Mo Li

SparkLink Low Energy (SLE) is a new short-range wireless communication technology designed to satisfy the increasing demands of future short-range scenarios. While SLE promises fundamental improvements over traditional Bluetooth, a thorough real-world evaluation is lacking. This article provides a detailed evaluation framework that includes access layer benchmarking and end-to-end application validation, presenting the first comprehensive comparison between SLE and traditional Bluetooth technologies. Benchmarking reveals SLE offers a 4× increase in data rate, a 3.84× reduction in latency, and a 10 dB improvement in sensitivity. To evaluate SLE against actual application QoS requirements, we implement an end-to-end evaluation system for validation across three practical application scenarios. Based on experimentation and parameter analysis, we propose a design for QoS-centered optimization. These results underscore SLE’s fundamental advancements over Bluetooth and its potential in real-world applications, offering insights for further optimization of this communication technology.

With the widespread deployment of WiFi infrastructure, WiFi RSSI fingerprinting has been extensively explored for indoor localization. However, conventional fingerprinting approaches require labor-intensive data collection and are often constrained by limited sampling rates in practical deployments. To address these limitations, we propose WiMU, a real-time indoor localization system that integrates WiFi and inertial measurement unit (IMU) data to enhance the real-time performance and accuracy of localization. WiMU operates on commodity WiFi infrastructure without the need for additional hardware, leveraging crowdsourced user trajectories to learn spatial representations of access points (APs). These representations can be fine-tuned with minimal labeled data to support effective localization. Extensive evaluations show that WiMU outperforms three state-of-the-art methods, significantly reducing the overhead while maintaining high positioning accuracy. Notably, it achieves an average localization error of 5.247 meters using no more than 100 fingerprints with known locations. WiMU has also been successfully deployed in real-world environments, including a university campus and a warehouse, demonstrating its practical effectiveness for real-time indoor localization.

This paper introduces a novel mobile sensing application - life journaling - designed to generate semantic descriptions of users' daily lives. We present AutoLife, an automatic life journaling system based on commercial smartphones. AutoLife only inputs low-cost sensor data (without photos or audio) from smartphones and can automatically generate comprehensive life journals for users. To achieve this, we first derive time, motion, and location contexts from multimodal sensor data, and harness the zero-shot capabilities of Large Language Models (LLMs), enriched with commonsense knowledge about human lives, to interpret diverse contexts and generate life journals. To manage the task complexity and long sensing duration, a multilayer framework is proposed, which decomposes tasks and seamlessly integrates LLMs with other techniques for life journaling. This study establishes a real-life dataset as a benchmark and extensive experiment results demonstrate that AutoLife produces accurate and reliable life journals. We also demonstrate the real-world application of AutoLife by integrating it into a digital journaling app that automatically generates journals for users.