A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer

This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.

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

[2]  Timothy J. O'Shea,et al.  Radio Machine Learning Dataset Generation with GNU Radio , 2016 .

[3]  Lan-Xun Wang,et al.  Recognition of digital modulation signals based on high order cumulants and support vector machines , 2009, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management.

[4]  M. H. Valipour,et al.  Automatic digital modulation recognition in presence of noise using SVM and PSO , 2012, 6th International Symposium on Telecommunications (IST).

[5]  M. Zorzi,et al.  Learning and Adaptation in Cognitive Radios Using Neural Networks , 2008, 2008 5th IEEE Consumer Communications and Networking Conference.

[6]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[7]  Cyrill Stachniss,et al.  UAV-based crop and weed classification for smart farming , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[8]  Yong Wang,et al.  Predicting link quality using supervised learning in wireless sensor networks , 2007, MOCO.

[9]  Pei Liu,et al.  Realtime Scheduling and Power Allocation Using Deep Neural Networks , 2018, 2019 IEEE Wireless Communications and Networking Conference (WCNC).

[10]  Carey L. Williamson,et al.  Offline/realtime traffic classification using semi-supervised learning , 2007, Perform. Evaluation.

[11]  Eun Jong Cha,et al.  Classification Technique of Human Motion Context based on Wireless Sensor Network , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[12]  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).

[13]  Shan Wang,et al.  MAC protocol selection based on machine learning in cognitive radio networks , 2016, 2016 19th International Symposium on Wireless Personal Multimedia Communications (WPMC).

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

[15]  Muhammad Shahzad,et al.  Augmenting User Identification with WiFi Based Gesture Recognition , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[16]  Awais Ahmad,et al.  Efficient Graph-Oriented Smart Transportation Using Internet of Things Generated Big Data , 2015, 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[17]  A. Forster,et al.  Machine Learning Techniques Applied to Wireless Ad-Hoc Networks: Guide and Survey , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[18]  Yifeng Zhu,et al.  Localization using neural networks in wireless sensor networks , 2008, MOBILWARE.

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

[20]  Ingrid Moerman,et al.  Automated linear regression tools improve RSSI WSN localization in multipath indoor environment , 2011, EURASIP J. Wirel. Commun. Netw..

[21]  Luis Alonso,et al.  WSN4QoL: A WSN-Oriented Healthcare System Architecture , 2014, Int. J. Distributed Sens. Networks.

[22]  Yiyang Pei,et al.  Robust Modulation Classification under Uncertain Noise Condition Using Recurrent Neural Network , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[23]  Zhi Ding,et al.  Wavelet transform processing for cellular traffic prediction in machine learning networks , 2015, 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP).

[24]  Dusit Niyato,et al.  A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio , 2010, 2010 IEEE International Conference on Communications.

[25]  Haibo He,et al.  MAC protocol classification in a cognitive radio network , 2010, The 19th Annual Wireless and Optical Communications Conference (WOCC 2010).

[26]  Octavio José Salcedo Parra,et al.  Detection of the Primary User's Behavior for the Intervention of the Secondary User Using Machine Learning , 2018, FDSE.

[27]  Wilhelm Stork,et al.  Context-aware mobile health monitoring: Evaluation of different pattern recognition methods for classification of physical activity , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Kenneth N. Brown,et al.  ER-MAC: A Hybrid MAC Protocol for Emergency Response Wireless Sensor Networks , 2010, 2010 Fourth International Conference on Sensor Technologies and Applications.

[29]  Hao Wu,et al.  VHF radio signal modulation classification based on convolution neural networks , 2018 .

[30]  Bhaskar Krishnamachari,et al.  Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks , 2018, IEEE Transactions on Cognitive Communications and Networking.

[31]  Jian Pei,et al.  A dynamic clustering and scheduling approach to energy saving in data collection from wireless sensor networks , 2005, 2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005..

[32]  Hossein Jafari,et al.  IoT Devices Fingerprinting Using Deep Learning , 2018, MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM).

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

[34]  Meng Zhang,et al.  Automatic Modulation Recognition Using Deep Learning Architectures , 2018, 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[35]  Raquel Barco,et al.  Self-healing in mobile networks with big data , 2016, IEEE Communications Magazine.

[36]  Bernt Schiele,et al.  Weakly Supervised Recognition of Daily Life Activities with Wearable Sensors , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Timothy J. O'Shea,et al.  Spectral detection and localization of radio events with learned convolutional neural features , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

[38]  Tom Schaul,et al.  Unit Tests for Stochastic Optimization , 2013, ICLR.

[39]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[40]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

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

[42]  Reynold Xin,et al.  Apache Spark , 2016 .

[43]  Antonio Torralba,et al.  Through-Wall Human Pose Estimation Using Radio Signals , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[45]  Prasanna Chaporkar,et al.  A Learnable Distortion Correction Module for Modulation Recognition , 2018, IEEE Wireless Communications Letters.

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

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

[48]  Antonio Liotta,et al.  Ensembles of incremental learners to detect anomalies in ad hoc sensor networks , 2015, Ad Hoc Networks.

[49]  Marco Fiore,et al.  DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[50]  Augusto Aubry,et al.  Cumulants-based Radar Specific Emitter Identification , 2011, 2011 IEEE International Workshop on Information Forensics and Security.

[51]  Huseyin Ugur Yildiz,et al.  Neural network based instant parameter prediction for wireless sensor network optimization models , 2018, Wireless Networks.

[52]  Man-Gon Park,et al.  Bayesian Statistical Modeling of System Energy Saving Effectiveness for MAC Protocols of Wireless Sensor Networks , 2010, Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

[53]  Wang Ke,et al.  Attribute-based clustering for information dissemination in wireless sensor networks , 2005, 2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005..

[54]  Woongsup Lee,et al.  Resource Allocation for Multi-Channel Underlay Cognitive Radio Network Based on Deep Neural Network , 2018, IEEE Communications Letters.

[55]  Sihai Zhang,et al.  Survey of wireless big data , 2017, Journal of Communications and Information Networks.

[56]  Hamid Sharif,et al.  Performance Evaluation of Feature-based Automatic Modulation Classification , 2018, 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS).

[57]  Sunil Kumar,et al.  Medium Access Control protocols for ad hoc wireless networks: A survey , 2006, Ad Hoc Networks.

[58]  Lisandro Zambenedetti Granville,et al.  A Survey on the Programmability of Wireless MAC Protocols , 2019, IEEE Communications Surveys & Tutorials.

[59]  Sanqing Hu,et al.  MAC protocol identification approach for implement smart cognitive radio , 2012, 2012 IEEE International Conference on Communications (ICC).

[60]  Samir Ranjan Das,et al.  A multichannel CSMA MAC protocol with receiver-based channel selection for multihop wireless networks , 2001, Proceedings Tenth International Conference on Computer Communications and Networks (Cat. No.01EX495).

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

[62]  Tao Liu,et al.  Data-driven link quality prediction using link features , 2014, TOSN.

[63]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[64]  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).

[65]  Eiji Oki,et al.  Feature-Selection Based Data Prioritization in Mobile Traffic Prediction Using Machine Learning , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[66]  Y. Li,et al.  Specific emitter identification using geometric features of frequency drift curve , 2018 .

[67]  Nirvana Meratnia,et al.  Outlier Detection Techniques for Wireless Sensor Networks: A Survey , 2008, IEEE Communications Surveys & Tutorials.

[68]  Kalaiarasi Sonai Muthu,et al.  Classification Algorithms in Human Activity Recognition using Smartphones , 2012 .

[69]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[70]  Ingrid Moerman,et al.  Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial , 2016, Sensors.

[71]  Yonina C. Eldar,et al.  Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection , 2019, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[72]  Tim O'Shea,et al.  Learning robust general radio signal detection using computer vision methods , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

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

[74]  Klaus Wehrle,et al.  Bursty traffic over bursty links , 2009, SenSys '09.

[75]  Madhusmita Mohanty,et al.  Automatic modulation classification using S-transform based features , 2015, 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN).

[76]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[77]  Wei Heng,et al.  Base station sleeping mechanism based on traffic prediction in heterogeneous networks , 2015, 2015 International Telecommunication Networks and Applications Conference (ITNAC).

[78]  Bishal Thapa,et al.  Machine Learning Approach to RF Transmitter Identification , 2017, IEEE Journal of Radio Frequency Identification.

[79]  Yangyu Fan,et al.  Automatic Modulation Classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints , 2017, IEEE Signal Processing Letters.

[80]  Zwi Altman,et al.  Automated Diagnosis for UMTS Networks Using Bayesian Network Approach , 2008, IEEE Transactions on Vehicular Technology.

[81]  Yunhao Liu,et al.  ZiSense: towards interference resilient duty cycling in wireless sensor networks , 2014, SenSys.

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

[83]  Woongsup Lee,et al.  Deep Cooperative Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks , 2019, IEEE Transactions on Vehicular Technology.

[84]  Hang Su,et al.  Opportunistic MAC Protocols for Cognitive Radio Based Wireless Networks , 2007, 2007 41st Annual Conference on Information Sciences and Systems.

[85]  Wu Yang,et al.  Device-Free Passive Identity Identification via WiFi Signals , 2017, Sensors.

[86]  Maurizio Dusi,et al.  Traffic classification through simple statistical fingerprinting , 2007, CCRV.

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

[88]  Xiaojiang Du,et al.  Robust Device-Free Intrusion Detection Using Physical Layer Information of WiFi Signals , 2019, Applied Sciences.

[89]  Asoke K. Nandi,et al.  Automatic Modulation Classification Using Combination of Genetic Programming and KNN , 2012, IEEE Transactions on Wireless Communications.

[90]  Vasant Dhar,et al.  Data science and prediction , 2012, CACM.

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

[92]  Hao Wang,et al.  Interference Source Identification for IEEE 802.15.4 wireless Sensor Networks Using Deep Learning , 2018, 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

[93]  Tao Liu,et al.  Foresee (4C): Wireless link prediction using link features , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[94]  John Langford,et al.  Agnostic Active Learning Without Constraints , 2010, NIPS.

[95]  Aly El Gamal,et al.  Deep neural network architectures for modulation classification , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

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

[97]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[98]  Walid Saad,et al.  Learning How to Communicate in the Internet of Things: Finite Resources and Heterogeneity , 2016, IEEE Access.

[99]  Hazem H. Refai,et al.  Wireless technology identification using deep Convolutional Neural Networks , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[100]  C.G. Christodoulou,et al.  Signal classification with an SVM-FFT approach for feature extraction in cognitive radio , 2009, 2009 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference (IMOC).

[101]  Maria-Florina Balcan,et al.  Active Learning Algorithms for Graphical Model Selection , 2016, AISTATS.

[102]  P. Levis,et al.  RSSI is Under Appreciated , 2006 .

[103]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[104]  Erkki Mäkinen,et al.  A Neural Network Model to Minimize the Connected Dominating Set for Self-Configuration of Wireless Sensor Networks , 2009, IEEE Transactions on Neural Networks.

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

[106]  Tao Liu,et al.  Temporal Adaptive Link Quality Prediction with Online Learning , 2014, ACM Trans. Sens. Networks.

[107]  Ingrid Moerman,et al.  A Convolutional Neural Network Approach for Classification of LPWAN Technologies: Sigfox, LoRA and IEEE 802.15.4g , 2019, 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[108]  Indrajit Ray,et al.  Behavioral Fingerprinting of IoT Devices , 2018, ASHES@CCS.

[109]  Uwe Meier,et al.  Wireless interference identification with convolutional neural networks , 2017, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN).

[110]  Hong Jiang,et al.  Design of Learning Engine Based on Support Vector Machine in Cognitive Radio , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[111]  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).

[112]  Xiaoqiao Meng,et al.  Real-time forest fire detection with wireless sensor networks , 2005, Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005..

[113]  Kok-Lim Alvin Yau,et al.  Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues , 2012, J. Netw. Comput. Appl..

[114]  João B. Martins,et al.  An approach to localization scheme of wireless sensor networks based on artificial neural networks and Genetic Algorithms , 2012, 10th IEEE International NEWCAS Conference.

[115]  Marwan Krunz,et al.  CDMA-based MAC protocol for wireless ad hoc networks , 2003, MobiHoc '03.

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

[117]  Hamed S. Al-Raweshidy,et al.  A New Intelligent Approach for Optimizing 6LoWPAN MAC Layer Parameters , 2017, IEEE Access.

[118]  Sirui Duan,et al.  Automatic Multicarrier Waveform Classification via PCA and Convolutional Neural Networks , 2018, IEEE Access.

[119]  Zhuo Yang,et al.  MAC protocol identification using support vector machines for cognitive radio networks , 2014, IEEE Wireless Communications.

[120]  Stathes Hadjiefthymiades,et al.  Predicting the location of mobile users: a machine learning approach , 2009, ICPS '09.

[121]  Song Han,et al.  ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA , 2016, FPGA.

[122]  Yunming Ye,et al.  TW-k-means: Automated two-level variable weighting clustering algorithm for multiview data , 2013, IEEE Transactions on Knowledge and Data Engineering.

[123]  G. Ahmed,et al.  Cluster head selection using decision trees for Wireless Sensor Networks , 2008, 2008 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[124]  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.

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

[126]  Ekram Hossain,et al.  Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks , 2013, IEEE Journal on Selected Areas in Communications.

[127]  Victor C. M. Leung,et al.  Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges , 2017, IEEE Communications Magazine.

[128]  Frank Eliassen,et al.  A Communication-Efficient Distributed Clustering Algorithm for Sensor Networks , 2008, 22nd International Conference on Advanced Information Networking and Applications - Workshops (aina workshops 2008).

[129]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[130]  Donghai Guan,et al.  Activity Recognition Based on Semi-supervised Learning , 2007, 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2007).

[131]  Guangyi Liu,et al.  Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks , 2018, Sensors.

[132]  Tao Liu,et al.  TALENT: temporal adaptive link estimator with no training , 2012, SenSys '12.

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

[134]  Angelo M. Sabatini,et al.  Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers , 2010, Sensors.

[135]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[136]  Soung Chang Liew,et al.  Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks , 2019, IEEE J. Sel. Areas Commun..

[137]  Kai Yang,et al.  Active Learning for Wireless IoT Intrusion Detection , 2018, IEEE Wireless Communications.

[138]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[139]  Lior Rokach,et al.  Data Mining with Decision Trees - Theory and Applications , 2007, Series in Machine Perception and Artificial Intelligence.

[140]  Pramod K. Varshney,et al.  Asynchronous Linear Modulation Classification With Multiple Sensors via Generalized EM Algorithm , 2014, IEEE Transactions on Wireless Communications.

[141]  Sofie Pollin,et al.  Identifying Spectrum Usage by Unknown Systems using Experiments in Machine Learning , 2009, 2009 IEEE Wireless Communications and Networking Conference.

[142]  Ali Abdi,et al.  Survey of automatic modulation classification techniques: classical approaches and new trends , 2007, IET Commun..

[143]  Roland Siegwart,et al.  weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming , 2017, IEEE Robotics and Automation Letters.

[144]  Gregory Piatetsky-Shapiro,et al.  The KDD process for extracting useful knowledge from volumes of data , 1996, CACM.

[145]  Ameer Ahmed Abbasi,et al.  A survey on clustering algorithms for wireless sensor networks , 2007, Comput. Commun..

[146]  Xiaofei Wang,et al.  Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges , 2015, IEEE Access.

[147]  Timothy J. O'Shea,et al.  Semi-supervised radio signal identification , 2016, 2017 19th International Conference on Advanced Communication Technology (ICACT).

[148]  D. Larose.  k‐Nearest Neighbor Algorithm , 2005 .

[149]  Suman Banerjee,et al.  Airshark: detecting non-WiFi RF devices using commodity WiFi hardware , 2011, IMC '11.

[150]  Asoke K. Nandi,et al.  Automatic digital modulation recognition using artificial neural network and genetic algorithm , 2004, Signal Process..

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

[152]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[153]  Hazem H. Refai,et al.  Energy detection and machine learning for the identification of wireless MAC technologies , 2015, 2015 International Wireless Communications and Mobile Computing Conference (IWCMC).

[154]  C. Cordeiro,et al.  C-MAC: A Cognitive MAC Protocol for Multi-Channel Wireless Networks , 2007, 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[155]  Sangarapillai Lambotharan,et al.  Spatio-Temporal Spectrum Sensing in Cognitive Radio Networks Using Beamformer-Aided SVM Algorithms , 2018, IEEE Access.

[156]  Carlos León,et al.  Giving neurons to sensors. QoS management in wireless sensors networks. , 2006, 2006 IEEE Conference on Emerging Technologies and Factory Automation.

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

[158]  Xiaodong Ji,et al.  A dynamic affinity propagation clustering algorithm for cell outage detection in self-healing networks , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[159]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[160]  Ingrid Moerman,et al.  Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments , 2019, IEEE Access.

[161]  J. J. Popoola,et al.  A Novel Modulation-Sensing Method , 2011, IEEE Vehicular Technology Magazine.

[162]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.

[163]  Ingrid Moerman,et al.  Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices , 2017, Sensors.

[164]  Jun Fang,et al.  A Deep Learning Approach for Modulation Recognition via Exploiting Temporal Correlations , 2018, 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[165]  Ian F. Akyildiz,et al.  A survey on spectrum management in cognitive radio networks , 2008, IEEE Communications Magazine.

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

[167]  Wei Lin,et al.  Artificial Neural Network Based Spectrum Sensing Method for Cognitive Radio , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[168]  Muazzam Ali Khan,et al.  Automatic Modulation Recognition of Communication Signals. , 2012 .

[169]  Simon Duquennoy,et al.  TIIM: technology-independent interference mitigation for low-power wireless networks , 2015, IPSN.

[170]  David A. Freedman,et al.  Statistical Models: Theory and Practice: References , 2005 .

[171]  Amit P. Sheth,et al.  Machine learning for Internet of Things data analysis: A survey , 2017, Digit. Commun. Networks.

[172]  Michele Zorzi,et al.  A Neural Network Based Cognitive Controller for Dynamic Channel Selection , 2009, 2009 IEEE International Conference on Communications.

[173]  Hans-Werner Gellersen,et al.  Multimodal recognition of reading activity in transit using body-worn sensors , 2012, TAP.

[174]  Chenyang Lu,et al.  Self-Adapting MAC Layer for Wireless Sensor Networks , 2013, 2013 IEEE 34th Real-Time Systems Symposium.

[175]  Xiang Cheng,et al.  Idle Time Window Prediction in Cellular Networks with Deep Spatiotemporal Modeling , 2019, IEEE Journal on Selected Areas in Communications.

[176]  Walid G. Morsi,et al.  Nonintrusive Load Monitoring Using Wavelet Design and Machine Learning , 2016, IEEE Transactions on Smart Grid.

[177]  Tariq Rahim Soomro,et al.  Big Data Analysis: Apache Storm Perspective , 2015 .

[178]  Alagan Anpalagan,et al.  SVM-based classification of digital modulation signals , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[179]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[180]  Yi Zhang,et al.  Spectrum Monitoring for Radar Bands Using Deep Convolutional Neural Networks , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[181]  Teemu Roos,et al.  Semi-supervised Learning for WLAN Positioning , 2011, ICANN.

[182]  Pramod K. Varshney,et al.  Distributed asynchronous modulation classification based on hybrid maximum likelihood approach , 2015, MILCOM 2015 - 2015 IEEE Military Communications Conference.

[183]  Holger Ziekow,et al.  Towards a Big Data Analytics Framework for IoT and Smart City Applications , 2015 .

[184]  Yingshu Li,et al.  Real time clustering of sensory data in wireless sensor networks , 2009, 2009 IEEE 28th International Performance Computing and Communications Conference.

[185]  Timothy J. O'Shea,et al.  Applications of Machine Learning to Cognitive Radio Networks , 2007, IEEE Wireless Communications.

[186]  Parth H. Pathak,et al.  WiWho: WiFi-Based Person Identification in Smart Spaces , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[187]  Ingrid Moerman,et al.  Poster: Towards a Cognitive MAC Layer: Predicting the MAC-level Performance in Dynamic WSN using Machine Learning , 2017, EWSN.

[188]  Dong Han,et al.  Spectrum sensing for cognitive radio based on convolution neural network , 2017, 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[189]  T. Charles Clancy,et al.  Over-the-Air Deep Learning Based Radio Signal Classification , 2017, IEEE Journal of Selected Topics in Signal Processing.

[190]  Cyrus Shahabi,et al.  The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks , 2007, TOSN.

[191]  Sundeep Prabhakar Chepuri,et al.  Performance evaluation of an IEEE 802.15.4 cognitive radio link in the 2360-2400 MHz band , 2011, 2011 IEEE Wireless Communications and Networking Conference.

[192]  Marion Berbineau,et al.  Automatic Modulation Recognition Using Wavelet Transform and Neural Networks in Wireless Systems , 2010, 2009 9th International Conference on Intelligent Transport Systems Telecommunications, (ITST).

[193]  Shahram Latifi,et al.  A survey on data compression in wireless sensor networks , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[194]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[195]  Abbas Javed,et al.  Critical Analysis of Learning Algorithms in Random Neural Network Based Cognitive Engine for LTE Systems , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[196]  Chung-Horng Lung,et al.  Mobile Network Traffic Prediction Using MLP, MLPWD, and SVM , 2016, 2016 IEEE International Congress on Big Data (BigData Congress).

[197]  T. Charles Clancy,et al.  Convolutional Radio Modulation Recognition Networks , 2016, EANN.

[198]  Shauna Revay,et al.  Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks , 2018, IEEE Journal of Selected Topics in Signal Processing.

[199]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[200]  Luiz A. DaSilva,et al.  Context-aware cognitive radio using deep learning , 2017, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

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

[202]  Ya Tu,et al.  Digital Signal Modulation Classification With Data Augmentation Using Generative Adversarial Nets in Cognitive Radio Networks , 2018, IEEE Access.

[203]  Junde Song,et al.  Signal Classification Based on Spectral Correlation Analysis and SVM in Cognitive Radio , 2008, 22nd International Conference on Advanced Information Networking and Applications (aina 2008).

[204]  Wei Wang,et al.  Understanding and Modeling of WiFi Signal Based Human Activity Recognition , 2015, MobiCom.

[205]  Erik G. Larsson,et al.  Adversarial Attacks on Deep-Learning Based Radio Signal Classification , 2018, IEEE Wireless Communications Letters.

[206]  Yu-Dong Yao,et al.  Modulation classification using convolutional Neural Network based deep learning model , 2017, 2017 26th Wireless and Optical Communication Conference (WOCC).

[207]  Awais Khawar,et al.  Robust Signal Classification Using Unsupervised Learning , 2011, IEEE Transactions on Wireless Communications.

[208]  Ingrid Moerman,et al.  Towards low-complexity wireless technology classification across multiple environments , 2019, Ad Hoc Networks.

[209]  Kwang-Cheng Chen,et al.  Carrier Sensing Based Multiple Access Protocols for Cognitive Radio Networks , 2008, 2008 IEEE International Conference on Communications.

[210]  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.

[211]  Anis Laouiti,et al.  TDMA-Based MAC Protocols for Vehicular Ad Hoc Networks: A Survey, Qualitative Analysis, and Open Research Issues , 2015, IEEE Communications Surveys & Tutorials.

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

[213]  Deniz Gündüz,et al.  A Learning Theoretic Approach to Energy Harvesting Communication System Optimization , 2012, IEEE Transactions on Wireless Communications.

[214]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[215]  Sofie Pollin,et al.  Electrosense: Open and Big Spectrum Data , 2017, IEEE Communications Magazine.

[216]  Jun Won Choi,et al.  Deep neural network-based automatic modulation classification technique , 2016, 2016 International Conference on Information and Communication Technology Convergence (ICTC).

[217]  Jean Hennebert,et al.  A Survey on Intrusive Load Monitoring for Appliance Recognition , 2014, 2014 22nd International Conference on Pattern Recognition.

[218]  H. T. Kung,et al.  Embedded Binarized Neural Networks , 2017, EWSN.

[219]  Ran Wolff,et al.  Noname manuscript No. (will be inserted by the editor) In-Network Outlier Detection in Wireless Sensor Networks , 2022 .

[220]  Lenan Wu,et al.  Automatic Modulation Classification: A Deep Learning Enabled Approach , 2018, IEEE Transactions on Vehicular Technology.

[221]  Alan J. Michaels,et al.  Signal detection effects on deep neural networks utilizing raw IQ for modulation classification , 2017, MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM).

[222]  Steven Latré,et al.  Evaluating Deep Neural Networks to Classify Modulated and Coded Radio Signals , 2018, CrownCom.

[223]  Yifan Wu,et al.  Radio Classify Generative Adversarial Networks: A Semi-supervised Method for Modulation Recognition , 2018, 2018 IEEE 18th International Conference on Communication Technology (ICCT).

[224]  Ingrid Moerman,et al.  End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications , 2017, IEEE Access.

[225]  Bernt Schiele,et al.  Towards Less Supervision in Activity Recognition from Wearable Sensors , 2006, 2006 10th IEEE International Symposium on Wearable Computers.

[226]  N. Sidiropoulos,et al.  Learning to Optimize: Training Deep Neural Networks for Interference Management , 2017, IEEE Transactions on Signal Processing.

[227]  Bernt Schiele,et al.  A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.

[228]  Pramod K. Varshney,et al.  Hybrid Maximum Likelihood Modulation Classification Using Multiple Radios , 2013, IEEE Communications Letters.

[229]  Ganesh K. Venayagamoorthy,et al.  Neural network based secure media access control protocol for wireless sensor networks , 2009, 2009 International Joint Conference on Neural Networks.

[230]  Josh Petersen,et al.  Modulation recognition using hierarchical deep neural networks , 2017, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).

[231]  Yourong Lu,et al.  Channel and Modulation Selection Based on Support Vector Machines for Cognitive Radio , 2006, 2006 International Conference on Wireless Communications, Networking and Mobile Computing.

[232]  Roger H. L. Chiang,et al.  Big Data Research in Information Systems: Toward an Inclusive Research Agenda , 2016, J. Assoc. Inf. Syst..

[233]  Carlo Meghini,et al.  Deep learning for decentralized parking lot occupancy detection , 2017, Expert Syst. Appl..

[234]  Hong-Tzer Yang,et al.  Load Identification in Neural Networks for a Non-intrusive Monitoring of Industrial Electrical Loads , 2007, CSCWD.

[235]  Raheem A. Beyah,et al.  A passive technique for fingerprinting wireless devices with Wired-side Observations , 2013, 2013 IEEE Conference on Communications and Network Security (CNS).

[236]  Dries Naudts,et al.  Enhancing the Coexistence of LTE and Wi-Fi in Unlicensed Spectrum Through Convolutional Neural Networks , 2019, IEEE Access.

[237]  Xiaofan Li,et al.  A Survey on Deep Learning Techniques in Wireless Signal Recognition , 2019, Wirel. Commun. Mob. Comput..

[238]  Hongguang Li,et al.  Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles , 2018, Sensors.

[239]  Robert D. Nowak,et al.  Minimax Bounds for Active Learning , 2007, IEEE Transactions on Information Theory.

[240]  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).

[241]  Thiemo Voigt,et al.  SoNIC: Classifying interference in 802.15.4 sensor networks , 2013, 2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[242]  Zahra Taghikhaki,et al.  Distributed Event Detection in Wireless Sensor Networks for Disaster Management , 2010, 2010 International Conference on Intelligent Networking and Collaborative Systems.

[243]  Yonina C. Eldar,et al.  Fast Deep Learning for Automatic Modulation Classification , 2019, ArXiv.

[244]  Yong Li,et al.  Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach , 2016, IEEE Transactions on Services Computing.

[245]  Xianfu Chen,et al.  Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory , 2017, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall).

[246]  Marina Petrova,et al.  Multi-class classification of analog and digital signals in cognitive radios using Support Vector Machines , 2010, 2010 7th International Symposium on Wireless Communication Systems.

[247]  Nirvana Meratnia,et al.  Adaptive and Online One-Class Support Vector Machine-Based Outlier Detection Techniques for Wireless Sensor Networks , 2009, 2009 International Conference on Advanced Information Networking and Applications Workshops.

[248]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[249]  Ian Witten,et al.  Data Mining , 2000 .

[250]  Uwe Meier,et al.  Multi-Label Wireless Interference Classification with Convolutional Neural Networks , 2018, 2018 IEEE 16th International Conference on Industrial Informatics (INDIN).

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

[252]  Ganesh K. Venayagamoorthy,et al.  Computational Intelligence in Wireless Sensor Networks: A Survey , 2011, IEEE Communications Surveys & Tutorials.

[253]  Timothy J. O'Shea,et al.  Unsupervised representation learning of structured radio communication signals , 2016, 2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE).

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

[255]  Saleh A. Alshebeili,et al.  Automatic modulation classification of digital modulations in presence of HF noise , 2012, EURASIP Journal on Advances in Signal Processing.

[256]  Jorge-Arnulfo Quiané-Ruiz,et al.  Efficient Big Data Processing in Hadoop MapReduce , 2012, Proc. VLDB Endow..

[257]  Stratis Ioannidis,et al.  Deep Learning Convolutional Neural Networks for Radio Identification , 2018, IEEE Communications Magazine.

[258]  Fereidoon Behnia,et al.  Automatic Digital Modulation Recognition Based on Novel Features and Support Vector Machine , 2016, 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[259]  Y.A. Sekercioglu,et al.  Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[260]  Anand Paul,et al.  IoT-based smart city development using big data analytical approach , 2016, 2016 IEEE International Conference on Automatica (ICA-ACCA).

[261]  Christos V. Verikoukis,et al.  Cooperative Energy Harvesting-Adaptive MAC Protocol for WBANs , 2015, Sensors.

[262]  Steve Hanneke,et al.  Theory of Disagreement-Based Active Learning , 2014, Found. Trends Mach. Learn..

[263]  Duc A. Tran,et al.  Localization In Wireless Sensor Networks Based on Support Vector Machines , 2008, IEEE Transactions on Parallel and Distributed Systems.