| Description |
1 online resource (296 pages). |
| Physical Medium |
polychrome |
| Description |
text file |
| Series |
Advances in Computing Communications and Informatics Ser.
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Advances in Computing Communications and Informatics Ser.
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| Note |
Description based upon print version of record. |
| Contents |
Cover -- Title -- Copyright -- End User License Agreement -- Contents -- Preface -- List of Contributors -- An Overview of Smart Grid in the Current Age -- Reinaldo Padilha França1,*, Ana Carolina Borges Monteiro1,*, Rangel Arthur2 and Yuzo Iano1 -- 1. INTRODUCTION -- 2. SMART CITIES CONCEPTS -- 3. SMART GRID CONCEPTS -- 3.1. Smart Grid and IoT -- 3.2. Smart Grid Applications -- 3.3. Smart Grid Advantages -- 4. SMART GRID INFRASTRUCTURE -- 5. DISCUSSION -- 5.1. Infrastructure generated by Smart Grid -- 5.2. Smart Grid as an Instrument of Innovation -- 5.3. Smart Grid Benefits |
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5.4. Blockchain for Smart Grids -- CONCLUSION -- TRENDS -- CONSENT FOR PUBLICATION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENT -- REFERENCES -- Dynamic Strategies of Machine Learning for Extenuation of Security Breaches in Wireless Sensor Networks -- Shweta Paliwal1,* -- 1. INTRODUCTION -- 2. SECURITY CONCERNS IN WIRELESS SENSOR NETWORKS -- 2.1. Eavesdropping Attack -- 2.2. Jamming Attack -- 2.3. Tampering -- 2.4. Exhaustion and Collision Attack -- 2.5. Sybil Attack -- 2.6. Blackhole Attack -- 2.7. Wormhole Attack -- 2.8. Grayhole Attack -- 2.9. Sinkhole Attack -- 2.10. Hello Flood Attack |
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3. MACHINE LEARNING EMERGING AS A SAFEGUARD TO WSNS -- 4. SUPERVISED LEARNING -- 4.1. Linear Regression and Logistic Regression -- 4.2. Artificial Neural Networks (ANN) -- 4.3. Decision Tree -- 4.4. Random Forest -- 4.5. Bayesian Learning -- 4.6. Support Vector Machine (SVM) -- 4.7. K- Nearest Neighbor (K-NN) -- 5. UNSUPERVISED LEARNING -- 5.1. K- Means Clustering -- 5.2. Fuzzy C- Means Clustering -- 6. SEMI-SUPERVISED LEARNING -- 7. REINFORCEMENT LEARNING -- 8. MACHINE LEARNING ADDRESSING ISSUES IN WSNS -- 8.1. Machine Learning Addressing Issue of Security |
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8.2. Machine Learning Addressing Issue of Routing in Wireless Sensor Network -- 8.3. Data Aggregation in WSNs with Machine Learning -- 9. PROPOSED FEATURE SELECTION METHOD AND COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS -- 9.1. Dataset Gathering -- 9.2. Feature Selection Methodology -- 9.2.1. Correlation-based Feature -- 9.2.2. Info Gain Method -- 9.2.3. CFS Subset Evaluation -- 10. EXPERIMENTAL RESULTS AND ANALYSIS -- CONCLUSION AND FUTURE WORK -- CONSENT FOR PUBLICATION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENT -- REFERENCES -- IoT- Fundamentals and Challenges |
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Mohammad Maksuf Ul Haque1, Shazmeen Shamsi1, Khwaja M. Rafi2 and Mohammad Sufian Badar3,4,* -- 1. INTRODUCTION -- 2. HISTORY OF IOT -- 2.1. Realizing the Concept -- 3. INTERNET OF THINGS -- 3.1. Meaning of IoT -- 3.2. Importance of IoT -- 3.2.1. Things that Collect and Send Information -- 3.2.2. Things that Receive Information and then Act on it -- 3.2.3. Things that Can Do Both -- 3.3. Scope of IoT: Applications and Examples -- 3.3.1. Increasing Efficiency -- 3.3.2. Improved Health and Safety -- 3.3.3. Enhancing Experience -- 4. FUNDAMENTALS OF IOT -- 4.1. IoT Device Architecture |
| Note |
4.2. IoT Reference Architecture. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Internet of things.
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Internet of things. |
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Blockchains (Databases)
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Blockchains (Databases) |
| Genre/Form |
Electronic books.
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| Added Author |
Bhushan, Bharat.
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Astya, Parma N.
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| Other Form: |
Print version: Sharma, Sudhir K. Blockchain Applications for Secure IoT Frameworks: Technologies Shaping the Future Sharjah : Bentham Science Publishers,c2021 9781681088631 |
| ISBN |
1681088622 |
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9781681088624 (electronic book) |
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