{"product_id":"tiny-machine-learning-design-principles-and-applications-hardback-9781394294541","title":"Tiny Machine Learning: Design Principles and Applications (Hardback) 9781394294541","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eTiny Machine Learning: Design Principles and Applications\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAgbotiname Lucky Imoize (Edited by), Imoize (Author), Dinh-Thuan Do (Edited by), Houbing Herbert Song (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394294541, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 6 January 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e784 pages\u003cbr\u003e28 x 19 x 2.6 cm, 1.302 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eAn expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn \u003ci\u003eTiny Machine Learning: Design Principles and Applications,\u003c\/i\u003e a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eTiny Machine Learning\u003c\/i\u003e explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. \u003c\/p\u003e\n\u003cp\u003eAdditional topics covered in the book include: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eA thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes\u003c\/li\u003e\n\u003cli\u003eIncisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML\u003c\/li\u003e\n\u003cli\u003ePractical discussions of TinyML research targeting microcontrollers for data extraction and synthesis\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003ePerfect for industry and academic researchers, scientists, and engineers, \u003ci\u003eTiny Machine Learning\u003c\/i\u003e will also benefit lecturers and graduate students interested in machine learning.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eAbout the Editors xxiii\u003c\/p\u003e \u003cp\u003eList of Contributors xxvii\u003c\/p\u003e \u003cp\u003ePreface xxxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to TinyML 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eFrancisca Onyiyechi Nwokoma, Chidi Ukamaka Betrand, Juliet Nnenna Odii, Euphemia Chioma Nwokorie, and Ikechukwu Ignatius Ayogu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Evolution of TinyML 6\u003c\/p\u003e \u003cp\u003e1.3 Key Milestones and Current Trends 8\u003c\/p\u003e \u003cp\u003e1.4 TinyML System Development 11\u003c\/p\u003e \u003cp\u003e1.5 Challenges and Bottlenecks 17\u003c\/p\u003e \u003cp\u003e1.6 Cost–Benefit Analysis 20\u003c\/p\u003e \u003cp\u003e1.7 Key Findings 26\u003c\/p\u003e \u003cp\u003e1.8 Limitations of TinyML 27\u003c\/p\u003e \u003cp\u003e1.9 Conclusion 29\u003c\/p\u003e \u003cp\u003eReferences 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Learning Panorama Under TinyML 35\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIkechukwu Ignatius Ayogu, Euphemia Chioma Nwokorie, Juliet Nnenna Odii, Francisca Onyiyechi Nwokoma, and Chidi Ukamaka Betrand\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 35\u003c\/p\u003e \u003cp\u003e2.2 Challenges and Opportunities for Improved TinyML Model Design 37\u003c\/p\u003e \u003cp\u003e2.3 Frontiers in Model Optimization for TinyML 47\u003c\/p\u003e \u003cp\u003e2.4 Learning Frameworks and Tools for TinyML Development 59\u003c\/p\u003e \u003cp\u003e2.5 Frontiers for Algorithmic Innovations for TinyML 63\u003c\/p\u003e \u003cp\u003e2.6 TinyML Development Process 68\u003c\/p\u003e \u003cp\u003e2.7 Key Findings 69\u003c\/p\u003e \u003cp\u003e2.8 Conclusion 70\u003c\/p\u003e \u003cp\u003eReferences 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 TinyML for Anomaly Detection 85\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRichard Govada Joshua, Peter Anuoluwapo Gbadega, Agbotiname Lucky Imoize, and Samuel Oluwatobi Tofade\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 85\u003c\/p\u003e \u003cp\u003e3.2 Context and Literature Review 93\u003c\/p\u003e \u003cp\u003e3.3 Lessons Learned 149\u003c\/p\u003e \u003cp\u003e3.4 Future Scope 152\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 156\u003c\/p\u003e \u003cp\u003eReferences 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 TinyML Power Consumption and Memory in IoT MCUs 163\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePeter Anuoluwapo Gbadega, Agbotiname Lucky Imoize, Richard Govada Joshua, and Samuel Oluwatobi Tofade\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 163\u003c\/p\u003e \u003cp\u003e4.2 Context and Literature Review 171\u003c\/p\u003e \u003cp\u003e4.3 Methodology 174\u003c\/p\u003e \u003cp\u003e4.4 Results and Discussion: Bibliometric Analysis 184\u003c\/p\u003e \u003cp\u003e4.5 Conclusion and Future Directions 198\u003c\/p\u003e \u003cp\u003eReferences 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Efficient Data Cleaning and Anomaly Detection in IoT Devices Using TinyCleanEDF 205\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIlker Kara\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 205\u003c\/p\u003e \u003cp\u003e5.2 IoT and TinyCleanEDF 206\u003c\/p\u003e \u003cp\u003e5.3 The Importance of Data Cleaning in IoT Systems 207\u003c\/p\u003e \u003cp\u003e5.4 Anomaly Detection and Its Importance in IoT Systems 208\u003c\/p\u003e \u003cp\u003e5.5 Data Preprocessing and Cleaning Workflow 209\u003c\/p\u003e \u003cp\u003e5.6 Implementation Details 213\u003c\/p\u003e \u003cp\u003e5.7 Case Studies and Applications 216\u003c\/p\u003e \u003cp\u003e5.8 Future Directions 219\u003c\/p\u003e \u003cp\u003e5.9 Conclusion and Future Scope 221\u003c\/p\u003e \u003cp\u003eReferences 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 TinyML Devices and Tools 225\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAbeeb Akorede Bello, Agbotiname Lucky Imoize, and Abiodun Temitope Odewale\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 225\u003c\/p\u003e \u003cp\u003e6.2 Related Work 227\u003c\/p\u003e \u003cp\u003e6.3 TinyML Devices 234\u003c\/p\u003e \u003cp\u003e6.4 TinyML Tools 237\u003c\/p\u003e \u003cp\u003e6.5 Deployment Procedure of TinyML 241\u003c\/p\u003e \u003cp\u003e6.6 Lesson Learned and Prospects 250\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 253\u003c\/p\u003e \u003cp\u003eReferences 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Privacy-Preserving Techniques in TinyML for IoT 259\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eOleksandr Kuznetsov, Emanuele Frontoni, Kateryna Kuznetsova, Marco Arnesano, and Pavlo Usik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 259\u003c\/p\u003e \u003cp\u003e7.2 Related Works 261\u003c\/p\u003e \u003cp\u003e7.3 Homomorphic Encryption in TinyML 262\u003c\/p\u003e \u003cp\u003e7.4 Differential Privacy for TinyML 271\u003c\/p\u003e \u003cp\u003e7.5 Secure Multi-Party Computation in TinyML 277\u003c\/p\u003e \u003cp\u003e7.6 Case Studies and Applications of Privacy-Preserving TinyML 281\u003c\/p\u003e \u003cp\u003e7.7 Discussion and Future Directions 293\u003c\/p\u003e \u003cp\u003e7.8 Conclusion 296\u003c\/p\u003e \u003cp\u003eAcknowledgment 296\u003c\/p\u003e \u003cp\u003eReferences 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Enhancing Cybersecurity in TinyML with Lightweight Cryptographic Algorithms 303\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eOleksandr Kuznetsov, Roman Minailenko, Aigul Shaikhanova, Yelyzaveta Kuznetsova, and Agbotiname Lucky Imoize\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 303\u003c\/p\u003e \u003cp\u003e8.2 Literature Review 305\u003c\/p\u003e \u003cp\u003e8.3 Lightweight Cryptographic Algorithms for TinyML 306\u003c\/p\u003e \u003cp\u003e8.4 Comparative Analysis of Lightweight Block Ciphers 315\u003c\/p\u003e \u003cp\u003e8.5 Comparative Analysis of Lightweight Hash Functions 318\u003c\/p\u003e \u003cp\u003e8.6 Comparative Analysis of Lightweight Stream Ciphers 322\u003c\/p\u003e \u003cp\u003e8.7 Implementation Strategies for Lightweight Cryptography in TinyML 325\u003c\/p\u003e \u003cp\u003e8.8 Conclusion 327\u003c\/p\u003e \u003cp\u003eAcknowledgment 328\u003c\/p\u003e \u003cp\u003eReferences 329\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Tiny Machine Learning for Enhanced Edge Intelligence 335\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eEmmanuel Alozie, Agbotiname Lucky Imoize, Hawau I. Olagunju, Nasir Faruk, Salisu Garba, and Ayobami P. Olatunji\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 335\u003c\/p\u003e \u003cp\u003e9.2 Overview of Tiny Machine Learning (TinyML) 337\u003c\/p\u003e \u003cp\u003e9.3 TinyML as a Service (TMLaaS) Architecture 349\u003c\/p\u003e \u003cp\u003e9.4 Results and Discussion 354\u003c\/p\u003e \u003cp\u003e9.5 Open Challenges and Further Research Directions 357\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 358\u003c\/p\u003e \u003cp\u003eReferences 359\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Advanced Security Schemes for TinyML Devices 367\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eWasswa Shafik and Mumin Adam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 367\u003c\/p\u003e \u003cp\u003e10.2 Fundamentals of TinyML 369\u003c\/p\u003e \u003cp\u003e10.3 Privacy Concerns in TinyML 374\u003c\/p\u003e \u003cp\u003e10.4 Security and Privacy Solutions 376\u003c\/p\u003e \u003cp\u003e10.5 The Implementation of Novel Security Schemes for TinyML Applications 384\u003c\/p\u003e \u003cp\u003e10.6 Privacy-Enhancing Techniques for TinyML 388\u003c\/p\u003e \u003cp\u003e10.7 Future Directions of Security and Privacy in TinyML Devices 391\u003c\/p\u003e \u003cp\u003e10.8 Lessons Learned 393\u003c\/p\u003e \u003cp\u003e10.9 Limitations of this Study 394\u003c\/p\u003e \u003cp\u003e10.10 Conclusion 395\u003c\/p\u003e \u003cp\u003eReferences 396\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Robust Ground Truth Data Mining for Enhanced Privacy and Accuracy in Noisy TinyML Environments 403\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYuichi Sei and Agbotiname Lucky Imoize\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 403\u003c\/p\u003e \u003cp\u003e11.2 Related Research Work 406\u003c\/p\u003e \u003cp\u003e11.3 Models 408\u003c\/p\u003e \u003cp\u003e11.4 Gdp 412\u003c\/p\u003e \u003cp\u003e11.5 Evaluation 416\u003c\/p\u003e \u003cp\u003e11.6 Discussion 420\u003c\/p\u003e \u003cp\u003e11.7 Conclusions 423\u003c\/p\u003e \u003cp\u003eReferences 424\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Security and Privacy of TinyML Devices 431\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eEftychia Mistillioglou, Evangelia Konstantopoulou, Nicolas Sklavos, and Andronikos Kyriakou\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 431\u003c\/p\u003e \u003cp\u003e12.2 Related Work 433\u003c\/p\u003e \u003cp\u003e12.3 Secure and Privacy-Aware Training of TinyML Models 437\u003c\/p\u003e \u003cp\u003e12.4 Implementation of Novel Security Schemes for TinyML Applications 455\u003c\/p\u003e \u003cp\u003e12.5 Lessons, Challenges, and Future Directions 463\u003c\/p\u003e \u003cp\u003e12.6 Conclusions and Outlook 464\u003c\/p\u003e \u003cp\u003eReferences 465\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Semantic Management of TinyML for Industrial Application 469\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKinzah Noor, Hasnain Ahmad, and Agbotiname Lucky Imoize\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 469\u003c\/p\u003e \u003cp\u003e13.2 Introduction to TinyML 472\u003c\/p\u003e \u003cp\u003e13.3 Recent Advances in TinyML 477\u003c\/p\u003e \u003cp\u003e13.4 Methodology 488\u003c\/p\u003e \u003cp\u003e13.5 Results and Discussion 495\u003c\/p\u003e \u003cp\u003e13.6 Conclusions and Future Scope 498\u003c\/p\u003e \u003cp\u003eReferences 498\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Fight Poison with Poison: Tiny Machine Learning Resilience Against Poisoning Attacks 503\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTomoki Chiba, Yasuyuki Tahara, Akihiko Ohsuga, Agbotiname Lucky Imoize, and Yuichi Sei\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 503\u003c\/p\u003e \u003cp\u003e14.2 Problem Definition 505\u003c\/p\u003e \u003cp\u003e14.3 Related Work 507\u003c\/p\u003e \u003cp\u003e14.4 Proposed Method 512\u003c\/p\u003e \u003cp\u003e14.5 Evaluation Experiment 523\u003c\/p\u003e \u003cp\u003e14.6 Discussion 541\u003c\/p\u003e \u003cp\u003e14.7 Conclusion 543\u003c\/p\u003e \u003cp\u003eReferences 544\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 TinyML for Real-Time Medical Image Classification and Diagnosis 549\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJelil O. Agbo-Ajala, Lateef A. Akinyemi, Olufisayo S. Ekundayo, and Ernest Mnkandla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 549\u003c\/p\u003e \u003cp\u003e15.2 Literature Review 551\u003c\/p\u003e \u003cp\u003e15.3 Methodology 566\u003c\/p\u003e \u003cp\u003e15.4 Results and Discussion 567\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 578\u003c\/p\u003e \u003cp\u003eReferences 578\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Biometric Authentication in TinyML: Opportunities and Challenges 587\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eOleksandr Kuznetsov, Emanuele Frontoni, Marco Arnesano, Oleksii Smirnov, and Boris Khruskov\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 587\u003c\/p\u003e \u003cp\u003e16.2 Related Work 589\u003c\/p\u003e \u003cp\u003e16.3 Overview of Biometric Authentication Techniques 591\u003c\/p\u003e \u003cp\u003e16.4 Comparative Analysis of Biometric Authentication Methods 609\u003c\/p\u003e \u003cp\u003e16.5 Adapting Biometric Techniques for TinyML Systems 615\u003c\/p\u003e \u003cp\u003e16.6 Discussion and Future Directions 625\u003c\/p\u003e \u003cp\u003e16.7 Conclusion 628\u003c\/p\u003e \u003cp\u003eAcknowledgment 628\u003c\/p\u003e \u003cp\u003eReferences 629\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Secure Deployment of TinyML Applications: Strategies and Practices 635\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eOleksandr Kuznetsov, Sergii Kavun, and Gulvira Bekeshova\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 635\u003c\/p\u003e \u003cp\u003e17.2 Related Work 636\u003c\/p\u003e \u003cp\u003e17.3 Security Architectures for TinyML Deployments 638\u003c\/p\u003e \u003cp\u003e17.4 Secure Bootstrapping and Key Management 643\u003c\/p\u003e \u003cp\u003e17.5 Secure Communication Protocols for TinyML 646\u003c\/p\u003e \u003cp\u003e17.6 Data Privacy in TinyML Applications 650\u003c\/p\u003e \u003cp\u003e17.7 Future Directions and Emerging Technologies 652\u003c\/p\u003e \u003cp\u003e17.8 Conclusion 655\u003c\/p\u003e \u003cp\u003eAcknowledgment 657\u003c\/p\u003e \u003cp\u003eReferences 657\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 TinyML for Environmental Applications 665\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDuy Nam Khanh Vu and Anh Khoa Dang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 665\u003c\/p\u003e \u003cp\u003e18.2 Related Work 667\u003c\/p\u003e \u003cp\u003e18.3 Methodology 669\u003c\/p\u003e \u003cp\u003e18.4 Case Study: New Insights on Air Writing from Pławiak and Alblehai 689\u003c\/p\u003e \u003cp\u003e18.5 Results and Discussion 692\u003c\/p\u003e \u003cp\u003e18.6 Conclusion and Future Scope 697\u003c\/p\u003e \u003cp\u003eReferences 697\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Benchmarking TinyML Encrypted Federated Learning with Secret Sharing in Medical Computer Vision 701\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRuduan B. F. Plug, Putu H. P. Jati, Samson Y. Amare, and Mirjam van Reisen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 701\u003c\/p\u003e \u003cp\u003e19.2 Related Work 702\u003c\/p\u003e \u003cp\u003e19.3 Methodology 703\u003c\/p\u003e \u003cp\u003e19.4 Results 711\u003c\/p\u003e \u003cp\u003e19.5 Conclusion 715\u003c\/p\u003e \u003cp\u003eReferences 716\u003c\/p\u003e \u003cp\u003eIndex 721\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Brand New","offer_id":52433310810392,"sku":"9781394294541","price":96.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394294541.jpg?v=1784853185","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/tiny-machine-learning-design-principles-and-applications-hardback-9781394294541","provider":"Freshly Printed Books","version":"1.0","type":"link"}