FLAME auto-labels mobile data efficiently on diverse processors.
problem Accurately and efficiently labeling mobile data with unknown labels on heterogeneous processors.
method Self-adaptive auto-labeling system Flame that schedules and executes workloads on mobile processors.
result Flame achieves high labeling accuracy and performance on heterogeneous mobile processors.
A real-time context-aware system for IoT using mobile devices.
problem Challenges in running machine learning on mobile devices.
method Developed a context-learning algorithm for mobile devices that updates itself periodically from the server.
result Achieved mean accuracy of 97.51% with only 11ms execution time.
Federated Learning system for mobile devices.
problem Training models on decentralized data.
method High-level design of a scalable Federated Learning system.
result Solutions to challenges in federated learning.
A DRL approach optimizes computation offloading in MEC systems for mobile users.
problem Optimizing computation offloading in MEC systems with mobile users and stochastic task arrivals.
method Deep Deterministic Policy Gradient (DDPG) for decentralized dynamic computation offloading.
result The DDPG-based strategy outperforms conventional strategies in terms of computation cost and power-delay tradeoff.
Introduces mobility algebra for modeling geodesics on n-spheres.
problem Modeling geodesics on n-spheres using algebraic structures.
method Introduces mobility algebra and mobility spaces, showing connections to modules and affine spaces.
result Shows geodesics on n-spheres as mobility spaces over unit interval mobility algebra.
Method discovers user habits from mobile data.
problem Understanding human mobility patterns and habits.
method Density-based clustering for spatio-temporal data and Gaussian Mixture Model (GMM).
result Many unique habits were identified from the datasets.
Mobile SoCs can improve ML inference by 2x when all components are used concurrently.
problem Power-performance trade-offs in mobile ML inference on heterogeneous SoCs.
method Quantitative evaluation of different ML components on mobile SoCs.
result Mobile SoCs can achieve up to 2x improvement in inference performance by using all components concurrently.
Study predicts traffic congestion based on population mobility data.
problem Predicting traffic congestion in multimodal transport networks.
method Machine learning methods applied to population mobility data.
result Likely prediction of congestion based on population movements.
Mobile app for neonatal EEG interpretation helps non-experts diagnose brain health.
problem Limited EEG interpretation skills among neonatal healthcare professionals.
method Low-cost, low-power EEG acquisition system with AI-assisted sonification.
result Improves diagnostic capabilities of non-expert clinicians.
RFN models urban mobility demand by separating temporal and spatial variability.
problem Aligning supply and demand in MoD systems for efficient transportation.
method Recurrent flow networks with latent variables and normalizing flows.
result RFN models explicitly disentangle temporal and spatial variability in urban mobility.
Neural pedagogical agent updates user models in real-time for mobile education apps.
problem Real-time user modeling for dynamic mobile education platforms.
method Bidirectional recurrent neural networks with attention mechanism over embedded question-response pairs.
result Model outperforms existing approaches in predicting user response correctness.
The study introduces measures of collective mobility from aggregated OD data.
problem Understanding large-scale mobility patterns from aggregated data.
method Developed a framework using synthetic and real data to interpret network-level mobility.
result Aggregated mobility measures reveal network structure and flow constraints.
UrbanRhythm reveals urban dynamics from mobility data.
problem Understanding changing urban activities over time.
method Extracting staying, leaving, arriving attributes; using Saak transform; clustering for city states; motif analysis for short-term regularity.
result Characterized urban dynamics as city state transformations over time.
HASP enhances mobile security against ASR by adding imperceptible noise.
problem Security leakage from ASR on mobile devices.
method Adversarial noise to perturb WER and enhance security.
result HASP reduces WER by 84.55% and speeds up data processing.
Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of d…
Mobile training improves speech recognition for users with unique speech characteristics.
problem Limited generalization of speaker-independent speech recognition models for users with very different speech characteristics.
method Securely training personalized end-to-end speech recognition models on mobile devices, splitting gradient computation to reduce memory usage.
result On-device personalization achieved 58.1% relative word error rate reduction compared to 63.7% in a server environment, with 18.7% performance degradation.
A DRL approach optimizes resource allocation in BFL to reduce latency and energy consumption.
problem Energy and CPU constraints of mobile devices and increased training latency due to blockchain mining.
method Deep Reinforcement Learning (DRL) to derive optimal decisions for MLMO.
result Optimal resource allocation and block generation rate to minimize system latency, energy consumption, and incentive cost.
This paper develops synthetic mobility datasets to protect privacy while maintaining realism.
problem Privacy concerns restrict sharing real mobility datasets, leading to lack of reproducibility.
method The paper benchmarks RNNs, GANs, and copulas to generate realistic synthetic trajectories.
result The approach generates trajectories that are statistically and semantically similar to real-world data.
Robotic grasping system learns to target objects from a single image.
problem Robotic grasping in unstructured environments.
method Distributed reinforcement learning, active vision, synchronous SGD.
result System learns to grasp unseen objects without retraining.
System detects social interactions in crowds using mobile phone sensors.
problem Detecting social interactions in crowded settings.
method Multi-modal mobile sensing (BLE, accelerometer, gyroscope) and machine learning.
result 77.8% precision and 86.5% recall for predicting social interactions.
Unbiased wealth exchanges always lead to inequality.
problem Understanding wealth distribution in unbiased binary exchange systems.
method Analytical demonstration of unbiased binary exchanges leading to perfect inequality.
result Any system driven by unbiased binary exchanges will reach perfect inequality and zero mobility.
Develops a framework for analyzing multi-agent and many-body systems with feedback loops.
problem Optimal order of multi-agent and general many-body systems
method Derive macroscopic properties and optimal degree of order
result Optimal degree of order balances productivity, stability, and adaptability
GANs improve path planning for smart mobility applications.
problem Improving path planning for smart mobility applications.
method Generative Adversarial Networks (GANs) for path planning.
result Generated paths are correct and reliable with high accuracy and quality.
Optimizes mobile notifications for multiple objectives using reinforcement learning.
problem Optimizing mobile notification systems for multiple objectives.
method End-to-end offline reinforcement learning with Double Deep Q-network and Conservative Q-learning.
result Demonstrates improved performance and benefits of the proposed approach.
A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the …
New method reduces mobile device energy for image classification.
problem High energy consumption of CNNs on mobile devices.
method Global optimization of CNN architectures with Bayesian optimization.
result Reduces energy consumption by up to 6x compared to blackbox CNNs.
A framework combining HSMM and survival analysis for lifecycle-oriented mobility analysis.
problem Understanding individual metro usage dynamics over multi-year horizons.
method A state-based lifecycle modeling framework integrating HSMM and discrete-time survival analysis.
result Identification of interpretable mobility states, transition dynamics, and state-dependent exit and re-entry processes.
This paper optimizes mobile device computation offloading using deep reinforcement learning.
problem Optimizing computation offloading for mobile devices in virtual edge computing systems.
method Modeling the problem as a Markov decision process and using double deep Q-networks for learning optimal offloading policies.
result The proposed algorithms significantly improve computation offloading performance.
ADRL improves participant selection in MCS systems.
problem Designing a participant selection algorithm for different MCS systems with multiple goals.
method Auxiliary-task based deep reinforcement learning (ADRL) using transformers and pointer networks.
result ADRL outperforms other baselines in various MCS settings.
Deep learning predicts V2I channel responses for high-mobility wireless communications.
problem Efficient channel estimation for high-mobility V2I communications.
method Developed a deep neural network-based channel prediction method.
result Deep neural networks can learn and predict V2I channel properties in real-time.
Pattern ensembling fills in missing or inaccurate trajectory data.
problem Incompleteness, missing information, and inaccuracies in geolocation data.
method Probabilistically ensemble similar trajectory patterns from the vicinity.
result Reconstructs missing or unreliable trajectory segments effectively.
Survey on deep neural networks for vision and speech.
problem Improving intelligent vision and speech systems.
method Review of deep learning models and challenges.
result Emerging technologies show promise for future systems.
Study develops a new model for predicting individual mobility based on activity patterns.
problem Existing mobility prediction methods overlook underlying activity patterns.
method Activity-based hidden Markov model (IOHMM) using transit smart card data.
result Proposed model achieves similar prediction performance to LSTM but also provides behavioral insights.
The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-l…
ARDEN improves deep learning performance on mobile devices by protecting privacy in the cloud.
problem Balancing privacy and performance in mobile deep learning with limited device capacity.
method ARDEN partitions DNN across mobile devices and cloud, using data transformation and noise addition for privacy, and noisy training for robustness.
result ARDEN enhances inference performance on cloud while maintaining strong privacy.
System recommends workouts and predicts success rates using RNNs.
problem Promoting healthy lifestyles through personalized exercise recommendations.
method Two interconnected recurrent neural networks (RNNs) using historical workout data.
result Interconnected-RNN model predicts exercise success rates with improved accuracy.
Mobile phones detect mosquitoes for malaria research.
problem Manual mosquito detection is inefficient and costly.
method Low-cost smartphone app for automated mosquito detection.
result Mobile system achieves excellent multi-species detection.
Resource allocation improved using machine learning from terminal positions.
problem Optimizing resource allocation in next-gen wireless systems with fast-changing channel conditions.
method Supervised machine learning using position information of mobile terminals.
result Coordinates-based resource allocation performs similarly to traditional CSI-based methods.
Mobile health data predicts chronic conditions better than daily activity.
problem Improving chronic condition prediction accuracy.
method Intra-day step and sleep data from mobile health devices, Convolutional Neural Networks, multi-task learning.
result Intra-day activity data significantly improves chronic condition classification.
Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal resolution, or are significantly delayed. In this paper, we describe how geoloc…
Study on adversarial attacks on user identification systems using motion sensors.
problem Adversarial attacks on deep learning models for user identification based on motion sensors.
method Study of adversarial example generation methods and their impact on user identification systems.
result Deep neural networks trained for user identification based on motion sensors are vulnerable to adversarial attacks, leading to high misclassification rates.
Mobile agents classify images via reinforcement learning and consensus.
problem Image classification using multiple mobile agents.
method Proposed network architecture for local belief formation and feature extraction. Decentralized consensus protocol using reinforcement learning.
result Effectiveness of the proposed framework demonstrated on MNIST dataset.
Unified model of urban consumer behavior and mobility patterns.
problem Understanding the lifestyles and infrastructure of urban regions.
method Collective matrix factorization for dual view modeling of consumer behavior and mobility.
result Unified model reveals deeper insights into consumer behavior and mobility patterns.
SPINN optimizes neural network inference on devices and cloud.
problem Inference on mobile devices is challenging due to high computational demands and dynamic connectivity.
method Synergistic progressive inference with a novel scheduler.
result SPINN achieves up to 2x higher throughput and reduces server cost by up to 6.8x.
DRL optimizes complex mobile networks with imperfect info.
problem Optimizing mobile networks with scarce data and complex dynamics.
method Sim-to-Real framework using graph CNN, domain randomization, multi-task learning, and self-play.
result First successful transfer of DRL from simulation to real-world mobile networks.
Deep actor-critic learning optimizes power control in mobile networks.
problem Optimizing power control in large-scale wireless mobile networks.
method Multi-agent deep reinforcement learning with deep deterministic policy gradient.
result The algorithm maximizes a global utility function in a distributed manner.
Survey of mobility studies using mobile phone data.
problem Understanding human mobility patterns.
method Data Science techniques applied to mobile phone datasets.
result Applications in urban planning, data traffic prediction, etc.
Proposes FairDRL-ST for fair spatio-temporal mobility prediction.
problem Fairness concerns in spatio-temporal AI applications.
method Disentangled representation learning, adversarial learning.
result Achieves fairness in spatio-temporal mobility prediction without performance loss.