Description and composition of bio-inspired design patterns: a complete overview <s> Quorum sensing pattern <s> Quorum sensing is the regulation of gene expression in response to fluctuations in cell-population density. Quorum sensing bacteria produce and release chemical signal molecules called autoinducers that increase in concentration as a function of cell density. The detection of a minimal threshold stimulatory concentration of an autoinducer leads to an alteration in gene expression. Gram-positive and Gram-negative bacteria use quorum sensing communication circuits to regulate a diverse array of physiological activities. These processes include symbiosis, virulence, competence, conjugation, antibiotic production, motility, sporulation, and biofilm formation. In general, Gram-negative bacteria use acylated homoserine lactones as autoinducers, and Gram-positive bacteria use processed oligo-peptides to communicate. Recent advances in the field indicate that cell-cell communication via autoinducers occurs both within and between bacterial species. Furthermore, there is mounting data suggesting that bacterial autoinducers elicit specific responses from host organisms. Although the nature of the chemical signals, the signal relay mechanisms, and the target genes controlled by bacterial quorum sensing systems differ, in every case the ability to communicate with one another allows bacteria to coordinate the gene expression, and therefore the behavior, of the entire community. Presumably, this process bestows upon bacteria some of the qualities of higher organisms. The evolution of quorum sensing systems in bacteria could, therefore, have been one of the early steps in the development of multicellularity. <s> BIB001 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Quorum sensing pattern <s> In this paper, we examine the SECOAS project which aims to design and field a self-organising, wireless sensor network for environmental monitoring. We examine the background of the project and follow the design through to an implementation in field trials. We show how implementation issues led to several significant changes in the direction of the project. We explore one aspect of the project—the operating system kOS—in detail, and explain the rationale for various design decisions. The lessons learnt during this process will be of interest to other researchers and developers in the areas of wireless sensor networks. <s> BIB002
Quorum sensing is a decision-making process for coordinating behaviour and for taking collective decisions in a decentralised way. The goal of the Quorum Sensing Pattern is to provide an estimation of the number of agents (or of the density of the agents) in the system using only local interactions. The number of agents in the system is crucial in those applications, where a minimum number of agents are needed to collaborate on specified tasks. Alias: none to our knowledge. Problem: collective decisions in large-scale decentralised systems, requiring a threshold number of agents or estimation of the density of agents in a system, using only local interactions. Solution: the Quorum Sensing Pattern allows to take collective decisions through an estimation by individual agents of the agents' density (assessing the number of other agents they interact with) and by determination of a threshold number of agents necessary to take the decision. Inspiration: the Quorum Sensing Pattern is inspired by the Quorum Sensing process (QS), which is a type of intercellular signal used by bacteria to monitor cell density for a variety of purposes. An example is the bioluminescent bacteria (Vibrio Fischeri) found in some species of squids. These bacteria self-organise their behaviour to produce light only when the density of bacteria is sufficiently high BIB001 . The bacteria constantly produce and secrete certain signalling molecules called auto-inducers. In presence of a high number of bacteria, the level of autoinducers increases exponentially (the higher the autoinducer level a bacteria detects, the more auto-inducer it produces). Another interesting example is given by the colonies of ants (Leptothorax albipennis)  , when the colony must find a new nest site. A small portion of the ants search for new potential nest sites and assess their quality. When they return to the old nest, they wait for a certain period of time before recruiting other ants (higher assessments produce lower waiting periods). Recruited ants visit the potential nest site and make their own assessment about the nest quality returning to the old nest and repeating the recruitment process. Because of the waiting periods, the number of ants present in the best nest will tend to increase. When the ants in this nest sense that the rate at which they encounter other ants exceeds a particular threshold, the quorum number is reached. Other swarms like honeybees or wasps use the same technique for nest finding. Forces: the Quorum Sensing Pattern uses gradients presenting the same parameters as the Gradient Pattern (Sect. 5.2.1). The threshold, indicating that the quorum number has been reached, triggers the collaborative behaviour. Quorum Sensing provides an estimation of the density of agents in the system. However, this pattern does not provide a solution to calculate the number of agents necessary to carry out a collaborative task (i.e. to identify the threshold value). Entities-Dynamic-Environment: the entities involved in the Quorum Sensing Pattern are the same as in the Gradient Pattern. Namely, Agents, Hosts, and Infrastructural Agents. The concentration is estimated by the aggregation of the gradients. The transition rule for the Quorum Pattern can be modelled through Transition Rule (12), where the evolution function p(D) has the form given by Eq. (13): Implementation: there is no specific implementation for the Quorum Sensing Pattern. However, biological systems presented above give us some ideas about how to implement the pattern. Here we propose two different approaches to implement the Quorum Sensing Pattern: (1) to use the Gradient Pattern to simulate the auto-inducers like in the bioluminescent bacteria. In this case the gradient concentration provides the agents with an estimation of the agents' density; (2) as in ants' systems, the agents' density can be estimated through the frequency to which agents are in communication range. The use of gradients provides better estimations than the use of frequencies. However, it is more expensive computationally and it requires more network communications. Diagram 17a, b show agents identifying whether the concentration gradient has reached the threshold, in response to gradient information propagated by neighbouring hosts. Known uses: the Quorum Sensing Pattern is used to increase the power saving in Wireless Sensor Networks BIB002 . Quorum sensing permits to create clusters based on the structure of the observed parameters of interest, and then only one node for each cluster sends the information on behalf of the quorum. Another known example is the coordination of Autonomous Swarm Robots  . Consequences: each agent can estimate the density of nodes or the density of other agents in the system using only local information received from neighbours, even when the system is really large and agents are anonymous. Related Patterns: the Quorum Sensing Pattern, depending on its implementation, uses the Gradient Pattern (Sect. 5.2.1).