Internet of things, smart spaces, and next generation networks and systems : 18th International Conference, NEW2AN 2018, and 11th Conference, ruSMART 2018, St. Petersburg, Russia, August 27-29, 2018, Proceedings /
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| Meeting name: | NEW2AN (Conference) (18th : 2018 : Saint Petersburg, Russia) |
|---|---|
| Imprint: | Cham, Switzerland : Springer, 2018. |
| Description: | 1 online resource (xvi, 705 pages) : illustrations. |
| Language: | English |
| Series: | Lecture notes in computer science ; 11118 LNCS sublibrary. SL 5, Computer communication networks and telecommunications Lecture notes in computer science ; 11118. LNCS sublibrary. SL 5, Computer communication networks and telecommunications. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11706442 |
Table of Contents:
- Intro
- Preface
- Organization
- Contents
- ruSMART: New Generation of Smart Services
- Requirements for Energy Efficient Edge Computing: A Survey
- Abstract
- 1 Introduction
- 2 Definition of Edge and Fog
- 3 Benefits of the Edge Computing
- 4 Edge and Fog Computing Challenges
- 4.1 Methods for Reducing Energy Consumption in Wireless Sensor Networks
- 4.2 Data Compression Methods in Edge Device: Lossy and Lossless Methods
- 5 Wireless Technologies for Energy Efficient IoT
- 6 Energy Efficient IoT Protocols
- 7 Security and Privacy Issues in the Edge
- 8 Conclusions
- 5 Sample Scenarios and Testing
- 6 Conclusion
- Acknowledgements
- References
- An Artificial Intelligence Based Forecasting in Smart Parking with IoT
- Abstract
- 1 Introduction
- 2 Related Work
- 3 AI Genetic Algorithm for SC Generated Parking Data
- 3.1 IoT Generated Data at the SC Parking Places
- 3.2 Incorporated Evaluation Real Datasets
- 3.3 Genetic Algorithm
- 3.4 AI Experimental Results
- 4 AI Recurrent Neural Networks (RNN) and Data Forecasting
- 4.1 Incorporating AI RNN
- 4.2 Data Preparation
- 4.3 Learning on Datasets
- 5 Decision System
- 5.1 Car System and Use Case
- 5.2 Finding the Best Parking Lot Through the Prediction System
- 6 Conclusions and Future Work
- Acknowledgments
- References
- On Data Stream Processing in IoT Applications
- Abstract
- 1 Introduction
- 2 On Streams-Based Architectures
- 3 IoT Applications and Streams
- 4 On Streams-Based Algorithms
- 5 Conclusion
- Acknowledgement
- References
- Analysis of Assets for Threat Risk Model in Avatar-Oriented IoT Architecture
- 1 Introduction
- 2 Place of Assets in Threat Risk Model
- 3 Architecture of IoT
- 3.1 Classical IoT Architecture
- 3.2 Avatar-Oriented IoT Architecture
- 4 Stakeholders and Assets
- 5 Conclusions
- References
- State of the Art Literature Review on Network Anomaly Detection with Deep Learning
- 1 Introduction
- 2 Network Anomaly Detection with Deep Learning
- 2.1 Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
- 2.2 DeepDefense: Identifying DDoS Attack via Deep Learning
- 2.3 Network Anomaly Detection Using Artificial Neural Networks
- 2.4 Network Anomaly Detection with Stochastically Improved Autoencoder Based Models
- 2.5 An Anomaly-Based Network Intrusion Detection System Using Deep Learning