An agent-based state engine also alleviates the need for frequent database queries and the use of time-consuming pointers that drastically drag down ms access times and erase any optimization gains from first defining the network. The antnet agent-based engine only on exception writes back to a database and instead keeps its own table-based approach to mapping the network while synchronizing the key elements suggested for inclusion to antnet agents within this section. Taxonomy creation algorithms and shared intelligence approaches to ensuring all ants have perfect knowledge of the network's structure (taxonomy). This is critical as antnet routing needs to include the ability not just map, but learn specific networks' characteristics and either equate the network structure and behavior to previously-learned models, or quickly create one through a series of network definition routines that scope, classify and optimize the network structure.

Support for Directed Diffusion data elements. Included within an antnet agent there also needs to be descriptors of each node to further add intelligence to the definition of the taxonomy. This includes the type of node, distance, location (also IP address if available), intensity and confidence (measures of reliability of the node) and timestamps. Directed diffusion also assumes that networks' sensor-based definition assist in continual environmental scanning and the creation of environmental definition of the entire network definition. Combining directed diffusion characteristics in the context of antnet agents brings in the necessary environmental sensing aspects critical for the continued growth and support of a taxonomy of the network that moves beyond being simply dimensional of distance between nodes. Antnet agents then become part of the learning algorithm which is inherent in the optimization of a complete network. Taking this concept further and applying it to the unmet needs on MANETs the pervasive use of GPS positioning in all mobile devices gives each node in any mobile-based ad hoc network a signature and identity which can be sensed throughout the network and then used in the development of a unique network taxonomy. For mobile ad-hoc networks specifically, the creation of taxonomies is essential for the development of increased performance routing and the use of antnet algorithms in the creation of greater efficiencies in Transient Hotspot discovery and as Table 1 has shown, the critical need for being able to capture Failure Nodes throughout a network. Today antnet routing is struggling specifically in this area of performance yet with a taxonomy of the entire network created, those nodes exhibiting characteristics that indicate they have a higher than average level of probability of failing could be flagged and routed around. That way optimization and network traffic could be routed around any node showing probabilistic tendencies to fail and decay.

Constraint-based logic that interpolates the taxonomy and creates an optimized set of node-based strategies for goal attainment. By incorporating constraint-based logic into the antnet agent, goal optimization can be used as the master constraint, followed by the agent's state engine providing a series of goal attainment alternatives through the network's taxonomy. This constraint-based logic is essential for inclusion in antnet agents as it capitalizes on directed diffusion data elements in terms of sensory-based reading of the network, and the extrapolation of this data into a taxonomy which acts as the total constraint base for this goal-seeking logic flow. Constraint-based approaches to goal attainment in the context of the taxonomy-defined network also give antnet routing algorithms the potential of finding and reacting to failed nodes and increasing performance in finding Transient Hotspots as well. This is possible given the continual streaming of goal-based queries throughout the network on the one hand and the use of antnet agents to sense and report back to all other agents the current environmental, directed diffusion and ultimately constraint-based structure of the network.

Future Developments to Antnet Agents

The need...

The next steps beyond these design objectives are the ability of antnet agents to create their own composite applications driven by constraint logic for a specific objective. Composite applications comprised of a series of antnet agents would be defined through a series of constraint-based strategies that would seek to optimize the performance of a given process. An example would be the use of antnet optimization to re-route and manage cell phone service for text messaging through a large mobile ad-hoc network when one specific node becomes inoperable. Another approach to using antnet-based agents is in the definition of new add-on services which could be incrementally added into existing MANETs. The objective would be the optimization of a new communications service using the ability of antnet agents to first create a taxonomy and increase the performance of a new service due to network-level as opposed to node-level optimizations.
Summary

Antnet-based routing algorithms today are not performing at the level they have the potential to due to many factors. The intent of this paper is to recommend the inclusion of antnet-based agent components provide a higher level of optimization performance through the definition of taxonomies, execution of constraint-based logic to navigate and use these taxonomies, and lead to the eventual definition of composite applications that align with common process areas or in the case of accentuating performance over a MANETs, the definition and delivery of a new high speed communications service. Ideally this would be possible in amore controlled environment, such as a company-based Intranet. Ultimately however the creation of taxonomies over and above the use of database tables provides a solution for antnet agent decay, in addition to monitoring and optimizing the patterns of generation. Antnet routing today is in need of these agent-based improvements to fulfill its theoretical potential of being efficient across a network-wide set of nodes and performing faster than node-to-node based algorithms.

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Chalermek Intanagonwiwat and Ramesh Govindan and Deborah Estrin,."Ibid.