Network Science Approach for Device Discovery in Mobile Device-to-Device Communications

Abstract

With the emerging demands for local area services, mobile device-to-device (D2D) communications is conceived as a vital component for next-generation wireless networks to improve spectral reuse, to bring hop gains, and to enhance system capacity. In mobile D2D networks, energy-efficient probing schemes are vital to prolonging the limited battery life of mobile devices. Current solutions mainly focus on optimizing the probing interval during contacts by assumption of exponential distributed contact. However, studies in the area of network science have shown that the contact interval follows a power-law distribution, which indicates that the current solutions are not optimal while, on the other hand, dramatically increasing the difficulty of analyzing this problem. In this paper, we propose a network science approach for adaptive wakeup schedule based on power-law distributed contacts. Our approach requires nodes to stay asleep when a contact is unlikely to happen and to wake up only when the possibility that it successfully contacts another node is relatively high. By predicting node contacts in the future based on network science, our approach significantly reduces energy consumption without degrading the performance of opportunistic networks. Extensive simulations with real-life human and vehicular traces demonstrate the outstanding performance of our approach. The results show that our scheme saves 30% in energy while keeping the same performance in most scenarios, and it enhances the performance in terms of average delivery ratio and delivery delay by over 15%, compared with the existing best wakeup techniques, without considering network science.