The sixth-generation network (6G) is expected to achieve a fully connected world, which makes full use of large amount sensitive data. Federated Learning (FL) an emerging distributed computing paradigm. In Vehicular Edge Computing (VEC), FL used protect consumer data privacy. However, using in VEC will lead expensive communication overheads, thereby occupying regular resources. the traditional FL, massive rounds before convergence enormous costs. Furthermore, each round, many clients upload...

The development of Industrial Internet Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number intensive computing tasks. Resource-constrained devices often cannot meet real-time requirements these As promising paradigm, mobile-edge (MEC) system migrates computation tasks from resource-constrained to nearby MEC servers, thereby obtaining lower delay energy consumption. However, considering...

In this paper, a new one-dimensional fractional chaotic map is proposed and an image encryption scheme based on parallel DNA coding designed by using the map. The mathematical model of system combines sine fraction operation. Compared with some traditional systems, has larger range parameters better characteristics, which makes it more suitable for applications in information encryption. addition, algorithm proposed, overcomes shortcoming common coding-based algorithms. Parallel computing...

Energy harvesting (EH) from ambient energy sources can potentially reduce the dependence on supply of grid or battery energy, providing many benefits to green communications. In this paper, we investigate device-to-device (D2D) user equipments (DUEs) multiplexing cellular (CUEs) downlink spectrum resources problem for EH-based D2D communication heterogeneous networks (EH-DHNs). Our goal is maximize average efficiency all links, in case guaranteeing quality service CUEs and EH constraints...

Remote clouds are gradually unable to achieve ultra-low latency meet the requirements of mobile users because intolerable long distance between remote and network congestion caused by tremendous number users. Mobile edge computing, a new paradigm, has been proposed mitigate aforementioned effects. Existing studies mostly assume servers have deployed properly they just pay attention how minimize delay In this paper, considering practical environment, we investigate deploy effectively...

Geographic routing is a research hotspot of the Internet Vehicles (IoV) and intelligent traffic system (ITS). In practice, vehicle movement not only affected by its characteristics relationship between position but also some implicit factors. Pointing to this problem, we combine moving probability matrix, association factors study influence potential features propose algorithm based on (RAVP) analysis, which can obtain more accurate prediction trajectory. Then, distance obtained By...

The number of smart devices newly connected to the Internet has grown exponentially in recent years. These are interwoven into huge Things. There is a contradiction between mass data transmission and communication bandwidth, distance supercomputing power processing object, demand frequent interaction real-time response. As new computing paradigm, edge processes tasks on resources close sources. Considering limited energy mobile terminal user's for low delay, making decisions about executed...

As the demand for Internet of Things (IoT) technologies continues to grow, IoT devices have been viable targets malware infections. Although deep learning-based detection has achieved great success, models are usually trained based on collected user records, thereby leading significant privacy risks. One promising solution is leverage federated learning (FL) enable distributed on-device training without centralizing private records. However, it non-trivial users label these where quality and...

The integration of Mobile Edge Computing (MEC) and microservice architecture drives the implementation sustainable Internet Vehicles (IoV). enables decomposition a service into multiple independent, fine-grained microservices working independently. With MEC, can be placed on Service Providers (ESPs) dynamically, responding quickly reducing latency resource consumption. However, burgeoning IoV leads to high computation overheads, making requirements an imminent issue. What's more, due limited...

Artificial Intelligence (AI) technology has been widely applied to Internet of Thing (IoT) and one key application is intelligence data collection from billions IoT devices. However, many AI based approaches lack security considerations leading availability restricted. In this paper, an Intelligent Trust Collaboration Network System (ITCN) established collect through collaboration with mobile vehicles Unmanned Aerial Vehicle (UAV) for IoT. The first, a deadline-aware network framework...

The energy harvesting cognitive wireless sensor network (EHCWSN) introduces technology and radio into the traditional (WSN), which significantly prolongs working life of node effectively alleviates congestion problem unlicensed spectrum. Due to uncertainty process behavior primary user (PU), how allocate manage limited resources is a crucial in EHCWSN. In this work, new Q-learning-based channel selection method proposed for randomness PU's network. By continuously interacting learning with...

Multi-modal fusion is a promising approach to boost the autonomous driving performance and has already received large amount of attention. Meanwhile, increase reliability under distinct scenarios, it important handle unforeseen weather events in training dataset, which known as an Out-Of-Distribution (OOD) problem, for algorithms. In this paper, we consider those two aspects propose end-to-end multi-modal domain-enhanced framework, namely CrossFuser, meet safety orientated requirements....

The Internet of Things (IoT) mainly consists a large number Internet-connected devices. proliferation untrusted third-party IoT applications has led to an increase in IoT-based malware attacks. In addition, it is infeasible for the devices support sophisticated detection systems due restricted resources. Edge computing considered be promising. It provides solutions data security and privacy leakage brought by applications. this article, intelligent trusted secure edge (ITEC) system proposed...

Camouflaged object detection has been considered a challenging task due to its inherent similarity and interference from background noise. It requires accurate identification of targets that blend seamlessly with the environment at pixel level. Although existing methods have achieved considerable success, they still face two key problems. The first one is difficulty in removing texture noise thus obtaining edge frequency domain information, leading poor performance when dealing complex...

Heterogeneous communication environments and broadcast feature of safety-critical messages bring great challenges to mode selection resource allocation problem. In this paper, we propose a federated multi-agent deep reinforcement learning (DRL) scheme with action awareness solve problem for ensuring quality service (QoS) in heterogeneous V2X environments. The proposed includes an action-observation-based DRL model parameter aggregation algorithm considering local historical parameters. By...

Task offloading is a widely used technology in Edge Computing (EC), which declines the makespan of user task with aid resourceful edge servers. How to solve competition for computation and communication resources among tasks fundamental issue offloading. Besides, real-life often comprise multiple interdependent subtasks. Dependencies subtasks significantly raises complexity offloading, makes it difficult propose generalized approaches scenarios different size. In this paper, we study...