Description and composition of bio-inspired design patterns: a complete overview <s> Chemotaxis pattern <s> Monitoring a diffuse event with a wireless sensor network differs from well studied applications such as target tracking and habitat monitoring and therefore we suggest that new approaches are needed. In this paper we propose a novel low power technique based on a multiple agent framework. We show how a set of simple rules can produce complex behavior that encompasses event characterization and data routing. We demonstrate the approach and examine its accuracy and scalability using a simulated gaseous plume monitoring scenario. <s> BIB001 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Chemotaxis pattern <s> To support and engineer the spatial coordination of distributed pervasive services, we propose a chemical-inspired model, which extends tuple spaces with the ability of evolving tuples mimicking chemical systems, that is, in terms of reaction and diffusion rules that apply to tuples modulo semantic match. The suitability of this model is studied by considering a self-adaptive display infrastructure providing people nearby with several visualization services (advertisements, news, personal and social content). The key result of this article is that general-purpose chemical reactions inspired by population dynamics can be used in pervasive applications to enact spatial computing patterns of competition and gradient-based interaction. <s> BIB002
The Chemotaxis Pattern provides a mechanism to perform motion coordination in large scale systems. Chemotaxis was initially proposed by Nagpal (Nagpa 2004) . The Chemotaxis Pattern extends the Gradient Pattern: agents identify the gradient direction to decide the direction of their next movements. Alias: none to our knowledge. Problem: decentralised motion coordination aiming at detecting sources or boundaries of events. Solution: agents locally sense gradient information and follow the gradient in a specified direction (i.e. follow higher gradient values, lower gradient values, or equipotential lines of gradients). Inspiration: in biology, chemotaxis is the phenomenon in which single or multi-cellular organisms direct their  Description and composition of bio-inspired design patterns 59 movements according to certain chemicals present in their environment. Examples in nature include: leukocyte cells moving towards a region of a bacterial inflammation or bacteria migrating towards higher concentrations of nutrients  . Notice that in biology, chemotaxis is also a basic mechanism of morphogenesis. It guides cells during development so that they will be placed in the final right position. In this paper, following (Nagpa 2004), the term chemotaxis is used as motion coordination following gradients, while the term morphogenesis is used for triggering specific behaviours based on relative positions determined through a gradient. Forces: the Chemotaxis Pattern exploits the Gradient Pattern (see Sect. 5.2.1 to find information about the forces involved in the Gradient Pattern). In the Chemotaxis Pattern the communication range plays an important role. When the communication range is long, agents move faster following the gradients. This, however, causes problems for precisely locating sources. On the other hand, short communication ranges need a higher number of hops to follow the gradient, but they allow to find sources with high precision. Entities-Dynamic-Environment: the concentration of gradient guides the agents' movements in three different ways, as shown in Fig. 15: (1) attractive movement, when agents change their positions following higher gradient values, (2) repulsive movement, when agents follow lower gradient values, incrementing the distance between the agent and the gradient source, and (3) equipotential movement, when agents follow gradients between thresholds. Given the Transition Rule (7) that creates the gradient, Transition Rule (11) determines the agent movement towards the highest, lowest, or equipotential gradient value (depending on the cases). move :: hL; ½D 1 ; Ci; . . .; hL n ; ½D n ; CiÀ! r move hL i ; ½D i ; Ci Implementation: chemotaxis can be implemented in two different ways. First, using gradients existing in the environment to coordinate the agent's positions or directions (e.g. using attractive and equipotential movements to detect the contour of diffuse events BIB001 , or using attractive movements to detect diffuse event sources (Fernandez-Marquez et al. 2012 ) through a multi-agent approach over a sensor network infrastructure). Second, using gradient fields generated by agents (e.g. using a gradient-based approach to coordinate the position of bots in the Quake 3 Arena video game ). Diagram 14a, b show a particular case of implementation, where agents get information about neighbouring gradients, before taking a decision about where to go next. As shown in Diagram 14a, each agent chooses n random neighbouring host and sends them a gradient concentration request. The agent chooses the neighbouring host that has a highest gradient concentration and moves there. By repeating this process the agent is able to find the gradient source. Known uses: use Chemotaxis to coordinate the position of a swarm of simple mobile robots. Chemotaxis is also used in BIB002 , where chemotaxis is applied to route messages in pervasive computing scenarios. Related Patterns: the Chemotaxis Pattern extends the Gradient Pattern (Sect. 5.2.1).