## 3 Materials and Methods
### Dynamic Habitat
The cumulative impact of their emissions can alter atmospheric composition, they require water supply and waste management, they interconnect with one another to form a complex system (a city), and they can harness solar energy through photovoltaic systems. Therefore, understanding how intelligent adaptation to the environment occurs in the plant world can be highly relevant for defining the concept of the SBE. Studies on plant intelligence [40; 41; 42; 43; 44] suggest that the intelligent adaptive capacity of plants to optimise their growth conditions relies on gathering information about their environment through their sensory capabilities. In terms of the SBE, this concept is comparable to IoT ecosystems. Research has shown that plants can receive stimuli from their surround ings through specialised structures, enabling them to detect and respond to changes in their environment without relying on a central control organ [40]. It has been proven that trees have sensory cells that allow them to detect soil moisture, light intensity, and ambient temperature. Additionally, they can sense the presence of herbivores and certain pathogens, responding to them by producing specific chemical compounds [41]. They exhibit growth patterns influenced by gravity, the location of minerals, water composition, the presence of other species, wind, and more. They even emit chemical and electrical signals to communicate with one another, exchanging information. Following these premises, an intelligent system would not rely on its capacity for consciousness or mobility but rather on its ability to make decisions in an environment of uncertainty. As demonstrated with plant systems, buildings can become intelligent by incorporating systems to capture environmental signals, process information, and respond with more adaptive (and therefore intelligent) behaviour [42]. Additionally, plants (as well as ants and bees) exhibit characteristics of distributed intelligence [43], which challenges the notion of intelligence as an individual property. From these approaches, the definition of the SBE should be expanded to include complex and highly collaborative behaviours as models of distributed intelligence, where multiple nodes (agents) operate autonomously or in a decentralised manner to achieve a common goal. Biologically inspired algorithms, such as _swarm intelligence_[44], are based on the idea that each node (agent) processes and responds to the environment independently while contributing to the overall system behaviour. As plants, an SBE should gather environmental data (through IoT), process and analyse it, and make decisions (in a collaborative and interconnected way) toward a goal. Defining a specific goal is crucial to understanding how a system solves complex problems, moving it closer to that goal, even under changing and complex conditions. In biological systems, these goals are typically related to survival and reproduction. In artificial systems, the goals must be defined to guide adaptive decisions. However, in the SBE, defining goals presents challenges. Beyond survival, human-built environments also encompass ethical, aesthetic, political, cultural, economic, energy-related, and technical values. Figure 1 shows a visual representation of habitat evolution that elucidates the notion of SBE as a complex and multifaceted system, underscoring the necessity of a holistic approach to building design and maintenance. This holistic approach, when all these purposes are intertwined, becomes challenging to clearly establish a primary (and measurable) goal for the SBE. While some studies are beginning to consider social and cultural aspects in the design of smart buildings, most research tends to prioritise operational efficiency and sustainability without addressing other objectives. These overlooked objectives often play a significant role in shaping architectural decisions and heavily influence their engineering.