Delay-Tolerant Networking for Remote and Challenging Environments

Abstract

Delay-Tolerant Networking (DTN) addresses communication challenges in remote and harsh environments where continuous connectivity, low latency, and stable infrastructure are unavailable. Such contexts include deep-space missions, rural regions, disaster zones, and underwater networks. DTN’s core principle—storecarry-forward routing—enables message delivery despite frequent disconnections, long delays, and intermittent links. This paper presents a comprehensive overview of DTN applied to remote and challenging environments, synthesizing pre-2019 work. We outline fundamental architectures like Bundle Protocol, highlight routing strategies (e.g., Epidemic, Spray-and-Wait, PRoPHET), and examine environment-specific adaptations (e.g., underwater acoustic DTN, interplanetary DTN). The research methodology comprises systematic literature review, scenario-based performance comparison, and criteria like delivery ratio, latency, overhead, and resource use. Key findings reveal that simple epidemic routing achieves high delivery rates at cost of overhead, while probabilistic or quota-based schemes balance performance with efficiency. Environmental factors—like node mobility, contact predictability, and energy constraints—significantly affect protocol suitability. A general workflow traces from environment characterization through protocol selection, simulation or emulation testing, parameter tuning, deployment calibration, and iterative refinement. Advantages of DTN include resilience to disruption, extended reach, and flexibility across domains. Disadvantages involve resource inefficiencies, high latency, and complex security/trust issues. Results from comparative evaluations show, for example, that Spray-and-Wait reduces overhead by over 50% versus Epidemic routing with only a minor drop in delivery success. The conclusion underscores DTN’s essential role in enabling connectivity where traditional networks fail. Future work possibilities include the integration of machine learning for contact prediction, energy-aware routing strategies, and cross-layer protocols optimized for opportunistic, sparse, and harsh environments. This work captures the state of DTN through the end of 2018 and charts directions for its continued evolution.