ABSTRACT
Cognitive information-centric sensor networks represent a paradigm of wireless sensor networks in which sensory information is identified from the network using named data; elements of cognition are used to deliver information to the sink with quality that satisfies the end user requirements. Specialized nodes called local cognitive nodes (LCNs) implement knowledge representation, reasoning, and learning as elements of cognition in the network. These LCNs identify user-requested sensory information and establish data delivery paths to the sink by prioritizing quality of information (QoI) attributes (e.g., latency, reliability, and throughput) at each hop based on the network traffic type. Analytic hierarchy processing (AHP) is the reasoning tool used to identify these paths based on QoI-attribute priorities set by the user. From extensive simulations, parameters that can be controlled to improve the values of QoI attributes along each hop were identified, and performance of the AHP-based data delivery technique was compared with two traditional data-centric techniques in terms of the number of transmission rounds and QoI attribute performance. It was found that the use of cognition improves the number of successful transmissions to the GCN by close to 30 percent, while closely adapting the data delivery paths to the QoI requirements of the user.
