Deepan Nagarajan, D. Jayakody, Rebekka Balakrishnan
Unmanned aerial vehicle (UAV) has been considered as a widespread technical solution in recent years to meet the explosive data and massive device connections demands. On the other hand, Backscatter communication (BackComm) also equally evolved as a potential candidate to realize future Internet-of-Things (IoT) networks. By deploying BackComm in UAV-enabled IoT system allows an efficient utilization of the network resources, especially, in remote areas and smart cities. In this paper, we investigate the performance of a UAV-assisted multi-node BackComm network over generalized k - μ shadowed fading channel. Closed-form expressions are derived to study the system performance through outage probability and average BER. Additionally, we obtain a simplified asymptotic expression in a high SNR regime, from which we gain insight into how the channel and system parameters affect the overall performance. Finally, simulation results are provided to validate the derived theoretical results.
{"title":"Performance analysis of UAV-enabled backscatter wireless communication network","authors":"Deepan Nagarajan, D. Jayakody, Rebekka Balakrishnan","doi":"10.1145/3414045.3415942","DOIUrl":"https://doi.org/10.1145/3414045.3415942","url":null,"abstract":"Unmanned aerial vehicle (UAV) has been considered as a widespread technical solution in recent years to meet the explosive data and massive device connections demands. On the other hand, Backscatter communication (BackComm) also equally evolved as a potential candidate to realize future Internet-of-Things (IoT) networks. By deploying BackComm in UAV-enabled IoT system allows an efficient utilization of the network resources, especially, in remote areas and smart cities. In this paper, we investigate the performance of a UAV-assisted multi-node BackComm network over generalized k - μ shadowed fading channel. Closed-form expressions are derived to study the system performance through outage probability and average BER. Additionally, we obtain a simplified asymptotic expression in a high SNR regime, from which we gain insight into how the channel and system parameters affect the overall performance. Finally, simulation results are provided to validate the derived theoretical results.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115514207","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}
M. K. Shehzad, Syed Ali Hassan, M. Luque-Nieto, J. Poncela, Haejoon Jung
The enormous increase in cellular users requires novel advancements in the existing cellular infrastructure. Therefore, small cell networks (SCNs) are a promising solution to meet the ever-growing demands of cellular users as they are beneficial in terms of coverage and providing higher data rates. However, one of the challenging parts is the deployment of small cell base stations (SBs) and their connectivity with the backhaul network. In this paper, we use the scalable idea of replacing the terrestrial backhaul network with an aerial network to provide fronthaul connectivity to SBs. In particular, we address the optimum placement of unmanned aerial vehicles (UAVs) to associate the SBs such that the sum-rate of the overall network is maximized. We achieve such an objective by proposing a two-layer framework, i.e., unsupervised learning and iterative algorithm (defined as UAV equalizer), and we call this two-layer framework as a hybrid approach. Simulation results show that the proposed hybrid approach outperforms the traditional approaches in terms of maximizing the sum-rate, minimum bandwidth consumption, moreover, maximizing link utilization and energy efficiency.
{"title":"Energy efficient placement of UAVs in wireless backhaul networks","authors":"M. K. Shehzad, Syed Ali Hassan, M. Luque-Nieto, J. Poncela, Haejoon Jung","doi":"10.1145/3414045.3415936","DOIUrl":"https://doi.org/10.1145/3414045.3415936","url":null,"abstract":"The enormous increase in cellular users requires novel advancements in the existing cellular infrastructure. Therefore, small cell networks (SCNs) are a promising solution to meet the ever-growing demands of cellular users as they are beneficial in terms of coverage and providing higher data rates. However, one of the challenging parts is the deployment of small cell base stations (SBs) and their connectivity with the backhaul network. In this paper, we use the scalable idea of replacing the terrestrial backhaul network with an aerial network to provide fronthaul connectivity to SBs. In particular, we address the optimum placement of unmanned aerial vehicles (UAVs) to associate the SBs such that the sum-rate of the overall network is maximized. We achieve such an objective by proposing a two-layer framework, i.e., unsupervised learning and iterative algorithm (defined as UAV equalizer), and we call this two-layer framework as a hybrid approach. Simulation results show that the proposed hybrid approach outperforms the traditional approaches in terms of maximizing the sum-rate, minimum bandwidth consumption, moreover, maximizing link utilization and energy efficiency.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126952610","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}
Ahmed Al-Hilo, M. Samir, C. Assi, S. Sharafeddine, Dariush Ebrahimi
Intelligent Transportation Systems (ITS) are gaining substantial attention owing to the great benefits offered to the vehicle users. In ITS paradigm, content data is normally obtained from road side units (RSUs). However, in some scenarios, terrestrial networks are partially/temporarily out-of-service. Unmanned Aerial Vehicle (UAV) or drone cells are expected to be one of the pillars of future networks to assist the vehicular networks in such scenarios. To this end, we propose a collaborative framework between UAVs and in-service RSUs to partial service vehicles. Our objective is to maximize the amount of downloaded contents to vehicles while considering the dynamic nature of the network. Motivated by the success of machine learning (ML) techniques particularly deep Reinforcement learning in solving complex problems, we formulate the scheduling and content management policy problem as a Markov Decision Process (MDP) where the system state space considers the vehicular network dynamics. Proximal Policy Optimization (PPO) is utilized to govern the content decisions in the vehicular network. The simulation-based results show that during the mission time, the proposed algorithm learns the vehicular environment and its dynamics to handle the complex action space.
{"title":"Cooperative content delivery in UAV-RSU assisted vehicular networks","authors":"Ahmed Al-Hilo, M. Samir, C. Assi, S. Sharafeddine, Dariush Ebrahimi","doi":"10.1145/3414045.3415947","DOIUrl":"https://doi.org/10.1145/3414045.3415947","url":null,"abstract":"Intelligent Transportation Systems (ITS) are gaining substantial attention owing to the great benefits offered to the vehicle users. In ITS paradigm, content data is normally obtained from road side units (RSUs). However, in some scenarios, terrestrial networks are partially/temporarily out-of-service. Unmanned Aerial Vehicle (UAV) or drone cells are expected to be one of the pillars of future networks to assist the vehicular networks in such scenarios. To this end, we propose a collaborative framework between UAVs and in-service RSUs to partial service vehicles. Our objective is to maximize the amount of downloaded contents to vehicles while considering the dynamic nature of the network. Motivated by the success of machine learning (ML) techniques particularly deep Reinforcement learning in solving complex problems, we formulate the scheduling and content management policy problem as a Markov Decision Process (MDP) where the system state space considers the vehicular network dynamics. Proximal Policy Optimization (PPO) is utilized to govern the content decisions in the vehicular network. The simulation-based results show that during the mission time, the proposed algorithm learns the vehicular environment and its dynamics to handle the complex action space.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134106493","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}
Wei Yang Bryan Lim, Zehui Xiong, Jiawen Kang, D. Niyato, Yang Zhang, Cyril Leung, C. Miao
The enhanced capabilities of Unmanned Aerial Vehicles have promoted the rapid growth of the Drones-as-a-Service (DaaS) market. To enable privacy-preserving collaborative machine learning among independent DaaS providers, we propose a Federated Learning (FL) based approach. There exists a tradeoff between Service Latency (SL), i.e., the time taken for the training request to be completed, and Age of Information (AoI), i.e., the time elapsed between data aggregation to completion of the FL based training. Given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner. Performance evaluation validates the incentive compatibility and flexibility of our contract design amid information asymmetry.
无人机性能的增强促进了无人机即服务(DaaS)市场的快速增长。为了在独立的DaaS提供商之间实现保护隐私的协作机器学习，我们提出了一种基于联邦学习(FL)的方法。在服务延迟(Service Latency, SL)，即完成训练请求所需的时间，和信息年龄(Age of Information, AoI)，即从数据聚合到完成基于FL的训练之间所经过的时间之间存在权衡。考虑到不同的训练任务可能有不同的AoI要求，我们提出了一个契约理论的任务感知激励方案，该方案可以基于模型所有者的加权偏好进行校准。绩效评估验证了信息不对称条件下契约设计的激励兼容性和灵活性。
{"title":"An incentive scheme for federated learning in the sky","authors":"Wei Yang Bryan Lim, Zehui Xiong, Jiawen Kang, D. Niyato, Yang Zhang, Cyril Leung, C. Miao","doi":"10.1145/3414045.3415935","DOIUrl":"https://doi.org/10.1145/3414045.3415935","url":null,"abstract":"The enhanced capabilities of Unmanned Aerial Vehicles have promoted the rapid growth of the Drones-as-a-Service (DaaS) market. To enable privacy-preserving collaborative machine learning among independent DaaS providers, we propose a Federated Learning (FL) based approach. There exists a tradeoff between Service Latency (SL), i.e., the time taken for the training request to be completed, and Age of Information (AoI), i.e., the time elapsed between data aggregation to completion of the FL based training. Given that different training tasks may have varying AoI requirements, we propose a contract-theoretic task-aware incentive scheme that can be calibrated based on the weighted preferences of the model owner. Performance evaluation validates the incentive compatibility and flexibility of our contract design amid information asymmetry.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116353862","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}
The growing number of unmanned aerial vehicles (UAVs), typically referred to as drones, poses new challenges on how to manage their operations in various internet of things (IoT) use cases such as surveillance and monitoring, weather prediction, agriculture, etc. The latter includes a massive number of devices that sometimes produce invalid messages due to lack of energy or system shutdown and needs to be autonomously monitored with drones in rural areas. In this paper, we develop a blockchain-based platform for managing drone IoT operations while maintaining trust and security. The test-bed consists of IoT devices, a drone and blockchain-enabled gateways through which drones are controlled to replace malfunctioning devices. The latter are detected using Z-score observation algorithm which launches a smart contract and sends the drone with clear operation order. The results obtained in realistic agriculture use case highlight the utility of our proposition in decreasing signaling and operation time, improving the percentage of successful maintenance operations and providing trust and security when managing drones in an autonomous manner.
{"title":"Blockchain-based IoT platform for autonomous drone operations management","authors":"Samir Dawaliby, Arezki Aberkane, Abbas Bradai","doi":"10.1145/3414045.3415939","DOIUrl":"https://doi.org/10.1145/3414045.3415939","url":null,"abstract":"The growing number of unmanned aerial vehicles (UAVs), typically referred to as drones, poses new challenges on how to manage their operations in various internet of things (IoT) use cases such as surveillance and monitoring, weather prediction, agriculture, etc. The latter includes a massive number of devices that sometimes produce invalid messages due to lack of energy or system shutdown and needs to be autonomously monitored with drones in rural areas. In this paper, we develop a blockchain-based platform for managing drone IoT operations while maintaining trust and security. The test-bed consists of IoT devices, a drone and blockchain-enabled gateways through which drones are controlled to replace malfunctioning devices. The latter are detected using Z-score observation algorithm which launches a smart contract and sends the drone with clear operation order. The results obtained in realistic agriculture use case highlight the utility of our proposition in decreasing signaling and operation time, improving the percentage of successful maintenance operations and providing trust and security when managing drones in an autonomous manner.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121963369","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}
Bishmita Hazarika, Rakesh Matam, M. Mukherjee, Varun G. Menon
Routing protocol for low-power and lossy networks (RPL) is an widely-used IPv6 routing protocol for lossy wireless networks with the power constrained devices in Internet of Things (IoT). It is a proactive protocol that constructs a destination oriented directed acyclic graph (DODAG) rooted at the single destination called the root node that resides at unmanned aerial vehicle (UAV). Specifically, a DODAG is built with the help of different control messages like DODAG information object (DIO), DODAG advertisement object (DAO), and DODAG information solicitation (DIS). As the generation of these messages incur additional energy consumption, RPL uses the Trickle algorithm to dynamically adjust the transmission windows. In this paper, we analyze the effect of the two parameters, namely, DIO-INTERVAL-MINIMUM and DIO-INTERVAL-DOUBLING that have significant effect on the Trickle algorithm and the rate of message generation. Through experiments, we show that an optimal selection of these parameters saves a significant amount of energy with different parameter settings in UAV-assisted IoT networks.
{"title":"DIO messages and trickle timer analysis of RPL routing protocol for UAV-assisted data collection in IoT","authors":"Bishmita Hazarika, Rakesh Matam, M. Mukherjee, Varun G. Menon","doi":"10.1145/3414045.3415944","DOIUrl":"https://doi.org/10.1145/3414045.3415944","url":null,"abstract":"Routing protocol for low-power and lossy networks (RPL) is an widely-used IPv6 routing protocol for lossy wireless networks with the power constrained devices in Internet of Things (IoT). It is a proactive protocol that constructs a destination oriented directed acyclic graph (DODAG) rooted at the single destination called the root node that resides at unmanned aerial vehicle (UAV). Specifically, a DODAG is built with the help of different control messages like DODAG information object (DIO), DODAG advertisement object (DAO), and DODAG information solicitation (DIS). As the generation of these messages incur additional energy consumption, RPL uses the Trickle algorithm to dynamically adjust the transmission windows. In this paper, we analyze the effect of the two parameters, namely, DIO-INTERVAL-MINIMUM and DIO-INTERVAL-DOUBLING that have significant effect on the Trickle algorithm and the rate of message generation. Through experiments, we show that an optimal selection of these parameters saves a significant amount of energy with different parameter settings in UAV-assisted IoT networks.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128599113","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}
S. Chavhan, P. Ramesh, R. Chhabra, Deepak Gupta, Ashish Khanna, J. Rodrigues
The objective of this paper is to explore visualization and the performance analysis of 5G network slicing for drones to achieve a better understanding of the concept in terms of expenditure and performance. Network slicing is the first basic part of the progressed 5G cell network availability. It offers the division of the single physical network into various advanced networks so one can achieve specific targets comprehensive of wellbeing, versatility, and the observing of the network. This paper considers a scalable area divided into sub-zones and each sub-zone contains a designated amount of base stations and is subjected to analysis by simulating different client mobility patterns and its effect on the network performance parameters. This analysis is further extended by using all base stations from the four quadrants to create a single network which is then subjected to the same analysis.
{"title":"Visualization and performance analysis on 5G network slicing for drones","authors":"S. Chavhan, P. Ramesh, R. Chhabra, Deepak Gupta, Ashish Khanna, J. Rodrigues","doi":"10.1145/3414045.3416208","DOIUrl":"https://doi.org/10.1145/3414045.3416208","url":null,"abstract":"The objective of this paper is to explore visualization and the performance analysis of 5G network slicing for drones to achieve a better understanding of the concept in terms of expenditure and performance. Network slicing is the first basic part of the progressed 5G cell network availability. It offers the division of the single physical network into various advanced networks so one can achieve specific targets comprehensive of wellbeing, versatility, and the observing of the network. This paper considers a scalable area divided into sub-zones and each sub-zone contains a designated amount of base stations and is subjected to analysis by simulating different client mobility patterns and its effect on the network performance parameters. This analysis is further extended by using all base stations from the four quadrants to create a single network which is then subjected to the same analysis.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121832747","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}
{"title":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","authors":"","doi":"10.1145/3414045","DOIUrl":"https://doi.org/10.1145/3414045","url":null,"abstract":"","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126975358","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}
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