With the increasing demand for accurate indoor localization in smart homes, robotics, and commercial scenarios, achieving precise location using minimal infrastructure has become essential yet challenging. Traditional RSSI-based fingerprinting methods typically require multiple APs, limiting their practicality in single-AP scenarios commonly found in real-world home environments. To overcome this limitation, we propose a single-AP localization method that employs multi-directional beamforming to generate rich RSSI fingerprints. By sweeping the AP beamforming angle, the method captures directional RSSI features, significantly improving localization accuracy. Additionally, we introduce an angular embedding mechanism, explicitly encoding beamforming direction into neural network inputs to enhance robustness, especially under limited labeled data conditions. Extensive simulation and real-world experiments demonstrate that our proposed approach significantly outperforms conventional single-AP RSSI methods, consistently achieving sub-meter accuracy with small training datasets, thereby validating its effectiveness.

The explosive growth of mobile data traffic demands scalable, resilient, and energy-efficient solutions for content dissemination, particularly at the network edge. In this context, 5G networks can rely on Outband Device-to-Device (O-D2D) communications, using technologies such as Wi-Fi Direct, to offload infrastructure, enhance coverage at the cell edge, and enable proximity-based services. However, O-D2D communications face critical challenges related to peer discovery, reliable data retrieval over lossy wireless links, and energy efficiency constraints. In this paper, we propose a novel framework that integrates Named Data Networking (NDN) and Chinese Remainder Theorem (CRT)-based fragmentation to support autonomous O-D2D content delivery. By leveraging name-based forwarding and in-network caching, NDN facilitates distributed content retrieval, making it inherently suited for dynamic and infrastructure-less D2D environments. CRT-based fragmentation, on the other hand, enhances reliability and energy efficiency by allowing data reconstruction from partial fragment sets, mitigating the effects of packet losses typical of wireless links. We implement the proposed architecture in ndnSIM v2.9, extending the NDN forwarding plane to support CRT-based multi-source data retrieval. Simulation results demonstrate that our solution significantly improves content delivery and energy efficiency compared to existing NDN retrieval schemes, particularly under lossy wireless conditions.