Design and Implementation of a Cognitive Wireless Sensor Network: Application to Environment Monitoring
Wireless Sensor Network , Cognition
Wireless sensor networks have applications in many places from wildlife environments to urban areas. Implementation of such a network is a challenging task because each specific application may require different constraints and objectives. To better meet the application requirements, cognitive wireless sensor network has been recently introduced. However, almost all the previous work in this area has been in theory or by simulation. Hence there is a demand to provide implementable ideas of cognition, implement, and analyze the results. The goal of this thesis is to implement a cognitive wireless sensor network with application in environment monitoring which is aware of the surrounding environment, updates its information based on the dynamic changes in the network status, makes appropriate decisions based on the gained awareness, and forwards required actions to involving nodes. An implementable cognitive idea is proposed based on the characteristics and goals of a cognitive system. Since transmission is one of the most power consuming processes in sensor nodes and non-efficient transmissions of data can lead to a shorter lifetime, this work tries to schedule nodes' transmission rate by the means of cognition and benefits from efficient scheduling of the redundant nodes to improve lifetime. To enhance a wireless sensor network with cognition, new nodes should be added to the architecture called cognitive nodes. Cognitive nodes will take care of most of the tasks in the cognition process while still there is a need to add a level of cognition to each individual node. The main contribution of this work is that it provides an implementable approach to cognition in wireless sensor networks, proposes a low complexity and low cost implementable idea for cognition, addresses implementation issues, and provides experimental results of different setups of the cognitive wireless sensor network.