Algorithm Deployment Optimization

[1]  Kai Ding,et al.  Battery-Management System (BMS) and SOC Development for Electrical Vehicles , 2011, IEEE Transactions on Vehicular Technology.

[2]  Trevor N. Mudge,et al.  Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge , 2017, ASPLOS.

[3]  Kashem M. Muttaqi,et al.  Real-time State-of-charge Tracking System Using Mixed Estimation Algorithm for Electric Vehicle Battery System , 2018, 2018 IEEE Industry Applications Society Annual Meeting (IAS).

[4]  Weisong Shi,et al.  LAVEA: latency-aware video analytics on edge computing platform , 2017, SEC.

[5]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[6]  Carlo Curino,et al.  WANalytics: Geo-Distributed Analytics for a Data Intensive World , 2015, SIGMOD Conference.

[7]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[8]  Qun Li,et al.  Efficient service handoff across edge servers via docker container migration , 2017, SEC.

[9]  Weisong Shi,et al.  Making Disk Failure Predictions SMARTer! , 2020, FAST.

[10]  Sidi Lu,et al.  Edge Compression: An Integrated Framework for Compressive Imaging Processing on CAVs , 2020, 2020 IEEE/ACM Symposium on Edge Computing (SEC).

[11]  Ji-Hyun Kim,et al.  Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap , 2009, Comput. Stat. Data Anal..

[12]  Mahadev Satyanarayanan,et al.  Eureka: Edge-Based Discovery of Training Data for Machine Learning , 2019, IEEE Internet Computing.

[13]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[14]  Lingjia Tang,et al.  Neurosurgeon , 2017 .

[15]  Fanglin Chen,et al.  CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones , 2013, MobiSys 2013.

[16]  Stefano Longo,et al.  A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur , 2016 .

[17]  MengChu Zhou,et al.  Security and trust issues in Fog computing: A survey , 2018, Future Gener. Comput. Syst..

[18]  J. Friedman.  Stochastic gradient boosting , 2002 .

[19]  Tamitza Toroyan,et al.  Global status report on road safety , 2009, Injury Prevention.

[20]  Diana Golodnitsky,et al.  Parameter analysis of a practical lithium- and sodium-air electric vehicle battery , 2011 .

[21]  Mats Jirstrand,et al.  OODIDA: On-board/Off-board Distributed Data Analytics for Connected Vehicles , 2019, ArXiv.

[22]  Byoungheon Shin,et al.  Application-Aware IoT Camera Virtualization for Video Analytics Edge Computing , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[23]  Olof Mogren,et al.  C-RNN-GAN: Continuous recurrent neural networks with adversarial training , 2016, ArXiv.

[24]  Javam C. Machado,et al.  Predicting Failures in Hard Drives with LSTM Networks , 2017, 2017 Brazilian Conference on Intelligent Systems (BRACIS).

[25]  Paramvir Bahl,et al.  VideoEdge: Processing Camera Streams using Hierarchical Clusters , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[26]  José Antonio Lozano,et al.  Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Zhaohui Zheng,et al.  Stochastic gradient boosted distributed decision trees , 2009, CIKM.

[28]  Li Ran,et al.  Design method of CAN BUS network communication structure for electric vehicle , 2010, International Forum on Strategic Technology 2010.

[29]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[30]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[31]  Weisong Shi,et al.  Collaborative Learning on the Edges: A Case Study on Connected Vehicles , 2019, HotEdge.

[32]  Giancarlo Fortino,et al.  Data Mining at the IoT Edge , 2019, 2019 28th International Conference on Computer Communication and Networks (ICCCN).

[33]  Peter Richtárik,et al.  Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.

[34]  Faisal Nawab,et al.  Collaborative Edge and Cloud Neural Networks for Real-Time Video Processing , 2018, Proc. VLDB Endow..

[35]  Michael Pecht,et al.  Battery Management Systems in Electric and Hybrid Vehicles , 2011 .

[36]  Fengyuan Xu,et al.  Learning from Differentially Private Neural Activations with Edge Computing , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[37]  Guangzhong Sun,et al.  Driving with knowledge from the physical world , 2011, KDD.

[38]  Xiaopei Wu,et al.  OpenVDAP: An Open Vehicular Data Analytics Platform for CAVs , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[39]  Suyog Gupta,et al.  To prune, or not to prune: exploring the efficacy of pruning for model compression , 2017, ICLR.

[40]  Mahadev Satyanarayanan,et al.  The Emergence of Edge Computing , 2017, Computer.

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

[42]  Yang Cui,et al.  Electric Vehicle Battery SOC Estimation based on GNL Model Adaptive Kalman Filter , 2018 .

[43]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[44]  Mahadev Satyanarayanan,et al.  Edge-Based Discovery of Training Data for Machine Learning , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[45]  Jianqiu Li,et al.  Design of the control system for a four-wheel driven micro electric vehicle , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.

[46]  M. Amaç Güvensan,et al.  Driver Behavior Analysis for Safe Driving: A Survey , 2015, IEEE Transactions on Intelligent Transportation Systems.

[47]  Hong-Peng Li,et al.  The research of electric vehicle's MCU system based on ISO26262 , 2017, 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS).

[48]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[49]  Jae-Yoon Jung,et al.  LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks , 2018, Sensors.

[50]  Kang G. Shin,et al.  Battery State-of-Health Estimation for Mobile Devices , 2017, 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems (ICCPS).

[51]  Nianbo Liu,et al.  DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration , 2017, Mob. Inf. Syst..

[52]  Jason Flinn,et al.  Gremlin: scheduling interactions in vehicular computing , 2017, SEC.

[53]  Giancarlo Fortino,et al.  A Simulation-driven Methodology for IoT Data Mining Based on Edge Computing , 2021, ACM Trans. Internet Techn..

[54]  Lingling Fan,et al.  Real-Time Simulation of Electric Vehicle Battery Charging Systems , 2018, 2018 North American Power Symposium (NAPS).

[55]  Xiaopei Wu,et al.  CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[56]  Giancarlo Fortino,et al.  Autonomic computation offloading in mobile edge for IoT applications , 2019, Future Gener. Comput. Syst..

[57]  Weisong Shi,et al.  SafeShareRide: Edge-Based Attack Detection in Ridesharing Services , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[58]  Weina Wang,et al.  Study on HIL system of electric vehicle controller based on NI , 2018 .

[59]  B. V. Alsenoy.  General Data Protection Regulation , 2019, Data Protection Law in the EU: Roles, Responsibilities and Liability.