Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network’s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.
This paper presents a Swarm Intelligence based approach for sensor management of a multi sensor network. Alternate sensor configurations and fusion strategies are evaluated by swarm agents, and an optimum configuration and fusion strategy evolves. An evolutionary algorithm, particle swarm optimization, is modified to optimize two objectives: accuracy and time. The output of the algorithm is the choice of sensors, individual sensor’s thresholds and the optimal decision fusion rule. The results achieved show the capability of the algorithm in selecting optimal configuration for a given requirement consisting of multiple objectives.
This paper introduces a new algorithm called “Adaptive Multimodal Biometric Fusion Algorithm”(AMBF), which is a combination
of Bayesian decision fusion and particle swarm optimization. A Bayesian framework is implemented to fuse decisions received
from multiple biometric sensors. The system’s accuracy improves for a subset of decision fusion rules. The optimal rule is a function of the
error cost and a priori probability of an intruder. This Bayesian framework formalizes the design of a system that can adaptively increase
or reduce the security level. Particle swarm optimization searches the decision and sensor operating points (i.e. thresholds) space to
achieve the desired security level. The optimization function aims to minimize the error in a Bayesian decision fusion. The particle swarm
optimization algorithm results in the fusion rule and the operating points of sensors at which the system can work. This algorithm is important
to systems designed with varying security needs and user access requirements. The adaptive algorithm is found to achieve desired
security level and switch between different rules and sensor operating points for varying needs.