The emergence of Unmanned Aerial Vehicles (UAVs) has transformed static Wireless Sensor Networks (WSNs) into dynamic mobile sensing networks, enabling large-scale data acquisition for applications such as surveillance and precision agriculture. In these missions, multiple heterogeneous UAVs equipped with onboard sensors (e.g., cameras, multispectral sensors or LiDAR units) must collaboratively perform area coverage. The efficiency of the planned paths, governed by the Heterogeneous Multi-robot Coverage Path Planning (HMCPP) problem, directly impacts mission success. However, existing approaches to HMCPP are often limited by either prohibitive computational complexity or suboptimal path efficiency. To address this challenge, the HMCPP problem is modeled from a graph-based perspective, and a novel framework, termed Heterogeneous Cost Balancing (HCB), is introduced to efficiently generate high-quality solutions. HCB transforms existing solutions into HMCPP solutions by integrating fine-grained planning with cost-balancing strategies. Specifically, the proposed fine-grained planning approach refines traditional spiral routes, reducing path redundancy and enhancing coverage efficiency. Experimental results demonstrate that HCB outperforms baseline methods in most instances while maintaining high computation efficiency. Notably, on a large-scale real-world map (366 × 376 with 40 UAVs), HCB achieves improvements in makespan of 33.82%, 9.79%, 6.37%, and 12.34% over four heuristic-based baselines, and an 86.66% improvement over MIP-based approach.
Human Activity Recognition (HAR) is foundational to intelligent systems delivering personalized healthcare and wellbeing services. Wearable devices equipped with inertial measurement units (IMUs), such as smartwatches and earables, have enabled non-intrusive recognition of motion-driven activities. However, real-time recognition of contextual composite activities – temporally overlapping actions initiated by distinct body parts – remains challenging due to asynchronous multi-IMU data streams, where data collected from multiple IMU-enabled wearable devices may exhibit heterogeneous sampling rates, independent device clocks, and communication delays, resulting in temporally misaligned sensor readings, variability in sensor quality, and limited contextual cues from low-resolution streaming data. To address these challenges, we propose Multisource Self-Supervised Learning (MSSL), a lightweight framework that exploits unlabeled multisource-IMU data to enhance composite activity recognition in real time. MSSL incorporates (1) a dynamic source selection mechanism leveraging probability density estimation to adaptively identify primary and auxiliary sensor sources, and (2) a multimodal contrastive learning strategy to improve feature representations across heterogeneous sensor modalities. Evaluations on an in-house dataset and four benchmark HAR datasets demonstrate that MSSL achieves (i) 95.26% accuracy on our in-house composite activity dataset, (ii) 85.93% on PAMAP2-ADL, (iii) 80.08% on Opportunity, (iv) 91.18% on RealDisp, and (v) 69.41% on PAMAP2-Locomotions. Notably, MSSL maintains robust performance even with only 10% labeled data (78.05% accuracy) while achieving low inference latency of 0.6328 milliseconds. These results underscore MSSL’s potential for reliable, context-aware composite activity recognition in real-time applications, paving the way for more responsive and personalized services.
Non-intrusive load monitoring (NILM) aims to disaggregate total electricity consumption into individual appliance usage. Although deep learning has advanced NILM, it remains limited by its dependence on labeled data, restricted generalization, and lack of explainability. This paper introduces a prompt-based NILM framework that uses large language models (LLMs) with in-context learning. We integrate appliance features, contextual history, and one-shot examples into a unified prompting strategy and evaluate it on the REDD and UK-DALE datasets. Prompt-only LLMs deliver basic NILM capability but lag behind specialized deep learning models in accuracy, while achieving zero-shot generalization across houses and regions by adapting the injected appliance features. To characterize the unique value of LLMs for NILM, we propose a three-layer faithfulness framework that evaluates LLM-generated explanations along factual validity, internal coherence, and external correctness. The framework reveals a traceable-but-misaligned pattern in which LLMs often cite numerical values supported by the input but fail to align the cited events with true appliance activity. Rather than replacing specialized models, LLMs are better positioned as a semantic interface for energy disaggregation. We release our framework to support follow-up research.
Crowd monitoring in sports stadiums is essential for public safety and audience experience. Traditional methods rely on manual observation, cameras, and microphones, which can be disruptive and raise privacy concerns. Floor vibration sensing offers a non-intrusive alternative, but the lack of open-source vibration datasets limits data-driven model development. In this paper, we propose Vi bration L everages A udio (ViLA) , a vibration-based crowd monitoring framework that reduces reliance on labeled vibration data through cross-modal pre-training. Specifically, ViLA is first pre-trained on general-purpose audio data in an unsupervised manner, then fine-tuned using a small amount of labeled vibration data. This approach enables the model to learn general spectral representations from audio and transfer this knowledge to the vibration domain, which is especially useful in environments with high variability and limited sensing duration, such as live sports events. We evaluate ViLA in two stadium environments across different sports. We also analyze audio–vibration relationships to understand how pre-training influences model performance. In a preliminary single-run modality comparison, general-purpose audio produced the highest accuracy among the evaluated pretraining modalities. Across five-seed same-day evaluations, audio pretraining improved performance in some games, particularly when the scratch baseline was weak, while the magnitude of benefit varied across games.
Efficient firmware distributions in energy-harvesting (EH) LoRa networks require that EH LoRa sensors simultaneously receive data fragments from a server without facing power failures. This requirement is difficult to satisfy due to the impact of EH rates and LoRa transmission parameters on the efficiency of firmware distributions. We present FioRa+, a novel energy neutrality-aware multicast firmware distribution framework for EH LoRa networks. It gradually distributes a firmware image to EH LoRa sensors in an energy-neutral manner according to their future energy availability predicted using embedded machine learning models. Consequently, the need for additional firmware distributions caused by unsuccessful firmware image reconstructions is reduced. Through one-hop neighbor discovery, on-demand relay, flexible energy query, and coverage assessment mechanisms, FioRa+ ensures that all EH LoRa sensors can receive data fragments from the server at the scheduled time using high data rates. Equipped with a relay scheduling algorithm, it circumvents the collision of data fragments relayed by EH LoRa sensors using identical data rates. The experimental results show that FioRa+ renders up to 113 & times; shorter distribution time and 22.7 & times; less distribution overhead than the state-of-the-art.
