In this paper, GeoFL develops a hierarchical federated learning (FL) framework to address the unique challenges in large-scale geo-distributed scenarios. The key idea is to deploy multiple aggregators to geo-distributed clients and aggregate the local model and the global model efficiently and effectively. By assigning each aggregator as a relay layer, GeoFL can elaborately aggregate the geo-distributed clients and systematically determine when to upload the model to the central server based on bandwidth to efficiently update the global model under inadequate and heterogeneous WAN bandwidth constraints. GeoFL designs three key components to optimize the inefficient model aggregation and cope with the non-importance model updates. It further addresses the statistical heterogeneity across geo-distributed aggregators by considering the clients’ graph relationship, delivering an end-to-end clien-taggregator- server architecture for large-scale clients. Compared with existing works, our results on large-scale real-life datasets show that GeoFL speeds up the training process by 1.4×–8× and reduces 6%–80% unnecessary communication rounds between the aggregator and the central server.

Abstract Navigation is a fundamental component of modern information application systems, ranging from military, transportations, and logistic, to explorations. Traditional navigations are based on an absolute coordination system that provides a precise map of the physical world, the locations of the moving objects, and the optimized navigation routes. In recent years, many new emerging applications have presented new demands for navigation, e.g., underwater/underground navigations where no GPS or other localizations are available, an un-explored area with no maps, and task-oriented navigations without specific routes. The advances in IoT and AI enable us to design new navigation paradigms, embodied navigation that allows the moving object to interact with the physical world to obtain the local map, localize the objects, and optimize the navigation routes accordingly. We make a systematic and comprehensive review of research in embodied navigation, encompassing key aspects on perceptions, navigation and efficiency optimization. Beyond advancements in these areas, we also examine the emerging tasks enabled by embodied navigation which require flexible mobility in diverse and evolving environments. Moreover, we identify the challenges associated with deploying embodied navigation systems in the real world and extend them to substantial areas. We aim for this article to provide valuable insights into this rapidly developing field, fostering future research to close existing gaps and advance the development of general-purpose autonomous systems grounded in embodied navigation.

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains. We have also created an accompanying GitHub repository, where we compile the papers included in this survey: https://github.com/AIoT-MLSys-Lab/AIoT-Survey. This repository will be actively maintained and updated with new research as it becomes available. As both IoT and AI become increasingly critical to our society, we believe that AIoT is emerging as an essential research field at the intersection of IoT and modern AI. It is our hope that this survey will serve as a valuable resource for those engaged in AIoT research and act as a catalyst for future explorations to bridge gaps and drive advancements in this exciting field.

LoRa has been widely used for the Internet of Things (IoT) due to its low power consumption and long communication range. The standard LoRa demodulation process condenses the energy of LoRa chirps to combat noise. However, there is an intrinsic frequency jump in real-life LoRa signals that standard demodulation neglects, reducing communication range in practice. We thoroughly study the frequency jump phenomenon and observe that it affects LoRa demodulation mainly in two folds: First, it makes each section of the signal shorter than the standard FFT perception range, introducing additional noise; Second, it induces a random phase jump that causes destructive addition of signal power. To mitigate the influence of frequency jump on LoRa demodulation, we propose LoRaTrimmer, a novel, fast, and noise-resilient LoRa decoding algorithm that optimally condenses LoRa signal power. LoRaTrimmer contains two innovative designs: First, we trim the perception range of FFT at the frequency jump, trimming off the additional noise; Second, we bypass the phase jump induced by frequency jump by probabilistic modeling and add up signal power constructively. Furthermore, we performed theoretical analysis to guarantee the performance of our method. Thorough experiments in various real-life environments show 1.70 to 2.49 dB SNR gain over the state-of-the-art and 3.44 to 3.79 dB SNR gain over FFT-based methods, translating to at most 1.67 times gain of coverage area. LoRaTrimmer is also robust under complex noise patterns, and capable of real-time decoding, with the only overhead being a slight increase in computational cost (0.51 to 3.17 ms per packet, compared with 0.23 to 0.94 ms of baseline methods).

Leaf Wetness Duration (LWD), the time that water remains on leaf surfaces, is crucial in the development of plant diseases. Existing LWD detection lacks standardized measurement techniques, and variations across different plant characteristics limit its effectiveness. Prior research proposes diverse approaches, but they fail to measure real natural leaves directly and lack resilience in various environmental conditions. This reduces the precision and robustness, revealing a notable practical application and effectiveness gap in real-world agricultural settings. This paper presents Hydra, an innovative approach that integrates millimeter-wave (mm-Wave) radar with camera technology to detect leaf wetness by determining if there is water on the leaf. We can measure the time to determine the LWD based on this detection. Firstly, we design a Convolutional Neural Network (CNN) to selectively fuse multiple mm-Wave depth images with an RGB image to generate multiple feature images. Then, we develop a transformer-based encoder to capture the inherent connection among the multiple feature images to generate a feature map, which is further fed to a classifier for detection. Moreover, we augment the dataset during training to generalize our model. Implemented using a frequency-modulated continuous-wave (FMCW) radar within the 76 to 81 GHz band, Hydra's performance is meticulously evaluated on plants, demonstrating the potential to classify leaf wetness with up to 96% accuracy across varying scenarios. Deploying Hydra in the farm, including rainy, dawn, or poorly light nights, it still achieves an accuracy rate of around 90%.

Rural Internet of Things (IoT) systems connect sensors and actuators in remote areas, serving crucial roles in agriculture and environmental monitoring. Given the absence of networking infrastructure for backhaul in these regions, satellite IoT techniques offer a cost-effective solution for connectivity. However, current satellite IoT architectures often struggle to deliver high performance due to temporal and spatial link challenges. This paper presents SateRIoT, a new network architecture with temporal link estimation and spatial link sharing that fully exploits the capability of space low-cost low-earth-orbit (LEO) IoT satellites and ground low-power wide area (LPWA) IoT techniques in rural areas. First, we introduce a bursty link model that predicts the number of transmittable packets within a transmission window, reducing energy waste from failed uplink transmissions. Moreover, we enhance the model by selecting informative features and optimizing the window length. Additionally, we develop a multi-hop flooding protocol that enables gateways to buffer and share data packets across the network while incorporating a priority data queue to avoid duplicate transmissions. We implement SateRIoT with commercial-off-the-shelf (COTS) IoT satellite and LoRa radios, then evaluate its performance based on real deployment and real-world collected traces. The results show that SateRIoT can consume 3.3X less energy consumption for an individual gateway. Moreover, SateRIoT offers up to a 5.6X reduction in latency for a single packet and a 1.9X enhancement in throughput.

Passive human tracking using Wi-Fi has been researched broadly in the past decade. Besides straightforward anchor point localization, velocity is another vital sign adopted by the existing approaches to infer user trajectory. However, state-of-the-art Wi-Fi velocity estimation relies on Doppler-Frequency-Shift (DFS), which suffers from the inevitable signal noise incurring unbounded velocity errors, further degrading the tracking accuracy. In this article, we present WiVelo, which explores new spatial-temporal signal correlation features observed from different antennas to achieve accurate velocity estimation. First, we use subcarrier shift distribution (SSD) extracted from channel state information (CSI) to define two correlation features for direction and speed estimation, separately. Then, we design a mesh model calculated by the antennas’ locations to enable a fine-grained velocity estimation with bounded direction error. Finally, with the continuously estimated velocity, we develop an end-to-end trajectory recovery algorithm to mitigate velocity outliers with the property of walking velocity continuity. We implement WiVelo on commodity Wi-Fi hardware and extensively evaluate its tracking accuracy in various environments. The experimental results show our median and 90-percentile tracking errors are 0.47 m and 1.06 m, which are half and a quarter of state-of-the-art. The datasets and source codes are published through Github ( https://github.com/research-source/code ).

Leaf wetness detection is one of the key technologies for preventing plant diseases in agriculture. In this poster, we propose mmLeaf, leveraging a commercial off-the-shelf millimeter-wave (mmWave) radar to detect actual leaf wetness in diverse environments and lighting conditions. mmLeaf captures mmWave signals reflected by monitored leaves with a two-dimensional (2D) scanning system. Then, we use a multiple-input multiple-output (MIMO) array and synthetic aperture radar (SAR) to reconstruct the signal distribution of different planes of the leaves. A deep learning model takes the fused signal distribution as inputs to classify the leaf wetness. We implement mmLeaf using a frequency-modulated continuous-wave (FMCW) radar and evaluate its performance with a potted plant indoors. By exploring the use of mmWave signals, mmLeaf delivers an end-to-end detection framework that achieves up to 90% accuracy in classifying leaf wetness under different distances.

Internet-of-Things (IoT) aims to connect billions of low-date rate and energy-constrained end-devices in the near future. Although many IoT systems have been commercialized, most of them focus on home and body scale applications. To establish a low-cost IoT at the city scale, LoRa Wide Area Networks (LoRaWAN) have become attractive in recent years due to their desirable kilometer or even longer communication distance with low energy consumption. However, due to the expensive cost of densely deploying end-nodes, the understanding of LoRa link behavior is still coarse-grained, and hard to fully realize the link dynamics, networking coverage, and localization accuracy of LoRaWAN in an urban environment. This paper shows a fine-grained LoRa link-level measurement via mobile end-nodes. We deploy two gateways and six mobile end-nodes and collect data packets over four months at a$6\times 6\ km^{2}$urban area. The evaluation mainly focuses on answering three questions: 1) Does a LoRa link stably perform in both spatial and temporal dimensions? 2) How large area can be covered for reliable communication by each gateway in the urban environment? 3) What accuracy can be achieved to localize an end-node through LoRa links? According to our measurement, our key findings are 1) The spatial and temporal behavior of LoRa links is quite dynamic due to the different types of land covers and the frequent micro-environment changes in the urban areas; 2) Each gateway can cover about 11.3 km2area and marginal SNR gains (e.g., 2 dB) of LoRa links are efficient enough to enlarge 32.6% coverage area of a gateway; and 3). The median localization error is about 400 m. Without densely deployed LoRa gateways, the SOTA LoRa localization can support road-level localization, even when an end node is close to one of the gateways.

Passive human tracking via Wi-Fi has been re-searched broadly in the past decade. Besides straight-forward anchor point localization, velocity is another vital sign adopted by the existing approaches to infer user trajectory. However, state-of-the-art Wi-Fi velocity estimation relies on Doppler-Frequency-Shift (DFS) which suffers from the inevitable signal noise incurring unbounded velocity errors, further degrading the tracking accuracy. In this paper, we present WiVelo11Code&datasets are available at https://github.com/liecn/WiVelo_SECON22 that explores new spatial-temporal signal correlation features observed from different antennas to achieve accurate velocity estimation. First, we use sub carrier shift distribution (SSD) extracted from channel state information (CSI) to define two correlation features for direction and speed estimation, separately. Then, we design a mesh model calculated by the antennas' locations to enable a fine-grained velocity estimation with bounded direction error. Finally, with the continuously estimated velocity, we develop an end-to-end trajectory recovery algorithm to mitigate velocity outliers with the property of walking velocity continuity. We implement WiVelo on commodity Wi-Fi hardware and extensively evaluate its tracking accuracy in various environments. The experimental results show our median and 90% tracking errors are 0.47 m and 1.06 m, which are half and a quarter of state-of-the-arts.

LoRa (Long Range) is an emerging wireless technology that enables long-distance communication and keeps low power consumption. Therefore, LoRa plays a more and more important role in Low-Power Wide-Area Networks (LPWANs), which easily extend many large-scale Internet of Things (IoT) applications in diverse scenarios (e.g., industry, agriculture, city). In lots of environments where various types of land-covers usually exist, it is challenging to precisely predict a LoRa link's path loss. As a result, how to deploy LoRa gateways to ensure reliable coverage and develop precise fingerprint-based localization becomes a difficult issue in practice. In this paper, we propose DeepLoRa, a deep learning-based approach to accurately estimate the path loss of long-distance links in complex environments. Specifically, DeepLoRa relies on remote sensing to automatically recognize land-cover types along a LoRa link. Then, DeepLoRa utilizes Bi-LSTM (Bidirectional Long Short Term Memory) to develop a land-cover aware path loss model. We implement DeepLoRa and use the data gathered from a real LoRaWAN deployment on campus to evaluate its performance extensively in terms of estimation accuracy and model transferability. The results show that DeepLoRa reduces the estimation error to less than 4 dB, which is 2× smaller than state-of-the-art models.