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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
An Efficient Method for Optimizing RFID Reader Deployment...
Ching-Hsien Hsu, Daqiang Zhang, Chao-Tung Yang, Hai-Cheng Chu · 2013-05-15 · via cs.DC updates on arXiv.org

The rapid proliferation of Radio Frequency IDentification (RFID) systems realizes integration of physical world with the cyber ones. One of the most promising is the Internet of Things (IoT), a vision in which the Internet extends into our daily activities through wireless networks of uniquely identifiable objects. Given that modern RFID systems are being deployed in large-scale for different applications, without optimizing reader's distribution, many of the readers will be redundant, resulting waste of energy. Additionally, eliminating redundant eaders can also decrease probability of reader collisions, as a result, enhancing system performance and efficiency. In this paper, an overlap aware (OA) technique is proposed for eliminating redundant readers. The OA is a distributed approach, which does not need to collect global information for centralizing control, aims to detect maximum amount of redundant readers could be safely removed or turned off with preserving original RFID network coverage. A significant improvement of the OA scheme is that the amount of "write-to-tag" operations could be largely reduced during the redundant reader identification phase. In order to accurately evaluate the performance of the proposed method, it was performed in a variety of scenarios. The experiment results show that the proposed method can provide reliable performance with detecting higher redundancy and has lower algorithm overheads as compared with several well known methods, such as the RRE, LEO, the hybrid algorithm (LEO+RRE) and the DRRE.