A Low-Complexity Architecture and Framework for Enabling Cognition in Heterogenous Wireless Sensor Networks
Abedi Khoozani, Parisa
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Rapid advances in hardware technology are making it possible to manufacture different types of Sensor Nodes (SN) that results in fast growing heterogeneous Wireless Sensor Networks (WSNs). These WSN’s are applicable for a wide range of applications relevant for military, industry and domestic use. However, WSNs have particular features such as scarce resources which can affect their performance. In addition, WSNs are subject to experience changes that can occur both within the condition of the network, due to factors such as node mobility or node failure (prevalent in harsh environments), and with regards to user requirements. Consequently, it is vital for WSNs to sense the current network conditions and user requirements to be able to perform efficiently. Cognition is necessarily introduced in WSNs as a response to this need. Cognition in the context of WSNs deals with the ability to be aware of the environment and user requirements and to proactively adapt to changes. This thesis proposes a hierarchical architecture along with a cognitive network management protocol capable of enabling cognition in WSNs. Specifically; this research introduces Cognitive Nodes (CN) into WSNs so that they can manage the cognitive network. The cognitive network management process is composed of three sub-processes: 1) scanning the network, 2) decision-making, and 3) updating the nodes from taken decisions.Scanning the network process aims to provide an awareness of current network conditions. Therefore, at the first execution, each CN creates a profile table for each node in its purview and updates the tables periodically during the network operation. In decision making process, CNs make necessary decisions in terms of the working state of SNs (active/sleep), the duration of this state, and the Frequency of Sensing (FoS). Decision making process uses an optimization scheme to find the optimal number of active SNs in order to prolong the lifetime of the network. Finally, the nodes will be informed of the taken decisions. Based on the simulation and implementation results, the proposed cognitive WSN shows a significant enhancement in terms of the network’s longevity, its ability to negotiate competing objectives, and its ability to serve users more efficiently.