Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors

YUNJI LIANG, School of Computer Science, Northwestern Polytechnical University XIN WANG, School of Software, Northwestern Polytechnical University ZHIWEN YU, School of Computer Science, Northwestern Polytechnical University BIN GUO, School of Computer Science, Northwestern Polytechnical University XIAOLONG ZHENG, Institute of Automation Chinese Academy of Sciences SAGAR SAMTANI, Kelley School of Business, Indiana University

[1]  Tanapat Anusas-Amornkul.  Strengthening Password Authentication using Keystroke Dynamics and Smartphone Sensors , 2019, ICICM 2019.

[2]  Sajal K. Das,et al.  Optimizing Sensing, Computing, and Communication for Energy Harvesting IoTs: A Survey , 2019, ArXiv.

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

[4]  Makoto Koshino,et al.  Representation Learning by Convolutional Neural Network for Smartphone Sensor Based Activity Recognition , 2019, CIIS.

[5]  Rong Du,et al.  Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing , 2015, IEEE Journal on Selected Areas in Communications.

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

[7]  Suman Nath,et al.  On-line sensing task optimization for shared sensors , 2010, IPSN '10.

[8]  Meng-Shiuan Pan,et al.  Precise Trajectories Derivation Using Smartphone Sensors , 2019, ICCMS 2019.

[9]  Kazuya Murao,et al.  Automatic annotation for human activity and device state recognition using smartphone notification , 2019, UbiComp/ISWC Adjunct.

[10]  Claudio Pastrone,et al.  Heterogeneous Applications, Tools, and Methodologies in the Car Manufacturing Industry Through an IoT Approach , 2017, IEEE Systems Journal.

[11]  Haotian Wang.  Residual Mask Based on MobileNet-V2 for Driver's Dangerous Behavior Recognition , 2019, CSAI.

[12]  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).

[13]  Mahbub Hassan,et al.  SolarGest: Ubiquitous and Battery-free Gesture Recognition using Solar Cells , 2018, MobiCom.

[14]  Konstantin Beznosov,et al.  Towards Understanding the Link Between Age and Smartphone Authentication , 2019, CHI.

[15]  Pradipta De,et al.  Does emotion influence the use of auto-suggest during smartphone typing? , 2019, IUI.

[16]  Najwa Hayaati Mohd Alwi,et al.  Measuring sensor to cloud energy consumption , 2017, ICC.

[17]  Paolo Bellavista,et al.  CoLearn: enabling federated learning in MUD-compliant IoT edge networks , 2020, EdgeSys@EuroSys.

[18]  Michael Mitzenmacher,et al.  Detecting Novel Associations in Large Data Sets , 2011, Science.

[19]  Qing Yang,et al.  HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users , 2015, IEEE Transactions on Information Forensics and Security.

[20]  Bruno Crispo,et al.  DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms , 2019, Comput. Secur..

[21]  Andrew McCallum,et al.  Energy and Policy Considerations for Deep Learning in NLP , 2019, ACL.

[22]  Romit Roy Choudhury,et al.  Listening through a Vibration Motor , 2016, MobiSys.

[23]  Jiangtao Wang,et al.  Energy Saving Techniques in Mobile Crowd Sensing: Current State and Future Opportunities , 2018, IEEE Communications Magazine.

[24]  Soheil Ghiasi,et al.  Machine Intelligence on Resource-Constrained IoT Devices , 2017, ACM Trans. Embed. Comput. Syst..

[25]  Xukan Ran,et al.  Deep Learning With Edge Computing: A Review , 2019, Proceedings of the IEEE.

[26]  Ahmed E. Kamal,et al.  Spatial and Temporal Management of Cellular HetNets with Multiple Solar Powered Drones , 2019, IEEE Transactions on Mobile Computing.

[27]  Rui Wang,et al.  CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia , 2016, UbiComp.

[28]  Xin Liu,et al.  Cost-aware compressive sensing for networked sensing systems , 2015, IPSN.

[29]  Masato Oguchi,et al.  Saving power consumption of smartphones in the screen-off state with disabling the Wi-Fi , 2018, 2018 IEEE International Conference on Consumer Electronics (ICCE).

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

[31]  Michael W. Condry,et al.  Using Smart Edge IoT Devices for Safer, Rapid Response With Industry IoT Control Operations , 2016, Proceedings of the IEEE.

[32]  Nei Kato,et al.  Automobile Driver Fingerprinting: A New Machine Learning Based Authentication Scheme , 2020, IEEE Transactions on Industrial Informatics.

[33]  Zhiwen Yu,et al.  Behavioral Biometrics for Continuous Authentication in the Internet-of-Things Era: An Artificial Intelligence Perspective , 2020, IEEE Internet of Things Journal.

[34]  Leila Ismail,et al.  Computing Server Power Modeling in a Data Center , 2020, ACM Comput. Surv..

[35]  Wen Hu,et al.  Ear-Phone assessment of noise pollution with mobile phones , 2009, SenSys '09.

[36]  Marco Caccamo,et al.  A low-power architecture for high frequency sensor acquisition in many-DOF UAVs , 2014, 2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

[37]  Yuan He,et al.  Parallel Backscatter in the Wild: When Burstiness and Randomness Play with You , 2018, MobiCom.

[38]  Ling Chen,et al.  An Initial Study of Multisensory Interaction for Outdoor Heritage Sites , 2018, CHI Extended Abstracts.

[39]  Mintra Ruensuk.  Detecting Emotions using Smartphone Sensors: Technique to Raise Self-Awareness for Social Media Users , 2018, UbiComp/ISWC Adjunct.

[40]  Song Han,et al.  Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.

[41]  Zhihan Lv,et al.  Multimodal Hand and Foot Gesture Interaction for Handheld Devices , 2014, TOMM.

[42]  Xingshe Zhou,et al.  Energy-Efficient Motion Related Activity Recognition on Mobile Devices for Pervasive Healthcare , 2014, Mob. Networks Appl..

[43]  Xinyang Deng,et al.  A new probability transformation method based on a correlation coefficient of belief functions , 2019, Int. J. Intell. Syst..

[44]  Duy Trong Ngo,et al.  A Distributed Energy-Harvesting-Aware Routing Algorithm for Heterogeneous IoT Networks , 2018, IEEE Transactions on Green Communications and Networking.

[45]  Sofie Pollin,et al.  When Autonomous Drones Meet Driverless Cars , 2018, MobiSys.

[46]  Vivienne Sze,et al.  Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  Vladlen Koltun,et al.  An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling , 2018, ArXiv.

[48]  Sid Chi-Kin Chau,et al.  Efficient Online Classification and Tracking on Resource-constrained IoT Devices , 2020, ACM Trans. Internet Things.

[49]  Mubarak Shah,et al.  Norm-Preservation: Why Residual Networks Can Become Extremely Deep? , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Wei Sun,et al.  Combating Replay Attacks Against Voice Assistants , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[51]  Sujata Pal,et al.  Vehicle Air Pollution Monitoring Using IoTs , 2018, SenSys.

[52]  Aleksandr Ometov,et al.  Challenges of Multi-Factor Authentication for Securing Advanced IoT Applications , 2019, IEEE Network.

[53]  Gierad Laput,et al.  Enhancing Mobile Voice Assistants with WorldGaze , 2020, CHI.

[54]  Pijush Kanti Dutta Pramanik,et al.  Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage , 2019, IEEE Access.

[55]  R. B. Deshmukh,et al.  A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications , 2018, IEEE Access.

[56]  Gokhan Memik,et al.  Using Built-In Sensors to Predict and Utilize User Satisfaction for CPU Settings on Smartphones , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[57]  Yoshihiko Suhara,et al.  DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks , 2017, WWW.

[58]  Jiabin Yuan,et al.  Quality-based Score Level Fusion for Continuous Authentication with Motion Sensor and Face , 2020, ICCSP.

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

[60]  Feng Qian,et al.  DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning , 2017, IEEE Transactions on Mobile Computing.

[61]  Xu Chen,et al.  Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing , 2019, Proceedings of the IEEE.

[62]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[63]  Yu-Chee Tseng,et al.  Activity sense organs: energy-efficient activity sensing with adaptive duty cycle control , 2016, UbiComp Adjunct.

[64]  Victor O. K. Li,et al.  Task Allocation in Spatial Crowdsourcing: Current State and Future Directions , 2018, IEEE Internet of Things Journal.

[65]  Daniel Dajun Zeng,et al.  A survey on big data-driven digital phenotyping of mental health , 2019, Inf. Fusion.

[66]  Yue Wang,et al.  Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..

[67]  Zhi Wang,et al.  PRSS: A Prejudiced Random Sensing Strategy for Energy-Efficient Information Collection in the Internet of Things , 2019, IEEE Internet of Things Journal.

[68]  Yusuke Sato,et al.  Height Estimation based on Sensor Data on Smartphone , 2017, MoMM.

[69]  Jorge Gonçalves,et al.  Does Smartphone Use Drive our Emotions or vice versa? A Causal Analysis , 2020, CHI.