Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive h -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks

Incorporating the 10th Edition Institute of Traffic Engineers (ITE) Trip Generation Rates Into Virginia Department of Transportation Guidelines

The Institute of Transportation Engineers (ITE) released the Trip Generation (TG) 10th edition in 2017, which significantly updated its database, and some of its trip generation rates were substantially lower than those of earlier editions. This study aims to investigate the applicability of the TG 10th edition in various Virginia contexts and to recommend how to incorporate the TG 10th edition into state guidelines. The research team surveyed 31 state transportation agencies to obtain a clear understanding of current practices in the adoption of trip rates and trip estimation approaches. We systematically compared trip rates of TG 9th and 10th editions using hypothesis tests and identified land uses with significant rate reduction. Trip generation data were collected from 37 sites in Virginia during weekday PM peaks for the mixed-use sites and single-use sites with significantly reduced 10th edition rates (multi-family low-rise and general office). To investigate the use of trip rates in different settings, general offices in both general urban/suburban and dense multi-use urban were considered. For mixed-use developments, we explored the combinations of four internal trip capture models and TG rates of 9th and 10th editions to identify the best trip estimation approach. Given that all trip data were collected after the outbreak of the COVID-19 pandemic, Streetlight data were used to adjust trip counts to account for the impacts of COVID. This study recommends that VDOT’s Office of Land Use provide guidance to VDOT districts to accept traffic impact analysis reports using ITE’s 10th Edition Trip Generation and the 3rd Edition of the Trip Generation Handbook. It is further recommended that the Office of Land Use provide guidance to the districts to accept traffic impact analysis reports prepared using the methodology presented in the 3rd edition of the Trip Generation Handbook to estimate internal capture for mixed-use developments

Automating Intersection Marking Data Collection and Condition Assessment at Scale With An Artificial Intelligence-Powered System

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with existing roadway Geographic information systems (GIS) data and aerial images. The system harnesses emerging artificial intelligence (AI) techniques such as deep learning and multi-task learning to enhance its robustness, accuracy, and computational efficiency. AI models were developed to detect lane-use arrows (85% mean average precision) and crosswalks (89% mean average precision) and to assess the degradation conditions of markings (91% overall accuracy for lane-use arrows and 83% for crosswalks). Data acquisition and computer vision modules developed were integrated and a graphical user interface (GUI) was built for the system. The proposed system can fully automate the processes of marking data collection and condition assessment on a large scale with almost zero cost and short processing time. The developed system has great potential to propel urban science forward by providing fundamental urban infrastructure data for analysis and decision-making across various critical areas such as data-driven safety management and prioritization of infrastructure maintenance

Deep learning based joint resource scheduling algorithms for hybrid MEC networks

In this paper, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial vehicle (UAVs), all with mobile edge cloud installed to enable user equipments (UEs) or Internet of thing (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real-time, while considering the dynamic environment. To this end, we propose a hybrid deep learning based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LSFCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Secondly, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed integer nonlinear programming (MINLP) problems and generate the sample datasets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Thirdly, a DNN with the scheduling layer is introduced to provide user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from traditional DNN predictor, we only input one UE’s information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN

Seasonal Variations in the Organization and Structure of Apis cerana cerana Swarm Queen Cells

This paper describes the organization and structure of the swarm queen cells of Apis cerana cerana in spring, summer, and autumn in Kunming, Yunnan Province, China. We measured the following indices to reveal the organization rule of swarm cells: number of swarm cells built by each colony during different seasons; the shortest distance between two adjacent swarm cells on the comb; distance between swarm cell base and bottom bar of movable frame. We revealed the swarm cells structural characteristics using the following indicators: maximum diameter of swarm cell, the length between mouth and bottom of swarm cell, depth between maximum diameter and bottom of swarm cell, and the ratio of maximum diameter to depth between maximum diameter and bottom of swarm cell. Regarding seasonal differences, results indicated a significant variation in the distance between the swarm cell base and the bottom bar of the movable frame. Still, no such effect was observed in the shortest distance between two adjacent swarm cells. The maximum swarm cell diameter was not considerably influenced either, while the distance between the maximum diameter and the bottom of the swarm cell had substantial variation. The detected ratio of the maximum diameter to the depth between the maximum diameter and the bottom of theswarm cell indicated seasonal changes in the bottom shape of the swarm cell. This study clarifies the temporal and spatial distribution and structure of swarm cells of A. c. cerana. It establishes the basis for predicting the time and position of appearing swarm cells, thus allowing for a more precise determination of the shape and size of queen-cell punch and the ideal position of a cell cup on the bar of queen cup frames in artificial queen rearing

Large AI Model Empowered Multimodal Semantic Communications

Multimodal signals, including text, audio, image and video, can be integrated into Semantic Communication (SC) for providing an immersive experience with low latency and high quality at the semantic level. However, the multimodal SC has several challenges, including data heterogeneity, semantic ambiguity, and signal fading. Recent advancements in large AI models, particularly in Multimodal Language Model (MLM) and Large Language Model (LLM), offer potential solutions for these issues. To this end, we propose a Large AI Model-based Multimodal SC (LAM-MSC) framework, in which we first present the MLM-based Multimodal Alignment (MMA) that utilizes the MLM to enable the transformation between multimodal and unimodal data while preserving semantic consistency. Then, a personalized LLM-based Knowledge Base (LKB) is proposed, which allows users to perform personalized semantic extraction or recovery through the LLM. This effectively addresses the semantic ambiguity. Finally, we apply the Conditional Generative adversarial networks-based channel Estimation (CGE) to obtain Channel State Information (CSI). This approach effectively mitigates the impact of fading channels in SC. Finally, we conduct simulations that demonstrate the superior performance of the LAM-MSC framework.Comment: To be submitted for journal publicatio