## 1 Introduction
### Multicasting in DTN  
IoT devices can generate or collect data as usual and transmit them through DTN. The nodes store the message bundles in their buffer and carry bundles until a communication link is available. In the case of environmental sensing, end-to-end communication paths between nodes do not exist due to node sparsity in the networks. To overcome this shortcoming, data mules are used to bridge the gaps between partitioned networks. For example, Figure 2 shows a scenario where workers with smartphones, known as data mules, traverse the monitored area according to schedule. The IoT devices establish a connection with the smartphone and forward the collected data. The smartphone distributes the data to neighboring devices if it is intended for multiple recipients. The smartphone can deliver configuration updates from the control center to different groups of participating devices using multicasting services in DTN. Multicast is an efficient mechanism for data dissemination in IoT applications. It is required because it can save bandwidth, increase resource utilization, and reduce transmission costs compared to unicast routing. However, since multicast approaches are proposed for the Internet or a well-connected MANET and they always assume available end-to-end paths between data sources and destinations, they are not suitable for DTN environments that have sparse connectivity. For example, the Reliable Multicast Transport Protocol (RMTP) [14] requires continuous network connectivity, and EraMobile [15] works only in MANETs that require temporary network topology establishment. However, data collection in remote areas with limited coverage is error-prone, and node update verification is a tedious task. Reliable multicast capability in DTN would overcome these limitations and enable the support of data dissemination among a group of participating nodes in a DTN-based IoT network. Figure 2: Data mule in bridging partitioned networks. In this paper, we survey recent multicast approaches according to the design intention of their proposed algorithm and provide an overview of multicasting services and open issues in DTN for future research into the Reliable Multicast Disruption Tolerant Networking (RMDTN) protocol. A more up-to-date state-of-the-art study of DTN multicast protocols is presented and compared to the existing surveys. Key solutions and performance evaluation of each of the protocols are summarized. Furthermore, new insights and discussions are provided on the enabling of DTN multicasting in IoT applications, which is one of the key research trends in this field. Nevertheless, unicast routing strategies in DTN are not covered in this paper, since they have been studied extensively and several related surveys have been published. For example, [PERSON] [16] categorized unicast routing approaches based on the predictability of the mobility model into deterministic and stochastic schemes. [PERSON] and [PERSON] [17] combined the works from References [16; 18] with improvement by classifying the unicast approaches according to the availability of infrastructure assistance. They introduced a hybrid category combining naive-replication and utility-forwarding-based routing with infrastructure assistance. [PERSON] et al. [6] reviewed a large number of unicast approaches along with multicast and anycast routing according to how protocols make forwarding decisions to relay nodes. A reference architecture for DTN routing protocols is proposed in Reference [19] with three basic components: Queue management, forwarding policies, and message replication. The authors classify relevant DTN unicast routing protocols in the literature into a single technique or combination of techniques, according to the components used. The remainder of this paper is structured as follows. Section 2 lists some of the related survey studies in DTN multicast routing approaches. Section 3 describes the motivation of this survey and some applications of multicast routing in the IoT context.