The Internet-of-things facilitates the development of many groundbreaking applications. A large number of these applications involve mobile end nodes and a sparsely deployed network of base stations that operate as gateways to the Internet. Most of the mobile nodes, at least within city areas, are connected through low power wide area networking technologies (LPWAN) using public frequencies. Mobility and sparse network coverage result in long delays and intermittent connectivity for the end nodes. Disruption Tolerant Networks and utilization of heterogeneous wireless interfaces have emerged as key technologies to tackle the problem at hand. The first technology renders communication resilient to intermittent connectivity by storing and carrying data while the later increases the communication opportunities of the end nodes and at the same time reduces energy consumption whenever short-range communication is possible. However, one has to consider that end nodes are typically both memory and energy constrained devices which makes finding an energy efficient data transmission policy for heterogeneous disruption tolerant networks imperative. In this work we utilize information related to the spatial availability of network resources and localization information to formulate the problem at hand as a dynamic programming problem. Next, we utilize the framework of Markov Decision Processes to derive approximately optimal and suboptimal data transmission policies. We also prove that we can achieve improved packet transmission policies and reduce energy consumption, extending battery lifetime. This is achieved by knowing the spatial availability of heterogeneous network resources combined with the mobile node's location information. Numerical resultsshow significant gains achieved by utilizing the derived approximately optimal and suboptimal policies.
Keywords: Internet-of-things; data transmission; disruption tolerant IoT; dynamic programming; heterogeneous networks.