Brevis factor ρ to evaluate data aggregation tree scheduling heuristics in sensor networks

Abstract

Summary In Sensor Networks (SNs), Network Lifetime (NL) enhancement using Data Aggregation Trees (DATs) is an extensively studied NP complete problem. The study in this paper investigates the existing DAT scheduling heuristics and proposes a metric termed as Brevis Factor ρ to evaluate the merit of the employed heuristics. Then, the ρ metric is applied to the state‐of‐art DAT scheduling heuristics which are typically based on Residual Energy (RE) parameter of the sensor nodes. Further, this paper proposes a new heuristics parameter for DAT scheduling based on the Number of Neighboring nodes (NN). Performance of NN‐based heuristics is evaluated with rigorous simulations, and the results demonstrate improvement in NL as compared with the state‐of‐art. The values of ρ are observed for the existing RE‐based heuristics as well as the proposed NN‐based heuristics. The results demonstrate how ρ metric values evaluate the DAT scheduling heuristics. In addition, the results facilitate to understand the trade‐off associated with the overheads to reconstruct DATs while improving NL.