Cognitive-Node Architecture and a Deployment Strategy for the Future WSNs

2019-10-01
Al-Turjman, Fadi
The advent of sensing and communication technologies represents the next step in the evolution of wireless sensor networks (WSNs) and future applications. Future WSNs systems demand that connected devices could be able to work autonomously, while surfing on-line generated data and process them for self-decision making. Accordingly, we propose a cognitive Information-Centric Sensor Network (ICSN) framework. The fundamentals of cognition in ICSN can be recognized as a promising direction in addressing opportunities and challenges posed by the needs of WSNs. Nevertheless, these fundamental concepts could be difficult to implement due to the wide spectrum of tasks to be covered from the perspective of each device in the WSN. Consequently, we propose the use of Cognitive Nodes (CNs) in typical sensor networks to provide intelligent information processing and knowledge-based services to the end-users. The CNs act on queries from the end-user, containing information about the nature of the request, without the need to modify and/or change any component at the end nodes from where data is collected. We present the detailed architecture and deployment strategy of the cognitive inspired framework that learns and exploits an expanded network information from relays and clustered sensors. Extensive simulation results show that in a network with randomly deployed sensor nodes, CNs can be strategically deployed at pre-determined positions, to ensure that end-user's Quality of Information (QoI) requirements are met, even under heavy traffic conditions and extensive packets' payloads. This shows the potential use of ICSN framework in producing the future cognitive applications, while catering to user-desired information quality.
MOBILE NETWORKS & APPLICATIONS

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Citation Formats
F. Al-Turjman, “Cognitive-Node Architecture and a Deployment Strategy for the Future WSNs,” MOBILE NETWORKS & APPLICATIONS, pp. 1663–1681, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/63416.