Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond最新文献

QoS-aware dynamic controller implantation over vSDN-enabled UAV networks for real-time service delivery 在支持vsdn的无人机网络上进行qos感知动态控制器植入，用于实时服务交付

Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond Pub Date : 2021-10-21 DOI: 10.1145/3477090.3481055

Deborsi Basu, Abhishek Jain, Uttam Ghosh, R. Datta

{"title":"QoS-aware dynamic controller implantation over vSDN-enabled UAV networks for real-time service delivery","authors":"Deborsi Basu, Abhishek Jain, Uttam Ghosh, R. Datta","doi":"10.1145/3477090.3481055","DOIUrl":"https://doi.org/10.1145/3477090.3481055","url":null,"abstract":"The advancement of wireless communication networks has been highly influenced by the development of UAV networks. The real-time realization and quick installation of UAV networks make it extremely suitable for emergency services. Due to limited energy and processing memory, vSDN-enabled UAV networks are brought into the picture where the central SDN controller is used to manage the Data Plane UAV activities. The placement of the controller is a critical issue due to random mobility and distant coverage. In this work, we have proposed a controller implantation technique for low latency communication and service delivery. A two-tier hierarchical data plane (D-plane) segmentation has been introduced to place the UAV entities at D-plane. Our algorithmic approach shows that the centralization of SDN controller causes comparatively low latency with respect to other potential regions. We have relaxed the traffic overheads considering minimal data exchange between D-plane and C-plane. The latency trade-off significantly helps to identify the most suitable positions to deploy the Controller units. This work also contributes towards the CPP-UAV (Controller Placement Problem in UAV-networks).","PeriodicalId":261033,"journal":{"name":"Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129323027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond 第四届ACM MobiCom 5G及以后无人机辅助无线通信研讨会论文集

Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond Pub Date : 2021-10-21 DOI: 10.1145/3477090

引用次数: 0

An intelligent self-learning drone assistance approach towards V2V communication in smart city 智慧城市V2V通信的智能自学习无人机辅助方法

Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond Pub Date : 2021-10-21 DOI: 10.1145/3477090.3481050

R. DhineshKumar, Suresh Chavhan, Deepak Gupta, Ashish Khanna, J. Rodrigues

{"title":"An intelligent self-learning drone assistance approach towards V2V communication in smart city","authors":"R. DhineshKumar, Suresh Chavhan, Deepak Gupta, Ashish Khanna, J. Rodrigues","doi":"10.1145/3477090.3481050","DOIUrl":"https://doi.org/10.1145/3477090.3481050","url":null,"abstract":"The objective of the study is to investigate the efficient packet transfer among vehicles in the smart city. With the evolution of Intelligent Transportation Systems (ITS), Vehicle to Vehicle (V2V) communication is becoming more prominent for safety and non-safety related applications. The V2V communication facilitates vehicles to interconnect with each other to support numerous applications that will be highly helpful for drivers and passenger's welfare. However, due to the extreme dynamic nature of transportation, the Vehicular Ad hoc Network (VANET) faces many challenges for transferring information among vehicles. Therefore, efficient clustering and dynamic routing are becoming a supreme area of improvement to increase the Packet Delivery Ratio (PDR) and reduce the End-to-End delay for data transfer. In order to overcome the major obstacle, in this paper, we propose an intelligent self-learning approach-based hybrid clustering by integrating Adaptive Network-based Fuzzy Inference System (ANFIS) and dynamic Dijkstra routing for packet transfer between vehicles. Also, experiments were carried out to support the data transfer with the help of drones to provide higher coverage in high dynamic vehicle mobility scenarios. The proposed algorithm is modeled, trained, and tested for performance evaluations metrics such as Packet Delivery Ratio (PDR), End to End delay, CH selection delay.","PeriodicalId":261033,"journal":{"name":"Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134531095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Deep learning based crowd counting model for drone assisted systems 基于深度学习的无人机辅助系统人群计数模型

Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond Pub Date : 2021-10-21 DOI: 10.1145/3477090.3481054

M. Woźniak, J. Siłka, Michal Wieczorek

引用次数: 19

Cross-domain authentication for 5G-enabled UAVs: a blockchain approach 5g无人机的跨域认证:区块链方法

Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond Pub Date : 2021-10-21 DOI: 10.1145/3477090.3481053

B. Liu, Keping Yu, Chaosheng Feng, Kim-Kwang Raymond Choo

引用次数: 7

Position optimization and resource allocation for cooperative heterogeneous aerial networks 协同异构空中网络的位置优化与资源分配

Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond Pub Date : 2021-10-21 DOI: 10.1145/3477090.3481049

D. Zhai, Qiqi Shi, Ruonan Zhang, Haotong Cao, Bin Li, Dawei Wang

{"title":"Position optimization and resource allocation for cooperative heterogeneous aerial networks","authors":"D. Zhai, Qiqi Shi, Ruonan Zhang, Haotong Cao, Bin Li, Dawei Wang","doi":"10.1145/3477090.3481049","DOIUrl":"https://doi.org/10.1145/3477090.3481049","url":null,"abstract":"Unmanned aerial vehicle (UAV) has great potential in the future wireless networks. In this paper, we investigate the system optimization algorithms for the heterogeneous aerial networks. Specifically, we propose a cooperative heterogeneous aerial network, where several low-altitude aerial base stations (LABSs) with high frequency are dynamically deployed to enhance the coverage of a high-altitude aerial base station (HABS) with low frequency. For this network, we formulate a joint position optimization, channel allocation, and power allocation problem with the objective to maximize the total data rate of all users under the constraint of the minimum rate requirement of each user. To tackle this hard problem, we first adopt the particle-and-fish swarm algorithm to optimize the positions of the LABSs. Then, the channel-and-power allocation algorithms are designed based on the matching theory and the Lagrangian dual decomposition technique. Simulation results indicate that our proposed algorithms can greatly improve the network performance.","PeriodicalId":261033,"journal":{"name":"Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128765330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Camera-enabled joint robotic-communication paradigm for UAVs mounted with mmWave radios 用于安装毫米波无线电的无人机的摄像头联合机器人通信范例

Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond Pub Date : 2021-10-21 DOI: 10.1145/3477090.3481052

Saray Sanchez, Rishabh Shukla, K. Chowdhury

{"title":"Camera-enabled joint robotic-communication paradigm for UAVs mounted with mmWave radios","authors":"Saray Sanchez, Rishabh Shukla, K. Chowdhury","doi":"10.1145/3477090.3481052","DOIUrl":"https://doi.org/10.1145/3477090.3481052","url":null,"abstract":"UAVs mounted with millimeter wave base stations will enable last-mile high bandwidth access, as well as help in rapidly deploying point to point aerial backhaul links. Because such transmitters use directional beamforming to increase capacity, UAV deployments require careful selection of the beamwidth. Even under regular hovering conditions, UAVs display minor relative rotations and displacements caused by GPS inaccuracies and environmental factors like wind. To ensure narrow beams are perfectly aligned in such practical conditions, we propose a beamforming framework that (i) fuses out-of-band information obtained from cameras and (ii) leverages antenna beam-patterns characterized online during flight. These inputs provide the UAV pair forming the link with an improved estimate of relative orientation, and furthermore, guide controlled and coordinated movements to ensure the mmWave beams remain aligned. We implement this joint robotics-communication framework within the robot operating system and evaluate the performance for emulated DJI M100 UAVs. Our results reveal 33% improvement in physical bitrate and 60.4% reduction in latency when compared to RF-only beam sweeping methods.","PeriodicalId":261033,"journal":{"name":"Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125201998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Federated learning-based aerial image segmentation for collision-free movement and landing 基于联邦学习的航空图像无碰撞运动与着陆分割

Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond Pub Date : 2021-10-21 DOI: 10.1145/3477090.3481051

P. Chhikara, Rajkumar Tekchandani, Neeraj Kumar, S. Tanwar

{"title":"Federated learning-based aerial image segmentation for collision-free movement and landing","authors":"P. Chhikara, Rajkumar Tekchandani, Neeraj Kumar, S. Tanwar","doi":"10.1145/3477090.3481051","DOIUrl":"https://doi.org/10.1145/3477090.3481051","url":null,"abstract":"The utilization of drones has recently revolutionized remote sensing with their high spatial resolution and flexibility in capturing images. In the proposed work, we employ a swarm of drones that communicate in a wireless network. Each drone captures the image frames, and each frame is further used to locate and differentiate different objects in an image frame. The semantic segmentation of the captured images is done using deep learning algorithms. To identify the most suitable, cost-efficient, and accurate segmentation method, various state-of-the-art models, are appraised and compared based on different evaluation metrics. Resnet50 model with U-net segmentation model performs the best out of all used models by providing 91.51% pixel accuracy. Also, to give real-time predictions, we have used federated learning with the drone network. Each drone trains a local model using its accumulated data and then transfers the locally trained model to the central server that aggregates the received models, generates a global federated learning model, and transmits it in the swarm network.","PeriodicalId":261033,"journal":{"name":"Proceedings of the 4th ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122325346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2