16 Exploring the potential of artificial intelligence in wireless sensor networks
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Vijaya Gunturu
Abstract
In various monitoring, tracking, and control applications, wireless sensor networks (WSNs) are gaining popularity. To fully utilize the promise of WSNs in various applications, two significant challenges - power efficiency and scalability - must be overcome. Recent developments in artificial intelligence (AI) methods, such as deep learning, machine learning (ML), and reinforcement learning, present fresh perspectives and development opportunities for WSNs to make wise decisions and effectively use resources. For example, AI-based techniques can enable WSNs to learn the underlying patterns and trends in the data streams exchanged among different sensor nodes, and choose optimal parameters for efficient data collection and analysis.
Abstract
In various monitoring, tracking, and control applications, wireless sensor networks (WSNs) are gaining popularity. To fully utilize the promise of WSNs in various applications, two significant challenges - power efficiency and scalability - must be overcome. Recent developments in artificial intelligence (AI) methods, such as deep learning, machine learning (ML), and reinforcement learning, present fresh perspectives and development opportunities for WSNs to make wise decisions and effectively use resources. For example, AI-based techniques can enable WSNs to learn the underlying patterns and trends in the data streams exchanged among different sensor nodes, and choose optimal parameters for efficient data collection and analysis.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- List of authors IX
- About the editors XIII
- 1 Introduction to artificial intelligence 1
- 2 AI technologies, tools, and industrial use cases 21
- 3 Classification and regression algorithms 53
- 4 Clustering and association algorithm 87
- 5 Reinforcement learning 109
- 6 Evaluation of AI model performance 125
- 7 Methods of cross-validation and bootstrapping 145
- 8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies 167
- 9 AI: issues, concerns, and ethical considerations 189
- 10 The future with AI and AI in action 213
- 11 A survey of AI in industry: from basic concepts to industrial and business applications 233
- 12 The intelligent implications of artificial intelligence-driven decision-making in business management 251
- 13 An innovative analysis of AI-powered automation techniques for business management 269
- 14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications 287
- 15 Utilizing AI technologies to enhance e-commerce business operations 309
- 16 Exploring the potential of artificial intelligence in wireless sensor networks 331
- 17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining 345
- 18 Exploring the potential of artificial intelligence for automated sentiment 361
- 19 A novel blockchain-based artificial intelligence application for healthcare automation 373
- 20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions 387
- Index 401
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- List of authors IX
- About the editors XIII
- 1 Introduction to artificial intelligence 1
- 2 AI technologies, tools, and industrial use cases 21
- 3 Classification and regression algorithms 53
- 4 Clustering and association algorithm 87
- 5 Reinforcement learning 109
- 6 Evaluation of AI model performance 125
- 7 Methods of cross-validation and bootstrapping 145
- 8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies 167
- 9 AI: issues, concerns, and ethical considerations 189
- 10 The future with AI and AI in action 213
- 11 A survey of AI in industry: from basic concepts to industrial and business applications 233
- 12 The intelligent implications of artificial intelligence-driven decision-making in business management 251
- 13 An innovative analysis of AI-powered automation techniques for business management 269
- 14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications 287
- 15 Utilizing AI technologies to enhance e-commerce business operations 309
- 16 Exploring the potential of artificial intelligence in wireless sensor networks 331
- 17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining 345
- 18 Exploring the potential of artificial intelligence for automated sentiment 361
- 19 A novel blockchain-based artificial intelligence application for healthcare automation 373
- 20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions 387
- Index 401
