# Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
## 2 Wireless Sensor Networks and Routing--Background/Survey
### Introduction to Complex Adaptive Systems (CASs)  
We consider our ad hoc environment as a CAS. A CAS is a system made up of many individual parts or agents. The individual parts, or agents, in a CAS follow simple rules. There is no leader or individual who is coordinating the actions of others. The interactions of the agents leads to generation of emergent patterns.  
1. Many simple agents or items compared to the entire system.
2. Nonlinear exchange among components--communication.
3. No central control.
4. Emergent behaviors; 1. Hierarchical organization; 2. Data processing--computation; 3. Dynamic--changing behavior; 4. Evolution and learning--adapting frequently; 5. Uncertainty; 6. Unordered. Adaptation in complex systems is a basic concept that considers the ability of a system to adjust and evolve in response to changes in its environment or internal conditions. The theory of adaptation is a key component of the broader framework of complex adaptive systems (CASs). Here are some key aspects of the theory of adaptation in complex systems:  
* Dynamic environment: Complex systems exist in dynamic environments where the conditions are the continually changing structures and behaviors of systems. Adaptation is essential for a system to cope with these changes effectively. The environment can include both external factors, such as climate or market conditions and stock prices, and internal factors, such as the behavior of other agents within the system. Examples include the study of planetary dynamics.
* Learning and memory: Adaptation often involves learning from experience and the ability to retain information over time. Agents within a complex system may adjust their behavior based on past outcomes, forming a kind of \"memory\" that allows them to make more informed decisions in the future.
* Feedback mechanisms: Feedback loops play a crucial role in adaptation. Positive feedback loops can reinforce successful strategies or behaviors, leading to adaptation and evolution. On the other hand, negative feedback loops can function as stabilizing forces, helping to regulate and maintain the system within certain bounds.
* Variation and diversity: Adaptation is facilitated by the presence of variation and diversity within the system. In a population of agents, having diverse strategies or traits increases the likelihood that at least some individuals will be well suited to changing conditions. This diversity provides the raw material for natural selection and adaptation.
* Robustness and resilience: Adaptive systems tend to be robust and resilient, meaning functionality can be maintained and recovery achieved from disturbances. The ability to adapt allows a system to absorb shocks, navigate uncertainties, and continue functioning in the face of changing circumstances.  
* Evolutionary processes: Adaptation in complex systems often involves evolutionary processes. Over time, successful strategies or traits may become more prevalent in the system, while less successful ones may decline. This process of \"survival of the fittest\" is a fundamental mechanism of adaptation and evolution.
* Decentralised nature: In many complex systems, adaptation is a decentralised process. Individual agents or components within the system often have the autonomy to adapt based on local information and feedback. The collective behaviour of these adaptive agents then gives rise to emergent system-level patterns.