Biologically-inspired Algorithms Research Articles

Local control of cognitive radio networks

This research examines the configuration and coordination of cognitive radio networks and seeks whether it is possible to efficiently configure a cognitive radio network using control algorithms that can independently manage and coordinate connections among radio nodes. Such control algorithms have to manage a cognitive radio in two ways - first, they must internally configure the radio to appropriately fulfill the users demand without causing interference to outside users; and second, they must configure the radio to allow satisfactory communication with external nodes since the cognitive radios objective is to exchange data. To address this coordination and configuration problem, this thesis examines two distinct problems. The first problem relates to improving the choice of internal configuration parameters and involves using fractional factorial designs to run a series of line experiments to determine a predictive model for performance among a set of nodes for a specific set of inputs. Through simulation and implementation, this thesis demonstrates that the fractional factorial design can be used to accelerate the convergence of a variety of optimization techniques (e.g., genetic algorithms and gradient ascent techniques). The second problem relates to the issue of channel assignment for cognitive radio networks, wherein different nodes must agree on which channels they will use to communicate. Through a series of theoretical analysis, software simulation and hardware implementation, this work demonstrates that biologically-inspired local control algorithms are a feasible and worthwhile avenue for cognitive radio coordination and shows promising prospects for other areas in wireless systems such as sensor and ad hoc networks. This thesis demonstrates that local control based on biologically-inspired algorithms is well suited to the coordination of cognitive radio nodes and that this approach can cope well with limited sensing capabilities and only partial knowledge about the system. The algorithm however showed certain limitations when large network sizes came into play due to its probabilistic inner workings, however there exist additional components that could address these issues and warrant further investigation.

Selforganisation in a Storage for Semantic Information

Scalable distributed semantic storage infrastructures are hard to realize. We propose the usage of principles of selforganization for the storage and retrieval of RDF triples. We use a biology-inspired algorithm for clustering of triples based on a purely syntactical similarity measure.

SPIRAL: A novel biologically-inspired algorithm for gas/odor source localization in an indoor environment with no strong airflow

This work describes the design and experimental results of an algorithm, designed to localize a gas source in an indoor environment with no strong airflow by using an autonomous agent. This condition exacerbates the patchiness and intermittency of odor distribution, typical of turbulent flows in the presence of strong mean flows. Furthermore, no information about the wind can be used to detect the position of the source. In the approach proposed here, the robot moves along spirals. A spiral can be reset and a new one started, based on the information acquired about gas distribution. This enables the robot to get close to the ejecting source, without relying on airflow measurements. Results from experiments are also described and discussed, to assess the efficiency of the proposed method.

The DCA: Some Comparison a Comparative Study between Two Biologically-Inspired Algorithms

The Dendritic Cell Algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation which results in a ‘context aware’ detection system. Previous applications of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a Self-Organizing Map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce anomaly detection results to the same standard as an established technique.