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.

Platooning within autonomous vehicles has proven effective in addressing driver shortages and reducing fuel consumption. However, as platooning lengths increase, traditional C-V2X (cellular vehicle-to-everything) architectures are susceptible to end-to-end (E2E) latency increases. This is due to the necessity of relaying information through multiple hops from the leader vehicle to the last vehicle. To address this problem, this paper proposes a hybrid communication architecture and evaluates its end-to-end (E2E) delay through simulation in a multi-platoon scenario that integrates light fidelity (LiFi) and C-V2X. The proposed architecture introduces multiple-leader vehicles equipped with outdoor LiFi communication nodes in platoons to achieve high-speed and low-delay communication between leader vehicles, thereby reducing E2E delay.

With the increasing demand for indoor localization, received signal strength indicator (RSSI)-based fingerprint localization has gained widespread attention due to its low equipment costs. Traditional methods only use RSSI data collected from user devices to train localization models, but the coarse granularity of RSSI often limits accuracy. Additionally, changes in the environment, such as door movements or furniture rearrangements, can render these models ineffective. While resource-intensive and time-consuming, data re-collection and model retraining are essential for capturing updated signal characteristics after environment changes, ensuring the model remains accurate and effective. To enhance localization accuracy, we expand on traditional approaches by incorporating RSSI data measured between wireless routers as additional fingerprint features, achieving nearly a 20% accuracy improvement. Furthermore, we address the challenges of dynamic environments by introducing a multi-task domain-adversarial transfer learning method, which extracts consistent features before and after environment changes. Transfer learning allows us to leverage knowledge from the environment before the change, thereby reducing the need for data re-collection after the change. Experiment results from simulated, real-world, and open dataset environments confirm the effectiveness of the proposed method in dynamic indoor localization. Our approach reduced the mean error distance (MED) by 35%, 44%, and 28%, respectively, with only 16%, 20%, and 17% of the data re-collected.

Summary In previous work, we introduced a distributed collections library for the APGAS for Java programming model. This library makes it possible for programmers to develop complex distributed programs thanks to the many abstractions and computation patterns supported. In particular, programmers can fully and dynamically change the distribution of data entries through high‐level abstractions. However, the problem of balancing the load between processes remains, especially in cases where multiple processes may be concurrently executing on a single host, or when the performance of the hosts used differs. To address this issue and to relieve the burden of programming a load balancing strategy for a specific application, we created a dynamic load balancer integrated into our library. This load balancer operates within a specific context in a manner which does not interfere with the program legibility. Internally, we implement a scheme inspired by the lifeline‐based global load balancer scheme first introduced in 10. We evaluate the performance of our integrated load balancer on a small‐scale Beowulf cluster.

Summary Global load balancer libraries should be easy to use and allow users to easily obtain good performance for their applications on a variety of distributed systems. In this article, we introduce a new tuning mechanism to our Java implementation of the lifeline‐based global load balancer which automatically adjusts the task granularity to reach good performance based on some selected runtime metrics. We evaluate our system against four backtrack‐search problems on both a many‐core supercomputer environment and on a beowulf server, achieving ideal performance with our tuning mechanism on the supercomputer. We also identify the limits of our mechanism in handling situations with reduced imbalance.