Location-based data dissemination with human mobility using online density estimation

The emerging wave of technology in human-centric devices such as smart phones, tablets, and other small wearable sensor modules facilitates pervasive systems and applications to be economically deployed on a large scale with human participation. To exploit such environment, data gathering and dissemination based on opportunistic contact times among humans is a fundamental requirement. To tackle the lack of contemporaneous end-to-end connectivity in Delay-tolerant Networks (DTNs), most current algorithms assess the probability of the contact times to gradually convey a message towards its destination. These contact-based approaches do not perform well when historical locations of nodes have mixture distribution. In this paper, we formulate routing problems in spatial and spatiotemporal domains as an online unsupervised learning problem given location data. The key insight is that nodes frequently appearing nearer the message destinations are regarded as possessing higher delivery probability even if they have low contact times. We show how to solve the formulated problems with two basic algorithms, Location-Mean and Location-Cluster, by estimating the means of historical locations to calculate delivery probability of nodes. To our best knowledge, this is the first work to tackle DTN routing problem using online unsupervised learning on geographical locations. In the context of human mobility, simulation results of the Location-Mean algorithm show that the online unsupervised learning approach given node locations achieves better routing performances in term of delivery ratio, latency, transmission cost, and computation efficiency compared to the contact-based approach.