Artificial Intelligence for Enhanced Mobility and 5G Connectivity in UAV-Based Critical Missions

Lins, Silvia; Cardoso, Kleber Vieira; Both, Cristiano Bonato; Mendes, Luciano Leonel; Rezende, José Ferreira de; Silveira, Antonio Morais da; Linder, Neiva; Klautau, Aldebaro

doi:10.1109/access.2021.3103041

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…Some results exploring UAVs in critical missions using the testbed are presented in [52]. This study presents how the AI agents and the network can be adapted to assist mobile network users in Search, Diagnostic and Rescue (SDAR) missions.…”

Section: Resultsmentioning

confidence: 99%

Network resource allocation for emergency management based on closed-loop analysis

Blessed¹,

Aliyu²,

Agajo³

et al. 2022

ITU J-FET

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The telecommunication system being a critical pillar of emergency management, intelligent deployment and management of slices in an affected area will help emergency responders. Techniques such as automated management of Machine Learning (ML) pipelines across the edge and emergency responder devices, usage of hierarchical closed-loops, and offloading inference tasks closer to the edge can minimize latencies for first responders in case of emergencies. This study describes the major results from building a Proof of Concept (PoC) for network resource allocation for emergency management using a hierarchical autonomous Artificial Intelligence (AI)/ML-based closed-loops in the mobile network, organized by the Internal Telecommunication Union Focus Group on Autonomous Networks (ITU FG-AN). The background scenario for this PoC included the interaction between a higher closed-loop in the Operations Support System (OSS) and a lower closed-loop in Radio Access Network (RAN) to intelligently share RAN resources between the public and the emergency responder slice. Representation of closed-loop "controllers" in a declarative fashion (intent), triggering "imperative actions" in the "underlay" based on the intent, setup of a data pipeline between various components, and methods of "influencing" lower layer loops using specific logic/models, were some of the essential aspects investigated by various teams. The main conclusions are summarised in this paper, including the significant observations and limitations from the PoC as well as future directions.

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Section: Resultsmentioning

confidence: 99%

Network resource allocation for emergency management based on closed-loop analysis

Blessed¹,

Aliyu²,

Agajo³

et al. 2022

ITU J-FET

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“…Lins et al 27 put forward the ideas of System Intelligence (SI) and Edge Intelligence (EI) to use 5G networks for UAV-based SAR (UAV-SAR)operations. Critical to mission efficiency, it provides a virtualized testbed to show how DNN partitioning affects communication costs and latency.…”

Section: Literature Reviewmentioning

confidence: 99%

“… S. no. Literature Method Advantages Limitations 1 26 Cloud-edge hybrid system (C-EHS) Precision in remote sensing, real-time data transmission, integration of object identification and tracking Reliance on cloud connectivity, potential latency issues 2 27 System intelligence (SI) and edge intelligence (EI) for UAV-based SAR operations Utilization of 5G networks, virtualized testbed for DNN partitioning analysis, and efficiency in mission-critical tasks Communication costs, latency issues, dependency on network stability 3 28 UAV-assisted edge computation framework (UAV-ECF) Real-time catastrophe scenario categorization, significant reduction in model size, increased throughput without accuracy loss Initial setup and configuration complexity, potential hardware compatibility issues 4 29 AI model optimization approaches Insights into edge intelligence applications, optimization of AI models for resource-constrained devices Complexity in implementation, potential trade-offs between optimization and model accuracy 5 30 PULP-Frontnet Real-time human pose estimation, energy-efficient autonomous navigation, and high accuracy compared to optimal sensor setups Dependency on vision quality, potential computational overhead 6 31 Optimization of vision-based CNNs on ULP processors for nano-UAVs Memory efficiency improvement, enhanced obstacle avoidance, free flight, and lane following power conservation Hardware compatibility, potential performance trade-offs 7 32 Integration of AI and backscatter communication for IoT enhancement Advancement in AI algorithms for...…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic task offloading edge-aware optimization framework for enhanced UAV operations on edge computing platform

Suganya,

Gopi,

Kumar

et al. 2024

Sci Rep

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Resource optimization, timely data capture, and efficient unmanned aerial vehicle (UAV) operations are of utmost importance for mission success. Latency, bandwidth constraints, and scalability problems are the problems that conventional centralized processing architectures encounter. In addition, optimizing for robust communication between ground stations and UAVs while protecting data privacy and security is a daunting task in and of itself. Employing edge computing infrastructure, artificial intelligence-driven decision-making, and dynamic task offloading mechanisms, this research proposes the dynamic task offloading edge-aware optimization framework (DTOE-AOF) for UAV operations optimization. Edge computing and artificial intelligence (AI) algorithms integrate to decrease latency, increase mission efficiency, and conserve onboard resources. This system dynamically assigns computing duties to edge nodes and UAVs according to proximity, available resources, and the urgency of the tasks. Reduced latency, increased mission efficiency, and onboard resource conservation result from dynamic task offloading edge-aware implementation framework (DTOE-AIF)'s integration of AI algorithms with edge computing. DTOE-AOF is useful in many fields, such as precision agriculture, emergency management, infrastructure inspection, and monitoring. UAVs powered by AI and outfitted with DTOE-AOF can swiftly survey the damage, find survivors, and launch rescue missions. By comparing DTOE-AOF to conventional centralized methods, thorough simulation research confirms that it improves mission efficiency, response time, and resource utilization.

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“…The case study offered in this work focuses on SAR missions. For this purpose, the presented X-IoCA architecture was implemented in this particular use case, being able to test the whole system with 5G communications, suitable for SAR missions since it supports real-time requirements, high bandwidth, and low latencies [ 47 ]. Because of that, this use case was included in a 5G pilot network developed by Vodafone and Huawei.…”

Section: Implementation Of X-ioca For Sar Missions: Sar-iocamentioning

confidence: 99%

The Internet of Cooperative Agents Architecture (X-IoCA) for Robots, Hybrid Sensor Networks, and MEC Centers in Complex Environments: A Search and Rescue Case Study

Bravo-Arrabal

Toscano-Moreno

Lozano

et al. 2021

Sensors

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Cloud robotics and advanced communications can foster a step-change in cooperative robots and hybrid wireless sensor networks (H-WSN) for demanding environments (e.g., disaster response, mining, demolition, and nuclear sites) by enabling the timely sharing of data and computational resources between robot and human teams. However, the operational complexity of such multi-agent systems requires defining effective architectures, coping with implementation details, and testing in realistic deployments. This article proposes X-IoCA, an Internet of robotic things (IoRT) and communication architecture consisting of a hybrid and heterogeneous network of wireless transceivers (H2WTN), based on LoRa and BLE technologies, and a robot operating system (ROS) network. The IoRT is connected to a feedback information system (FIS) distributed among multi-access edge computing (MEC) centers. Furthermore, we present SAR-IoCA, an implementation of the architecture for search and rescue (SAR) integrated into a 5G network. The FIS for this application consists of an SAR-FIS (including a path planner for UGVs considering risks detected by a LoRa H-WSN) and an ROS-FIS (for real-time monitoring and processing of information published throughout the ROS network). Moreover, we discuss lessons learned from using SAR-IoCA in a realistic exercise where three UGVs, a UAV, and responders collaborated to rescue victims from a tunnel accessible through rough terrain.

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Artificial Intelligence for Enhanced Mobility and 5G Connectivity in UAV-Based Critical Missions

Cited by 10 publications

References 16 publications

Network resource allocation for emergency management based on closed-loop analysis

Network resource allocation for emergency management based on closed-loop analysis

Dynamic task offloading edge-aware optimization framework for enhanced UAV operations on edge computing platform

The Internet of Cooperative Agents Architecture (X-IoCA) for Robots, Hybrid Sensor Networks, and MEC Centers in Complex Environments: A Search and Rescue Case Study

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