Improving Traffic Load Distribution Fairness in Mobile Social Networks

Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) network. A few these are highly mobile, and proposed social-based routing algorithms likely choose most “social” as best message relays. Finally, this could lead inequitable resource utilisation, such faster battery drain and/or storage consumption (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) improve balancing across network present novel method for calculating node popularity which takes into account both inherent social-relations popularity. The former is purely determined by node’s sociability level network, TraLDA computed using Kalman prediction considers periodicity behaviour. However, latter benefit interactions with more neighbours (acquaintances) boost lower (social) Using extensive simulations Opportunistic Network Environment (ONE) driven real human scenarios, we show that our strategy enhances fairness classical, yet social-aware BubbleRap SimBet without negatively impacting overall delivery performance.