Delay/Disruption-Tolerant Networking-based the Integrated Deep-Space Relay Network: State-of-the-Art

Communication with deep-space elements poses significant challenges due to vast distances, orbital motions, and harsh environmental conditions that restrict point-to-point or end-to-end communication. Disruption/DelayTolerant Networking (DTNs) is a special kind of computer network architecture, that overcomes the shortcomings of the TCP/IP suite in meeting deep-space networking requirements with intermittent end-to-end connectivity. In DTN, data transmission follows a store-carry-forward approach, where bundles (data) are transferred as custody between relay nodes. This necessitates more complex routing strategies involving additional route computations and network resource consumption but provides greater flexibility for unstable connectivity. This study is an updated literature review in the context of DTN-based deep-space communication. It covers the recent studies that address the architecture of current and future deep-space communication systems, the environmental challenges of deep-space communication, various implementations and demonstrations of DTN's architecture, and the validation of its related routing strategies over the Deep Space Relay network. Additionally, this review explores the integration of machine learning techniques into DTN routing strategies, along with ongoing research directions and open issues in DTN-based DSN. Examining the state-of-the-art literature reveals that developing an intelligent unified n-copy-based forwarding routing scheme holds promise for effectively integrating into deep space communications and achieving optimal networking among the diverse segments of the future unified Interplanetary Internet.

Automated summary

This article introduces the Disruption/Delay Tolerant Networking (DTNs) architecture for deep-space communication, which overcomes intermittent connectivity issues through store-carry-forward data transmission. It provides an overview of the current and future deep-space communication systems' architecture, environmental challenges, DTN implementations and demonstrations, and validation of related routing strategies. Additionally, it explores the integration of machine learning techniques into DTN routing strategies and discusses ongoing research directions and open issues in DTN-based Deep Space Networking.