Anmerkung: Intro -- Preface -- Contents -- About the Editors -- The Rudiments of Energy Conservation and IoT -- 1 Introduction -- 2 Paradigmatic View of Energy-Efficient IoT -- 3 Pragmatic Energy-Efficient IoT System Architecture -- 4 Issues of Energy Conservation in IoT -- 5 Energy Conservation Approaches for IoT Devices and Its Perspectives -- 5.1 Node Activity Management -- 5.2 Data Aggregation and Transmission Process -- 5.3 Media Access Control (MAC) Protocol -- 5.4 Security Management -- 5.5 Topology Management -- 5.6 Routing -- 6 Energy-Efficient System Design for IoT Devices -- 7 Conclusions -- References -- Existing Enabling Technologies and Solutions for Energy Management in IoT -- 1 Introduction -- 2 Architectures of IoT -- 2.1 Three-Layer Architecture -- 2.2 Four Layer Architecture -- 2.3 Five-Layer Architecture -- 3 Components of IoT -- 3.1 Identification -- 3.2 Sensing -- 3.3 Communication -- 3.4 Computation -- 3.5 Services -- 3.6 Semantics -- 4 Applications -- 4.1 Home Automation -- 4.2 Health care -- 4.3 Transportation -- 4.4 Logistics -- 4.5 Smart Environment and Agriculture -- 5 Challenges in IoT -- 6 Energy Management -- 6.1 Energy Harvesting -- 6.2 Energy Conservation -- 7 Research Directions -- 8 Conclusion -- References -- Energy-Efficient System Design for Internet of Things (IoT) Devices -- 1 Introduction -- 2 Operation -- 3 Energy Conservation -- 3.1 Solar Energy Harvesting -- 3.2 Thermal Energy Harvesting -- 3.3 Vibrational Energy Harvesting -- 3.4 Electrostatic Energy Harvesting -- 3.5 Wind Energy Harvesting -- 3.6 RF Energy Harvesting -- 4 Harvesting Module -- 4.1 Rectenna Model -- 4.2 Sensing Antenna -- 4.3 DC-DC Converter -- 4.4 Power Management Unit -- 5 Wireless Energy Harvesting -- 5.1 Near Field Communication -- 5.2 Inductive Coupling -- 6 Applications -- 6.1 Home Appliances -- 6.2 Healthcare Devices. , 6.3 Automatic Vehicles -- 6.4 Business Infrastructure -- 6.5 Farming and Poultry -- 6.6 Smart Utilities -- References -- Models and Algorithms for Energy Conservation in Internet of Things -- 1 Introduction -- 2 Data Centers -- 2.1 Big Data -- 2.2 Cloud Computing -- 3 Virtualization -- 4 Load Balancing -- 4.1 Hardware Versus Software Load Balancing -- 5 Energy Consumptions in Data Centers -- 5.1 Green Computing -- 5.2 Power Calculation at Data Center -- 6 Static Energy-Efficient Algorithms -- 6.1 Exact Allocation Algorithm -- 6.2 Best Fit Heuristic Algorithm -- 7 Dynamic Energy-Efficient Algorithms -- 7.1 Hardware Level Solution -- 7.2 Software Level Solution -- 8 Summary -- References -- An Energy-Efficient IoT Group-Based Architecture for Smart Cities -- 1 Introduction -- 2 Related Work -- 3 System Description -- 3.1 The WSN for e-Health and Human Well-Being Monitoring -- 3.2 Utilities Monitoring Systems -- 3.3 Air Quality and Climate Monitoring Systems -- 3.4 Emergency Situations Monitoring -- 3.5 Other Systems -- 4 Proposed Architecture for the Smart City -- 5 Conclusion and Future Work -- References -- Context-Aware Automation Based Energy Conservation Techniques for IoT Ecosystem -- 1 Introduction -- 1.1 Communication Technologies -- 1.2 Pricing Policies -- 2 Introduction -- 3 Related Work -- 3.1 Demand-Side Management -- 3.2 Usage of Renewable Energy Source -- 3.3 Context-Aware Automation -- 3.4 Feedback-Based Automation -- 4 Case Studies -- 5 Proposed Framework -- 6 Future Directions and Challenges -- 7 Conclusion -- References -- Energy Conservation in IoT-Based Smart Home and Its Automation -- 1 Introduction -- 2 Electrical Network End-to-End System -- 2.1 Generation -- 2.2 Transmission and Distribution -- 2.3 Automation in Demand, Supply, and Monitoring -- 2.4 Load Shedding and Control -- 3 Causes of Energy Losses and Preventive Actions. , 3.1 Electrical Network Improvement -- 3.2 Smart Energy Monitoring Devices -- 4 Automation and Control in Electrical Network -- 4.1 Automation Devices -- 4.2 Standards for Automation Devices -- 4.3 Communication Hardware and Automation Protocols -- 5 Energy Conservation Key Area -- 5.1 Smart Buildings -- 5.2 Smart Homes -- 5.3 Smart Appliances -- 6 Energy Conservation in Smart Home and IoT -- 6.1 Automation and Sensors in Smart Home -- 6.2 Industry Trends and Present Technology -- 6.3 Energy Conservation Components of Smart Home -- 6.4 Renewable Energy Sources with IoT in Smart Home -- 7 Artificial Intelligence in Energy Conservation-Methods and Technology -- 7.1 Digital Signal Processing and IoT -- 7.2 Artificial Intelligence in Smart Home -- 8 Cloud Data Processing Using IoT Devices -- 9 Conclusions -- References -- IoT Architecture for Preventive Energy Conservation of Smart Buildings -- 1 Introduction -- 1.1 Prevalent Smart Components -- 1.2 IoT System Architectures -- 1.3 Smart Buildings -- 1.4 Energy Efficiency in Smart Building IoT Systems -- 2 Requirements and Approaches for Energy Efficiency in Smart Buildings -- 2.1 Requirements for Environmental Conservation -- 2.2 Requirement for Energy Modeling -- 2.3 Requirement for Energy Consumption Monitoring and Evaluation -- 3 Existing Application Architectures -- 3.1 Smart Energy Metering Architectures -- 3.2 Smart Lighting Architectures -- 3.3 Energy Management Interfaces for Buildings -- 3.4 Energy-Efficient Smart Building Automation Architectures -- 3.5 Energy-Efficient Implementations in Smart Grid -- 3.6 Energy-Efficient Comfort Management Systems in Smart Buildings -- 3.7 Energy Monitoring and Saving Methods in Smart Buildings -- 4 Open Challenges and Future Work -- 4.1 Lack of Interoperability for Currently Used Protocols -- 4.2 Need for a Cost-Effective Architecture that Conserves Energy. , 4.3 Integration of Renewable Energy Sources in Smart Buildings -- 4.4 Maintainability of Energy-Efficient Architectures -- 5 Conclusion -- References -- Designing Energy-Efficient IoT-Based Intelligent Transport System: Need, Architecture, Characteristics, Challenges, and Applications -- 1 Introduction -- 1.1 Intelligent Transport System -- 1.2 Motivations for IoT in Transportation -- 1.3 Architecture of ITS -- 2 Key Technologies and Related Power Optimization Bottlenecks -- 2.1 Perception Technology: Precision, Reliability, and Power Constraints -- 2.2 Communication Technology and Related Power Issues -- 2.3 Information Extraction and Underlying Power Issues -- 3 Energy Efficiency Challenges and Corresponding Solutions -- 3.1 Precision, Density, and Reliability of Perception and Smart Sensing Solutions -- 3.2 Information Exchange Based Solutions -- 3.3 Computational Feasibility and Distributed Computing Solutions -- 3.4 Data Collection and Pooling with Energy-Efficient Solutions -- 4 Further Challenges and Opportunities -- 4.1 Further Involvement of Internet of Vehicle (IoV) -- 4.2 Cooperative Automated Vehicle (CAV) Scheme -- 4.3 Utilization of Multiple-Source Data in ITS -- 4.4 Software-Defined Radio (SDR)-Based Communication -- 4.5 Energy Harvesting Corridors -- 5 Conclusion and Future Work -- References -- Capacity Estimation of Electric Vehicle Aggregator for Ancillary Services to the Grid -- 1 Historical Perspective -- 2 Development of Electric Vehicles -- 3 Motivation for Vehicle to Everything (V2X) and V2G Technology -- 4 Electric Vehicles and Solar Power Plants in Smart Grid Environment -- 5 Potential of EV to Grid Connection -- 6 Capacity Estimation of Aggregator -- 7 Battery Management System -- 8 Grid Connection and Performance Testing of V2G -- 9 Commercial Value of V2G -- 10 Challenges and Opportunities -- 11 Discussion and Conclusion. , References -- Need and Design of Smart and Secure Energy-Efficient IoT-Based Healthcare Framework -- 1 Introduction -- 2 Data Generation in IoT Environment -- 3 Applications of IoT -- 4 Publication Trends of IoT -- 5 Critical Human Disorders -- 6 Energy-Efficient IoT Systems (Related Works) -- 7 Role of IoT in Designing Energy-Optimized Systems -- 7.1 Proposed Energy-Efficient IoT-Based Healthcare System for Neurological and Psychological Disorder Patients -- 8 Conclusion -- References -- Medical Information Processing Using Smartphone Under IoT Framework -- 1 Introduction -- 1.1 Motivation -- 1.2 Objectives -- 1.3 Organization of the Chapter -- 2 System Model -- 3 System Requirement -- 4 Importance of Cloud for Smartphone-Enabled IoT -- 5 Internet of Medical Things (IoMT) Using Smartphone -- 6 Biomedical Data Processing -- 6.1 Transmission of Medical Image Signals -- 6.2 Transmission of Biomedical Signals (ECG, EEG, and EMG) -- 6.3 Transmission of Medical Video Signals -- 6.4 Teletrauma System -- 7 Application of IoT -- 7.1 Application Oriented to Health Care -- 8 Application Standards/Protocols Use in IoT (Health Care) -- 9 Challenges -- 10 Conclusion -- References -- Contributing Toward Green IoT: An Awareness-Based Approach -- 1 Introduction -- 2 A Walkthrough of Internets of Things and Its Applications -- 2.1 Challenges of Internet of Things -- 3 Green IoT: An Overview -- 3.1 Smart Homes -- 3.2 Smart Cities -- 3.3 Energy-Efficient Smart Health Care -- 4 Various Approaches to Achieve Green IoT -- 5 Awareness-Based Approach Toward Green IoT -- 5.1 Energy Awareness Campaigns -- 5.2 IoT-Based Smart Metering -- 5.3 Promoting Recycling -- 5.4 Creating Awareness About Green Information Communication Technology -- 5.5 Promoting the Usage of Sensor Cloud: A Step Toward Green IoT. , 6 Creating Awareness Through Prototyping: A Green IoT-Based Smart Home Model.

Anmerkung: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1. Introduction to Signal Processing and Machine Learning -- 1.1 Introduction -- 1.2 Basic Terminologies -- 1.2.1 Signal Processing -- 1.2.1.1 Continuous and Discrete Signals -- 1.2.1.2 Sampling and Quantization -- 1.2.1.3 Change of Basis -- 1.2.1.4 Importance of Time Domain and Frequency Domain Analyses -- 1.2.2 Machine Learning -- 1.3 Distance-Based Signal Classification, Nearest Neighbor Classifier, and Hilbert Space -- 1.3.1 Distance-Based Signal Classification -- 1.3.1.1 Metric Space -- 1.3.1.2 Normed Linear Space -- 1.3.1.3 Inner Product Space -- 1.3.2 Nearest Neighbor Classification -- 1.3.3 Hilbert Space -- 1.4 Fusion of Machine Learning in Signal Processing -- 1.5 Benefits of Adopting Machine Learning in Signal Processing -- 1.6 Conclusion -- References -- 2. Learning Theory (Supervised/Unsupervised) for Signal Processing -- 2.1 Introduction -- 2.1.1 Signal Processing -- 2.2 Machine Learning -- 2.2.1 Why Do We Need ML for Signal Processing? -- 2.2.2 Speaker ID - A Utilization of ML Calculations in Sign Handling -- 2.2.3 Discourse and Audio Processing -- 2.2.4 Discourse Recognition -- 2.2.5 Listening Devices -- 2.2.6 Independent Driving -- 2.2.7 Picture Processing and Analysis -- 2.2.8 Wearables -- 2.2.9 Information Science -- 2.2.10 Wireless Systems and Networks -- 2.3 Machine Learning Algorithms -- 2.4 Supervised Learning -- 2.5 Unsupervised Learning -- 2.6 Semi-Supervised Learning -- 2.7 Reinforcement Learning -- 2.8 Use Case of Signal Processing Using Supervised and Unsupervised Learning -- 2.8.1 Features and Classifiers -- 2.8.2 Linear Classifiers -- 2.8.3 Decision Hyperplanes -- 2.8.4 Least Squares Methods -- 2.8.5 Mean Square Estimation -- 2.8.6 Support Vector machines -- 2.8.7 Non-Linear Regression. , 2.8.8 Non-Linearity of Activation Functions -- 2.8.8.1 Sigmoid Function -- 2.8.8.2 Rectified Linear Unit (ReLU) -- 2.8.9 Classification -- 2.8.9.1 Linear Classification -- 2.8.9.2 Two-Class Classification -- 2.8.9.3 Geometrical Interpretation of Derivatives -- 2.8.9.4 Multiclass Classification: Loss Function -- 2.8.10 Mean Squared Error -- 2.8.11 Multilabel Classification -- 2.8.12 Gradient Descent -- 2.8.12.1 Learning Rate -- 2.8.13 Hyperparameter Tuning -- 2.8.13.1 Validation -- 2.8.14 Regularization -- 2.8.14.1 How Does Regularization Work? -- 2.8.15 Regularization Techniques -- 2.8.15.1 Ridge Regression (L2 Regularization) -- 2.8.16 Lasso Regression (L1 Regularization) -- 2.8.17 K-Means Clustering -- 2.8.18 The KNN Algorithm -- 2.8.19 Clustering -- 2.8.20 Clustering Methods -- 2.9 Deep Learning for Signal Data -- 2.9.1 Traditional Time Series Analysis -- 2.9.2 Recurrence Domain Analysis -- 2.9.3 Long Short-Term Memory Models for Human Activity Recognition -- 2.9.4 External Device HAR -- 2.9.5 Signal Processing on GPUs -- 2.9.6 Signal Processing on FPGAs -- 2.9.7 Signal Processing is coming to the Forefront of Data Analysis -- 2.10 Conclusion -- References -- 3. Supervised and Unsupervised Learning Theory for Signal Processing -- 3.1 Introduction -- 3.1.1 Supervised Learning -- 3.1.2 Unsupervised Learning -- 3.1.3 Reinforcement Learning -- 3.1.4 Semi-Supervised Learning -- 3.2 Supervised Learning Method -- 3.2.1 Classicfiation Problems -- 3.2.2 Regression Problems -- 3.2.3 Examples of Supervised Learning -- 3.3 Unsupervised Learning Method -- 3.3.1 Illustrations of Unsupervised Learning -- 3.4 Semi-Supervised Learning Method -- 3.5 Binary Classification -- 3.5.1 Different Classes -- 3.5.2 Classification in Preparation -- 3.5.2.1 Logistic Regression Model -- 3.5.2.2 Odds Ratio -- 3.5.2.3 Logit Function -- 3.5.2.4 The Sigmoid Function. , 3.5.2.5 Support Vector Machines -- 3.5.2.6 Maximum Margin Lines -- 3.6 Conclusion -- References -- 4. Applications of Signal Processing -- 4.1 Introduction -- 4.2 Audio Signal Processing -- 4.2.1 Machine Learning in Audio Signal Processing -- 4.2.1.1 Spectrum and Cepstrum -- 4.2.1.2 Mel Frequency Cepstral Coefficients -- 4.2.1.3 Gammatone Frequency Cepstral Coefficients -- 4.2.1.4 Building the Classifier -- 4.3 Audio Compression -- 4.3.1 Modeling and Coding -- 4.3.2 Lossless Compression -- 4.3.3 Lossy Compression -- 4.3.4 Compressed Audio with Machine Learning Applications -- 4.4 Digital Image Processing -- 4.4.1 Fields Overlapping with Image Processing -- 4.4.2 Digital Image Processing System -- 4.4.3 Machine Learning with Digital Image Processing -- 4.4.3.1 Image Classification -- 4.4.3.2 Data Labelling -- 4.4.3.3 Location Detection -- 4.5 Video Compression -- 4.5.1 Video Compression Model -- 4.5.2 Machine Learning in Video Compression -- 4.5.2.1 Development Savings -- 4.5.2.2 Improving Encoder Density -- 4.6 Digital Communications -- 4.6.1 Machine Learning in Digital Communications -- 4.6.1.1 Communication Networks -- 4.6.1.2 Wireless Communication -- 4.6.1.3 Smart Infrastructure and IoT -- 4.6.1.4 Security and Privacy -- 4.6.1.5 Multimedia Communication -- 4.6.2 Healthcare -- 4.6.2.1 Personalized Medical Treatment -- 4.6.2.2 Clinical Research and Trial -- 4.6.2.3 Diagnosis of Disease -- 4.6.2.4 Smart Health Records -- 4.6.2.5 Medical Imaging -- 4.6.2.6 Drug Discovery -- 4.6.2.7 Outbreak Prediction -- 4.6.3 Seismology -- 4.6.3.1 Interpreting Seismic Observations -- 4.6.3.2 Machine Learning in Seismology -- 4.6.4 Speech Recognition -- 4.6.5 Computer Vision -- 4.6.6 Economic Forecasting -- 4.7 Conclusion -- References -- 5. Dive in Deep Learning: Computer Vision, Natural Language Processing, and Signal Processing -- 5.1 Deep Learning: Introduction. , 5.2 Past, Present, and Future of Deep- Learning -- 5.3 Natural Language Processing -- 5.3.1 Word Embeddings -- 5.3.1.1 Word2vec -- 5.3.2 Global Vectors for Word Representation -- 5.3.3 Convolutional Neural Networks -- 5.3.4 Feature Selection and Preprocessing -- 5.3.4.1 Tokenization -- 5.3.4.2 Stop Word Removal -- 5.3.4.3 Stemming -- 5.3.4.4 Lemmatization -- 5.3.5 Named Entity Recognition -- 5.4 Image Processing -- 5.4.1 Introduction to Image Processing and Computer Vision -- 5.4.1.1 Scene Understanding -- 5.4.2 Localization -- 5.4.3 Smart Cities and Surveillance -- 5.4.4 Medical Imaging -- 5.4.5 Object Representation -- 5.4.6 Object Detection -- 5.5 Audio Processing and Deep Learning -- 5.5.1 Audio Data Handling Using Python -- 5.5.2 Spectrogram -- 5.5.3 Wavelet- Based Feature Extraction -- 5.5.4 Current Methods -- 5.5.4.1 Audio Classification -- 5.5.4.2 Audio Fingerprinting -- 5.5.4.3 Feature Extraction -- 5.5.4.4 Speech Classification -- 5.5.4.5 Music Processing -- 5.5.4.6 Natural Sound Processing -- 5.5.4.7 Technological Tools -- 5.6 Conclusion -- References -- 6. Brain-Computer Interfacing -- 6.1 Introduction to BCI and Its Components -- 6.1.1 BCI Components -- 6.2 Framework/Architecture of BCI -- 6.3 Functions of BCI -- 6.3.1 Correspondence and Control -- 6.3.2 Client State Checking -- 6.4 Applications of BCI -- 6.4.1 Healthcare -- 6.4.1.1 Prevention -- 6.4.1.2 Detection and Diagnosis -- 6.4.1.3 Rehabilitation and Restoration -- 6.4.2 Neuroergonomics and Smart Environment -- 6.4.3 Neuromarketing and Advertisement -- 6.4.4 Pedagogical and Self-Regulating Oneself -- 6.4.5 Games and Entertainment -- 6.4.6 Security and Authentication -- 6.5 Signal Acquisition -- 6.5.1 Invasive Techniques -- 6.5.1.1 Intracortical -- 6.5.1.2 ECoG and Cortical Surface -- 6.5.2 Noninvasive Techniques -- 6.5.2.1 Magneto-encephalography (MEG). , 6.5.2.2 fMRI (functional Magnetic Resonance Imaging) -- 6.5.2.3 fNIRS (functional Near-Infrared Spectroscopy) -- 6.5.2.4 EEG (Electroencephalogram) -- 6.6 Electrical Signal of BCI -- 6.6.1 Evoked Potential (EP) or Evoked Response -- 6.6.2 Event-Related Desynchronization and Synchronization -- 6.7 Challenges of BCI and Proposed Solutions -- 6.7.1 Challenges of Usability -- 6.7.2 Technical Issues -- 6.7.3 Proposed Solutions -- 6.7.3.1 Noise Removal -- 6.7.3.2 Disconnectedness of Multiple Classes -- 6.8 Conclusion -- References -- 7. Adaptive Filters and Neural Net -- 7.1 Introduction -- 7.1.1 Adaptive Filtering Problem -- 7.2 Linear Adaptive Filter Implementation -- 7.2.1 Stochastic Gradient Approach -- 7.2.2 Least Square Estimation -- 7.3 Nonlinear Adaptive Filters -- 7.3.1 Volterra-Based Nonlinear Adaptive Filter -- 7.4 Applications of Adaptive Filter -- 7.4.1 Biomedical Applications -- 7.4.1.1 ECG Power-Line Interference Removal -- 7.4.1.2 Maternal-Fetal ECG Separation -- 7.4.2 Speech Processing -- 7.4.2.1 Noise Cancelation -- 7.4.3 Communication Systems -- 7.4.3.1 Channel Equalization in Data Transmission Systems -- 7.4.3.2 Multiple Access Interference Mitigation in CDMA -- 7.4.4 Adaptive Feedback Cancellation in Hearing Aids -- 7.5 Neural Network -- 7.5.1 Learning Techniques in ANN -- 7.6 Single and Multilayer Neural Net -- 7.6.1 Single-Layer Neural Networks -- 7.6.2 Multilayer Neural Net -- 7.7 Applications of Neural Networks -- 7.7.1 ECG Classicafition -- 7.7.1.1 Methodology -- 7.7.2 Speech Recognition -- 7.7.2.1 Methodology -- 7.7.3 Communication Systems -- 7.7.3.1 Mobile Station Location Identification Using ANN -- 7.7.3.2 ANN-Based Call Handoff Management Scheme for Mobile Cellular Network -- 7.7.3.3 A Hybrid Path Loss Prediction Model based on Artificial  Neural Networks -- 7.7.3.4 Classification of Primary Radio Signals. , 7.7.3.5 Channel Capacity Estimation Using ANN.