Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

[1]  Xuanzhe Liu,et al.  DeepCache: Principled Cache for Mobile Deep Vision , 2017, MobiCom.

[2]  Nei Kato,et al.  A Tensor Based Deep Learning Technique for Intelligent Packet Routing , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[3]  Bartlomiej Placzek,et al.  Fully Connected Neural Networks Ensemble with Signal Strength Clustering for Indoor Localization in Wireless Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[4]  Blaise Agüera y Arcas,et al.  Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.

[5]  Navrati Saxena,et al.  A Survey on 5G Network Technologies from Social Perspective , 2017 .

[6]  Jinoh Kim,et al.  A survey of deep learning-based network anomaly detection , 2017, Cluster Computing.

[7]  Mahesh K. Marina,et al.  Towards multimodal deep learning for activity recognition on mobile devices , 2016, UbiComp Adjunct.

[8]  Zuzana Komínková Oplatková,et al.  Detection of mobile botnets using neural networks , 2016, 2016 Future Technologies Conference (FTC).

[9]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[10]  Jason Jianjun Gu,et al.  Deep Neural Networks for wireless localization in indoor and outdoor environments , 2016, Neurocomputing.

[11]  Roy E. Welsch,et al.  Comprehensive Predictions of Tourists' Next Visit Location Based on Call Detail Records Using Machine Learning and Deep Learning Methods , 2017, 2017 IEEE International Congress on Big Data (BigData Congress).

[12]  Xin Wang,et al.  Machine Learning for Networking: Workflow, Advances and Opportunities , 2017, IEEE Network.

[13]  Rabi N. Mahapatra,et al.  Energy efficient scheduling for real-time systems , 2011 .

[14]  Yanfang Ye,et al.  Deep4MalDroid: A Deep Learning Framework for Android Malware Detection Based on Linux Kernel System Call Graphs , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW).

[15]  Marco Fiore,et al.  Not All Apps Are Created Equal: Analysis of Spatiotemporal Heterogeneity in Nationwide Mobile Service Usage , 2017, CoNEXT.

[16]  Hamid Reza Naji,et al.  Energy efficient data aggregation in wireless sensor networks using neural networks , 2017, Int. J. Sens. Networks.

[17]  Langford B. White,et al.  Reinforcement Learning With Network-Assisted Feedback for Heterogeneous RAT Selection , 2017, IEEE Transactions on Wireless Communications.

[18]  Ali Feizollah,et al.  Evaluation of machine learning classifiers for mobile malware detection , 2014, Soft Computing.

[19]  Laura Pierucci,et al.  A Neural Network for Quality of Experience Estimation in Mobile Communications , 2016, IEEE MultiMedia.

[20]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[21]  Nicholas D. Lane,et al.  Can Deep Learning Revolutionize Mobile Sensing? , 2015, HotMobile.

[22]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[23]  Vitaly Shmatikov,et al.  Privacy-preserving deep learning , 2015, Allerton.

[24]  Jaime Lloret,et al.  Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT , 2017, Sensors.

[25]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[26]  Xinlei Chen,et al.  DeepTP: An End-to-End Neural Network for Mobile Cellular Traffic Prediction , 2018, IEEE Network.

[27]  Stephen J. Wright,et al.  Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent , 2011, NIPS.

[28]  Nicholas D. Lane,et al.  Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables , 2016, SenSys.

[29]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.

[30]  Stefano Ermon,et al.  Generative Adversarial Imitation Learning , 2016, NIPS.

[31]  Plamen Angelov,et al.  A general purpose intelligent surveillance system for mobile devices using Deep Learning , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[32]  Kaishun Wu,et al.  FIFS: Fine-Grained Indoor Fingerprinting System , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[33]  Mahmood Yousefi-Azar,et al.  Autoencoder-based feature learning for cyber security applications , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[34]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[35]  Bing Liu,et al.  Lifelong machine learning: a paradigm for continuous learning , 2017, Frontiers of Computer Science.

[36]  S. Z. Iliya,et al.  A Comprehensive Survey of Pilot Contamination in Massive MIMO—5G System , 2016, IEEE Communications Surveys & Tutorials.

[37]  Paul Rad,et al.  Deep learning control for complex and large scale cloud systems , 2017, Intell. Autom. Soft Comput..

[38]  Jie Tang,et al.  Enabling Deep Learning on IoT Devices , 2017, Computer.

[39]  Ivan Poupyrev,et al.  Interacting with Soli: Exploring Fine-Grained Dynamic Gesture Recognition in the Radio-Frequency Spectrum , 2016, UIST.

[40]  Chi Harold Liu,et al.  Experience-driven Networking: A Deep Reinforcement Learning based Approach , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[41]  Xiaohui Peng,et al.  Deep Learning for Sensor-based Activity Recognition: A Survey , 2017, Pattern Recognit. Lett..

[42]  Xiao Zeng Mobile Sensing Through Deep Learning , 2017, MobiSys PhDForum.

[43]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[44]  Vijay Varadharajan,et al.  A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection , 2019, IEEE Communications Surveys & Tutorials.

[45]  Saverio Niccolini,et al.  Net2Vec: Deep Learning for the Network , 2017, Big-DAMA@SIGCOMM.

[46]  Mingxuan Sun,et al.  Intelligent wireless communications enabled by cognitive radio and machine learning , 2017, China Communications.

[47]  Marcin Andrychowicz,et al.  Learning to learn by gradient descent by gradient descent , 2016, NIPS.

[48]  Steven Latré,et al.  A neural-network-based MF-TDMA MAC scheduler for collaborative wireless networks , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[49]  Stefano Ermon,et al.  Label-Free Supervision of Neural Networks with Physics and Domain Knowledge , 2016, AAAI.

[50]  Xiang Cheng,et al.  Mobile Big Data: The Fuel for Data-Driven Wireless , 2017, IEEE Internet of Things Journal.

[51]  Dawei Li,et al.  DeepCham: Collaborative Edge-Mediated Adaptive Deep Learning for Mobile Object Recognition , 2016, 2016 IEEE/ACM Symposium on Edge Computing (SEC).

[52]  Xiang Cheng,et al.  Mobile Demand Forecasting via Deep Graph-Sequence Spatiotemporal Modeling in Cellular Networks , 2018, IEEE Internet of Things Journal.

[53]  Junaid Qadir,et al.  Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges , 2017, IEEE Access.

[54]  Yi Liu,et al.  Indoor Fingerprint Positioning Based on Wi-Fi: An Overview , 2017, ISPRS Int. J. Geo Inf..

[55]  Tarek F. Abdelzaher,et al.  FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices , 2018, SenSys.

[56]  Trishul M. Chilimbi,et al.  Project Adam: Building an Efficient and Scalable Deep Learning Training System , 2014, OSDI.

[57]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[58]  Haralabos C. Papadopoulos,et al.  Predicting Wireless Channel Features Using Neural Networks , 2018, 2018 IEEE International Conference on Communications (ICC).

[59]  Hong Wen,et al.  The Rayleigh Fading Channel Prediction via Deep Learning , 2018, Wirel. Commun. Mob. Comput..

[60]  Cong Wang,et al.  DeepMag: Sniffing Mobile Apps in Magnetic Field through Deep Convolutional Neural Networks , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[61]  Sozo Inoue,et al.  Recognition of multiple overlapping activities using compositional CNN-LSTM model , 2017, UbiComp/ISWC Adjunct.

[62]  Kwangjo Kim,et al.  Detecting Impersonation Attack in WiFi Networks Using Deep Learning Approach , 2016, WISA.

[63]  Zheng Zhang,et al.  MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.

[64]  Navrati Saxena,et al.  Next Generation 5G Wireless Networks: A Comprehensive Survey , 2016, IEEE Communications Surveys & Tutorials.

[65]  Jaime Lloret,et al.  ELDC: An Artificial Neural Network Based Energy-Efficient and Robust Routing Scheme for Pollution Monitoring in WSNs , 2020, IEEE Transactions on Emerging Topics in Computing.

[66]  Andrea Zanella,et al.  COBANETS: A new paradigm for cognitive communications systems , 2016, 2016 International Conference on Computing, Networking and Communications (ICNC).

[67]  Zhi-Quan Luo,et al.  An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[68]  Dafang Zhang,et al.  A Deep Learning Approach to Android Malware Feature Learning and Detection , 2016, 2016 IEEE Trustcom/BigDataSE/ISPA.

[69]  Ian J. Goodfellow,et al.  NIPS 2016 Tutorial: Generative Adversarial Networks , 2016, ArXiv.

[70]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[71]  Alagan Anpalagan,et al.  Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach , 2017, IEEE Communications Letters.

[72]  Yoshua Bengio,et al.  Deep Learning for NLP (without Magic) , 2012, ACL.

[73]  Georg Carle,et al.  Learning and Generating Distributed Routing Protocols Using Graph-Based Deep Learning , 2018, Big-DAMA@SIGCOMM.

[74]  Paul Patras,et al.  ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network , 2017, CoNEXT.

[75]  Kazuhiko Fukawa,et al.  Neural Network Based Transmit Power Control and Interference Cancellation for MIMO Small Cell Networks , 2016, IEICE Trans. Commun..

[76]  Daqing Zhang,et al.  Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization , 2018, J. Netw. Comput. Appl..

[77]  Jakob Hoydis,et al.  An Introduction to Deep Learning for the Physical Layer , 2017, IEEE Transactions on Cognitive Communications and Networking.

[78]  Xiangyang Li,et al.  Finding the Stars in the Fireworks: Deep Understanding of Motion Sensor Fingerprint , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[79]  Xiang Lu,et al.  Device-Free Localization Based on CSI Fingerprints and Deep Neural Networks , 2018, 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[80]  Pedro M. Domingos A few useful things to know about machine learning , 2012, Commun. ACM.

[81]  Mohammad S. Obaidat,et al.  Deep Learning-Based Content Centric Data Dissemination Scheme for Internet of Vehicles , 2018, 2018 IEEE International Conference on Communications (ICC).

[82]  Pan Zhou,et al.  A Convolutional Neural Network for Leaves Recognition Using Data Augmentation , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[83]  Sergio Gomez Colmenarejo,et al.  Hybrid computing using a neural network with dynamic external memory , 2016, Nature.

[84]  Fei Wang,et al.  Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..

[85]  Zhisheng Niu,et al.  DeepNap: Data-Driven Base Station Sleeping Operations Through Deep Reinforcement Learning , 2018, IEEE Internet of Things Journal.

[86]  Shafiq R. Joty,et al.  Sleep Quality Prediction From Wearable Data Using Deep Learning , 2016, JMIR mHealth and uHealth.

[87]  Neil D. Lawrence,et al.  Deep Gaussian Processes , 2012, AISTATS.

[88]  Jürgen Schmidhuber,et al.  Learning to forget: continual prediction with LSTM , 1999 .

[89]  Hamed Haddadi,et al.  Private and Scalable Personal Data Analytics Using Hybrid Edge-to-Cloud Deep Learning , 2018, Computer.

[90]  Depeng Jin,et al.  Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment , 2015, Internet Measurement Conference.

[91]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[92]  Xianfu Chen,et al.  Deep Reinforcement Learning for Network Slicing , 2018, ArXiv.

[93]  Navdeep Jaitly,et al.  Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[94]  Xuan Song,et al.  DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level , 2016, IJCAI.

[95]  Max Welling,et al.  Semi-supervised Learning with Deep Generative Models , 2014, NIPS.

[96]  Shiwen Mao,et al.  DeepFi: Deep learning for indoor fingerprinting using channel state information , 2015, 2015 IEEE Wireless Communications and Networking Conference (WCNC).

[97]  George D. Magoulas,et al.  Deep learning Parkinson's from smartphone data , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[98]  Ci Ani,et al.  Artificial Neural Network Approach to Mobile Location Estimation in GSM Network , 2017 .

[99]  Maryam Var Naseri,et al.  Mobile botnet detection model based on retrospective pattern recognition , 2016 .

[100]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[101]  Zhenlong Yuan,et al.  DroidDetector: Android Malware Characterization and Detection Using Deep Learning , 2016 .

[102]  James T. Kwok,et al.  Asynchronous Distributed Semi-Stochastic Gradient Optimization , 2015, AAAI.

[103]  Antonio Torralba,et al.  RF-based 3D skeletons , 2018, SIGCOMM.

[104]  Nikos D. Sidiropoulos,et al.  Learning to optimize: Training deep neural networks for wireless resource management , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[105]  Xuan Song,et al.  Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference , 2016, AAAI.

[106]  Ruiyun Yu,et al.  Evaluation and Improvement of Activity Detection Systems with Recurrent Neural Network , 2018, 2018 IEEE International Conference on Communications (ICC).

[107]  Wei Xiang,et al.  Big data-driven optimization for mobile networks toward 5G , 2016, IEEE Network.

[108]  Alex Graves,et al.  Neural Turing Machines , 2014, ArXiv.

[109]  Jonathan P. How,et al.  Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability , 2017, ICML.

[110]  Ching-Yung Lin,et al.  Deep Convolutional Neural Network on iOS Mobile Devices , 2016, 2016 IEEE International Workshop on Signal Processing Systems (SiPS).

[111]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

[112]  Mohsen Guizani,et al.  Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services , 2018, IEEE Internet of Things Journal.

[113]  Bjørn-Atle Reme,et al.  Deep Learning Applied to Mobile Phone Data for Individual Income Classification , 2016 .

[114]  Alec Radford,et al.  Proximal Policy Optimization Algorithms , 2017, ArXiv.

[115]  Ian Goodfellow,et al.  Deep Learning with Differential Privacy , 2016, CCS.

[116]  Zhifeng Zhao,et al.  Deep Learning-Based Intelligent Dual Connectivity for Mobility Management in Dense Network , 2018, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall).

[117]  G. Casella,et al.  Explaining the Gibbs Sampler , 1992 .

[118]  Leonard Barolli,et al.  Performance Evaluation of a Deep Q-Network Based Simulation System for Actor Node Mobility Control in Wireless Sensor and Actor Networks Considering Three-Dimensional Environment , 2017, INCoS.

[119]  Jong-Min Kim,et al.  A load balancing scheme based on deep-learning in IoT , 2017, Cluster Computing.

[120]  Jaime Lloret,et al.  Distributed flood attack detection mechanism using artificial neural network in wireless mesh networks , 2016, Secur. Commun. Networks.

[121]  Qi Hao,et al.  Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey , 2018, IEEE Communications Surveys & Tutorials.

[122]  Dario Pompili,et al.  Deep Learning with Edge Computing for Localization of Epileptogenicity Using Multimodal rs-fMRI and EEG Big Data , 2017, 2017 IEEE International Conference on Autonomic Computing (ICAC).

[123]  Moustafa Youssef,et al.  DeepLoc: a ubiquitous accurate and low-overhead outdoor cellular localization system , 2018, SIGSPATIAL/GIS.

[124]  Laurence T. Yang,et al.  An Improved Stacked Auto-Encoder for Network Traffic Flow Classification , 2018, IEEE Network.

[125]  Nei Kato,et al.  A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks , 2018, IEEE Network.

[126]  Geoffrey Ye Li,et al.  Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems , 2017, IEEE Wireless Communications Letters.

[127]  Michal R. Nowicki,et al.  Low-effort place recognition with WiFi fingerprints using deep learning , 2016, AUTOMATION.

[128]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[129]  Heiga Zen,et al.  Fast, Compact, and High Quality LSTM-RNN Based Statistical Parametric Speech Synthesizers for Mobile Devices , 2016, INTERSPEECH.

[130]  Hubert Eichner,et al.  Towards Federated Learning at Scale: System Design , 2019, SysML.

[131]  Ali Feizollah,et al.  The Evolution of Android Malware and Android Analysis Techniques , 2017, ACM Comput. Surv..

[132]  Laurence T. Yang,et al.  Energy-Efficient Scheduling for Real-Time Systems Based on Deep Q-Learning Model , 2019, IEEE Transactions on Sustainable Computing.

[133]  Wei Li,et al.  Tux2: Distributed Graph Computation for Machine Learning , 2017, NSDI.

[134]  Jonathan Masci,et al.  Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[135]  Alex Pentland,et al.  Using Deep Learning to Predict Demographics from Mobile Phone Metadata , 2015, ArXiv.

[136]  Shaohan Hu,et al.  DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing , 2016, WWW.

[137]  Yuqing Chen,et al.  A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[138]  Randy C. Paffenroth,et al.  Anomaly Detection with Robust Deep Autoencoders , 2017, KDD.

[139]  Martín Abadi,et al.  Learning to Protect Communications with Adversarial Neural Cryptography , 2016, ArXiv.

[140]  VALENTIN RADU,et al.  Multimodal Deep Learning for Activity and Context Recognition , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[141]  Hiroshi Suzuki,et al.  Intercell-Interference Cancellation and Neural Network Transmit Power Optimization for MIMO Channels , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).

[142]  Soung Chang Liew,et al.  Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks , 2017, 2018 IEEE International Conference on Communications (ICC).

[143]  Lei Shu,et al.  Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges , 2018, IEEE Communications Surveys & Tutorials.

[144]  Fabio Martinelli,et al.  Evaluating Convolutional Neural Network for Effective Mobile Malware Detection , 2017, KES.

[145]  Marco Fiore,et al.  Large-Scale Mobile Traffic Analysis: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[146]  Miodrag Potkonjak,et al.  Pruning Filters and Classes: Towards On-Device Customization of Convolutional Neural Networks , 2017, EMDL '17.

[147]  Gabriel Maciá-Fernández,et al.  Survey and taxonomy of botnet research through life-cycle , 2013, CSUR.

[148]  Zhu Han,et al.  Joint User Scheduling and Content Caching Strategy for Mobile Edge Networks Using Deep Reinforcement Learning , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).

[149]  Jiheon Kang,et al.  Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems , 2018, IEEE Transactions on Industrial Electronics.

[150]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[151]  Nicholas D. Lane,et al.  An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices , 2015, IoT-App@SenSys.

[152]  Miguel Nicolau,et al.  A Hybrid Autoencoder and Density Estimation Model for Anomaly Detection , 2016, PPSN.

[153]  Mathias Niepert,et al.  Network Data Monetization Using Net2Vec , 2017, SIGCOMM Posters and Demos.

[154]  Muhammad Ali Imran,et al.  Challenges in 5G: how to empower SON with big data for enabling 5G , 2014, IEEE Network.

[155]  Marco Pavone,et al.  Cellular Network Traffic Scheduling With Deep Reinforcement Learning , 2018, AAAI.

[156]  Sihao Huang,et al.  Fully optical spacecraft communications: implementing an omnidirectional PV-cell receiver and 8 Mb/s LED visible light downlink with deep learning error correction , 2017, IEEE Aerospace and Electronic Systems Magazine.

[157]  Richard P. Martin,et al.  Continuous Low-Power Ammonia Monitoring Using Long Short-Term Memory Neural Networks , 2018, SenSys.

[158]  Yoshua Bengio,et al.  Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs , 2013, NIPS.

[159]  Ian McGraw,et al.  Personalized speech recognition on mobile devices , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[160]  Pan Li,et al.  Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach , 2020, IEEE Transactions on Network Science and Engineering.

[161]  Iakovos S. Venieris,et al.  Changing the Game of Mobile Data Analysis with Deep Learning , 2017 .

[162]  Xiao Zhang,et al.  Device-free wireless localization and activity recognition with deep learning , 2016, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[163]  Gabriel Falcão Paiva Fernandes,et al.  On the Evaluation of Energy-Efficient Deep Learning Using Stacked Autoencoders on Mobile GPUs , 2017, 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP).

[164]  Teng Teng,et al.  Facial expressions recognition based on convolutional neural networks for mobile virtual reality , 2016, VRCAI.

[165]  Yiran Chen,et al.  MoDNN: Local distributed mobile computing system for Deep Neural Network , 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.

[166]  Maria Trocan,et al.  Personal Health Indicators by Deep Learning of Smart Phone Sensor Data , 2017, 2017 3rd IEEE International Conference on Cybernetics (CYBCON).

[167]  Yang Yi,et al.  Reservoir Computing Meets Smart Grids: Attack Detection Using Delayed Feedback Networks , 2018, IEEE Transactions on Industrial Informatics.

[168]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[169]  Yasuyuki Matsushita,et al.  Detecting State Changes of Indoor Everyday Objects using Wi-Fi Channel State Information , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[170]  Arslan Munir,et al.  Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks , 2017, MLDM.

[171]  Xianfu Chen,et al.  Deep Reinforcement Learning for Resource Management in Network Slicing , 2018, IEEE Access.

[172]  Shiwen Mao,et al.  DeepML: Deep LSTM for Indoor Localization with Smartphone Magnetic and Light Sensors , 2018, 2018 IEEE International Conference on Communications (ICC).

[173]  Fabio Giust,et al.  Distributed mobility management for future 5G networks: overview and analysis of existing approaches , 2015, IEEE Communications Magazine.

[174]  Tara N. Sainath,et al.  Making Deep Belief Networks effective for large vocabulary continuous speech recognition , 2011, 2011 IEEE Workshop on Automatic Speech Recognition & Understanding.

[175]  Toshiaki Miyazaki,et al.  QoE-Based Big Data Analysis with Deep Learning in Pervasive Edge Environment , 2018, 2018 IEEE International Conference on Communications (ICC).

[176]  Cecilia Mascolo,et al.  Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[177]  Xuan Song,et al.  DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction , 2018, AAAI.

[178]  Niranjan Balasubramanian,et al.  MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU , 2017, EMDL '17.

[179]  Demis Hassabis,et al.  A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.

[180]  Yiannis Kompatsiaris,et al.  Deep Learning Advances in Computer Vision with 3D Data , 2017, ACM Comput. Surv..

[181]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[182]  Yunchao Wei,et al.  Perceptual Generative Adversarial Networks for Small Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[183]  Juan M. Corchado,et al.  Deep neural networks and transfer learning applied to multimedia web mining , 2017, DCAI.

[184]  Matthew D. Zeiler ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[185]  Seungjin Choi,et al.  Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[186]  Ning Zhang,et al.  iHear Food: Eating Detection Using Commodity Bluetooth Headsets , 2016, 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).

[187]  Abdulsalam Yassine,et al.  Using distance estimation and deep learning to simplify calibration in food calorie measurement , 2015, 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA).

[188]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[189]  Pan Li,et al.  Online Power Control for 5G Wireless Communications: A Deep Q-Network Approach , 2018, 2018 IEEE International Conference on Communications (ICC).

[190]  Khulumani Sibanda,et al.  The Generalization Ability of Artificial Neural Networks in Forecasting TCP/IP Traffic Trends: How Much Does the Size of Learning Rate Matter? , 2015 .

[191]  Lei Xu,et al.  Can machine learning aid in delivering new use cases and scenarios in 5G? , 2016, NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium.

[192]  Shie Mannor,et al.  A Deep Hierarchical Approach to Lifelong Learning in Minecraft , 2016, AAAI.

[193]  Shantanu Sharma,et al.  A survey on 5G: The next generation of mobile communication , 2015, Phys. Commun..

[194]  Sarvar Patel,et al.  Practical Secure Aggregation for Privacy-Preserving Machine Learning , 2017, IACR Cryptol. ePrint Arch..

[195]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[196]  Yi Yang,et al.  Big Data Meet Cyber-Physical Systems: A Panoramic Survey , 2018, IEEE Access.

[197]  Hao Jiang,et al.  DeepSpace: An Online Deep Learning Framework for Mobile Big Data to Understand Human Mobility Patterns , 2016, ArXiv.

[198]  Xiangyu Wang,et al.  Deep Convolutional Neural Networks for Indoor Localization with CSI Images , 2020, IEEE Transactions on Network Science and Engineering.

[199]  Ami Wiesel,et al.  Deep MIMO detection , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[200]  Vrizlynn L. L. Thing,et al.  IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[201]  Shiho Moriai,et al.  Privacy-Preserving Deep Learning: Revisited and Enhanced , 2017, ATIS.

[202]  Hwee Pink Tan,et al.  Rate-Distortion Balanced Data Compression for Wireless Sensor Networks , 2016, IEEE Sensors Journal.

[203]  Kok-Lim Alvin Yau,et al.  Application of reinforcement learning to routing in distributed wireless networks: a review , 2013, Artificial Intelligence Review.

[204]  Li Ma,et al.  Temperature error correction based on BP neural network in meteorological wireless sensor network , 2017, Int. J. Sens. Networks.

[205]  David B. Smith,et al.  Heterogeneous Machine-Type Communications in Cellular Networks: Random Access Optimization by Deep Reinforcement Learning , 2018, 2018 IEEE International Conference on Communications (ICC).

[206]  Nei Kato,et al.  On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control , 2018, IEEE Wireless Communications.

[207]  Kevin Waugh,et al.  DeepStack: Expert-level artificial intelligence in heads-up no-limit poker , 2017, Science.

[208]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[209]  Linda Doyle,et al.  A neural-network-based realization of in-network computation for the Internet of Things , 2017, 2017 IEEE International Conference on Communications (ICC).

[210]  Nadra Guizani,et al.  Recent Advances and Challenges in Mobile Big Data , 2018, IEEE Communications Magazine.

[211]  Dawei Li,et al.  DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices , 2017, AAAI.

[212]  Song Guo,et al.  Big Data Meet Green Challenges: Big Data Toward Green Applications , 2016, IEEE Systems Journal.

[213]  Martin Pielot,et al.  Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions , 2017, EMDL '17.

[214]  Chi Harold Liu,et al.  Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach , 2018, IEEE Journal on Selected Areas in Communications.

[215]  Shui Yu,et al.  Network Traffic Prediction Based on Deep Belief Network in Wireless Mesh Backbone Networks , 2017, 2017 IEEE Wireless Communications and Networking Conference (WCNC).

[216]  Piet Demeester,et al.  Distributed Neural Networks for Internet of Things: The Big-Little Approach , 2015, IoT 360.

[217]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[218]  Anshumali Shrivastava,et al.  Scalable and Sustainable Deep Learning via Randomized Hashing , 2016, KDD.

[219]  Etienne Perot,et al.  Deep Reinforcement Learning framework for Autonomous Driving , 2017, Autonomous Vehicles and Machines.

[220]  Oriol Vinyals,et al.  Matching Networks for One Shot Learning , 2016, NIPS.

[221]  Ursula Challita,et al.  Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial , 2017, IEEE Communications Surveys & Tutorials.

[222]  Guo-Jun Qi,et al.  Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities , 2017, International Journal of Computer Vision.

[223]  Yan Chen,et al.  Intelligent 5G: When Cellular Networks Meet Artificial Intelligence , 2017, IEEE Wireless Communications.

[224]  Cong Shen,et al.  Exploiting Noise Correlation for Channel Decoding with Convolutional Neural Networks , 2018, 2018 IEEE International Conference on Communications (ICC).

[225]  Tim Kraska,et al.  MLbase: A Distributed Machine-learning System , 2013, CIDR.

[226]  Mike Wu,et al.  Beyond Sparsity: Tree Regularization of Deep Models for Interpretability , 2017, AAAI.

[227]  Zhi Chen,et al.  Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach , 2017, IEEE Access.

[228]  Sudharman K. Jayaweera,et al.  A Survey on Machine-Learning Techniques in Cognitive Radios , 2013, IEEE Communications Surveys & Tutorials.

[229]  Brian L. Evans,et al.  Deep Reinforcement Learning for Improving Downlink mmWave Communication Performance , 2017, 1707.02329.

[230]  Ying-Chang Liang,et al.  Applications of Deep Reinforcement Learning in Communications and Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[231]  Shaocheng Tong,et al.  Reinforcement Learning Design-Based Adaptive Tracking Control With Less Learning Parameters for Nonlinear Discrete-Time MIMO Systems , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[232]  Kiran Karra,et al.  Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention , 2016, 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[233]  Bolei Zhou,et al.  Network Dissection: Quantifying Interpretability of Deep Visual Representations , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[234]  Christopher Mutschler,et al.  Convolutional Neural Networks for Position Estimation in TDoA-Based Locating Systems , 2018, 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[235]  John Tran,et al.  cuDNN: Efficient Primitives for Deep Learning , 2014, ArXiv.

[236]  Tiejun Lv,et al.  Deep reinforcement learning based computation offloading and resource allocation for MEC , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[237]  Honglak Lee,et al.  Supplementary Material : Zero-Shot Task Generalization with MultiTask Deep Reinforcement Learning , 2017 .

[238]  F. Richard Yu,et al.  Optimization of cache-enabled opportunistic interference alignment wireless networks: A big data deep reinforcement learning approach , 2017, 2017 IEEE International Conference on Communications (ICC).

[239]  Zhenming Liu,et al.  Delivering Deep Learning to Mobile Devices via Offloading , 2017, VR/AR Network@SIGCOMM.

[240]  Hyungshin Kim,et al.  Reducing distraction of smartwatch users with deep learning , 2016, MobileHCI Adjunct.

[241]  Fredrik Tufvesson,et al.  Deep convolutional neural networks for massive MIMO fingerprint-based positioning , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[242]  Hongli Zhang,et al.  Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world , 2015, IEEE Network.

[243]  D. PraveenKumar,et al.  Machine learning algorithms for wireless sensor networks: A survey , 2019, Inf. Fusion.

[244]  Zheng Dou,et al.  Anomaly detection of spectrum in wireless communication via deep auto-encoders , 2017, Journal of Supercomputing.

[245]  Jianfeng Liu,et al.  FitCNN: A cloud-assisted lightweight convolutional neural network framework for mobile devices , 2017, 2017 IEEE 23rd International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA).

[246]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[247]  Timothy J. O'Shea,et al.  Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent , 2016, ArXiv.

[248]  Nicholas D. Lane,et al.  Squeezing Deep Learning into Mobile and Embedded Devices , 2017, IEEE Pervasive Computing.

[249]  Onur Mutlu,et al.  Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds , 2017, NSDI.

[250]  Emmanuel Prouff,et al.  Breaking Cryptographic Implementations Using Deep Learning Techniques , 2016, SPACE.

[251]  Pramod K. Varshney,et al.  Artificial Neural Network Based Automatic Modulation Classification over a Software Defined Radio Testbed , 2018, 2018 IEEE International Conference on Communications (ICC).

[252]  Dejan S. Milojicic,et al.  A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade , 2018 .

[253]  Fernando Pérez-Cruz,et al.  PassGAN: A Deep Learning Approach for Password Guessing , 2017, ACNS.

[254]  Abdelkader Outtagarts,et al.  Deep Reinforcement Learning Based QoS-Aware Routing in Knowledge-Defined Networking , 2018, QSHINE.

[255]  Jia-Ching Wang,et al.  Transportation Mode Detection on Mobile Devices Using Recurrent Nets , 2016, ACM Multimedia.

[256]  Wei Zhang,et al.  DeepPositioning: Intelligent Fusion of Pervasive Magnetic Field and WiFi Fingerprinting for Smartphone Indoor Localization via Deep Learning , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).

[257]  Toshiaki Koike-Akino,et al.  Nonlinear Equalization with Deep Learning for Multi-Purpose Visual MIMO Communications , 2018, 2018 IEEE International Conference on Communications (ICC).

[258]  Li Deng,et al.  A tutorial survey of architectures, algorithms, and applications for deep learning , 2014, APSIPA Transactions on Signal and Information Processing.

[259]  Dusit Niyato,et al.  A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization , 2018, Applied Soft Computing.

[260]  Yue Xu,et al.  Handover Optimization via Asynchronous Multi-User Deep Reinforcement Learning , 2018, 2018 IEEE International Conference on Communications (ICC).

[261]  Shervin Shirmohammadi,et al.  Mobile Multi-Food Recognition Using Deep Learning , 2017, ACM Trans. Multim. Comput. Commun. Appl..

[262]  Luis Perez,et al.  The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.

[263]  Mahesh K. Marina,et al.  Network Slicing in 5G: Survey and Challenges , 2017, IEEE Communications Magazine.

[264]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[265]  Shiwen Mao,et al.  BiLoc: Bi-Modal Deep Learning for Indoor Localization With Commodity 5GHz WiFi , 2017, IEEE Access.

[266]  Siyuan Liu,et al.  Urban human mobility data mining: An overview , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[267]  Anatolij Zubow,et al.  ns3-gym: Extending OpenAI Gym for Networking Research , 2018, ArXiv.

[268]  Hui Liu,et al.  On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Selection Framework , 2018, MobiSys.

[269]  Victor C. M. Leung,et al.  Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks , 2017, IEEE Transactions on Vehicular Technology.

[270]  George Kurian,et al.  Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.

[271]  Maciá-FernándezGabriel,et al.  Survey and taxonomy of botnet research through life-cycle , 2013 .

[272]  Tarik Taleb,et al.  On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration , 2017, IEEE Communications Surveys & Tutorials.

[273]  Ali A. Ghorbani,et al.  A detailed analysis of the KDD CUP 99 data set , 2009, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications.

[274]  Shiwen Mao,et al.  CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach , 2016, IEEE Transactions on Vehicular Technology.

[275]  Philip S. Yu,et al.  Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud , 2018, KDD.

[276]  Jing Wang,et al.  Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[277]  Bruno Sericola,et al.  Distributed deep learning on edge-devices: Feasibility via adaptive compression , 2017, 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA).

[278]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[279]  Abhinav Gupta,et al.  Temporal Dynamic Graph LSTM for Action-Driven Video Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[280]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[281]  Ha Yoon Song,et al.  Method of predicting human mobility patterns using deep learning , 2017, Neurocomputing.

[282]  Jeff Donahue,et al.  Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.

[283]  Timothy J. O'Shea,et al.  Deep architectures for modulation recognition , 2017, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[284]  H. T. Kung,et al.  Inferring Origin Flow Patterns in Wi-Fi with Deep Learning , 2014, ICAC.

[285]  Nicolas Le Roux,et al.  Representational Power of Restricted Boltzmann Machines and Deep Belief Networks , 2008, Neural Computation.

[286]  Pablo Torres,et al.  An analysis of Recurrent Neural Networks for Botnet detection behavior , 2016, 2016 IEEE Biennial Congress of Argentina (ARGENCON).

[287]  Wu Liu,et al.  Deep learning hashing for mobile visual search , 2017, EURASIP J. Image Video Process..

[288]  Ying Li,et al.  Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems , 2018, IEEE Access.

[289]  Dusit Niyato,et al.  Optimal Auction for Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach , 2017, 2018 IEEE International Conference on Communications (ICC).

[290]  Daan Wierstra,et al.  One-Shot Generalization in Deep Generative Models , 2016, ICML.

[291]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[292]  Lei Zheng,et al.  DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection , 2017, KDD.

[293]  Yu Zheng,et al.  Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.

[294]  Yann LeCun,et al.  Learning a similarity metric discriminatively, with application to face verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[295]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[296]  Peng Jiang,et al.  Virtual MAC Spoofing Detection through Deep Learning , 2018, 2018 IEEE International Conference on Communications (ICC).

[297]  B. L. Roux,et al.  Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis , 2004 .

[298]  Adam Doupé,et al.  Deep Android Malware Detection , 2017, CODASPY.

[299]  Soheil Ghiasi,et al.  CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android , 2015, ACM Multimedia.

[300]  Chih-Wei Huang,et al.  A study of deep learning networks on mobile traffic forecasting , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[301]  Paul Patras,et al.  Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks , 2017, MobiHoc.

[302]  Zhezhuang Xu,et al.  Learning Transportation Modes From Smartphone Sensors Based on Deep Neural Network , 2017, IEEE Sensors Journal.

[303]  Xiang Cheng,et al.  Exploiting Mobile Big Data: Sources, Features, and Applications , 2017, IEEE Network.

[304]  Sven G. Bilen,et al.  Multi-Objective Reinforcement Learning for Cognitive Radio Based Satellite Communications , 2016 .

[305]  Mei-Ling Shyu,et al.  A Survey on Deep Learning , 2018, ACM Comput. Surv..

[306]  Luc Van Gool,et al.  DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[307]  B. S. Manjunath,et al.  Are Very Deep Neural Networks Feasible on Mobile Devices , 2016 .

[308]  Eric P. Xing,et al.  GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server , 2016, EuroSys.

[309]  Ming-Hsuan Yang,et al.  Generative Face Completion , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[310]  Walid Saad,et al.  Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks , 2017, ArXiv.

[311]  Andrea Cavallaro,et al.  Distributed One-Class Learning , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[312]  Berin Martini,et al.  A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[313]  Hwee Pink Tan,et al.  Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications , 2014, IEEE Communications Surveys & Tutorials.

[314]  Edward Kim,et al.  A deep semantic mobile application for thyroid cytopathology , 2016, SPIE Medical Imaging.

[315]  Sung-Bae Cho,et al.  Human activity recognition with smartphone sensors using deep learning neural networks , 2016, Expert Syst. Appl..

[316]  Zhisheng Niu,et al.  Deep learning based optimization in wireless network , 2017, 2017 IEEE International Conference on Communications (ICC).

[317]  Nei Kato,et al.  State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems , 2017, IEEE Communications Surveys & Tutorials.

[318]  Moshe Zukerman,et al.  Energy-Efficient Base-Stations Sleep-Mode Techniques in Green Cellular Networks: A Survey , 2015, IEEE Communications Surveys & Tutorials.

[319]  Hong Wang,et al.  Distributed Data Mining Based on Deep Neural Network for Wireless Sensor Network , 2015, Int. J. Distributed Sens. Networks.

[320]  Tie Luo,et al.  Distributed Anomaly Detection Using Autoencoder Neural Networks in WSN for IoT , 2018, 2018 IEEE International Conference on Communications (ICC).

[321]  Sandra Servia Rodríguez,et al.  Personal Model Training under Privacy Constraints , 2017, ArXiv.

[322]  Arindam Banerjee,et al.  Stable Gradient Descent , 2018, UAI.

[323]  Ashikur Rahman,et al.  Zone-Based Indoor Localization Using Neural Networks: A View from a Real Testbed , 2018, 2018 IEEE International Conference on Communications (ICC).

[324]  Lidong Wang,et al.  Big Data Analytics for Network Intrusion Detection: A Survey , 2017 .

[325]  Clément Farabet,et al.  Torch7: A Matlab-like Environment for Machine Learning , 2011, NIPS 2011.

[326]  H. T. Kung,et al.  Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[327]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[328]  Arindam Banerjee,et al.  Poster: Deep Learning Enabled M2M Gateway for Network Optimization , 2016, MobiSys '16 Companion.

[329]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[330]  Stephan ten Brink,et al.  On deep learning-based communication over the air , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

[331]  Richard D. Gitlin,et al.  Base station prediction and proactive mobility management in virtual cells using recurrent neural networks , 2017, 2017 IEEE 18th Wireless and Microwave Technology Conference (WAMICON).

[332]  Stefan Wermter,et al.  Lifelong learning of human actions with deep neural network self-organization , 2017, Neural Networks.

[333]  Sofie Pollin,et al.  Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors , 2017, IEEE Transactions on Cognitive Communications and Networking.

[334]  Osvaldo Simeone,et al.  A Brief Introduction to Machine Learning for Engineers , 2017, Found. Trends Signal Process..

[335]  Ivan Marsic,et al.  Deep Learning for RFID-Based Activity Recognition , 2016, SenSys.

[336]  Dongfeng Yuan,et al.  Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks , 2018, IEEE Communications Letters.

[337]  Hao Chen,et al.  ConFi: Convolutional Neural Networks Based Indoor Wi-Fi Localization Using Channel State Information , 2017, IEEE Access.

[338]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[339]  Andrea Zanella,et al.  Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence , 2015, IEEE Access.

[340]  Ian McGraw,et al.  On the compression of recurrent neural networks with an application to LVCSR acoustic modeling for embedded speech recognition , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[341]  Hong Cheng,et al.  Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network , 2016, Sensors.

[342]  Hua Yang,et al.  Neural networks for MANET AODV: an optimization approach , 2017, Cluster Computing.

[343]  Nei Kato,et al.  The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective , 2017, IEEE Wireless Communications.

[344]  Luca Foschini,et al.  Collecting and Analyzing Millions of mHealth Data Streams , 2017, KDD.

[345]  Laurence T. Yang,et al.  A Tucker Deep Computation Model for Mobile Multimedia Feature Learning , 2017, ACM Trans. Multim. Comput. Commun. Appl..

[346]  Chadi Assi,et al.  Deep reinforcement learning-based scheduling for roadside communication networks , 2017, 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

[347]  Timothy J. O'Shea,et al.  Deep Learning Based MIMO Communications , 2017, ArXiv.

[348]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[349]  Mahdi Jafari Siavoshani,et al.  Deep packet: a novel approach for encrypted traffic classification using deep learning , 2017, Soft Computing.

[350]  Zhenlong Yuan,et al.  Droid-Sec: deep learning in android malware detection , 2015, SIGCOMM 2015.

[351]  Sam Greydanus,et al.  Learning the Enigma with Recurrent Neural Networks , 2017, ArXiv.

[352]  Brian L. Evans,et al.  Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement , 2017, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.

[353]  Shuo Liu,et al.  Dynamic Spectrum Assignment for Land Mobile Radio with Deep Recurrent Neural Networks , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).

[354]  Chao Zhang,et al.  DeepMove: Predicting Human Mobility with Attentional Recurrent Networks , 2018, WWW.

[355]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[356]  Alberto Del Bimbo,et al.  Deep Artwork Detection and Retrieval for Automatic Context-Aware Audio Guides , 2017, ACM Trans. Multim. Comput. Commun. Appl..

[357]  Min Chen,et al.  A 5G Cognitive System for Healthcare , 2017, Big Data Cogn. Comput..

[358]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[359]  Bo Yu,et al.  Convolutional Neural Networks for human activity recognition using mobile sensors , 2014, 6th International Conference on Mobile Computing, Applications and Services.

[360]  Junmo Kim,et al.  Active Convolution: Learning the Shape of Convolution for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[361]  Antonio Pescapè,et al.  Mobile Encrypted Traffic Classification Using Deep Learning , 2018, 2018 Network Traffic Measurement and Analysis Conference (TMA).

[362]  Kaoru Ota,et al.  Deep Learning for Mobile Multimedia , 2017, ACM Trans. Multim. Comput. Commun. Appl..

[363]  Ruizhi Chen,et al.  A Survey of Human Activity Recognition Using Smartphones , 2016 .

[364]  Ilja Radusch,et al.  Indoor localization of vehicles using Deep Learning , 2016, 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[365]  Mauro Brunato,et al.  Statistical learning theory for location fingerprinting in wireless LANs , 2005, Comput. Networks.

[366]  Wei Wang,et al.  GENPass: A General Deep Learning Model for Password Guessing with PCFG Rules and Adversarial Generation , 2018, 2018 IEEE International Conference on Communications (ICC).

[367]  Sergey Levine,et al.  Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..

[368]  Huazi Zhang,et al.  Performance Evaluation of Channel Decoding with Deep Neural Networks , 2017, 2018 IEEE International Conference on Communications (ICC).

[369]  Samy Bengio,et al.  Device Placement Optimization with Reinforcement Learning , 2017, ICML.

[370]  Giuseppe Ateniese,et al.  Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning , 2017, CCS.

[371]  Sundeep Rangan,et al.  AMP-Inspired Deep Networks for Sparse Linear Inverse Problems , 2016, IEEE Transactions on Signal Processing.

[372]  Richard E. Overill,et al.  Detection of known and unknown DDoS attacks using Artificial Neural Networks , 2016, Neurocomputing.

[373]  Sherali Zeadally,et al.  Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities , 2018, Future Gener. Comput. Syst..

[374]  Keiji Yanai,et al.  DeepFoodCam: A DCNN-based Real-time Mobile Food Recognition System , 2016, MADiMa @ ACM Multimedia.

[375]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[376]  Tao Zhang,et al.  A Survey of Model Compression and Acceleration for Deep Neural Networks , 2017, ArXiv.

[377]  Randy Paffenroth,et al.  Multiobjective Reinforcement Learning for Cognitive Satellite Communications Using Deep Neural Network Ensembles , 2018, IEEE Journal on Selected Areas in Communications.

[378]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[379]  Kobi Cohen,et al.  Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access , 2017, IEEE Transactions on Wireless Communications.

[380]  Yan Zhang,et al.  Deep Learning for Secure Mobile Edge Computing , 2017, ArXiv.

[381]  Dong-Ho Cho,et al.  Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication , 2019, IEEE Systems Journal.

[382]  Gürsel Serpen,et al.  Adaptive and intelligent wireless sensor networks through neural networks: an illustration for infrastructure adaptation through Hopfield network , 2016, Applied Intelligence.

[383]  Ashish Payal,et al.  Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks , 2015, Wireless Personal Communications.

[384]  Alex Graves,et al.  Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.

[385]  John Salvatier,et al.  Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.

[386]  H. Vincent Poor,et al.  A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead , 2016, IEEE Journal on Selected Areas in Communications.

[387]  Vasu Jindal,et al.  Integrating Mobile and Cloud for PPG Signal Selection to Monitor Heart Rate during Intensive Physical Exercise , 2016, 2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft).

[388]  Hao Chen,et al.  High-precision approach to localization scheme of visible light communication based on artificial neural networks and modified genetic algorithms , 2017 .

[389]  Leonidas J. Guibas,et al.  PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.

[390]  Xiao Zeng,et al.  MobileDeepPill: A Small-Footprint Mobile Deep Learning System for Recognizing Unconstrained Pill Images , 2017, MobiSys.

[391]  Zhenxiang Gao,et al.  Large-Scale Wi-Fi Hotspot Classification via Deep Learning , 2017, WWW.

[392]  David Blaauw,et al.  14.7 A 288µW programmable deep-learning processor with 270KB on-chip weight storage using non-uniform memory hierarchy for mobile intelligence , 2017, 2017 IEEE International Solid-State Circuits Conference (ISSCC).

[393]  Jing Wang,et al.  A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs , 2017, 2017 IEEE International Conference on Communications (ICC).

[394]  Jean-François Paiement,et al.  Deep Generative Models of Urban Mobility , 2017 .

[395]  Xue-wen Chen,et al.  Big Data Deep Learning: Challenges and Perspectives , 2014, IEEE Access.

[396]  Grégoire Mercier,et al.  Convolutional Neural Networks for object recognition on mobile devices: A case study , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[397]  Mohammad Ashraf,et al.  Conceptual design of proactive SONs based on the Big Data framework for 5G cellular networks: A novel Machine Learning perspective facilitating a shift in the SON paradigm , 2016, 2016 International Conference System Modeling & Advancement in Research Trends (SMART).

[398]  Daniel Brand,et al.  MEC: Memory-efficient Convolution for Deep Neural Network , 2017, ICML.

[399]  Zhenming Liu,et al.  DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[400]  AKHIL GUPTA,et al.  A Survey of 5G Network: Architecture and Emerging Technologies , 2015, IEEE Access.

[401]  Anil A. Bharath,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[402]  Guang-Zhong Yang,et al.  A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices , 2017, IEEE Journal of Biomedical and Health Informatics.

[403]  Nicola Bui,et al.  A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques , 2016, IEEE Communications Surveys & Tutorials.

[404]  Shingo Mabu,et al.  Forecast chaotic time series data by DBNs , 2014, 2014 7th International Congress on Image and Signal Processing.

[405]  Samy Bengio,et al.  Links between perceptrons, MLPs and SVMs , 2004, ICML.

[406]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[407]  Hongzi Mao,et al.  Neural Adaptive Video Streaming with Pensieve , 2017, SIGCOMM.

[408]  Chao Zhang,et al.  Trajectory clustering via deep representation learning , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[409]  Geoffrey Ye Li,et al.  Deep Reinforcement Learning for Resource Allocation in V2V Communications , 2017, 2018 IEEE International Conference on Communications (ICC).

[410]  Mustafa ElNainay,et al.  CNN based Indoor Localization using RSS Time-Series , 2018, 2018 IEEE Symposium on Computers and Communications (ISCC).

[411]  Andrew Y. Ng,et al.  Zero-Shot Learning Through Cross-Modal Transfer , 2013, NIPS.

[412]  Marianne Winslett,et al.  Mercury: Metro density prediction with recurrent neural network on streaming CDR data , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[413]  Prasant Mohapatra,et al.  Using Deep Learning for Energy Expenditure Estimation with wearable sensors , 2015, 2015 17th International Conference on E-health Networking, Application & Services (HealthCom).

[414]  Xiao Zhang,et al.  Device-Free Wireless Localization and Activity Recognition: A Deep Learning Approach , 2017, IEEE Transactions on Vehicular Technology.

[415]  Athanasios V. Vasilakos,et al.  A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges , 2015, Wireless Networks.

[416]  Juyang Weng,et al.  Mobile Device Based Outdoor Navigation with On-Line Learning Neural Network: A Comparison with Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[417]  Ying-Chang Liang,et al.  Joint Transaction Transmission and Channel Selection in Cognitive Radio Based Blockchain Networks: A Deep Reinforcement Learning Approach , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[418]  Yee Whye Teh,et al.  Neural Processes , 2018, ArXiv.

[419]  Sofiène Affes,et al.  Robust ANNs-Based WSN Localization in the Presence of Anisotropic Signal Attenuation , 2016, IEEE Wireless Communications Letters.

[420]  Shiwen Mao,et al.  CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach , 2016, IEEE Internet of Things Journal.

[421]  Qiyue Li,et al.  WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs , 2017, IEEE Access.

[422]  Mohsen Guizani,et al.  Smart Cities: A Survey on Data Management, Security, and Enabling Technologies , 2017, IEEE Communications Surveys & Tutorials.

[423]  Leonard Barolli,et al.  Design and Implementation of a Simulation System Based on Deep Q-Network for Mobile Actor Node Control in Wireless Sensor and Actor Networks , 2017, 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA).

[424]  Marco De Nadai,et al.  A multi-source dataset of urban life in the city of Milan and the Province of Trentino , 2015, Scientific Data.

[425]  Zhu Han,et al.  Machine Learning Paradigms for Next-Generation Wireless Networks , 2017, IEEE Wireless Communications.

[426]  Yu Wang,et al.  A Deep Learning Approach for Blind Drift Calibration of Sensor Networks , 2017, IEEE Sensors Journal.

[427]  Mahbub Hassan,et al.  A Survey of Wearable Devices and Challenges , 2017, IEEE Communications Surveys & Tutorials.

[428]  Takehisa Yairi,et al.  Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction , 2014, MLSDA'14.

[429]  Jian Cheng,et al.  Quantized Convolutional Neural Networks for Mobile Devices , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[430]  Thomas La Porta,et al.  Demo Abstract: On-Demand Information Retrieval from Videos Using Deep Learning in Wireless Networks , 2017, 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI).

[431]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[432]  James A. Landay,et al.  Comparing Speech and Keyboard Text Entry for Short Messages in Two Languages on Touchscreen Phones , 2016, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[433]  Ivor W. Tsang,et al.  Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..

[434]  Bin Liu,et al.  Signal detection of MIMO-OFDM system based on auto encoder and extreme learning machine , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[435]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[436]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[437]  Biing-Hwang Juang,et al.  Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).

[438]  Chengzhu Yu,et al.  The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices , 2015, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).

[439]  Ming Zhu,et al.  Malware traffic classification using convolutional neural network for representation learning , 2017, 2017 International Conference on Information Networking (ICOIN).

[440]  Victor C. M. Leung,et al.  Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach , 2017, IEEE Communications Magazine.

[441]  Hwee Pink Tan,et al.  Mobile big data analytics using deep learning and apache spark , 2016, IEEE Network.

[442]  Xianfu Chen,et al.  TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks , 2012, IEEE Transactions on Wireless Communications.

[443]  Walid Saad,et al.  Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective , 2017, IEEE Transactions on Wireless Communications.

[444]  Ming Zhu,et al.  End-to-end encrypted traffic classification with one-dimensional convolution neural networks , 2017, 2017 IEEE International Conference on Intelligence and Security Informatics (ISI).

[445]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[446]  Mingxuan Sun,et al.  Machine Learning and Cognitive Technology for Intelligent Wireless Networks , 2017, ArXiv.

[447]  Xin Wang,et al.  UbiEar: Bringing Location-independent Sound Awareness to the Hard-of-hearing People with Smartphones , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[448]  H. Vincent Poor,et al.  A Secure Mobile Crowdsensing Game With Deep Reinforcement Learning , 2018, IEEE Transactions on Information Forensics and Security.

[449]  Timothy J. O'Shea,et al.  Radio transformer networks: Attention models for learning to synchronize in wireless systems , 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

[450]  Cong Xu,et al.  TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning , 2017, NIPS.

[451]  Mo Li,et al.  Urban Traffic Prediction from Mobility Data Using Deep Learning , 2018, IEEE Network.

[452]  Syed Hassan Ahmed,et al.  A Deep Learning Framework Using Passive WiFi Sensing for Respiration Monitoring , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[453]  Lionel M. Ni,et al.  A Survey on Wireless Indoor Localization from the Device Perspective , 2016, ACM Comput. Surv..

[454]  Junaid Qadir,et al.  Neural networks in wireless networks: Techniques, applications and guidelines , 2016, J. Netw. Comput. Appl..

[455]  Xuan Song,et al.  DeepMob , 2017, ACM Trans. Inf. Syst..

[456]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[457]  J. Venkata Subramanian,et al.  Implementation of Artificial Neural Network for Mobile Movement Prediction , 2014 .

[458]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[459]  Jason Yosinski,et al.  Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[460]  José M. F. Moura,et al.  A Deep Learning Approach to IoT Authentication , 2018, 2018 IEEE International Conference on Communications (ICC).

[461]  Tom Schaul,et al.  Rainbow: Combining Improvements in Deep Reinforcement Learning , 2017, AAAI.

[462]  Alun D. Preece,et al.  Interpretability of deep learning models: A survey of results , 2017, 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[463]  Yaoliang Yu,et al.  Petuum: A New Platform for Distributed Machine Learning on Big Data , 2015, IEEE Trans. Big Data.

[464]  Guan Gui,et al.  Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System , 2018, IEEE Transactions on Vehicular Technology.

[465]  Honglak Lee,et al.  Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning , 2017, ICML.

[466]  Xiao Zeng,et al.  NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision , 2018, MobiCom.

[467]  Xuan Song,et al.  Deep ROI-Based Modeling for Urban Human Mobility Prediction , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[468]  Lina Yao,et al.  Deep Learning Based Recommender System , 2017, ACM Comput. Surv..

[469]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[470]  Jia Chen,et al.  A Collaborative Privacy-Preserving Deep Learning System in Distributed Mobile Environment , 2016, 2016 International Conference on Computational Science and Computational Intelligence (CSCI).

[471]  Marc'Aurelio Ranzato,et al.  Large Scale Distributed Deep Networks , 2012, NIPS.

[472]  Ramachandra Raghavendra,et al.  Learning deeply coupled autoencoders for smartphone based robust periocular verification , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[473]  Qingyun Du,et al.  A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization , 2017, Sensors.

[474]  Guang-Zhong Yang,et al.  Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.

[475]  Md. Zakirul Alam Bhuiyan,et al.  A Survey on Deep Learning in Big Data , 2017, 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC).

[476]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[477]  Li Fei-Fei,et al.  Progressive Neural Architecture Search , 2017, ECCV.

[478]  Tom Schaul,et al.  Prioritized Experience Replay , 2015, ICLR.

[479]  Sergey Levine,et al.  Continuous Deep Q-Learning with Model-based Acceleration , 2016, ICML.

[480]  Byoung-Tak Zhang,et al.  Dual-Memory Deep Learning Architectures for Lifelong Learning of Everyday Human Behaviors , 2016, IJCAI.

[481]  Timothy Dozat,et al.  Incorporating Nesterov Momentum into Adam , 2016 .

[482]  Kaiming He,et al.  Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour , 2017, ArXiv.

[483]  Pavel Kordík,et al.  Neural Turing Machine for sequential learning of human mobility patterns , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[484]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[485]  Jiang Zhu,et al.  Fog Computing: A Platform for Internet of Things and Analytics , 2014, Big Data and Internet of Things.

[486]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[487]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[488]  Luis Rodero-Merino,et al.  Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing , 2014, CCRV.

[489]  Shiann-Shiun Jeng,et al.  Mobility prediction in mobile ad-hoc network using deep learning , 2018, 2018 IEEE International Conference on Applied System Invention (ICASI).

[490]  Hamed Haddadi,et al.  A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics , 2017, IEEE Internet of Things Journal.

[491]  Laurence T. Yang,et al.  Data Mining for Internet of Things: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[492]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[493]  Sepp Hochreiter,et al.  Self-Normalizing Neural Networks , 2017, NIPS.

[494]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[495]  Yangmin Lee,et al.  Classification of node degree based on deep learning and routing method applied for virtual route assignment , 2017, Ad Hoc Networks.

[496]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[497]  Nicholas D. Lane,et al.  DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[498]  Po-Jen Chuang,et al.  Effective neural network-based node localisation scheme for wireless sensor networks , 2014, IET Wirel. Sens. Syst..

[499]  Nei Kato,et al.  Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning , 2017, IEEE Transactions on Computers.

[500]  Yi Luo,et al.  All-optical machine learning using diffractive deep neural networks , 2018, Science.

[501]  Hao Jiang,et al.  DeepSense: Device-Free Human Activity Recognition via Autoencoder Long-Term Recurrent Convolutional Network , 2018, 2018 IEEE International Conference on Communications (ICC).

[502]  Mohsen Guizani,et al.  Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.

[503]  Wei-Lun Chao,et al.  Synthesized Classifiers for Zero-Shot Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[504]  Natalija Vlajic,et al.  Robustness of deep autoencoder in intrusion detection under adversarial contamination , 2018, HotSoS.

[505]  Nicholas D. Lane,et al.  From smart to deep: Robust activity recognition on smartwatches using deep learning , 2016, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[506]  Kobi Cohen,et al.  Deep Multi-User Reinforcement Learning for Dynamic Spectrum Access in Multichannel Wireless Networks , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[507]  Chan-Gun Lee,et al.  Deep learning–based real-time query processing for wireless sensor network , 2017, Int. J. Distributed Sens. Networks.

[508]  Kevin Skadron,et al.  Scalable parallel programming , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).

[509]  Stephen Lin,et al.  Deformable ConvNets V2: More Deformable, Better Results , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[510]  Adlen Ksentini,et al.  Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach , 2018, IEEE Network.

[511]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[512]  Heiga Zen,et al.  Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[513]  Gaurav Mittal,et al.  SpotGarbage: smartphone app to detect garbage using deep learning , 2016, UbiComp.

[514]  Naveen K. Chilamkurti,et al.  Distributed attack detection scheme using deep learning approach for Internet of Things , 2017, Future Gener. Comput. Syst..

[515]  Marco Fiore,et al.  Joint spatial and temporal classification of mobile traffic demands , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[516]  Shiwen Mao,et al.  PhaseFi: Phase Fingerprinting for Indoor Localization with a Deep Learning Approach , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[517]  Leonel Sousa,et al.  Beamformed Fingerprint Learning for Accurate Millimeter Wave Positioning , 2018, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall).

[518]  Moustafa Alzantot,et al.  RSTensorFlow: GPU Enabled TensorFlow for Deep Learning on Commodity Android Devices , 2017, EMDL '17.

[519]  Mehdi Bennis,et al.  A transfer learning approach for cache-enabled wireless networks , 2015, 2015 13th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

[520]  Timothy J. O'Shea,et al.  End-to-end radio traffic sequence recognition with recurrent neural networks , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[521]  Yi Li,et al.  Deformable Convolutional Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[522]  Erhan Guven,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2016, IEEE Communications Surveys & Tutorials.

[523]  Tong Zhang,et al.  Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings , 2016, ICML.

[524]  Shonali Krishnaswamy,et al.  Mobile Big Data Analytics: Research, Practice, and Opportunities , 2014, 2014 IEEE 15th International Conference on Mobile Data Management.

[525]  Hsin-Piao Lin,et al.  Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment , 2018, Applied Sciences.

[526]  Mehdi Agha Sarram,et al.  A New Range-Free and Storage-Efficient Localization Algorithm Using Neural Networks in Wireless Sensor Networks , 2018, Wirel. Pers. Commun..

[527]  Haichen Shen,et al.  TVM: An Automated End-to-End Optimizing Compiler for Deep Learning , 2018, OSDI.

[528]  Xiangyu Wang,et al.  CiFi: Deep convolutional neural networks for indoor localization with 5 GHz Wi-Fi , 2017, 2017 IEEE International Conference on Communications (ICC).

[529]  T. Nishimura,et al.  Traffic prediction for mobile network using Holt-Winter’s exponential smoothing , 2007, 2007 15th International Conference on Software, Telecommunications and Computer Networks.

[530]  Zhiyuan Liu,et al.  A Neural Network Approach to Joint Modeling Social Networks and Mobile Trajectories , 2016, ArXiv.

[531]  Yong Li,et al.  System architecture and key technologies for 5G heterogeneous cloud radio access networks , 2015, IEEE Netw..

[532]  Wenchao Huang,et al.  AppDNA: App Behavior Profiling via Graph-based Deep Learning , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[533]  Marcus Edel,et al.  Binarized-BLSTM-RNN based Human Activity Recognition , 2016, 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[534]  Mohamed Medhat Gaber,et al.  Imitation Learning , 2017, ACM Comput. Surv..

[535]  Xiangyu Zhang,et al.  ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[536]  Randy Paffenroth,et al.  Multi-objective reinforcement learning-based deep neural networks for cognitive space communications , 2017, 2017 Cognitive Communications for Aerospace Applications Workshop (CCAA).

[537]  Moustafa Youssef,et al.  The Horus WLAN location determination system , 2005, MobiSys '05.

[538]  Marc Peter Deisenroth,et al.  Deep Reinforcement Learning: A Brief Survey , 2017, IEEE Signal Processing Magazine.

[539]  Ji Feng,et al.  Deep Forest: Towards An Alternative to Deep Neural Networks , 2017, IJCAI.

[540]  Eryk Dutkiewicz,et al.  Cyberattack detection in mobile cloud computing: A deep learning approach , 2017, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[541]  Siti Mariyam Shamsuddin,et al.  A systematic literature review on features of deep learning in big data analytics , 2017 .

[542]  Liang Liu,et al.  Urban Resolution: New Metric for Measuring the Quality of Urban Sensing , 2015, IEEE Transactions on Mobile Computing.

[543]  Hyukjoon Lee,et al.  Dynamic bandwidth provisioning using ARIMA-based traffic forecasting for Mobile WiMAX , 2011, Comput. Commun..

[544]  Bandar Saleh Mouhammed ِAlmaslukh,et al.  An effective deep autoencoder approach for online smartphone-based human activity recognition , 2017 .

[545]  Philip S. Yu,et al.  Deep Learning towards Mobile Applications , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[546]  Vivienne Sze,et al.  Efficient Processing of Deep Neural Networks: A Tutorial and Survey , 2017, Proceedings of the IEEE.

[547]  Robert Piché,et al.  A Survey of Selected Indoor Positioning Methods for Smartphones , 2017, IEEE Communications Surveys & Tutorials.

[548]  Michael I. Jordan,et al.  Ray: A Distributed Framework for Emerging AI Applications , 2017, OSDI.

[549]  Jon Barker,et al.  The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines , 2015, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).

[550]  B. M. Bidgoli,et al.  Reduce energy consumption and send secure data wireless multimedia sensor networks using a combination of techniques for multi-layer watermark and deep learning , 2017 .

[551]  Yunhao Liu,et al.  Spatio-Temporal Analysis and Prediction of Cellular Traffic in Metropolis , 2019, IEEE Transactions on Mobile Computing.

[552]  Haiyong Luo,et al.  DePedo: Anti Periodic Negative-Step Movement Pedometer with Deep Convolutional Neural Networks , 2018, 2018 IEEE International Conference on Communications (ICC).

[553]  Wolfgang Utschick,et al.  Deep Channel Estimation , 2017, WSA.

[554]  Dina Katabi,et al.  Zero-Effort In-Home Sleep and Insomnia Monitoring using Radio Signals , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[555]  Li Zhang,et al.  A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks , 2016, Neural Computing and Applications.

[556]  David A. Patterson,et al.  In-datacenter performance analysis of a tensor processing unit , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).

[557]  Walid Saad,et al.  Deep Reinforcement Learning for Interference-Aware Path Planning of Cellular-Connected UAVs , 2018, 2018 IEEE International Conference on Communications (ICC).

[558]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[559]  Filipp Akopyan Design and Tool Flow of IBM's TrueNorth: an Ultra-Low Power Programmable Neurosynaptic Chip with 1 Million Neurons , 2016, ISPD.

[560]  Pietro Perona,et al.  One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[561]  Feng Liu,et al.  AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization , 2018, SIGCOMM.

[562]  Daniel Roggen,et al.  Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.

[563]  Jing Li,et al.  Wavelet-Based Stacked Denoising Autoencoders for Cell Phone Base Station User Number Prediction , 2016, 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[564]  Sicong Liu,et al.  Poster: MobiEar-Building an Environment-independent Acoustic Sensing Platform for the Deaf using Deep Learning , 2016, MobiSys '16 Companion.

[565]  Geoffrey E. Hinton,et al.  Zero-shot Learning with Semantic Output Codes , 2009, NIPS.

[566]  Yuxi Li,et al.  Deep Reinforcement Learning: An Overview , 2017, ArXiv.

[567]  Geoffrey E. Hinton Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[568]  Tao Zhang,et al.  Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges , 2018, IEEE Signal Processing Magazine.

[569]  Rajesh Krishna Balan,et al.  Demo: DeepMon: Building Mobile GPU Deep Learning Models for Continuous Vision Applications , 2017, MobiSys.

[570]  Geoffrey E. Hinton,et al.  On the importance of initialization and momentum in deep learning , 2013, ICML.

[571]  Xianfu Chen,et al.  Energy-Efficiency Oriented Traffic Offloading in Wireless Networks: A Brief Survey and a Learning Approach for Heterogeneous Cellular Networks , 2015, IEEE Journal on Selected Areas in Communications.

[572]  Richard Demo Souza,et al.  A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks , 2017, IEEE Communications Surveys & Tutorials.

[573]  Sofie Pollin,et al.  Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors , 2017, ArXiv.

[574]  Tao Jiang,et al.  Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues , 2018, IEEE Network.

[575]  Ning Wang,et al.  Backhauling 5G small cells: A radio resource management perspective , 2015, IEEE Wireless Communications.

[576]  Mehryar Mohri,et al.  AdaNet: Adaptive Structural Learning of Artificial Neural Networks , 2016, ICML.

[577]  Yike Guo,et al.  TensorLayer: A Versatile Library for Efficient Deep Learning Development , 2017, ACM Multimedia.

[578]  Sanming Zhou,et al.  Networking for Big Data: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[579]  Zhenzhong Chen,et al.  3-D BLE Indoor Localization Based on Denoising Autoencoder , 2017, IEEE Access.

[580]  Ying Wang,et al.  A Data-Driven Architecture for Personalized QoE Management in 5G Wireless Networks , 2017, IEEE Wireless Communications.