The blockchain technology based on the existing stack of the Internet (i.e., TCP/IP) faces significant challenges regarding scalability, throughput, and latency, which are critical to its operation. This paper proposes shifting from an IP-based approach to a content-centric paradigm using Named Data Networking (NDN) to address blockchain-related problems and improve performance. We introduce a novel NDN-based method to optimize the use of NDN caching and content-based routing in tackling issues such as scalability, delay, and congestion. The core novelty lies in the formal integration of a subscription-based mechanism for real-time block delivery with a batch-based mechanism for efficient node synchronization, both rigorously modeled and evaluated via a Hierarchical Colored Petri Net (HCPN). This dual-mechanism design, unlike prior works, minimizes NDN overheads. Our simulations in the CPN Tools environment prove that the proposed method achieves better throughput and faster synchronization of blockchain nodes than other methods.
Cell-free massive multi-input multi-output (MIMO) is one of the architectures that look highly favorable for 6G networks as geographically distributed access points (AP) are able to together serve users without any set cell boundaries. This article summarizes some recent trends on cell-free massive MIMO and concentrates on areas like handling the interference problem, creating a group of users around a common focus, adjusting the power levels, adopting AI, and those technologies that make a cell-free environment possible. Besides that, the paper describes interference-division with Q-learning as a tool for learning how to adjust power levels through reinforcement learning. The authors tested their model in simulations in different aspects including interference, energy and spectral efficiencies SINR (Signal-to-Interference-plus-Noise Ratio) convergence of the learner, learning behavior, and computational burden. From the results, seems like the RLIC model behaves almost in real-time as regards controlling interference and has an overall better performance when compared to baseline methods. But the paper also reports that RLIC brings an extra cost with computation. In the last section, the paper raises some important challenges and research areas related to massive MIMO-based networks without cells, which are highly scalable and intelligent ones.
Recent advances in self-supervised vision transformers—particularly DINOv2—have shown impressive capabilities in learning rich visual representations from natural images. However, their potential for building robust and deployable face-recognition systems for Internet of Things (IoT) environments remains largely unexplored. In this work, we introduce a unified architecture that employs pretrained DINOv2 models (small, base, and large) as frozen feature extractors on the Labeled Faces in the Wild (LFW) and CelebA datasets, and couples them with three complementary downstream identity-learning modules: (i) Graph Neural Networks (GNN) operating on similarity graphs of face embeddings, (ii) Siamese networks trained with contrastive and triplet objectives, and (iii) distance-based classifiers using learned metric spaces. We develop an edge–cloud IoT pipeline in which low-power cameras perform lightweight, quantized DINOv2 feature extraction and transmit compact embeddings to a secure edge gateway for real-time matching and decision-making. To ensure long-term resilience, we also propose optimal design elements, similarity search, optimized metric learning, and the integration of quantum-resistant cryptographic mechanisms for template storage and communication. The results show that DINOv2 using GNN-based reasoning and Siamese metric learning can compete with LFW and CelebA approximately 62 ms on Raspberry Pi 4, 28 ms on Jetson Nano, and less than 110 ms end-to-end latency. Altogether, the system achieves approximately 94
Free-space optical communication provide high bandwidth and flexible deployment, however its exposure to the atmosphere makes it vulnerable to environmental degradation and security threats. To address these challenges, this paper proposes a robust chaotic image encryption scheme based on Chua’s circuit, designed for direct integration into FSO image transmission systems. Exploiting the sensitivity and unpredictability of chaotic dynamics, the method enhances resistance to both differential and statistical attacks. Simulation results under clear atmospheric conditions show a BER of 1.07 × 10⁻20 and a Q-factor of 10.84, confirming reliable transmission. Under moderate fog, the system maintains a BER of 1.058 × 10⁻3 and a Q-factor of 3.04, ensuring link stability. The security evaluation reveals a key space of 8.6 × 10^200 , an information entropy of 7.9992, a NPCR of 99.95
Mobile edge computing (MEC) is a key enabler for latency-sensitive 6 G services; however, task offloading in multi-base-station multi-MEC systems remains highly challenging due to inter-BS resource contention, time-varying traffic dynamics, and the limited realism of synthetic task-generation models. This paper proposes a task-offloading framework for a two-tier MEC architecture that integrates spatio-temporal traffic forecasting with multi-agent decision making to jointly reduce processing delay, task drop rate, and energy consumption. Specifically, task generation is derived directly from the Abilene backbone origin–destination traffic matrices. A ConvLSTM encoder-decoder is trained to capture the spatial-temporal correlations of network traffic and to generate predictive features that are incorporated into the local observation of each base station. The offloading problem is then formulated as a cooperative Dec-POMDP and solved by multi-agent proximal policy optimization (MAPPO) under a centralized training and decentralized execution paradigm. Experimental results under four traffic scales show that the proposed method consistently outperforms No-offloading, Random, Greedy, and DQN baselines. At the highest traffic load, the proposed framework achieves an average delay of 6.320 slots, a task drop rate of 31.749
