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.