A Predictiveness-Enhanced PRoPHET Based on Triple Exponential Smoothing in Mobile Opportunistic Networks
Demin Peng, Yanan Chang, Xingzhuo Duan, Jianqun Cui · 2024
With the advancement in communication technologies in the last decade, mobile opportunistic networks (MONs) have received significant attention in facilitating spontaneous communication. Due to the intermittent connectivity and implicit end-to-end routing paths in MONs, one of the most challenging problems is the development of reliable routing algorithms for delivering more messages to their destinations faster and at a reduced cost. This paper primarily focuses on enhancing the traditional Probability Routing Protocol using History of Encounters and Transitivity (PRoPHET) which utilizes the history of transitivity and encounters. Here, we propose a predictiveness-enhanced PRoPHET based on triple exponential smoothing (TES-PRoPHET). We incorporate triple exponential smoothing, a nonlinear analytical prediction method, to smooth and adjust the delivery predictability time series data that exhibits non-linear tendencies in traditional PRoPHET, so as to compute the adaptive delivery predictability (ADP) of messages. Then, during message propagation, we choose relay nodes with exceptional ADP values, which aim to make more accurate routing decisions. The simulation results demonstrate that TES-PRoPHET not only significantly improves the delivery rate and reduces the average delay, but it also shows good performance in network overhead and average hop count compared to PRoPHET, PRoPHETV2, and AR-PRoPHET.