We consider a new blockchain empowered federated learning approach which uses wireless mobile miners at drones in the future sixth generation (6G) networks for a disaster response system. Our focus is on the blockchain latency, and energy consumption in the proposed architecture of the network of drones. Maintaining low delay in wireless communication between the drones is required to minimize blockchain forking events while performing blockchain operations. Therefore, we quantify the probability of occurrence of forking events to analyze the uncertainty of the system towards the additional energy wastage. The forked block (due to channel impairments or mobility) incurs re-computation energy. We develop pragmatic analyses of the expected energy consumption by considering the parameters like the number of miners as well as the power consumed during computing, block transfer and 6G channel dynamics for the system.
{"title":"Federated learning meets blockchain at 6G edge: a drone-assisted networking for disaster response","authors":"Shiva Raj Pokhrel","doi":"10.1145/3414045.3415949","DOIUrl":"https://doi.org/10.1145/3414045.3415949","url":null,"abstract":"We consider a new blockchain empowered federated learning approach which uses wireless mobile miners at drones in the future sixth generation (6G) networks for a disaster response system. Our focus is on the blockchain latency, and energy consumption in the proposed architecture of the network of drones. Maintaining low delay in wireless communication between the drones is required to minimize blockchain forking events while performing blockchain operations. Therefore, we quantify the probability of occurrence of forking events to analyze the uncertainty of the system towards the additional energy wastage. The forked block (due to channel impairments or mobility) incurs re-computation energy. We develop pragmatic analyses of the expected energy consumption by considering the parameters like the number of miners as well as the power consumed during computing, block transfer and 6G channel dynamics for the system.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"9 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":"125431007","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}
Sunil Jacob, Varun G. Menon, R. Parvathi, Shynu Gopalan Padinjappurathu, KS FathimaShemim, B. Mahapatra, M. Mukherjee
The number of vehicular collisions is on a toll worldwide. Despite enforcing stringent laws and incorporating various safety features, the causalities are still on the rise. Existing techniques such as vision zero strategy and safe system approach provides only post-crash aid. Although numerous works have been carried out on Intelligent Transportation Systems (ITS), a well-coordinated vehicular collision avoidance system is still missing. In this paper, we utilize the tremendous opportunity provided by ITS, Light Detection and Ranging (LIDAR), Wireless Sensor Networks (WSN), 5G, and propose an effective system using drones with swarm intelligence that can automatically control the vehicles to prevent the collision. The proposed method, Bidirectional Multi-Tier IoT drone with Swarm optimization (BMTD-IoT-S) uses intelligent coordination of the drone swarms with the vehicular networks and always ensures a safe distance between the vehicles using the principle of magnetic levitation. The system is further investigated for optimizing the power, altitude, and angular frequency allocation for static and dynamic BMTD-IoT-S'. The results from simulation confirm the excellent performance of the system in ensuring collision avoidance.
{"title":"Intelligent vehicle collision avoidance system using 5G-enabled drone swarms","authors":"Sunil Jacob, Varun G. Menon, R. Parvathi, Shynu Gopalan Padinjappurathu, KS FathimaShemim, B. Mahapatra, M. Mukherjee","doi":"10.1145/3414045.3415938","DOIUrl":"https://doi.org/10.1145/3414045.3415938","url":null,"abstract":"The number of vehicular collisions is on a toll worldwide. Despite enforcing stringent laws and incorporating various safety features, the causalities are still on the rise. Existing techniques such as vision zero strategy and safe system approach provides only post-crash aid. Although numerous works have been carried out on Intelligent Transportation Systems (ITS), a well-coordinated vehicular collision avoidance system is still missing. In this paper, we utilize the tremendous opportunity provided by ITS, Light Detection and Ranging (LIDAR), Wireless Sensor Networks (WSN), 5G, and propose an effective system using drones with swarm intelligence that can automatically control the vehicles to prevent the collision. The proposed method, Bidirectional Multi-Tier IoT drone with Swarm optimization (BMTD-IoT-S) uses intelligent coordination of the drone swarms with the vehicular networks and always ensures a safe distance between the vehicles using the principle of magnetic levitation. The system is further investigated for optimizing the power, altitude, and angular frequency allocation for static and dynamic BMTD-IoT-S'. The results from simulation confirm the excellent performance of the system in ensuring collision avoidance.","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":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123376424","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 study on data aggregation in Internet of Things (IoT) has drawn a great attention in recent years. Since a large-scale disaster could damage the entire communication network and cut off data aggregation completely, an Intelligent UAV based Data Aggregation Strategy, named (IDAS), is proposed for after disaster scenarios in IoT. Specifically, IDAS first employs an task distribution mechanism to achieve the trade-off between the aggregation ratio and the energy cost. Then, a deep reinforcement learning method is developed for UAV route design to perform corresponding task. Thus, all data are aggregated toward the rescue headquarter by UAV deployment. The simulation results indicate that IDAS has a higher aggregation ratio and a lower energy cost while compared with contemporary strategies.
{"title":"An intelligent UAV based data aggregation strategy for IoT after disaster scenarios","authors":"Xiaoding Wang, Jia Hu, Hui Lin","doi":"10.1145/3414045.3415940","DOIUrl":"https://doi.org/10.1145/3414045.3415940","url":null,"abstract":"The study on data aggregation in Internet of Things (IoT) has drawn a great attention in recent years. Since a large-scale disaster could damage the entire communication network and cut off data aggregation completely, an Intelligent UAV based Data Aggregation Strategy, named (IDAS), is proposed for after disaster scenarios in IoT. Specifically, IDAS first employs an task distribution mechanism to achieve the trade-off between the aggregation ratio and the energy cost. Then, a deep reinforcement learning method is developed for UAV route design to perform corresponding task. Thus, all data are aggregated toward the rescue headquarter by UAV deployment. The simulation results indicate that IDAS has a higher aggregation ratio and a lower energy cost while compared with contemporary strategies.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"39 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":"134151742","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}
Muhammad Usman Sheikh, M. Riaz, Furqan Jameel, R. Jäntti, Navuday Sharma, Vishal Sharma, M. Alazab
The use of Unmanned Aerial Vehicles (UAVs) is becoming common in our daily lives and cellular networks are effective in providing support services to UAVs for long-range applications. The main target of this paper is to propose a modified form of well-known graph search methods i.e., Dijkstra and A-star also known as A* algorithm, for quality-aware trajectory planning of the UAV. The aerial quality map of the propagation environment is used as an input for UAV trajectory planning, and the quality metric considered for this work is Signal to Interference plus Noise Ratio (SINR). The UAV trajectory is quantified in terms of three performance metrics i.e., path length, Quality Outage Ratio (QOR), and maximum Quality Outage Duration (QOD). The proposed path planning algorithm aims at achieving a trade-off between the path length and other quality metrics of the UAV trajectory. The simulations are performed using an agreed 3GPP macro cell LOS scenario for UAVs in MATLAB. Simulation results illustrate that the proposed algorithm significantly improves the QOR by slightly increasing the path length compared with the naive shortest path. Similarly, the outage avoidance path achieves high QOR at the expense of large path length, and our proposed method finds a compromise and provides an optimal quality-aware path.
{"title":"Quality-aware trajectory planning of cellular connected UAVs","authors":"Muhammad Usman Sheikh, M. Riaz, Furqan Jameel, R. Jäntti, Navuday Sharma, Vishal Sharma, M. Alazab","doi":"10.1145/3414045.3415943","DOIUrl":"https://doi.org/10.1145/3414045.3415943","url":null,"abstract":"The use of Unmanned Aerial Vehicles (UAVs) is becoming common in our daily lives and cellular networks are effective in providing support services to UAVs for long-range applications. The main target of this paper is to propose a modified form of well-known graph search methods i.e., Dijkstra and A-star also known as A* algorithm, for quality-aware trajectory planning of the UAV. The aerial quality map of the propagation environment is used as an input for UAV trajectory planning, and the quality metric considered for this work is Signal to Interference plus Noise Ratio (SINR). The UAV trajectory is quantified in terms of three performance metrics i.e., path length, Quality Outage Ratio (QOR), and maximum Quality Outage Duration (QOD). The proposed path planning algorithm aims at achieving a trade-off between the path length and other quality metrics of the UAV trajectory. The simulations are performed using an agreed 3GPP macro cell LOS scenario for UAVs in MATLAB. Simulation results illustrate that the proposed algorithm significantly improves the QOR by slightly increasing the path length compared with the naive shortest path. Similarly, the outage avoidance path achieves high QOR at the expense of large path length, and our proposed method finds a compromise and provides an optimal quality-aware path.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"20 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":"134180467","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. Patel, Hamza Abubakar Kheruwala, M. Alazab, N. Patel, R. Damani, Pronaya Bhattacharya, S. Tanwar, Neeraj Kumar
Modern cloud-based UAV communications authenticate user identity through hash-based biometric authentication schemes, but are limited in scope due to high-end processing and user template matching delays, coupled with latency and storage overheads. Motivated from the aforementioned discussions, the paper proposes a BC- envisioned identity framework to secure next-generation UAV communication. In BioUAV, a dual layer of security is exploited. In the first layer, user identity registration to UAVs are done in BC through input random oracles, that generates diffusion in biometric values. The values are then fed to a transformation function that generates biocodes as second layer of authentication. Based on generated biocodes values, smart contracts (SC) are executed for transaction verification through encrypted wallets with user public/private pairs. For 80 biohashes, BioUAV has an overall latency of 22.5 milliseconds (ms), compared to 33.78 ms for conventional matching schemes. The framework has a accuracy of 98.37% under receiver operating characteristic (ROC) curve, with an attack probability of less than 0.5 at a proposed low performance indicator (PI) of 0.48. For security evaluation, the computation cost (CC) of BioUAV is 144.58 ms, and communication cost (CCM) is 123 bytes, that indicates the viability of the proposed framework against conventional approaches.
{"title":"BioUAV","authors":"S. Patel, Hamza Abubakar Kheruwala, M. Alazab, N. Patel, R. Damani, Pronaya Bhattacharya, S. Tanwar, Neeraj Kumar","doi":"10.1145/3414045.3415945","DOIUrl":"https://doi.org/10.1145/3414045.3415945","url":null,"abstract":"Modern cloud-based UAV communications authenticate user identity through hash-based biometric authentication schemes, but are limited in scope due to high-end processing and user template matching delays, coupled with latency and storage overheads. Motivated from the aforementioned discussions, the paper proposes a BC- envisioned identity framework to secure next-generation UAV communication. In BioUAV, a dual layer of security is exploited. In the first layer, user identity registration to UAVs are done in BC through input random oracles, that generates diffusion in biometric values. The values are then fed to a transformation function that generates biocodes as second layer of authentication. Based on generated biocodes values, smart contracts (SC) are executed for transaction verification through encrypted wallets with user public/private pairs. For 80 biohashes, BioUAV has an overall latency of 22.5 milliseconds (ms), compared to 33.78 ms for conventional matching schemes. The framework has a accuracy of 98.37% under receiver operating characteristic (ROC) curve, with an attack probability of less than 0.5 at a proposed low performance indicator (PI) of 0.48. For security evaluation, the computation cost (CC) of BioUAV is 144.58 ms, and communication cost (CCM) is 123 bytes, that indicates the viability of the proposed framework against conventional approaches.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"51 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":"116149713","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}
Drone systems, the so-called Unmanned Autonomous Vehicles (UAVs), have been widely employed in military and civilian sectors. Drone systems have been used for cyber warfare, warfighting and surveillance purposes of modern military and civilian applications. However, they have increasingly suffered from sophisticated malicious activities that exploit their vulnerabilities through network communications. As drones comprise a complex infrastructure as piloted aircraft but without operators, they still need a reliable security control to assert their safe operations. This paper proposes an autonomous intrusion detection scheme for discovering advanced and sophisticated cyberattacks that exploit drone networks. A testbed was configured to launch malicious events against a drone network for collecting legitimate and malicious observations and evaluate the performances of machine learning in real-time. Machine learning algorithms, including decision tree, k-nearest neighbors, naive Bayes, support vector machine and deep learning multi-layer perceptron, were trained and evaluated using the data collections, with promising results in terms of detection accuracy, false alarm rates, and processing times.
{"title":"Autonomous detection of malicious events using machine learning models in drone networks","authors":"Nour Moustafa, A. Jolfaei","doi":"10.1145/3414045.3415951","DOIUrl":"https://doi.org/10.1145/3414045.3415951","url":null,"abstract":"Drone systems, the so-called Unmanned Autonomous Vehicles (UAVs), have been widely employed in military and civilian sectors. Drone systems have been used for cyber warfare, warfighting and surveillance purposes of modern military and civilian applications. However, they have increasingly suffered from sophisticated malicious activities that exploit their vulnerabilities through network communications. As drones comprise a complex infrastructure as piloted aircraft but without operators, they still need a reliable security control to assert their safe operations. This paper proposes an autonomous intrusion detection scheme for discovering advanced and sophisticated cyberattacks that exploit drone networks. A testbed was configured to launch malicious events against a drone network for collecting legitimate and malicious observations and evaluate the performances of machine learning in real-time. Machine learning algorithms, including decision tree, k-nearest neighbors, naive Bayes, support vector machine and deep learning multi-layer perceptron, were trained and evaluated using the data collections, with promising results in terms of detection accuracy, false alarm rates, and processing times.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"58 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":"126568599","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}
Yun Chen, Xingqin Lin, T. Khan, Mohammad Mozaffari
The growing deployment of drones in a myriad of applications relies on seamless and reliable wireless connectivity for safe control and operation of drones. Cellular technology is a key enabler for providing essential wireless services to drones flying in the sky. Existing cellular networks targeting terrestrial usage can support the initial deployment of low-altitude drone users, but there are challenges such as mobility support. In this paper, we propose a novel handover framework for providing efficient mobility support and reliable wireless connectivity to drones served by a terrestrial cellular network. Using tools from deep reinforcement learning, we develop a deep Q-learning algorithm to dynamically optimize handover decisions to ensure robust connectivity for drone users. Simulation results show that the proposed framework significantly reduces the number of handovers at the expense of a small loss in signal strength relative to the baseline case where a drone always connect to a base station that provides the strongest received signal strength.
{"title":"A deep learning approach to efficient drone mobility support","authors":"Yun Chen, Xingqin Lin, T. Khan, Mohammad Mozaffari","doi":"10.1145/3414045.3415948","DOIUrl":"https://doi.org/10.1145/3414045.3415948","url":null,"abstract":"The growing deployment of drones in a myriad of applications relies on seamless and reliable wireless connectivity for safe control and operation of drones. Cellular technology is a key enabler for providing essential wireless services to drones flying in the sky. Existing cellular networks targeting terrestrial usage can support the initial deployment of low-altitude drone users, but there are challenges such as mobility support. In this paper, we propose a novel handover framework for providing efficient mobility support and reliable wireless connectivity to drones served by a terrestrial cellular network. Using tools from deep reinforcement learning, we develop a deep Q-learning algorithm to dynamically optimize handover decisions to ensure robust connectivity for drone users. Simulation results show that the proposed framework significantly reduces the number of handovers at the expense of a small loss in signal strength relative to the baseline case where a drone always connect to a base station that provides the strongest received signal strength.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"14 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":"132654324","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}
Maninderpal Singh, G. Aujla, R. S. Bali, Sahil Vashisht, Amritpal Singh, Anish Jindal
COVID-19 made the world stop, with people trapped inside their homes and governments trying to restrict the public movement. However, to accomplish this, one big problem that emerged and outscored everything else was catering to the day to day necessary items of the people without human involvement. In this regard, we propose a blockchain-enabled secure communication framework for delivering the goods in COVID-19 like scenarios by leveraging the drones that are available with commercial retail providers. The blockchain scheme is used to create smart contracts to build the trust of buyers and sellers on the framework as the payments are made through the smart contract executions. The blockchain based order processing ensures the integrity and authenticity of the information. Moreover, a communication model is presented along with the order, delivery and payment phases. The results prove the effectiveness of the proposed scheme by evaluating it based on gas price, transaction time, and mining time.
{"title":"Blockchain-enabled secure communication for drone delivery: a case study in COVID-like scenarios","authors":"Maninderpal Singh, G. Aujla, R. S. Bali, Sahil Vashisht, Amritpal Singh, Anish Jindal","doi":"10.1145/3414045.3415937","DOIUrl":"https://doi.org/10.1145/3414045.3415937","url":null,"abstract":"COVID-19 made the world stop, with people trapped inside their homes and governments trying to restrict the public movement. However, to accomplish this, one big problem that emerged and outscored everything else was catering to the day to day necessary items of the people without human involvement. In this regard, we propose a blockchain-enabled secure communication framework for delivering the goods in COVID-19 like scenarios by leveraging the drones that are available with commercial retail providers. The blockchain scheme is used to create smart contracts to build the trust of buyers and sellers on the framework as the payments are made through the smart contract executions. The blockchain based order processing ensures the integrity and authenticity of the information. Moreover, a communication model is presented along with the order, delivery and payment phases. The results prove the effectiveness of the proposed scheme by evaluating it based on gas price, transaction time, and mining time.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"9 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":"116857868","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}
Ishan Budhiraja, Neeraj Kumar, M. Alazab, Sudhanshu Tyagi, S. Tanwar, G. Srivastava
Device-to-Device (D2D) communications underlaying Unmanned aerial vehicle (UAV) with its mobility extend the coverage and improve the data rate. In this paper, we propose an energy management scheme for wireless powered D2D users with NOMA underlaying full-duplex (FD) UAV. Here, the cellular transmitters (CTs) and D2D transmitters (DDTs) first harvest energy from the radio frequency (RF) signals of the UAV. Then, the CT communicates with the cellular receivers (CRs) using the FD-UAV as a relay. On the other hand, DDT communicates with its two D2D receivers (DDRs) using the NOMA. We formulate the problem as a mixed-integer non-linear programming (MINLP) form and then divide it into two sub-problems. In the first sub-problem, an optimal value of time allocation for energy harvesting (EH) for DMG is estimated, whereas, in the second subproblem, the power of DDT in each DMG is optimized using the variable changing technique. Finally, the joint time allocation and power control scheme is proposed to achieve the maximum energy-efficiency (EE). Numerical results demonstrated that the proposed scheme achieves better results as compared to the existing conventional NOMA and orthogonal multiple access (OMA) schemes.
{"title":"Energy management scheme for wireless powered D2D users with NOMA underlaying full duplex UAV","authors":"Ishan Budhiraja, Neeraj Kumar, M. Alazab, Sudhanshu Tyagi, S. Tanwar, G. Srivastava","doi":"10.1145/3414045.3415946","DOIUrl":"https://doi.org/10.1145/3414045.3415946","url":null,"abstract":"Device-to-Device (D2D) communications underlaying Unmanned aerial vehicle (UAV) with its mobility extend the coverage and improve the data rate. In this paper, we propose an energy management scheme for wireless powered D2D users with NOMA underlaying full-duplex (FD) UAV. Here, the cellular transmitters (CTs) and D2D transmitters (DDTs) first harvest energy from the radio frequency (RF) signals of the UAV. Then, the CT communicates with the cellular receivers (CRs) using the FD-UAV as a relay. On the other hand, DDT communicates with its two D2D receivers (DDRs) using the NOMA. We formulate the problem as a mixed-integer non-linear programming (MINLP) form and then divide it into two sub-problems. In the first sub-problem, an optimal value of time allocation for energy harvesting (EH) for DMG is estimated, whereas, in the second subproblem, the power of DDT in each DMG is optimized using the variable changing technique. Finally, the joint time allocation and power control scheme is proposed to achieve the maximum energy-efficiency (EE). Numerical results demonstrated that the proposed scheme achieves better results as compared to the existing conventional NOMA and orthogonal multiple access (OMA) schemes.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"176 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":"126180353","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. Wazid, Basudeb Bera, Ankush Mitra, A. Das, Rashid Ali
Internet of Drones (IoD) architecture is designed to support a co-ordinated access for the airspace using the unmanned aerial vehicles (UAVs) known as drones. Recently, IoD communication environment is extremely useful for various applications in our daily activities. Artificial intelligence (AI)-enabled Internet of Things (IoT)-based drone-aided healthcare service is a specialized environment which can be used for different types of tasks, for instance, blood and urine samples collections, medicine delivery and for the delivery of other medical needs including the current pandemic of COVID-19. Due to wireless nature of communication among the deployed drones and their ground station server, several attacks (for example, replay, man-in-the-middle, impersonation and privileged-insider attacks) can be easily mounted by malicious attackers. To protect such attacks, the deployment of effective authentication, access control and key management schemes are extremely important in the IoD environment. Furthermore, combining the blockchain mechanism with deployed authentication make it more robust against various types of attacks. To mitigate such issues, we propose a private-blockchain based framework for secure communication in an IoT-enabled drone-aided healthcare environment. The blockchain-based simulation of the proposed framework has been carried out to measure its impact on various performance parameters.
{"title":"Private blockchain-envisioned security framework for AI-enabled IoT-based drone-aided healthcare services","authors":"M. Wazid, Basudeb Bera, Ankush Mitra, A. Das, Rashid Ali","doi":"10.1145/3414045.3415941","DOIUrl":"https://doi.org/10.1145/3414045.3415941","url":null,"abstract":"Internet of Drones (IoD) architecture is designed to support a co-ordinated access for the airspace using the unmanned aerial vehicles (UAVs) known as drones. Recently, IoD communication environment is extremely useful for various applications in our daily activities. Artificial intelligence (AI)-enabled Internet of Things (IoT)-based drone-aided healthcare service is a specialized environment which can be used for different types of tasks, for instance, blood and urine samples collections, medicine delivery and for the delivery of other medical needs including the current pandemic of COVID-19. Due to wireless nature of communication among the deployed drones and their ground station server, several attacks (for example, replay, man-in-the-middle, impersonation and privileged-insider attacks) can be easily mounted by malicious attackers. To protect such attacks, the deployment of effective authentication, access control and key management schemes are extremely important in the IoD environment. Furthermore, combining the blockchain mechanism with deployed authentication make it more robust against various types of attacks. To mitigate such issues, we propose a private-blockchain based framework for secure communication in an IoT-enabled drone-aided healthcare environment. The blockchain-based simulation of the proposed framework has been carried out to measure its impact on various performance parameters.","PeriodicalId":189206,"journal":{"name":"Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond","volume":"33 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":"132279367","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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