverage can reduce data transmissions including the same regions.
Another interest is to develop a scalable distributed scheduling method. In the proposed algorithm, an RSU, which act as a central controller, determines the schedule based on information from all vehicles participating in cooperative perception. When the number of vehicles is large, it is difficult for a central controller to obtain accurate information from all vehicles and to perfectly control whole schedules because mmWave channels vary rapidly. Therefore, distributed scheduling including hybrid schemes of centralized and distributed scheduling should be developed to reduce the amount of vehicle information transmitted to the RSU and to allow vehicles to decide scheduling autonomously using their own information.