SMART: A Social- and Mobile-Aware Routing Strategy for Disruption-Tolerant Networks

Disruption-tolerant networks (DTNs) are sparse mobile ad hoc networks where nodes connect with each other intermittently and end-to-end communication paths do not exist. Data routing in DTNs is challenging and has drawn much attention from research communities recently. Although many DTN routing strategies have been proposed in the past, they confront problems such as blind spots and dead ends and lack efficient implementation in a decentralized, large-scale, mobile, and dynamic environment. To overcome these difficulties, we introduce a new solution for DTNs that leverages the social properties and mobility characteristics of users. Our observation of the mobile trajectories of three data sets collected from real DTNs reveals that user movements appear locally and they tend to form communities correlated to geographic locations. Based on these findings, we propose a social- and mobile-aware routing strategy (SMART). It exploits a distributed community partitioning algorithm to divide the DTN into smaller communities regarding user locations and interaction routines. For intracommunity communications, a decayed routing metric convoluting social similarity and social centrality is calculated, which is used to decide forwarding node efficiently while avoiding the blind-spot and dead-end problems. To enable efficient intercommunity communications, we choose the fringe nodes that travel remotely as relays, and we propose the node-to-community utilities for routing decision across communities. We present empirical analysis to show that SMART reduces the occurrence of blind spots and dead ends to a level below 1%. The efficiency of SMART is evaluated by extensive trace-driven experiments, which illustrate that it outperforms other routing strategies in various real DTN traces.

容断网络（DTNs）是一种稀疏的移动自组织网络，其中节点间歇性地相互连接，端到端的通信路径不存在。DTNs中的数据路由具有挑战性，最近引起了研究界的广泛关注。尽管过去已经提出了许多DTN路由策略，但它们面临诸如盲点和死胡同之类的问题，并且在分散的、大规模的、移动的和动态的环境中缺乏有效的实施。为了克服这些困难，我们为DTNs引入了一种新的解决方案，该方案利用了用户的社交属性和移动特性。我们对从真实DTNs收集的三个数据集的移动轨迹的观察表明，用户的移动是局部性的，并且他们倾向于形成与地理位置相关的社区。基于这些发现，我们提出了一种社交和移动感知路由策略（SMART）。它利用一种分布式社区划分算法，根据用户位置和交互惯例将DTN划分为更小的社区。对于社区内通信，计算一种融合社交相似性和社交中心性的衰减路由度量，该度量用于有效地决定转发节点，同时避免盲点和死胡同问题。为了实现高效的社区间通信，我们选择远程移动的边缘节点作为中继，并提出节点到社区的效用用于跨社区的路由决策。我们进行了实证分析，表明SMART将盲点和死胡同的出现降低到1%以下的水平。通过大量的跟踪驱动实验评估了SMART的效率，这些实验表明它在各种真实的DTN跟踪中优于其他路由策略。