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AI + Iot + Analytics for Driver Safety Monitoring: The Smartphone Sentinel - A Zero-Hardware Blueprint from Prototype to Client Pilot
Paperback
Publisher: Independently Published
Published: Sep 25 2026
Pages: 234
Weight: 0.63
Height: 0.59 Width: 6.00 Depth: 9.00
Language: English
How do you turn a real-world safety problem into a working technology solution?
A driver closes their eyes for two seconds. A quick glance at a phone becomes a dangerous distraction. A harsh-braking event happens on an otherwise normal journey.
When these signals are captured, interpreted, and analysed over time, they can reveal patterns of driving risk.
AI + IoT + Analytics for Driver Safety Monitoring presents an end-to-end approach to designing and building intelligent driver-safety systems.
Rather than treating AI, IoT, and Analytics separately, the book brings them together-from sensing and edge processing to machine learning, event pipelines, risk scoring, dashboards, alerts, and decision-making.
Inside the book, you will explore:- Driver-safety problem definition and system requirements
- AI, IoT, and Analytics fundamentals
- Camera, IMU, GPS, and sensor-layer design
- Edge computing and real-time data pipelines
- AI/ML approaches for drowsiness, distraction, and harsh-driving detection
- Explainability, model governance, security, and adversarial robustness
- Real-time alerts and escalation
- Fleet analytics and dashboard design
- Fleet telematics integration
- Hands-on prototype development
- Local storage, synchronization, backend ingestion, and risk scoring
- Testing, validation, field trials, ROI, privacy, compliance, and deployment
- Future directions including OBD-II/CAN, production hardware, wearables, Vision Transformers, federated learning, autonomous driving, and two-wheelers
The hands-on sections build major components including a drowsiness detector, distraction detection, phone-sensor-based harsh-driving detection, local storage and synchronization, backend event ingestion, risk scoring, fleet analytics, end-to-end alerts, and validation.
The book goes beyond model accuracy to examine edge vs. cloud responsibilities, offline operation, explainability, data protection, performance, evaluation, and real-world deployment constraints.
Beyond the PrototypeThe final sections address ROI modelling, privacy and compliance, driver adoption, consulting proposals, pilot design, pricing, phased delivery, staffing, and future production directions.
Designed for students, AI/ML and IoT practitioners, developers, automotive and fleet-technology professionals, educators, consultants, and technology leaders, the book follows a practical progression:
Problem → Architecture → Prototype → Evaluation → Deployment → Future Possibilities
Don't stop at the model. Build the system. Test the system. Understand the trade-offs. Then ask whether it creates enough real-world value to deploy.
Srinivas Yellugani is a technology leader, educator, author, and Professor of Practice with more than two decades of experience across AI, Analytics, IoT, digital transformation, and technology delivery. He has worked with organizations across banking, healthcare, manufacturing, utilities, and industrial domains, helping teams turn complex technology investments into measurable business outcomes. His journey spans technology leadership, delivery, mentoring, and talent development. As an educator, he has spent years helping students and professionals move from theory to practical application. His writing reflects the same philosophy: technology is best understood when concepts connect to real problems, systems, and decisions. His books explore AI engineering, predictive analytics, IoT, digital transformation, and the human dimensions of technology and leadership. Through his work as a practitioner, mentor, and author, Srinivas focuses on making complex ideas accessible, actionable, and relevant to people who build, deploy, and use technology.Also from
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