352 research outputs found
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
Saliency-Enabled Coding Unit Partitioning and Quantization Control for Versatile Video Coding
The latest video coding standard, versatile video coding (VVC), has greatly improved coding efficiency over its predecessor standard high efficiency video coding (HEVC), but at the expense of sharply increased complexity. In the context of perceptual video coding (PVC), the visual saliency model that utilizes the characteristics of the human visual system to improve coding efficiency has become a reliable method due to advances in computer performance and visual algorithms. In this paper, a novel VVC optimization scheme compliant PVC framework is proposed, which consists of fast coding unit (CU) partition algorithm and quantization control algorithm. Firstly, based on the visual saliency model, we proposed a fast CU division scheme, including the redetermination of the CU division depth by calculating Scharr operator and variance, as well as the executive decision for intra sub-partitions (ISP), to reduce the coding complexity. Secondly, a quantization control algorithm is proposed by adjusting the quantization parameter based on multi-level classification of saliency values at the CU level to reduce the bitrate. In comparison with the reference model, experimental results indicate that the proposed method can reduce about 47.19% computational complexity and achieve a bitrate saving of 3.68% on average. Meanwhile, the proposed algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality
Intelligent-Reflecting-Surface-Assisted UAV Communications for 6G Networks
In 6th-Generation (6G) mobile networks, Intelligent Reflective Surfaces
(IRSs) and Unmanned Aerial Vehicles (UAVs) have emerged as promising
technologies to address the coverage difficulties and resource constraints
faced by terrestrial networks. UAVs, with their mobility and low costs, offer
diverse connectivity options for mobile users and a novel deployment paradigm
for 6G networks. However, the limited battery capacity of UAVs, dynamic and
unpredictable channel environments, and communication resource constraints
result in poor performance of traditional UAV-based networks. IRSs can not only
reconstruct the wireless environment in a unique way, but also achieve wireless
network relay in a cost-effective manner. Hence, it receives significant
attention as a promising solution to solve the above challenges. In this
article, we conduct a comprehensive survey on IRS-assisted UAV communications
for 6G networks. First, primary issues, key technologies, and application
scenarios of IRS-assisted UAV communications for 6G networks are introduced.
Then, we put forward specific solutions to the issues of IRS-assisted UAV
communications. Finally, we discuss some open issues and future research
directions to guide researchers in related fields
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis
A wireless federated learning system is investigated by allowing a server and
workers to exchange uncoded information via orthogonal wireless channels. Since
the workers frequently upload local gradients to the server via
bandwidth-limited channels, the uplink transmission from the workers to the
server becomes a communication bottleneck. Therefore, a one-shot distributed
principle component analysis (PCA) is leveraged to reduce the dimension of
uploaded gradients such that the communication bottleneck is relieved. A
PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated
version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients
and the Nesterov's momentum. For the non-convex loss functions, a finite-time
analysis is performed to quantify the impacts of system hyper-parameters on the
convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is
theoretically certified to converge faster than the PCA-WFL algorithm. Besides,
the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal
the linear speedup with respect to the number of workers over the vanilla
gradient descent algorithm. Numerical results are used to demonstrate the
improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over
the benchmarks
Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey
Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.The work of Vinay Chamola and Fei Richard Yu was supported in part by the SICI SICRG Grant through the Project Artificial Intelligence Enabled Security Provisioning and Vehicular Vision Innovations for Autonomous Vehicles, and in part by the Government of Canada's National Crime Prevention Strategy and Natural Sciences and Engineering Research Council of Canada (NSERC) CREATE Program for Building Trust in Connected and Autonomous Vehicles (TrustCAV)
A Better Match for Drivers and Riders: Reinforcement Learning at Lyft
To better match drivers to riders in our ridesharing application, we revised
Lyft's core matching algorithm. We use a novel online reinforcement learning
approach that estimates the future earnings of drivers in real time and use
this information to find more efficient matches. This change was the first
documented implementation of a ridesharing matching algorithm that can learn
and improve in real time. We evaluated the new approach during weeks of
switchback experimentation in most Lyft markets, and estimated how it benefited
drivers, riders, and the platform. In particular, it enabled our drivers to
serve millions of additional riders each year, leading to more than $30 million
per year in incremental revenue. Lyft rolled out the algorithm globally in
2021
Inquiry web-based learning to enhance information problem solving competences in science
Early research on using web information indicates that secondary students fail to
explore much web tools, use them naively and have serious difficulties to
understand and integrate web information. In response to these challenges, the
main goal of this research has been to design, implement and evaluate an
instructional approach that helps students learn from web information. We have
developed on-line learning materials which focus on specific curricular contents
and provide specific scaffolds to help students accomplish web-based tasks and
develop specific information problem-solving competencies. These scaffolds have
intended to give support to students involved in information-seeking activities as
they were asked questions, searched for information, organised and assessed their
findings, and created rich representations of their newly-constructed
understandings. We have designed a one year long study to investigate the depth
and accuracy of 127 secondary students, as regards their content understanding as
well as their development of information problem-solving competencies when
using on-line resources to solve instructional tasks. Our research demonstrates that
the experimental group performed computer-based activities statistically better
than the control group. Our findings also suggest that students were able to
develop accurate and in-depth understanding from web information if they could
appropriately use search and managerial strategies. This research lends evidence
to questions regarding the value of students engaging in on-line inquiry web-based
learning to enhance content understanding and to develop more efficient
information problem-solving competencies in secondary education
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