Energy efficient wireless sensor networks based on machine learning

  • Mohammad Abdulaziz Alwadi

    Student thesis: Doctoral Thesis


    The field of wireless sensor networks have become a focus of intensive research in recent years, especially for monitoring and characterizing of large physical environments ,and for tracking various environmental or physical conditions such as temperature, pressure, wind and humidity. Wireless Sensor networks can be used in many applications, such as wildlife monitoring, military target tracking and surveillance, hazardous environment exploration, and natural disaster relief. Given the huge amount of sensed data, automatically classifying them becomes a critical task in many of these applications. Energy efficiency is a key issue in wireless sensor networks where the energy sources and battery capacity are very limited. To address some of key WSN challenges, a novel integrated framework for achieving energy efficiency is proposed consisting of three stages of modelling from data. The first stage is a joint energy efficiency–event detection model, where a novel sensor node selection technique is designed, that conserves the energy in the wireless sensor network and at the same time maximizes the event recognition performance. Here, the scheme utilises, fewer sensor nodes at a time, and placing unwanted sensor nodes in the sleep mode. For this, a novel objective quantitative metric is proposed to assess the energy efficiency achieved, namely, the life time extension factor (LTEF). It was shown with extensive experimental evaluation, that this joint scheme, allows selection of most significant and influential sensor nodes for participation in different WSN tasks, and contributes significantly towards energy savings and event detection accuracy. As the WSN needs to adapt to the state of the environment being monitored dynamically, the number of sensor nodes participating in the routing tree cannot remain fixed, and need to adapt, in order to accurately monitor and predict the physical environment, and the second stage in this framework, is a proposal for adaptive models for sensor selection and classifier learning for achieving energy efficiency and prediction accuracy, based on performance targets specified. The third stage is a joint energy efficiency–adaptive routing model, where an appropriate sensor selection and adaptive routing strategy allows addressing the WSN challenges corresponding to energy efficiency, prediction accuracy, and MAC layer adaptation. We show that this joint model, also meets non-functional performance targets, such as missing or faulty sensors, model building time, needed for adaptation of routing protocol.
    Date of Award2015
    Original languageEnglish
    SupervisorGirija Chetty (Supervisor) & Dat Tran (Supervisor)

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