相关论文: Age-Based Scheduling for Mobile Edge Computing: A …

We present two elegant solutions for modeling continuous-time dynamics, in a novel model-based reinforcement learning (RL) framework for semi-Markov decision processes (SMDPs), using neural ordinary differential equations (ODEs). Our models…

Age of information (AoI) is a performance metric that captures the freshness of status updates. While AoI has been studied thoroughly for point-to-point links, the impact of modern random-access protocols on this metric is still unclear. In…

In compute-first networking, maintaining fresh and accurate status information at the network edge is crucial for effective access to remote services. This process typically involves three phases: Status updating, user accessing, and user…

Age-of-Information (AoI) is a performance metric for scheduling systems that measures the freshness of the data available at the intended destination. AoI is formally defined as the time elapsed since the destination received the recent…

Mobile edge computing (MEC) allows appliances to offload workloads to neighboring MEC servers that have the potential for computation-intensive tasks with limited computational capabilities. This paper studied how deep reinforcement…

This paper studies the user scheduling problem in a multiuser multiple-input multi-output (MIMO) status update system, in which multiple single-antenna devices aim to send their latest statuses to a multiple-antenna information-fusion…

Data freshness, measured by Age of Information (AoI), is highly relevant in networked applications such as Vehicle to Everything (V2X), smart health systems, and Industrial Internet of Things (IIoT). However, freshness alone does not always…

Future real-time applications like smart cities will use complex Machine Learning (ML) models for a variety of tasks. Timely status information is required for these applications to be reliable. Offloading computation to a mobile edge cloud…

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical…

In Internet of Things (IoTs), the freshness of system status information is crucial for real-time monitoring and decision-making. This paper studies the transmission scheduling problem in wireless monitoring systems, where information…

Markov Decision Processes (MDPs), the mathematical framework underlying most algorithms in Reinforcement Learning (RL), are often used in a way that wrongfully assumes that the state of an agent's environment does not change during action…

The age of information (AoI) is used to measure the freshness of the data. In IoT networks, the traditional resource management schemes rely on a message exchange between the devices and the base station (BS) before communication which…

Wireless network optimization has been becoming very challenging as the problem size and complexity increase tremendously, due to close couplings among network entities with heterogeneous service and resource requirements. By continuously…

As Internet of Things (IoT) systems scale and device heterogeneity grows, multimodal data have become ubiquitous. Meanwhile, evaluating the freshness of multimodal data is essential, as stale updates would delay task execution, degrade…

Today many mobile users in various zones are invited to sense and send back real-time useful information (e.g., traffic observation and sensor data) to keep the freshness of the content updates in such zones. However, due to the sampling…

We summarize recent contributions in the broad area of age of information (AoI). In particular, we describe the current state of the art in the design and optimization of low-latency cyberphysical systems and applications in which sources…

Information freshness is a status update timeliness indicator of utmost importance to several real-time applications, such as connected and autonomous driving. The Ageof- Information (AoI) metric is widely considered as useful to quantify…

Information freshness is crucial for time-critical IoT applications, e.g., monitoring and control systems. We consider an IoT status update system with multiple users, multiple energy harvesting sensors, and a wireless edge node. The users…

Deep Reinforcement Learning (DRL) has emerged as a powerful solution for meeting the growing demands for connectivity, reliability, low latency and operational efficiency in advanced networks. However, most research has focused on…

Age of incorrect information (AoII) is a recently proposed freshness and mismatch metric that penalizes an incorrect estimation along with its duration. Therefore, keeping track of AoII requires the knowledge of both the source and…