2,061 research outputs found
Supporting UAVs with Edge Computing: A Review of Opportunities and Challenges
Over the last years, Unmanned Aerial Vehicles (UAVs) have seen significant
advancements in sensor capabilities and computational abilities, allowing for
efficient autonomous navigation and visual tracking applications. However, the
demand for computationally complex tasks has increased faster than advances in
battery technology. This opens up possibilities for improvements using edge
computing. In edge computing, edge servers can achieve lower latency responses
compared to traditional cloud servers through strategic geographic deployments.
Furthermore, these servers can maintain superior computational performance
compared to UAVs, as they are not limited by battery constraints. Combining
these technologies by aiding UAVs with edge servers, research finds measurable
improvements in task completion speed, energy efficiency, and reliability
across multiple applications and industries. This systematic literature review
aims to analyze the current state of research and collect, select, and extract
the key areas where UAV activities can be supported and improved through edge
computing
Trustworthy Edge Machine Learning: A Survey
The convergence of Edge Computing (EC) and Machine Learning (ML), known as
Edge Machine Learning (EML), has become a highly regarded research area by
utilizing distributed network resources to perform joint training and inference
in a cooperative manner. However, EML faces various challenges due to resource
constraints, heterogeneous network environments, and diverse service
requirements of different applications, which together affect the
trustworthiness of EML in the eyes of its stakeholders. This survey provides a
comprehensive summary of definitions, attributes, frameworks, techniques, and
solutions for trustworthy EML. Specifically, we first emphasize the importance
of trustworthy EML within the context of Sixth-Generation (6G) networks. We
then discuss the necessity of trustworthiness from the perspective of
challenges encountered during deployment and real-world application scenarios.
Subsequently, we provide a preliminary definition of trustworthy EML and
explore its key attributes. Following this, we introduce fundamental frameworks
and enabling technologies for trustworthy EML systems, and provide an in-depth
literature review of the latest solutions to enhance trustworthiness of EML.
Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table
Communication and Control in Collaborative UAVs: Recent Advances and Future Trends
The recent progress in unmanned aerial vehicles (UAV) technology has
significantly advanced UAV-based applications for military, civil, and
commercial domains. Nevertheless, the challenges of establishing high-speed
communication links, flexible control strategies, and developing efficient
collaborative decision-making algorithms for a swarm of UAVs limit their
autonomy, robustness, and reliability. Thus, a growing focus has been witnessed
on collaborative communication to allow a swarm of UAVs to coordinate and
communicate autonomously for the cooperative completion of tasks in a short
time with improved efficiency and reliability. This work presents a
comprehensive review of collaborative communication in a multi-UAV system. We
thoroughly discuss the characteristics of intelligent UAVs and their
communication and control requirements for autonomous collaboration and
coordination. Moreover, we review various UAV collaboration tasks, summarize
the applications of UAV swarm networks for dense urban environments and present
the use case scenarios to highlight the current developments of UAV-based
applications in various domains. Finally, we identify several exciting future
research direction that needs attention for advancing the research in
collaborative UAVs
A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions
The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network
A Survey on UAV-enabled Edge Computing: Resource Management Perspective
Edge computing facilitates low-latency services at the network's edge by
distributing computation, communication, and storage resources within the
geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent
advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new
opportunities for edge computing in military operations, disaster response, or
remote areas where traditional terrestrial networks are limited or unavailable.
In such environments, UAVs can be deployed as aerial edge servers or relays to
facilitate edge computing services. This form of computing is also known as
UAV-enabled Edge Computing (UEC), which offers several unique benefits such as
mobility, line-of-sight, flexibility, computational capability, and
cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices
are typically very limited in the context of UEC. Efficient resource management
is, therefore, a critical research challenge in UEC. In this article, we
present a survey on the existing research in UEC from the resource management
perspective. We identify a conceptual architecture, different types of
collaborations, wireless communication models, research directions, key
techniques and performance indicators for resource management in UEC. We also
present a taxonomy of resource management in UEC. Finally, we identify and
discuss some open research challenges that can stimulate future research
directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU
The future of UAS: standards, regulations, and operational experiences [workshop report]
This paper presents the outcomes of "The Future of UAS: Standards, Regulations and Operational Experiences" workshop, held on the 7th and 8th of December, 2006 in Brisbane, Queensland, Australia. The goal of the workshop was to identify recent international activities in the Unmanned Airborne Systems (UAS) airspace integration problem. The workshop attracted a broad cross-section of the UAS community, including: airspace and safety regulators, developers, operators and researchers. The three themes of discussion were: progress in the development of standards and regulations, lessons learnt from recent operations, and advances in new technologies. This paper summarises the activities of the workshop and explores the important outcomes and trends as perceived by the authors
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