On the integration of interest and power awareness in social-aware opportunistic forwarding algorithms

• Integrating interest and power awareness into opp. social forwarding algorithms.
• PI-SOFA integrates with the SocialCast, PeopleRank and SCAR algorithms.
• Our algorithms outperform the Epidemic, Profilecast and EBubbleRap algorithms.
• Performance metrics are effectiveness, efficiency, power consumption, and fairness.
• Real-data simulations show 500% more fmeasure, preserve 8% power with 41% less cost.

Social-aware Opportunistic forwarding algorithms are much needed in environments which lack network infrastructure or in those that are susceptible to frequent disruptions. However, most of these algorithms are oblivious to both the user’s interest in the forwarded content and the limited power resources of the available mobile nodes. This paper proposes PI-SOFA, a framework for integrating the awareness of both interest and power capability of a candidate node within the forwarding decision process. Furthermore, the framework adapts its forwarding decisions to the expected contact duration between message carriers and candidate nodes. The proposed framework is applied to three state-of-the-art social-aware opportunistic forwarding algorithms that target mobile opportunistic message delivery. A simulation-based performance evaluation demonstrates the improved effectiveness, efficiency, reduction of power consumption, and fair utilization of the proposed versions in comparison to those of the original algorithms. The results show more than 500% extra f-measure, mainly by disregarding uninterested nodes while focusing on the potentially interested ones. Moreover, power awareness preserves up to 8% power with 41% less cost to attain higher utilization fairness by focusing on power-capable interested nodes. Finally, this paper analyzes the proposed algorithms’ performance across various environments. These findings can benefit message delivery in opportunistic mobile networks.

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