Swarm Routing
  Clockless Processors

Autonomous Agents

  Mobile Communication
  Genetic Algorithms
  Swarm Intelligence
  Neural Networks
  Machine 2 Machine
  Grid Computing
Bluetronix, Inc.
35 River Street
Chagrin Falls, OH 44022



Swarm Intelligence
Swarm Intelligence is a design framework based on social insect behavior. Social insects such as ants, bees, and wasps are unique in the way these simple individuals cooperate to accomplish complex, difficult tasks. This cooperation is distributed among the entire population, without any centralized control. Each individual simply follows a small set of rules influenced by locally available information. This emergent behavior results in great achievements that no single member could complete by themselves. Additional properties swarm intelligent systems possess include: robustness against individual misbehavior or loss, the flexibility to change quickly in a dynamic environment, and an inherent parallelism or distributed action.

There are four principles governing the swarm intelligence. These are:

  • Positive Feedback - reinforces good solutions present in the system
  • Negative Feedback - removes old or poor solutions
  • Randomness - so solutions can be tested regardless of perceived quality, which in turn, result in creative and unconventional solutions
  • Multiple Interactions - key to building up the best solutions

By understanding these properties and applying them correctly, swarm intelligent systems may be designed. Each principle plays a clear role in governing the system.

The benefits of swarm intelligence can work effectively to resolve current issues in MANETs, or mobile ad-hoc networks. A MANET is a collection of computers, or nodes, participating and cooperating in a computer network. MANETs are increasingly appearing now that wireless devices become more and more ubiquitous. Information is communicated between nodes via a wireless link. There is a limited communications range for each node, and each node has only a few neighbors. Neighbors are nodes that can communicate directly. Nodes are assumed to be mobile; nodes can move relative to each other. Mobility causes the topology of the network to be quite dynamic.

Self-Optimizing Auction Systems

Bluetronix's new protocols are being developed to account for the dynamic topology of ad-hoc networks, rather than try to adapt old approaches to new problems. Ad-hoc networks pose a fundamentally different set of issues than traditional wired networks. Swarm intelligence is a novel approach which can account for a larger set of critical metrics, as well as to adapt to highly variable factors such as network size or node speed. It must also be mentioned that the very metrics to which the network must adapt are also subject to rapid change.

The principles of Swarm Intelligence can be applied to a variety of other applications reaching far beyond computer networks. Where a centralized design fails, Swarm's unique collective (or distributed) problem solving method may prove an attractive alternative. For example, traffic congestion is often reduced by increasing the number of lanes. This does not always work. Using a swarm paradigm to model for traffic patterns, making the road longer and manipulating the speed limits has been shown to reduce gridlock and actually decrease travel time in certain cases.

Optimizing scheduling or distribution tasks can be very time consuming, or even virtually impossible in some instances. Southwest Airlines has used swarm to develop a more efficient model of cargo handling, saving the company $2 million per year in labor costs. General Motors Corp. implemented software using adaptive technology to schedule car paint jobs and to avoid the scheduling conflicts from which the manual system suffered. The new system resulted in a 30% productivity improvement and 35% fewer business-process changes.

Real world examples:

White papers on our products and technologies are available upon request. E-mail us at innovation@bluetronix.net or call 440.247.3434.

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