Mobility-Aware Computation Offloading for Swarm Robotics using Deep Reinforcement Learning

Swarm robotics is envisioned to automate a large number of dirty, dangerous, and dull tasks. Robots have limited energy, computation capability, and communication resources. Therefore, current swarm robotics have a small number of robots, which can only provide limited spatio-temporal information. In this paper, we propose to leverage the mobile edge computing to alleviate the computation burden. We develop an effective solution based on a mobility-aware deep reinforcement learning model at the edge server side for computing scheduling and resource. Our results show that the proposed approach can meet delay requirements and guarantee computation precision by using minimum robot energy

Secure Communication Based on a Hyperchaotic System with Disturbances

This paper studies the problem on chaotic secure communication, and a new hyperchaotic system is included for the scheme design. Based on Lyapunov method and H∞ techniques, two kinds of chaotic secure communication schemes in the case that system disturbances exist are presented for the possible application in real engineering; corresponding theoretical derivations are also provided. In the end, some typical numerical simulations are carried out to demonstrate the effectiveness of the proposed schemes

Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles

By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden. However, existing model-based task offloading methods suffer from heavy computational complexity with the increase of vehicles and data-driven methods lack interpretability. To address these challenges, in this paper, we propose a knowledge-driven multi-agent reinforcement learning (KMARL) approach to reduce the latency of task offloading in cybertwin-enabled IoV. Specifically, in the considered scenario, the cybertwin serves as a communication agent for each vehicle to exchange information and make offloading decisions in the virtual space. To reduce the latency of task offloading, a KMARL approach is proposed to select the optimal offloading option for each vehicle, where graph neural networks are employed by leveraging domain knowledge concerning graph-structure communication topology and permutation invariance into neural networks. Numerical results show that our proposed KMARL yields higher rewards and demonstrates improved scalability compared with other methods, benefitting from the integration of domain knowledge

Intra- and intersexual interactions shape microbial community dynamics in the rhizosphere of Populus cathayana females and males exposed to excess Zn

In this study, we intended to investigate the responses of rhizospheric bacterial communities of Populus cathayana to excess Zn under different planting patterns. The results suggested that intersexual and intrasexual interactions strongly affect plant growth and Zn extraction in both sexes, as well as rhizosphere-associated bacterial com-munity structures. Females had a higher capacity of Zn accumulation and translocation than males under all planting patterns. Males had lower Zn accumulation and translocation under intersexual than under intrasexual interaction; the contrary was true for females. Females harbored abundant Streptomyces and Nocardioides in their rhizosphere, similarly to males under intersexual interaction, but differed from single-sex males under excess Zn. Conversely, intersexual interaction increased the abundance of key taxa Actinomycetales and Betaproteobacteria in both sexes exposed to excess Zn. Males improved the female rhizospheric microenvironment by increasing the abundance of some key tolerance taxa of Chloroflexi, Proteobacteria and Actinobacteria in both sexes under excess Zn in intersexual interaction. These results indicated that the sex of neighboring plants affected sexual differences in the choice of specific bacterial colonizations for phytoextraction and tolerance to Zn-contaminated soils, which might regulate the spatial segregation and phytoremediation potential of P. cathayana females and males under heavy metal contaminated soils.Peer reviewe

On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks

On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic. In this paper, we study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process. Specifically, we take the decision-making delay into the optimization problem. Then, a dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements. We further build a knowledge base representing the relationship among the service requirements, available computing resources, and the resource allocation performance. By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration. Simulation results show that the proposed scheme significantly outperforms the traditional static neural network, and also shows sufficient flexibility in on-demand service provisioning

Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach

As a fundamental problem, numerous methods are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-based optimization methods achieve strong performance, the high complexity raises the research of neural network (NN) based approaches to trade-off the performance and complexity. To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques. This approach aims to alleviate common issues encountered in each of these training paradigms, including the infeasibility of obtaining optimal solutions as labels and overfitting in supervised learning, ensuring higher convergence performance in unsupervised learning, and improving training efficiency in reinforcement learning. Simulation results demonstrate the enhanced performance of the proposed AD-based methods compared to traditional learning methods. Remarkably, this research paves the way for the integration of traditional optimization insights and emerging NN techniques in wireless communication system optimization

Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling

Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmitted to server at a time, while server processes tasks according to the transmission order, the problem is NP-hard. However, it is difficult for traditional optimization methods to quickly obtain the optimal solution, while approaches based on reinforcement learning face with the challenge of excessively large action space and slow convergence. In this paper, we propose a Digital Twin (DT)-assisted RL-based task scheduling method in order to improve the performance and convergence of the RL. We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT. In this way, the exploration efficiency of RL can be significantly improved via DT, and thus RL can converges faster and local optimality is less likely to happen. Particularly, two algorithms are designed to made task scheduling decisions, i.e., DT-assisted asynchronous Q-learning (DTAQL) and DT-assisted exploring Q-learning (DTEQL). Simulation results show that both algorithms significantly improve the convergence speed of Q-learning by increasing the exploration efficiency

Imperfect Digital Twin Assisted Low Cost Reinforcement Training for Multi-UAV Networks

Deep Reinforcement Learning (DRL) is widely used to optimize the performance of multi-UAV networks. However, the training of DRL relies on the frequent interactions between the UAVs and the environment, which consumes lots of energy due to the flying and communication of UAVs in practical experiments. Inspired by the growing digital twin (DT) technology, which can simulate the performance of algorithms in the digital space constructed by coping features of the physical space, the DT is introduced to reduce the costs of practical training, e.g., energy and hardware purchases. Different from previous DT-assisted works with an assumption of perfect reflecting real physics by virtual digital, we consider an imperfect DT model with deviations for assisting the training of multi-UAV networks. Remarkably, to trade off the training cost, DT construction cost, and the impact of deviations of DT on training, the natural and virtually generated UAV mixing deployment method is proposed. Two cascade neural networks (NN) are used to optimize the joint number of virtually generated UAVs, the DT construction cost, and the performance of multi-UAV networks. These two NNs are trained by unsupervised and reinforcement learning, both low-cost label-free training methods. Simulation results show the training cost can significantly decrease while guaranteeing the training performance. This implies that an efficient decision can be made with imperfect DTs in multi-UAV networks

Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph neural networks (GNN) can extract features of edge nodes when the network scales, they fail to handle a new scalability issue whereas the dimension of the decision space may change as the network scales. To address the issue, in this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC for an arbitrary number of edge nodes with extremely low algorithm inference delay. Moreover, a label-free unsupervised method is applied to train the LOGNN efficiently, where the gradient of edge tasks processing delay with respect to the LOGNN parameters is derived explicitly. In addition, a theoretical analysis of the scalability of the node-output GNN and link-output GNN is performed. Simulation results show that the proposed LOGNN can efficiently optimize the MEC resource allocation problem in a scalable way, with an arbitrary number of servers and users. In addition, the proposed unsupervised training method has better convergence performance and speed than supervised learning and reinforcement learning-based training methods. The code is available at \url{https://github.com/UNIC-Lab/LOGNN}

What leads to severe mountainous freeway crashes in southeast of China?

U radu je primijenjen hi-kvadrat test i binarna logistička regresija u analizi faktora koji dovode do teških sudara na brdskim cestama na jugoistoku Kine i određivanju njihova utjecaja na stupanj težine sudara u svrhu poduzimanja odgovarajućih mjera za poboljšanje sigurnosti na cesti. Rezultati su pokazali da mlađi i stariji vozači, žene, vožnja vikendom i rano ujutro, doprinose težini sudara. Uz to, vjerojatnije je da će se teški sudari dogoditi u proljeće, ljeto i jesen, nego zimi. Važnost dobivenih rezultata je u tome što vozači mogu razumjeti specifične karakteristike sudara na brdskoj autocesti na jugoistoku Kine te se mogu poduzeti odgovarajuće mjere da se stupanj sigurnosti poveća. Stoga se u radu daju sugestije za povećanje sigurnosti; mlađe i starije vozače treba upozoriti postavljanjem raznih prometnih znakova ili propagandom; vozačicama se savjetuje da ne voze na nekim dijelovima te ceste; upozorenja bi trebalo postaviti o pažljivoj vožnji noću naročito između 0 i 5:59 ujutro.This paper adopted chi-square test and binary logistic regression to analyse factors about mountainous freeway crash severity level in the southeast of China and to determine the factor impact on crash severity level to make corresponding measures to improve road safety. Results of this paper indicated that younger and older drivers, female drivers, weekends and driving early in morning all make contribution to severe mountainous freeway crashes. Besides, it is more likely to occur in severe crash in spring, summer and autumn compared to that in winter. The importance of study results to audience is that road users can understand the specific characteristics of mountainous freeway crashes in southeast of China and corresponding measurements could be made to improve the safety level. Therefore, this paper proposed some suggestions to improve mountainous freeway safety: younger and older drivers should be told the driving weakness by means of variable message sign or propaganda; female drivers are not encouraged to drive in some special mountainous freeway sections; traffic designs about careful driving at night are supposed to be set especially between 0 am and 5:59 am