This paper tackles efficient resource control in IoT edge computing using deep reinforcement learning.
problem Efficient allocation and scheduling of limited resources in IoT edge computing systems.
method Formulated as a CTMDP model, used deep reinforcement learning (RL) to approximate value functions and solve the MDP problem.
result Significant performance improvement over baseline algorithms and RL algorithms based on other architectures.
Edge computing addresses AI on IoT devices by processing data locally.
problem Processing AI on resource-constrained IoT devices is challenging.
method Deploying machine learning systems at the edge of the network.
result Edge computing reduces latency and communication costs.
Adaptive anomaly detection for IoT data reduces delay by 84%.
problem Real-time anomaly detection for IoT devices with limited resources.
method Hierarchical edge computing with adaptive anomaly detection models.
result Reduces detection delay by 84% while maintaining accuracy.
Adaptive anomaly detection for IoT data reduces delay without sacrificing accuracy.
problem Real-time anomaly detection for IoT data in distributed edge computing systems.
method Adaptive anomaly detection approach using contextual bandit and reinforcement learning.
result Significantly reduces detection delay (e.g., 71.4% for univariate data) without sacrificing accuracy.
EdgeAI aims to deploy deep learning on IoT devices.
problem Deep learning's high computational demands on IoT devices.
method Addressing data-independent deployment and communication-aware distributed inference.
result New directions to enable deep learning on IoT devices.
The paper addresses challenges in edge deep learning for IoT, proposing new directions.
problem Challenges in large-scale deep learning adoption for IoT devices.
method Unified view targeting three research directions: federated learning, data-independent deployment, and communication-aware inference.
result A network-centric approach is needed for edge intelligence.
The paper uses interpretable ML to secure data quality in IoT edge computing.
problem Ensuring data quality in IoT edge computing environments.
method Interpretable machine learning methods for feature selection and ensemble learning.
result The proposed method efficiently selects significant features for data processing.
Energy-efficient DL inference for IoT devices reduces power consumption and improves performance.
problem Energy inefficiency in deep learning models for IoT devices.
method Energy-aware early exiting policy to balance energy consumption and inference accuracy.
result Accuracy and service rate improved up to 25% and 35% respectively.
This paper explores deep reinforcement learning for IoT autonomy.
problem Autonomous control of IoT devices without human intervention.
method Deep reinforcement learning applied to IoT systems.
result A comprehensive survey of DRL applications in AIoT.
Paper presents a lightweight, unobtrusive method to protect edge device data privacy.
problem Protecting inference data privacy in IoT edge devices with limited compute power.
method A lightweight neural network at edge devices to obfuscate inference data without indicating obfuscation.
result Effectively protects inference data confidentiality while preserving backend accuracy.
To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing …
pAElla detects malware in DCs/SCs with high accuracy.
problem Real-time malware detection in high-resolution data centers.
method AI-powered edge computing with IoT-based monitoring and Power Spectral Density.
result pAElla achieves F1-score close to 1 with low false alarm and malware miss rates.
Paper presents ADEPOS framework for energy-efficient anomaly detection.
problem Energy and bandwidth limitations in IoT systems.
method Low precision computing and adaptive neural networks.
result 8.95X energy savings with no loss in detection accuracy.
Paper proposes a multi-phase pruning pipeline for deep ensemble learning on IIoT devices.
problem Computational limitations of IoT devices for deep learning models.
method Generates diverse pruned models, applies integer quantization, and uses clustering-based pruning.
result Significant reduction in model size (up to 90%) and improved performance (up to 7%) on IIoT devices.
Deep learning approach for efficient IoT task scheduling in MEC networks.
problem Minimizing task latency in IoT users with large-scale MEC systems.
method Stacked auto-encoder for data compression, adaptive simulated annealing, experience replay.
result Near-optimal performance with significantly reduced computational time.
Meta-ensemble scheme allocates queries to EC nodes for reduced latency.
problem Efficiently allocating queries to EC nodes to minimize latency.
method Combining ensemble models to decide query allocation based on node and query characteristics.
result Meta-ensemble scheme outperforms traditional allocation methods in reducing query processing latency.
The recent advent of `Internet of Things' (IOT) has increased the demand for enabling AI-based edge computing. This has necessitated the search for efficient implementations of neural networks in terms of both computations and storage. Although extreme quantization has proven to be a powerful tool to achieve significan…
PSO optimizes hyperparameters for edge ML models in FL.
problem Efficient local training and inference at edge devices.
method Particle Swarm Optimization (PSO) for hyperparameter tuning.
result PSO reduces the number of communication rounds by 2 orders of magnitude.
This paper proposes a communication-efficient deep anomaly detection framework for industrial IoT.
problem Accurately detecting anomalies in time-series data from edge devices in industrial IoT.
method A federated learning-based approach with an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model and gradient compression.
result The proposed framework accurately and timely detects anomalies with reduced communication overhead.
Paper proposes DP-PASGD for efficient, private IoT learning.
problem Privacy and resource constraints in IoT.
method Differentially private federated learning (DP-PASGD) for resource-constrained IoT.
result DP-PASGD achieves efficient training while maintaining privacy.
Federated Learning helps IoT devices learn without central servers.
problem Resource constraints in IoT devices hinder traditional ML approaches.
method Local training of IoT devices using global models.
result Federated Learning can be applied to IoT devices with varying resource capabilities.
This work surveys attacks and defenses on edge neural networks.
problem Security challenges of edge neural networks due to their compute and memory intensity, data-independence, and privacy risks.
method Taxonomy of attacks and defenses on edge-deployed neural networks.
result New security considerations and approaches are needed for edge DNNs.
Paper explores sustainable machine learning with energy harvesting.
problem Energy-efficient distributed machine learning in resource-constrained devices.
method Developed a practical learning framework with theoretical guarantees for distributed learning over energy-harvesting devices.
result Demonstrated significant performance improvement over non-harvesting benchmarks.
This paper reviews methods to create compact neural networks for IoT applications.
problem Complex deep neural networks are costly and slow, hindering real-world deployment.
method Automatic synthesis of compact, accurate DNN/LSTM models.
result Compact neural networks reduce energy consumption, memory, and inference time.
Paper discusses privacy issues in IoT and proposes a lightweight neural network approach.
problem Privacy concerns in IoT due to extensive data collection and processing.
method Developed a privacy-preserving inference approach for IoT objects and a deep neural network in the cloud.
result Satisfactory performance of the proposed approach on the MNIST dataset.
NoNN compresses deep networks into distributed IoT modules with minimal communication.
problem Memory and communication constraints in IoT devices for deep learning inference.
method NoNN compresses a large pretrained network into disjoint, highly-compressed student modules, optimizing for memory and communication.
result NoNN achieves higher accuracy than baselines and similar to the teacher model with minimal communication.
A framework for real-time edge intelligence using federated meta-learning.
problem Real-time intelligent decisions at edge devices with limited resources and data.
method Federated meta-learning approach for rapid adaptation of learned models.
result Effective framework demonstrated on various datasets.
New algorithms improve signal processing in federated learning.
problem Efficiently process distributed signal samples with privacy and communication constraints.
method Proposes overpredictive signal approximations using convex optimization.
result Quantifies tradeoffs between communication cost, sampling rate, and approximation error.
FANN-on-MCU enables efficient neural network inference on IoT devices.
problem Energy-efficient neural network inference on resource-constrained IoT devices.
method Open-source toolkit for ARM Cortex-M and RISC-V microcontrollers.
result Efficient neural network execution with low latency and power consumption.
Automates design of lightweight neural networks for image classification.
problem Designing efficient neural networks for edge devices with limited computational resources.
method Uses the Mesh Adaptive Direct Search (MADS) algorithm to optimize network architecture.
result Achieves comparable performance to standard methods with fewer design trials.
Deep learning identifies unknown IoT devices in network traffic.
problem Unauthorized IoT devices pose security risks in BYOD environments.
method Deep learning applied to network traffic images for IoT device identification.
result Over 99% accuracy in identifying 10 IoT devices and non-white-listed devices.
Implementing large-scale deep neural networks with high computational complexity on low-cost IoT devices may inevitably be constrained by limited computation resource, making the devices hard to respond in real-time. This disjunction makes the state-of-art deep learning algorithms, i.e. CNN (Convolutional Neural Networ…
Paper proposes a new ML approach using only additions and thresholding.
problem Energy efficiency and reduced complexity for IoT ML devices.
method Margin-Propagation (MP) network for inference and learning without MVMs.
result MP-based classifiers achieve comparable results to traditional ML methods with energy savings.
PCA-Triage optimizes sensor data sampling for IoT networks.
problem Excessive sensor data in IoT networks exceeds available bandwidth.
method PCA-Triage uses streaming incremental PCA loadings to adaptively triage sensor data.
result PCA-Triage achieves high inference performance with minimal bandwidth usage.
Paper designs distributed CNN execution for IoT units.
problem Memory and processing constraints in IoT units.
method Optimization problem to minimize latency, considering memory and processing constraints.
result Design supports multiple sources and CNNs in a single IoT system.
New method reduces cloud usage for mobile/IoT predictions.
problem Minimizing cloud usage for mobile/IoT predictions while maintaining accuracy.
method Formulating the BL problem via bracketing functions h−,h+ from a simple class. result Improved performance over prior methods, validated on real-world datasets.
Geometric Graph Alignment enhances IoT intrusion detection using NID data.
problem Data scarcity hinders IoT intrusion detection accuracy.
method Geometric Graph Alignment (GGA) approach to transfer knowledge between network intrusion detection and IoT intrusion detection domains.
result GGA approach boosts IoT intrusion detection performance on multiple datasets.
A new machine learning framework reduces IoT data transfer by two orders of magnitude.
problem Reducing data transfer in IoT devices over wireless channels.
method Developed a machine learning framework for distributed functional compression over GMAC and AWGN channels.
result The framework reduces communication by two orders of magnitude compared to cloud-based methods.
Machine learning and IoT improve steam flood oil production.
problem Optimizing oil production from heavy-oil wells using steam floods.
method Cutting-edge machine learning techniques applied to time-series IoT data.
result 3% improvement in oil production with optimized steam allocation.
FedHDPrivacy uses DP to improve FL in IoT, maintaining high accuracy.
problem Privacy threats in FL, especially in IoT environments.
method Integrates DP with neuro-symbolic computing, actively monitoring and adjusting noise.
result Maintains high performance in manufacturing monitoring, surpassing other FL methods.
Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth…
Predictive Q-learning algorithm for IoT networks with human operators.
problem Resilient and predictive actions for IoT networks with faulty components.
method Predictive and resilient Q-learning algorithm considering historical data and human operator feedback.
result Optimal scheduling policies avoiding attacked locations and faults.
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this work, we focus on …
The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy is…
Survey examines ML for IoT security, addressing new challenges.
problem IoT security challenges due to rapid growth and diverse attacks.
method Comprehensive literature review of ML-based security solutions.
result ML provides dynamic and efficient security for IoT.
Survey of IoT recommendation systems and their limitations.
problem Traditional recommender systems fail to handle IoT data.
method Comprehensive review of IoT recommender systems and techniques.
result Proposes a reference framework for future research.
State-of-the-art image recognition systems use sophisticated Convolutional Neural Networks (CNNs) that are designed and trained to identify numerous object classes. Such networks are fairly resource intensive to compute, prohibiting their deployment on resource-constrained embedded platforms. On one hand, the ability t…
DarkneTZ protects edge devices from DNN model leaks using TEE and model partitioning.
problem Privacy risks of pre-trained DNNs on edge devices through membership inference attacks.
method Model partitioning into sensitive and untrusted parts, leveraging TEE.
result DarkneTZ provides reliable model privacy with minimal performance overhead.