2016/10/19 by Merim Dzaferagic, Dzaferagic, Merim, Nicholas J. Kaminski +6 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Physics and Astronomy · #Complex Network Analysis Techniques #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Molecular Communication and Nanonetworks #Networking and Internet Architecture (cs.NI) #cs.NI
paper · pdf · doi:10.48550/arxiv.1610.05970
arxiv created 2016/10/19 · openalex publication_date 2016/10/19 · arxiv updated 2016/10/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In order to understand the underlying mechanisms that lead to certain network properties (i.e. scalability, energy efficiency) we apply a complex systems science approach to analyze clustering in Wireless Sensor Networks (WSN). We represent different implementations of clustering in WSNs with a functional topology graph. Different characteristics of the functional topology provide insight into the relationships between system parts that result in certain properties of the whole system. Moreover, we employ a complexity metric - functional complexity (CF) - to explain how local interactions give rise to the global behavior of the network. Our analysis shows that higher values of CF indicate higher scalability and lower energy efficiency.