# Energy Efficient Policies for Data Transmission in Disruption Tolerant Heterogeneous IoT Networks
); [EMAIL_ADDRESS] (A.T.)1 Institute of Computer Science, Foundation for Research and Technology Hellas, N. Plastira 100, 70013 Heraklstations, that operate as gateways to the Internet, is typically sparse. It is also true for applications that utilize mobile devices for data collection such as smart phones and smart vehicles. However, mobility and sparse deployment of base stations comes at the cost of intermittent network connectivity and large delays. Studies and experiments have also shown that interference in rural environments can also cause severe impact on the connectivity and the performance of the networks [4; 5]. Delay Tolerant Networks (DTNs) is a class of network architectures that could address the lack of continuous network connectivity in IoT networks [6]. On the other hand, DTNs follow a store, carry and forward pattern for data routing and turn the limited buffer space of mobile sensors to a valuable resource. Furthermore, a long battery lifetime is a prerequisite for most IoT applications due to the difficulties associated with charging or replacing batteries once the devices have been deployed. This turns energy consumption into another limited and thus valuable resource. To tackle both the problem of energy efficiency and intermittent connectivity, it has been considered [7] to equip mobile IoT devices with network interfaces that are heterogeneous. Wireless interfaces with a long communication range increase network coverage while short-range interfaces may reduce the amount of energy consumed by radio transmissions which are a major source of energy consumption [8]. In this work we deal with the problem of finding an energy efficient packet transmission policy for a mobile node that participates in a heterogeneous disruption tolerant IoT network. Typically, whenever a mobile node comes within the communication range of one or more base stations it transmits all the packets in its backlog using the most energy efficient wireless interface available at that time. However, this packet transmission policy completely disregards the potential availability of more energy efficient wireless networks in nearby locations and thus does not capitalize on the node's mobility in order to reduce the total energy consumed in packet transmissions over a long period of time. This some times results to the well-known \"ping-pong effect\" when the mobile node movement is not considered in the handover decisions [9]. In this work we consider the advantage offered by a map with spatial information regarding network coverage within the area traversed by the mobile node in devising an energy-efficient packet transmission policy. In real-world environments, there is temporal variation of the wireless network availability, which might induce inaccuracy in the network availability map that is a central point of the proposed system. However, even in this case, the proposed scheme does not become inapplicable, since temporal changes can be dealt with other network mechanisms or the network availability map can be constructed using longer term average availability measurements. To the best of our knowledge we are the first to formulate the problem at hand as a dynamic programming problem and utilize the framework of Markov Decision Processes (MDP) to derive approximately optimal and suboptimal packet transmission policies. Furthermore, we exhibit that knowledge of the spatial availability of heterogeneous network resources combined with mobile node's location information leads to improved packet transmission policies that significantly reduce energy consumption and thus extend battery lifetime. The remainder of this paper is organized as follows. In Section 2 we present past work related to the problem at hand. In Section 3 we present the specifics of the problem, our model and it's key assumptions. Section 4 presents a formulation of the problem as a MDP and iterative algorithms for the derivation of optimal and suboptimal policies. Section 5 presents numerical results for the evaluation of the proposed policies.