Node self-localization is a key research topic for wireless sensor networks (WSNs). There are two main algorithms, the triangulation method and maximum likelihood (ML) estimator, angle of arrival (AOA) based self-localization. The ML estimator requires good initialization close to true location avoid divergence, while cannot obtain closed-form solution with high efficiency. Here, we develop set efficient AOA algorithms using auxiliary variables methods. First, formulate problem as linear...

How can the advantages of deep learning be brought to emerging world embedded IoT devices? The authors discuss several core challenges in and mobile learning, as well recent solutions demonstrating feasibility building applications that are powered by effective, efficient, reliable models.

With recent advances, neural networks have become a crucial building block in intelligent IoT systems and sensing applications. However, the excessive computational demand remains serious impediment to their deployments on low-end devices. emergence of edge computing, offloading grows into promising technique circumvent end-device limitations. transferring data between local devices takes up large proportion time existing frameworks, creating bottleneck for low-latency services. In this...

Federated learning (FL) is a privacy-preserving distributed machine framework, which involves training statistical models over number of mobile users (i.e., workers) while keeping data localized. However, recent works have demonstrated that workers engaged in FL are still susceptible to advanced inference attacks when sharing model updates or gradients, would discourage them from participating. Most the existing incentive mechanisms for mainly account workers' resource cost, cost incurred by...

Sign language recognition (SLR) bridges the communication gap between hearing-impaired and ordinary people. However, existing SLR systems either cannot provide continuous or suffer from low accuracy due to difficulty of sign segmentation insufficiency capturing both finger arm motions. The latest system, SignSpeaker, has a significant limit in recognizing two-handed signs with only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">one</i>...

Recent advances in deep learning motivate the use of neural networks Internet-of-Things (IoT) applications. These are modelled after signal processing human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where underlying physics (such inertia, wireless propagation, or natural frequency oscillation) fundamentally a function frequencies, offering better features domain....

Recent proliferation of Internet Things (IoT) devices with enhanced computing and sensing capabilities has revolutionized our everyday life. The massive data from these ubiquitous motivate the creation intelligent IoT systems that can collectively learn. However, labelling for learning purposes is extremely time-consuming, which greatly hinders deployment. In this paper, we describe a semi-supervised deep framework, called SenseGAN, leverage abundant unlabelled thereby minimizing need...

Recent advances in deep learning have led various applications to unprecedented achievements, which could potentially bring higher intelligence a broad spectrum of mobile and ubiquitous applications. Although existing studies demonstrated the effectiveness feasibility running neural network inference operations on embedded devices, they overlooked reliability computing models. Reliability measurements such as predictive uncertainty estimations are key factors for improving decision accuracy...

The paper enhances deep-neural-network-based inference in sensing applications by introducing a lightweight attention mechanism called the global module for multi-sensor information fusion. This is capable of utilizing collected from higher layers neural network to selectively amplify influence informative features and suppress unrelated noise at fusion layer. We successfully integrate this into new end-to-end learning framework, GIobalFusion, where two modules are deployed spatial modality...

End-to-end deep neural networks have achieved remarkable success across various domains but are often criticized for their lack of interpretability. While post hoc explanation methods attempt to address this issue, they fail accurately represent these black-box models, resulting in misleading or incomplete explanations. To overcome challenges, we propose an inherently transparent model architecture called Neural Probabilistic Circuits (NPCs), which enable compositional and interpretable...

Along with the rapid increasing energy consumption, cost of Internet data centers (IDCs) has been skyrocketing. A novel scheme geographical load balancing was proposed to reduce electricity bills for service providers. However, one important challenge faced by providers not considered properly. In systems, delay consumers includes queuing and transmission delay. While existing work only consider delay, introduced overlooked. It is most factors affecting quality real-time systems. this paper,...

Deep neural networks are becoming increasingly popular in Internet of Things (IoT) applications. Their capabilities fusing multiple sensor inputs and extracting temporal relationships can enhance intelligence a wide range However, one key problem is the missing adaptation to heterogeneous on-device sensors. These low-end sensors on IoT devices possess different accuracies, granularities, amounts information, whose sensing qualities vary over time. The existing deep learning frameworks for...

The paper presents a real-time computing framework for intelligent edge services, on behalf of local embedded devices that are themselves unable to support extensive computations. work contributes new direction in realtime develops scheduling algorithms machine intelligence tasks enable anytime prediction. We show deep neural network workflows can be cast as imprecise computations, each with mandatory part and (several) optional parts whose execution utility depends input data. With our...

This paper develops a novel unsupervised algorithm for belief representation learning in polarized networks that (i) uncovers the latent dimensions of underlying space and (ii) jointly embeds users content items (that they interact with) into manner facilitates number downstream tasks, such as stance detection, prediction, ideology mapping. Inspired by total correlation information theory, we propose Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) learns to project both...

This paper describes a novel diffusion model, DyDiff-VAE, for information prediction on social media. Given the initial content and sequence of forwarding users, DyDiff-VAE aims to estimate propagation likelihood other potential users predict corresponding user rankings. Inferring interests from data lies foundation prediction, because often forward in which they are interested or those who share similar interests. Their also evolve over time as result dynamic influence neighbors...

This paper explores <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">criticality-based real-time scheduling</i> of neural-network-based machine inference pipelines in cyber-physical systems (CPS) to mitigate the effect algorithmic priority inversion. We specifically focus on perception subsystem, an important subsystem feeding other components (e.g., planning and control). In general, inversion occurs when computations that are lower performed...

Recent advances in deep-learning-based applications have attracted a growing attention from the IoT community. These highly capable learning models shown significant improvements expected accuracy of various sensory inference tasks. One important and yet overlooked direction remains to provide uncertainty estimates deep outputs. Since robustness reliability results are critical systems, indispensable for applications. To address this challenge, we develop ApDeepSense, an effective efficient...

Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation disentangled representation learning. However, the existing VAE models some limitations different applications. For example, easily suffers from KL vanishing language modeling low reconstruction quality for disentangling. To address these issues, we propose novel controllable variational autoencoder framework, ControlVAE, that combines controller,...

Federated learning (FL) is a distributed machine paradigm that addresses the challenges of privacy leakage and data silos by collaboratively training global model through parameter exchange, rather than data, between central server local clients. However, recent researches highlight vulnerability FL to gradient attacks where adversaries exploit shared parameters from clients reconstruct sensitive data. Differential (DP) effectively mitigates this threat adding noise parameters, yet...

This paper presents the design and evaluation of GreenDrive, a smartphone-based system that helps drivers save fuel by judiciously advising on driving speed to match signal phase timing (SPAT) upcoming signalized traffic intersections. In absence such advice, default driver behavior is usually accelerate (near) maximum legally allowable speed, conditions permitting. suboptimal if light ahead will turn red just before vehicle arrives at intersection. GreenDrive uses collected real-time...

This paper presents the design and implementation of VibeBin, a low-cost, non-intrusive easy-to-install waste bin level detection system. Recent popularity Internet-of-Things (IoT) sensors has brought us unprecedented opportunities to enable variety new services for monitoring controlling smart buildings. Indoor management is crucial healthy environment in Measuring fill-level helps building operators schedule garbage collection more responsively optimize quantity location bins. Existing...

Cell selection is a critical issue in sparse mobile crowdsensing (MCS) systems. However, the sensing cost heterogeneity among different cells (subareas) has long been ignored by existing works. Moreover, data provided participants are not always trustworthy, and some malicious may intend to launch positioning attacks, which raises new challenge for cell selection. In this paper, address these issues, we propose trustworthy cost-effective (TCECS) framework that takes into consideration...

The paper discusses an emerging suite of machine intelligence services that are increasing importance in the highly instrumented world Internet Things (IoT). suite, called Eugene, would offer a form intelligent behavior (based on deep neural networks) to otherwise simple embedded devices; clients service. These devices benefit from service resources learn data and perform inference, classification, prediction, estimation tasks they too limited carry out their own. taxonomy such state...

This paper reviews the novel concept of a controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives key analytic properties, and offers useful extensions applications. ControlVAE is new (VAE) framework that combines automatic control theory with basic VAE stabilize KL-divergence models specified value. It leverages non-linear PI controller, variant proportional-integral-derivative (PID) dynamically tune weight term in evidence lower...