Bio-Inspired Optimization in Fog and Edge Computing Environments: Principles, Algorithms, and Systems

A new era of complexity science is emerging in which nature- and bio-inspired principles are being applied to provide solutions. At the same time, systems complexity is increasing due to such models as the Internet of Things (IoT) and fog computing. Can complexity science applying the principles of nature be able to tackle the challenges posed by highly complex networked systems?

Bio-Inspired Optimization in Fog and Edge Computing: Principles, Algorithms, and Systems is an attempt to answer this question It presents and innovative bio-inspired solutions for fog and edge computing and highlights the role of machine learning and informatics. Nature- or biological-inspired techniques are successful tools to understand and analyze a collective behavior. As the book demonstrates, algorithms and mechanisms of self-organization of complex natural systems have been used to solve optimization problems, particularly in complex systems that are adaptive, ever evolving, and distributed in nature.

Chapters look at ways of enhancing the performance of fog networks in real-world applications using nature-based optimization techniques. They discuss challenges and provide solutions to such concerns as security, privacy, and power consumption in cloud data center nodes and fog computing networks. The book also examines how:

• The existing fog and edge architecture is used to provide solutions to future challenges
• A geographical information system (GIS0 can be used with fog computing to help users in an urban region access prime healthcare
• An optimization framework helps in cloud resource management.
• Fog computing can improve the quality, quantity, long-term viability, and cost-effectiveness of agricultural production
• How virtualization can support fog computing, increase resources to be allocated, and be applied to different network layers
• The combination of fog computing and IoT or cloud computing can help healthcare workers predict and analyze diseases in patients.

一個新的複雜性科學時代正在興起，其中運用自然和生物啟發的原則來提供解決方案。同時，由於物聯網（IoT）和雲霧計算等模型的出現，系統複雜性也在增加。複雜性科學能否應用自然原則來應對高度複雜的網絡系統所帶來的挑戰呢？

《雲霧和邊緣計算中的生物啟發優化：原則、算法和系統》試圖回答這個問題。它提出了創新的生物啟發解決方案，並強調機器學習和信息學的作用。自然或生物啟發的技術是理解和分析集體行為的成功工具。正如本書所展示的，自組織的算法和機制已被用於解決優化問題，特別是在適應性、不斷演化和分佈式的複雜系統中。

本書的章節探討了使用基於自然的優化技術來提升雲霧網絡在實際應用中的性能的方法。它們討論了在雲數據中心節點和雲霧計算網絡中的安全性、隱私性和功耗等問題，并提供了解決方案。本書還探討了以下方面：

- 現有的雲霧和邊緣架構如何用於應對未來的挑戰
- 地理信息系統（GIS）如何與雲霧計算結合，幫助城市地區的用戶獲得優質醫療服務
- 優化框架如何幫助雲資源管理
- 雲霧計算如何提高農業生產的質量、數量、長期可行性和成本效益
- 虛擬化如何支持雲霧計算，增加可分配的資源，并應用於不同的網絡層次
- 雲霧計算與物聯網或雲計算的結合如何幫助醫療工作者預測和分析患者的疾病。

Dr. Punit Gupta is currently working as associate professor in the department of computer and communication engineering at Manipal University Jaipur, India.

Dr. Dinesh Kumar Saini is a full professor at the School of Computing and Information Technology, Manipal University.

Dr. Pradeep Rawat is affiliated with DIT University, India.

Dr. Kashif Zia is currently working as an associate professor in the faculty of computing and information technology, Sohar University, Oman.

Dr. Punit Gupta目前在印度Manipal University Jaipur的電腦與通訊工程系擔任副教授。

Dr. Dinesh Kumar Saini是Manipal University的計算機與資訊技術學院的教授。

Dr. Pradeep Rawat隸屬於印度的DIT University。

Dr. Kashif Zia目前在阿曼Sohar University的計算機與資訊技術學院擔任副教授。