We consider some aspects of mixed H2/H∞control in a policy optimization setting. We study the convergence and robustness properties of our proposed policy scheme for autonomous systems described by stochastic differe...
详细信息
Viewport prediction is a crucial aspect of tile-based 360◦ video streaming system. However, existing trajectory based methods lack of robustness, also oversimplify the process of information construction and fusion be...
详细信息
In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been...
详细信息
ISBN:
(数字)9798350358261
ISBN:
(纸本)9798350358278
In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration; 2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively.
The use of unmanned aerial vehicles (UAVs) in IoT networks can provide an efficient means of collecting mission-critical data. However, ensuring the freshness of collected data is a significant challenge due to the st...
The use of unmanned aerial vehicles (UAVs) in IoT networks can provide an efficient means of collecting mission-critical data. However, ensuring the freshness of collected data is a significant challenge due to the stochastic status updates of sensor nodes (SN s), In this paper, we delve into the issue of timely data collection aided by a UAV in IoT networks, where the UAV collects status updates from SNs and offloads the data to the data center at an opportune location. SNs sample information from their surroundings at stochastic intervals to generate new status updates. To minimize the average total age of information (AoI), we describe the problem as a Markov decision process and propose a compound-action deep reinforcement learning (CADRL) approach to jointly optimize the trajectory, scheduling of SNs, offloading strategy, and offloading power of the UAV, which can handle both continuous and discrete actions of the UAV. Simulation results indicate that our algorithm effectively reduces the AoI when contrasted with baseline methods.
5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks t...
5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks than other proprietary fixed-function networks. Besides that, cloud-native architectures also present new security challenges. Cloud-native separates monolithic virtual machines into microservice pods, resulting in higher volumes of signaling and communication flowing through and between microservices. In addition, secure connections in monolithic applications have been replaced by untrusted communication between microservice pods, requiring additional cybersecurity capabilities. Access control systems were created to provide reliability and limit access to an organization’s assets. However, due to technology's constant evolution and dynamicity, these conventional security systems lack the security to protect an organization’s information because they were created to address access control for known users. For 5G based cloud native technology, these access controls need to be taken further by implementing a Zero Trust model to secure one’s essential assets for all users within the system. Zero Trust is implemented in an access control system under the concept "Never Trust, Always Verify". In this paper, we implement zero trust as a factor within access control systems by combining the principles of access control systems and zero-trust security by factoring in the user’s historical behavior and recommendations into the mix.
This paper proposes a novel robust reinforcement learning framework for discrete-time linear systems with model mismatch that may arise from the sim-to-real gap. A key strategy is to invoke advanced techniques from co...
详细信息
The Internet of Bio-Nano Things (IoBNT) is envisioned to be a heterogeneous network of artificial and natural units that are connected to the Internet. Hence, it extends the connectivity and control to unconventional ...
详细信息
ISBN:
(数字)9798350343199
ISBN:
(纸本)9798350343205
The Internet of Bio-Nano Things (IoBNT) is envisioned to be a heterogeneous network of artificial and natural units that are connected to the Internet. Hence, it extends the connectivity and control to unconventional domains, such as the human body. A potential use case for IoBNT is the communication from the outside to the inside of the human body. In this scenario, typically the Receiver (RX) inside the human body has limited computational complexity, while the Transmitter (TX) outside has large computational resources. In this paper, we address this scenario and propose a novel Asymmetric Auto-Encoder (AAEC) architecture for end-to-end learning of a Molecular Communication (MC) system. It applies a Neural Network (NN) at the TX and a low-complexity slope detector at the RX. We discuss the different layers of the NN-based TX and the corresponding training approach. Moreover, we investigate the explainability of the NN-based TX and show through the use of meta modeling that it can be approximated by a linear model. In addition, we demonstrate that the proposed AAEC resembles an MC system with Zero Forcing (ZF) precoding for low and moderate Inter Symbol Interference (ISI). Finally, through numerical results, we confirmed the aforementioned findings and showed that the proposed AAEC outperforms MC systems with and without ZF precoding, especially in high ISI scenarios.
The Large Language Model (LLM) has gained significant popularity and is extensively utilized across various domains. Most LLM deployments occur within cloud data centers, where they encounter substantial response dela...
详细信息
ISBN:
(数字)9798350368550
ISBN:
(纸本)9798350368567
The Large Language Model (LLM) has gained significant popularity and is extensively utilized across various domains. Most LLM deployments occur within cloud data centers, where they encounter substantial response delays and incur high costs, thereby impacting the Quality of Services (QoS) at the network edge. Leveraging vector database caching to store LLM request results at the edge can substantially mitigate response delays and cost associated with similar requests, which has been overlooked by previous research. Addressing these gaps, this paper introduces a novel Vector database-assisted cloud-Edge collaborative LLM QoS Optimization (VELO) framework. Firstly, we propose the VELO framework, which ingeniously employs vector database to cache the results of some LLM requests at the edge to reduce the response time of subsequent similar requests. Diverging from direct optimization of the LLM, our VELO framework does not necessitate altering the internal structure of LLM and is broadly applicable to diverse LLMs. Subsequently, building upon the VELO framework, we formulate the QoS optimization problem as a Markov Decision Process (MDP) and devise an algorithm grounded in Multi-Agent Reinforcement Learning (MARL) to decide whether to request the LLM in the cloud or directly return the results from the vector database at the edge. Moreover, to enhance request feature extraction and expedite training, we refine the policy network of MARL and integrate expert demonstrations. Finally, we implement the proposed algorithm within a real edge system. Experimental findings confirm that our VELO framework substantially enhances user satisfaction by concurrently diminishing delay and resource consumption for edge users utilizing LLMs.
The noise transition matrix $T$ , reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and build statistically consistent classifiers. The traditional transi...
详细信息
The noise transition matrix $T$ , reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and build statistically consistent classifiers. The traditional transition matrix is limited to model closed-set label noise, where noisy training data have true class labels within the noisy label set. It is unfitted to employ such a transition matrix to model open-set label noise, where some true class labels are outside the noisy label set. Therefore, when considering a more realistic situation, i.e., both closed-set and open-set label noises occur, prior works will give unbelievable solutions. Besides, the traditional transition matrix is mostly limited to model instance-independent label noise, which may not perform well in practice. In this paper, we focus on learning with the mixed closed-set and open-set noisy labels. We address the aforementioned issues by extending the traditional transition matrix to be able to model mixed label noise, and further to the cluster-dependent transition matrix to better combat the instance-dependent label noise in real-world applications. We term the proposed transition matrix as the cluster-dependent extended transition matrix. An unbiased estimator (i.e., extended $T$ -estimator) has been designed to estimate the cluster-dependent extended transition matrix by only exploiting the noisy data. Comprehensive experiments validate that our method can better cope with realistic label noise, following its more robust performance than the prior state-of-the-art label-noise learning methods.
Intelligent reflecting surface (IRS) has been recognized as a powerful technology for boosting communication performance. To reduce manufacturing and control costs, it is preferable to consider discrete phase shifts (...
详细信息
暂无评论