Spectrum crowdsensing is a paradigm where participants upload their collected spectrum data to the cloud for extracting analytics. First movers like Microsoft Spectrum Observatory and Electrosense, though with support from leading industry, research, and government, still suffer from sustainability challenges. In this paper, we present Fiesta, a sustainable framework for spectrum crowdsensing. On the technology side, we use federated learning and blockchain to decentralize the data analysis computations. For individual participants, minimal invasion of privacy suppresses concerns regarding large-scale adoption. From organizations’ perspectives, using blockchain avoids single point of failure and enhances the robustness of the entire system against malicious attacks. On the policy side, we propose a reward quantification mechanism to motivate engagement. Potential funding sources to ensure ongoing sustainability are also discussed. We have demonstrated Fiesta through simulation testbeds and real-world deployments with two demo tasks. Results show that Fiesta, as a decentralized framework, can preserve user privacy, enhance system robustness, maintain data fidelity compared with traditional methods, and fairly reward participants. We believe Fiesta is a stepping stone for the future spectrum crowdsensing paradigm.

Deploying large language models (LLMs) locally on mobile devices is advantageous in scenarios where transmitting data to remote cloud servers is either undesirable due to privacy concerns or impractical due to network connection. Recent advancements have facilitated the local deployment of LLMs. However, local deployment also presents challenges, particularly in balancing quality (generative performance), latency, and throughput within the hardware constraints of mobile devices. In this paper, we introduce our lightweight, all-in-one automated benchmarking framework that allows users to evaluate LLMs on mobile devices. We provide a comprehensive benchmark of various popular LLMs with different quantization configurations (both weights and activations) across multiple mobile platforms with varying hardware capabilities. Unlike traditional benchmarks that assess full-scale models on high-end GPU clusters, we focus on evaluating resource efficiency (memory and power consumption) and harmful output for compressed models on mobile devices. Our key observations include: i) differences in energy efficiency and throughput across mobile platforms; ii) the impact of quantization on memory usage, GPU execution time, and power consumption; and iii) accuracy and performance degradation of quantized models compared to their non-quantized counterparts; and iv) the frequency of hallucinations and toxic content generated by compressed LLMs on mobile devices.

Radio frequency (RF) techniques have shown promise for continuous contactless healthcare applications. However, real-world indoor environments pose challenges for existing systems, which may struggle to detect subtle physiological signals. This paper proposes Medusa, a novel wireless vital-sign sensing system designed for multi-view setups. It enables users to deploy distributed Multiple Input Multiple Output (MIMO) arrays into their daily living environments, facilitating vital-sign sensing in real-world settings. Unlike most existing single Commercial Off-The-Shelf (COTS) radar-based systems that operate under controlled settings Medusa's primary novelty lies in the design of a first-of-its-kind flexible multi-view vital sign sensing system that is view-agnostic, pose-agnostic, contactless, and can sense basic human vitals with good accuracy. Through our well-engineered hardware and software co-design, Medusa enables real-time processing of large distributed MIMO arrays, while balancing the tradeoff between Signal-to-Noise Ratio (SNR) and spatial diversity gain across each of its four distributed 4 × 4 sub-arrays for increased robustness. This is achieved using our novel unsupervised learning model which effectively recovers vital sign waveforms by decomposing the received signals. Extensive evaluations with 21 participants demonstrate Medusa's spatial diversity gain for real-world vital-sign monitoring, enabling free movement and orientation of subjects in both familiar and unfamiliar indoor environments.