Titlebook: Algorithmic Aspects in Information and Management; 18th International C Smita Ghosh,Zhao Zhang Conference proceedings 2024 The Editor(s) (i

chisel

NFTO: DAG-Based Task Offloading and?Energy Optimization Algorithmoading Algorithm based on Network Flow Theory. The core innovation lies in applying network flow theory to optimize task scheduling in dynamic environments, considering service caching and device energy constraints. We validate the algorithm’s advantages through comprehensive simulations.

無底

Minsum Problem for?Discrete and?Weighted Set Flow on?Dynamic Path Networks, need to move together and that some groups may be more important than others. To address these limitations, we modify the minsum flow problem to support flows represented as discrete and weighted sets. We also propose a 2-approximation pseudo-polynomial time algorithm to solve this modified problem for path networks with uniform capacity.

眉毛

ToI-Based Data Utility Maximization for?UAV-Assisted Wireless Sensor NetworksoI), which decays over time. Therefore, in this paper, we investigate the UAV Trajectory optimization problem for Maximizing the ToI-based data utility (TMT). We propose an improved deep reinforcement learning-based algorithm to address the problem, and the experience results demonstrate the effectiveness of our designs.

阻止

jeopardize

Algorithmic Aspects in Information and Management978-981-97-7798-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

倔強一點

ICBT for Severe Health Anxiety,quirement ., the goal of the . (MaxIMC) problem is to find a sub-collection of intervals . with . to maximize the number of fully-covered points, where a point . is fully-covered by . if it belongs to at least . intervals of .. In this paper, we present a .-approximation algorithm for the MaxIMC problem.

LEER

https://doi.org/10.1007/978-981-97-7798-3Algorithms and data structures; Algorithmic game theory and incentive analysis; Approximation algorith

frivolous

并入

貪婪地吃

The Agoras of the Twenty-First Century indicating the “staired coreness hierarchy” in each connected component. The edge weight here is defined as ., and .(.) is the corness of vertex .. Unlike the insertion maintenance problem of Maximum Spanning Tree (MST) which has known efficient algorithms, MCST insertion maintenance raises special