Description and composition of bio-inspired design patterns: a complete overview <s> Aggregation pattern <s> Modern UAV’s reduce the threat to human operators, but do not decrease the manpower requirements. Each aircraft requires a flight crew of one to three, so deploying large numbers of UAV’s requires committing and coordinating many human warfighters. Insects perform impressive feats of coordination without direct inter-agent coordination, by sensing and depositing pheromones (chemical scent markers) in the environment [14]. We have developed a novel technology for coordinating the movements of multiple UAV’s based on a computational analog of pheromone dynamics. The control logic is simple enough that it can be executed autonomously by a UAV, enabling a single human to monitor an entire swarm of UAV’s. This paper describes the technology, its application to UAV coordination, and the results we have obtained. <s> BIB001 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Aggregation pattern <s> Sensor Networks have recently emerged as a ubiquitous computing platform. However, the energy constrained and limited computing resources of the sensor nodes present major challenges in gathering data. In this work, we propose a self-organizing method for aggregating data in ad-hoc wireless sensor networks. We present new network architecture, CODA (Cluster-based self-Organizing Data Aggregation), based on the Kohonen Self-Organizing Map to aggregate sensor data in cluster. Before deploying the network, we train the nodes to have the ability to classify the sensor data. Thus, it increases the quality of data and reduces data traffic as well as energy-conserving. Our simulation results show that CODA increases the accuracy of data than traditional aggregation of database system. Finally, we show a real-world platform, TIP, on that we will implement the idea. <s> BIB002 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Aggregation pattern <s> For a wireless sensor network (WSN) with a random number of sensors, we propose a decision fusion rule that uses the total number of detections reported by local sensors as a statistic for hypothesis testing. We assume that the signal power attenuates as a function of the distance from the target, the number of sensors follows a Poisson distribution, and the locations of sensors follow a uniform distribution within the region of interest (ROI). Both analytical and simulation results for system-level detection performance are provided. This fusion rule can achieve a very good system-level detection performance even at very low signal-to-noise ratio (SNR), as long as the average number of sensors is sufficiently large. For all the different system parameters we have explored, the proposed fusion rule is equivalent to the optimal fusion rule, which requires much more prior information. The problem of designing an optimum local sensor-level threshold is investigated. For various system parameters, the optimal thresholds are found numerically by maximizing the deflection coefficient. Guidelines on selecting the optimal local sensor-level threshold are also provided. <s> BIB003 </s> Description and composition of bio-inspired design patterns: a complete overview <s> Aggregation pattern <s> One of the major problems that artificial intelligence needs to tackle is the combination of different and potentially conflicting sources of information. Examples are multi-sensor fusion, database integration and expert systems development. In this paper we are interested in the aggregation of propositional logic-based information, a problem recently addressed in the literature on information fusion. It has applications in multiagent systems that aim at aggregating the distributed agent-based knowledge into an (ideally) unique set of propositions. We consider a group of autonomous agents who individually hold a logically consistent set of propositions. Each set of propositions represents an agent's beliefs on issues on which the group has to make a collective decision. To make the collective decision, several aggregation procedures have been proposed in the literature. Assuming that all propositions in question are factually right or wrong, we ask how good belief fusion is as a truth tracker. Will it single out the true set of propositions? And how does information fusion compare with other aggregation procedures? We address these questions in a probabilistic framework and show that information fusion does especially well for agents with a middling competence of hitting the truth of an individual proposition. <s> BIB004
The Aggregation Pattern is a basic pattern used for information fusion. The dissemination of information in large scale systems, either deposited by the agents or taken from the environment, may produce network and memory overload. The Aggregation Pattern was introduced as a way to reduce the amount of information in the system by synthesising meaningful information  . Alias: aggregation is also known as fusion BIB003 . Problem: in large systems, excess of information produced by the agents may produce network and memory overloads. Information must be distributively processed in order to reduce the amount of information and to obtain meaningful information. Solution: aggregation consists in locally applying a fusion operator to process the information and synthesise macro information. This fusion operator can take many forms, such as filtering, merging, aggregating, or transforming . Inspiration: in nature, the aggregation (sum) of ant's pheromones allows the colony to find the shortest path to the food, and to discard longer paths. (i.e. two pheromone scents together create an attractive field bigger than a single pheromone scent). In nature the aggregation is a process performed by the environment. Even when there are no agents present in the system, the environment continues performing the aggregation process. Forces: aggregation applies to all the information available locally or only on part of that information. The parameter involved is the amount of information that is fused; it relates to the memory usage in the system. Entities-Dynamics-Environment: aggregation is executed either by agents or by infrastructural agents. In both cases the agents aggregate the information they access locally. Information may come from the environment or from other agents. Information coming from the environment is typically read by sensors (e.g. temperature, humidity, etc.). According to the model presented in Sect. 3, aggregation is executed by an agent that receives information from the host where the agent is residing. Such host is either a sensor reading information from the environment or a communication device receiving information from neighbouring hosts. Aggregation may be applied by any agent that receives information independently of the underlying infrastructure. The aggregation process is not repetitive and finishes when one agent executes the aggregation function. The Transition Rule for aggregation (2) is as follows: information in input (possibly a set of information) is transformed into a new set of information with smaller cardinality then the input set through an aggregation function a. Implementation: available information takes the form of a stream of events. Aggregation or fusion of information can take various forms: from a simple operator (sum, multiplication or average) like in ACO, to more complex operators (e.g. Kohonen Self-Organising Maps aggregating sensor data in clusters, BIB002 . Fusion operators are classified into four different groups : (1) filter: this operator selects a subset of the received events (e.g. the sensor takes 10 measures per second, but the application processes only 1 per second); (2) transformer: this operator changes the type of the information received in input (e.g. inputs are GPS coordinates and outputs are the countries where the positions are located); (3) merger: this operator unifies all information received and outputs all information received as a single piece of information (e.g. input is the position of many sensors and the output is the corresponding tuple of positions); (4) aggregator: this operator applies a specific operation (e.g. max, min or avg) to one or more incoming information; input and output types can all be different. The flow chart 6a shows that the aggregation process starts when the agent receives the information (an event). Then, it applies the fusion operator and sends the aggregated information back to the host. Figure 6b shows how the agent or infrastructural agent uses the interface provided by the host to get the data, applies a fusion operator, and deposits the aggregated data back in the host. Known uses: aggregation has been used in the ACO algorithm  to aggregate pheromones, emulating higher concentrations when two or more pheromones are close to each other. Aggregation is also used in digital pheromones for autonomous coordination of swarming UAVs BIB001 . Moreover, aggregation has been used in the field of information fusion, which studies how to aggregate individual belief bases into a collective one  , or for truth-tracking in MAS BIB004 . Consequences: aggregation increases the efficiency in networks (e.g. sensor networks, ad-hoc or P2P), by reducing the number of messages, i.e. increasing the battery life and the scalability of the system. Also aggregation provides a mechanism to extract macro-information in large-scale systems, such as extracting meaningful information from data reads obtained from many sensors. Thus, the amount of memory used by the system is reduced. Related Patterns: the Aggregation Pattern can be implemented together with Evaporation and Gradient Patterns to form digital pheromones BIB001 . Evaporation can be used with aggregation in order to aggregate information recently collected from the environment. The Gossip Pattern (Sect. 5.2.3) is a pattern composed of the Aggregation Pattern and the Spreading Pattern (Sect. 5.1.1).