Hybrid mobility in opportunistic networks: Insights into enhanced PIPeR variants for subway settings

Author's Department

Computer Science & Engineering Department

Second Author's Department

Computer Science & Engineering Department

Third Author's Department

Computer Science & Engineering Department

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https://doi.org/10.1016/j.comcom.2025.108277

All Authors

Sara ElSingergy Soumaia Al Ayyat Sherif G. Aly

Document Type

Research Article

Publication Title

Computer Communications

Publication Date

9-1-2025

doi

10.1016/j.comcom.2025.108277

Abstract

This paper explores the performance of the Power and Interest Aware PeopleRank (PIPeR) algorithm, a prominent opportunistic network forwarding algorithm, within a new context namely, subway mobility environments. While PIPeR has demonstrated strong performance in pedestrian mobility models by accounting for both interest in disseminated content and power conservation, its application in subway settings—characterized by hybrid mobility and unique challenges—has yet to be explored. Subway mobility scenarios are particularly relevant for contexts such as emergency response for civilians use during crises, where fixed network infrastructure is limited or unavailable. In this study, PIPeR is implemented and evaluated using the AnyLogic simulator, which accurately models subway passenger flows under hybrid mobility. Additionally, five enhanced variants of the original PIPeR algorithm are proposed, designed to address the unique challenges of subway environments and any other environments of similar mobility patterns, aiming to enhance the algorithm's overall efficiency. The best-performing variant is then identified and rigorously tested through experiments to evaluate its robustness under varying conditions, including various interest distributions, battery distributions, user density, and message volume per user. The results reveal that the PIPeR algorithm in the subway environment achieves a notable 64 % increase in the F-measure and a 63 % reduction in delay compared to the pedestrian mobility environment, but at the cost of increased power consumption and cost. The proposed variants mitigate these challenges, achieving an impressive 83 % reduction in power consumption and a 38 % decrease in cost, with a trade-off of a 20 % reduction in F-measure. These findings highlight a significant step towards green computing and sustainability in opportunistic networks. Moreover, the best-performing variant, when tested in a challenging scenario with a majority of uninterested users and a lack of intermediate forwarders, demonstrates excellent performance, further underscoring its adaptability and robustness.

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