Multifaceted Optimization of Energy Efficiency for Stationary WSN Applications

Pin Nie

    Research output: ThesisDoctoral ThesisCollection of Articles


    Stationary Wireless Sensor Networks (S-WSNs) consist of battery-powered and resource-constrained sensor nodes distributed at fixed locations to cooperatively monitor the environment or an object and provide persistent data acquisition. These systems are being practiced in many applications, ranging from disaster warning systems for instant event detection to structural health monitoring for effective maintenance. Despite the diversity of S-WSN applications, one common requirement is to achieve a long lifespan for a higher value-to-cost ratio. However, the variety of WSN deployment environments and use cases imply that there is no silver bullet to solve the energy issue completely. This thesis is a summary of six publications. Our contributions include four energy optimization techniques on three layers for S-WSN applications. From the bottom up, we designed an ultra-low power smart trigger to integrate environment perceptibility into the hardware. On the network layer, we propose a reliable clustering protocol and a cluster-based data aggregation scheme. This scheme offers topology optimization together with in-network data processing. On the application layer, we extend an industrial standard protocol XMPP to incorporate WSN characteristics for unified information dissemination. Our protocol extensions facilitate WSN application development by adopting IMPS on the Internet. In addition, we conducted a performance analysis of one lightweight security protocol for WSNs called HIP Diet Exchange, which is being standardized by IETF. We suggested a few improvements and potential applications for HIP DEX. In the process of improving energy efficiency, we explore modular and generic design for better system integration and scalability. Our hardware invention can extend features by adding new transducers onboard. The clustering protocol and data aggregation scheme provides a general self-adaptive method to increase information throughput per energy cost while tolerating network dynamics. The unified XMPP extensions aim to support seamless information flow for the Web of Things. The results presented in this thesis demonstrate the importance of multifaceted optimization strategy in WSN development. An optimal WSN system should comprehend multiple factors to boost energy efficiency in a holistic approach.
    Translated title of the contributionMultifaceted Optimization of Energy Efficiency for Stationary WSN Applications
    Original languageEnglish
    QualificationDoctor's degree
    Awarding Institution
    • Aalto University
    • Ylä-Jääski, Antti, Supervising Professor
    • Lukyanenko, Andrey, Thesis Advisor
    Print ISBNs978-952-60-4957-1
    Electronic ISBNs978-952-60-4958-8
    Publication statusPublished - 2012
    MoE publication typeG5 Doctoral dissertation (article)


    • energy efficiency
    • data aggregation
    • clustering
    • security
    • XMPP


    Dive into the research topics of 'Multifaceted Optimization of Energy Efficiency for Stationary WSN Applications'. Together they form a unique fingerprint.

    Cite this