The drastically increasing volume and the growing trend on types of data have brought in possibility realizing advanced applications such as enhanced driving safety, enriched existing vehicular services through sharing among vehicles analysis. Due to limited resources with vehicles, edge computing networks (VECONs) i.e., integration mobile networks, can provide powerful massive storage resources. However, road side units that primarily presume role servers cannot be fully trusted, which may...

Federated learning is a promising tool in the Internet-of-Things (IoT) domain for training machine model decentralized manner. Specifically, data owners (e.g., IoT device consumers) keep their raw and only share local computation results to train global of owner an service provider). When executing federated task, contribute communication resources. In this situation, have face privacy issues where attackers may infer property or recover based on shared information. Considering these...

Abstract Programmed death ligand 1 (PD‐L1) is highly expressed in cancer cells and participates the immune escape process of tumor cells. However, as one most promising biomarkers for immunotherapy monitoring, key problem ahead practical usage how to effectively improve detection sensitivity PD‐L1. Herein, an electrochemical aptasensor evaluation developed based on checkpoint protein The fundamental principle this method involves utilization DNA nanotetrahedron (NTH)‐based capture probes...

With the rapid increase of mobile devices, computing load roadside cloudlets is fast growing. When computation tasks cloudlet reach limit, overload may generate heat radiation problem and unacceptable delay to users. In this paper, we leverage characteristics buses propose a scalable fog paradigm with servicing offloading in bus networks. The servers not only provide services for users on bus, but also are motivated accomplish offloaded by cloudlets. By way, capability significantly...

Integrated terrestrial and non-terrestrial networks (TNTNs) have become promising architecture for enabling ubiquitous connectivity. Smart remote sensing is one of the typical applications TNTNs that collects analyzes various dimensions data by deploying Internet Things (IoT) sensors edge computing in terrestrial, space, aerial, underwater networks. To improve analysis accuracy data, owners different should conduct collaborative learning on while label privacy be jointly considered. However,...

Deep learning holds a great promise of revolutionizing healthcare and medicine. Unfortunately, various inference attack models demonstrated that deep puts sensitive patient information at risk. The high capacity neural networks is the main reason behind privacy loss. In particular, in training data can be unintentionally memorized by network. Adversarial parties extract given ability to access or query this paper, we propose novel privacy-preserving mechanism for networks. Our approach adds...

Abstract To better provide fast computing services, vehicular edge can improve the quality of service and experience for intelligent transportation in 6G by reducing task transmission delay. However, networks face network capability limitations privacy issues practice. High‐speed vehicles time‐varying environment make them unpredictable. In meantime, smart with distinct computation capabilities need to process various tasks different resource requirements, which will inevitably cause...

The coupling of federated learning (FL) and multi-access edge computing (MEC) has the potential to foster numerous applications. However, it poses great challenges train FL fast enough with limited communication resources mobile devices. Motivated by recent development in ultra wireless transmissions promising advances artificial intelligence (AI) hardware devices, this paper, we propose a time efficient over future called dynamic batch sizes assisted (DBFL) convergence guarantee. DBFL...

Federated learning (FL) is an emerging distributed paradigm widely used in vehicular networks, where vehicles are enabled to train the deep model for server while keeping private data locally. However, annotation of by users very difficult since high costs and professional needs, one solution that roadside infrastructures could provide label mapping according geographical coordinates. In this scenario hold labels, respectively, traditional FL not applicable it needs each participant have...

Split federated learning (SFL) has been regarded as an efficient paradigm to enable both and reduce the computation burdens at devices by allowing them train parts of model. However, deploying SFL over resource-constrained vehicular edge networks is challenging, a cost-effective scheme necessitated minimize total time energy consumption devices. To this end, we use improved reinforcement method present joint optimization that can efficiently determine optimal model partition point for each...

Cloud-enabled vehicular network is an emerging paradigm which utilizes cloud computing to enhance the performance of network. But some issues still need be addressed and we focus on pseudonym resources management, crucial for vehicles guarantee location privacy. A new three-plane hierarchical architecture with software defined technology proposed manage resources. We use two-sided matching theory solve allocation problem among pools in different roadside unit clouds. Numerical results show...

Deep learning has attracted broad interest in healthcare and medical communities. However, there been little research into the privacy issues created by deep networks trained for applications. Recently developed inference attack algorithms indicate that images text records can be reconstructed malicious parties have ability to query networks. This gives rise concern electronic health containing sensitive patient information are vulnerable these attacks. paper aims attract from researchers...

Collecting urban data has become a crucial issue for smart city. Unlike mobile devices and other vehicles, buses have unique characteristics: fixed mobility trajectories, strong periodicity, uniform sensor interfaces low influence of exposure privacy. We leverage the characteristics consider collaborative vehicle sensing paradigm in bus networks. Data center are two sides trading sense service paradigm. The coordinated to collect while they competitors reward from center. design...

The limited lifetime of wireless nodes has become the essential bottleneck system performance and wide-scale deployment networks. In this paper, we consider a practical mobile chargeable network in which each single charger is able to charge multiple target simultaneously. To tackle problem, cooperative grouping proposed reduce number traversing spots chargers. Meanwhile, charging among chargers studied conserve their energy consumption. numerical results show that scheme outperforms...

Vehicular CrowdSensing (VCS) has become a promising paradigm to employ mobile vehicles for performing sensing tasks in supporting location-based services and applications. In traditional VCS, central agency is highly depended on, from collecting task requests of requesters employment reward assignment workers. However, the centralized manner causes critical problems, such as potential privacy leakage unexpected free-riding false-reporting behaviors due lack recorded proofs. The challenging...