In large-scale Low Earth Orbit (LEO) satellite networks, cache placement and update are facing challenges such as limited cache volume, update capacity, frequently interrupted Inter-Satellite Links (ISLs) and sudden changes in content popularity, leading to the difficulty for obtaining fresh data for existing algorithms by optimizing average user access delay instead of timeliness and adaptability. To address this issue, in this work, we propose an age-driven Multi-Agent Deep Reinforcement Learning (MADRL) based cooperative cache and update mechanism called as Age-Driven Cooperative Cache and Update (ADCCU) algorithm, in which we establish an age-driven cache gain function to evaluate effective value of holding a data item from cache’s perspective. In particular, we formulate the age-driven cooperatively cache scheduling issue as an Integer Programming (IP) problem and solve it by exploiting a Markov Decision Process (MDP). Simulation results demonstrate that, under a cache capacity constraint ofci=3, the ADCCU algorithm significantly outperforms baseline caching strategies: the average cache gain is increased by approximately 1.09 compared to the Deep Deterministic Policy Gradient (DDPG) based cache, 2.88 compared to the Least Frequently Used (LFU), and 4.92 compared to the Least Recently Used (LRU), respectively, and the cache hit ratio is improved by approximately 5.31% over the DDPG-based cache, 56.4% over LFU, and 65.9% over LRU, respectively.

In long-span space communication, the current Licklider Transmission Protocol (LTP) confronts apparent challenges such as high packet loss rate and huge latency when carrying the bundles in the Disruption Tolerant Networks (DTN). These challenges incur obviously low freshness of satellite telemetry and instruction data with high timeliness requirements since the typical Automatic Repeat reQuest (ARQ) mechanism is exploited in the LTP for reliable transfer. To address this issue, in this paper, we propose an age-driven bundle delivery mechanism called as Semantic LTP (S-LTP) by considering the semantic correlations in the context-dependent data, which has excellent error-tolerant capability by a well-designed Semantic Supplement Hybrid Automatic Repeat reQuest (SS-HARQ), making it with high timeliness. In particular, a novel metric of semantic freshness of data called Age of Semantic Information (AoSI) is proposed to evaluate the timeliness contribution of information at the semantic level. The simulation results indicate that the proposed SS-HARQ scheme performs better in reducing the average AoSI and AoI by 62.24% and 64.52% respectively compared to the conventional LTP-ARQ with Cyclic Redundancy Check (CRC), 6.39% and 27.09% respectively compared to the Semantic Coding HARQ (SCHARQ) with a similarity detection network called Sim32.

In large-span space communication, the current bundle delivery mechanism using the Disruption-Tolerant Networks (DTN) technique confronts huge challenges such as high packet loss rate and huge latency. These challenges incur obviously low goodput when delivering scientific and engineering data for the target missions, such as instructions, text, and images. However, the context correlations in these data are not yet excavated to resist the above challenges by current works. To address this issue, therefore, we propose a semantic-aware bundle delivery mechanism for context-dependent data via a cross-layer design on Bundle Protocol (BP) and Licklider Transmission Protocol (LTP), which has the excellent error-tolerance capability by the well-designed semantic-oriented Automatic Repeat reQuest (ARQ) scheme. In particular, the jointed cross-layer design consists of a Semantic Blocking (SB) and Semantic Coding (SC)-based Bundle Updating (BU) mechanism and a dynamic Red/Green-part Allocation method based on Semantic Importance (RGA-SI) for bundles and segments in the two layers. Simulation results show that the proposed mechanism can reduce data latency and improve goodput from about 50% to 70% compared with the current bundle delivery mechanism in DTN with optimal segment size, especially under bad channel conditions.

Low orbit satellite constellation as a relay network provides a possible solution for remote real-time data gathering applications, in which freshness information updates will be forwarded via dynamical intersatellite links (ISLs). Modeling by a time-varying network, this article studies minimizing Age of Information (AoI) of delivering the data through a multihop path, in particular, focusing on the effect of frequent interruptions of ISLs. Subjected to two constraints of path and effective arrival rate, the minimizing AoI problem is formulated to find a pair of optimal transmission delay and arrival rate. In particular, the$\mathcal {H}$-approximate optimal algorithm, called a latest update routing (LUR) algorithm, is proposed with a well-designed continuous time-varying graph. Using LUR, a set of paths can be obtained with degraded transmission delay that satisfies a given arrival rate. By screening all the arrival rates satisfying the effective constraint, the maximum rate and a corresponding path set that minimizes age can be found. The simulation results verified that a degraded average AoI can be obtained by the proposed path selection mechanism compared with the typical shortest delay path (SDP) strategy, minimum spanning tree (MST) strategy, and MAoIG. In particular, the numerical findings show that the proposed LUR reduces average AoI by a maximum of 12.66% compared with SDP, 75.28% compared with MST, and 69.3% compared with MAoIG, respectively, under different scenarios.

In this article, we consider the data updating problem in the Satellite-integrated Internet of Things network for the time-critical scenarios, i.e., animal tracking and environmental monitoring. Due to the limited channel rate during contact of transmission, however, constantly updating data with huge volume over the uplink will incur obvious waiting and transmission delay bringing stale information to the satellite node. To address this issue, we propose a novel metric, spatially temporally correlative mutual information (STI), to characterize the information timeliness from perspective of information entropy by considering the correlations between the last update message and the status of the information source. By maximizing the averaged STI, we find the optimal allocation policy of channel slots with a fixed updating period by formulating the problem as a Markov decision process (MDP) with possibly infinite state space. Furthermore, we derive the optimal amounts of allocated time slots in a unit frame by solving a constrained range integer optimization problem with respect to the average STI. The simulation results show that the proposed periodically updating policy can significantly improve the information freshness compared with the original slot allocation strategy and current commonly used scheduled access strategies, i.e., slotted ALOHA and Threshold-ALOHA.

In a typical Satellite-integrated Internet of Things (SIoT), the limited transmission rate of a sensor causes a stale in data freshness due to unavoidable time waiting for transmission. Moreover, due to the high bit error rate (BER) of the satellite-to-ground link, data freshness will be further exacerbated by frequent retransmissions. Generally, this issue is partially solved using a powerful compression scheme that can reduce the data volume. However, conventional compression schemes will necessitate a significant amount of time to accumulate constant data to a certain quantity, posing a difficult challenge. Therefore, this study proposes an age-optimal hybrid temporal-spatial generalized deduplication and automatic repeat request (HARQ-GD) protocol for the high-sampling data collection in SIoT, considering data compression, and transmission collaboratively. A novel Age of Information (AoI) metric is developed for timeliness evaluation over a two-hop end-to-end link of SIoT, which is optimized to design the proposed HARQ-GD protocol by considering the temporal and spatial correlations of sampled data with specific encoding/decoding algorithms and packet formats. The simulation results indicate that the proposed HARQ-GD protocol performs better performance than typical generalized deduplication (GD) and hybrid automatic repeat request with chase combing (HARQ-CC) schemes in reducing AoI, because of its fewer transmission times and higher compression rate.

Space‐terrestrial integrated networks consisting of low earth orbit (LEO) satellites andterrestrial users are widely developed for potentially diversified requirementsof content distribution. With an obviously time‐varying topology, however, designing a distribution strategy faces several explicit challenges, such asprolonged content access latency and significant transmission overheads, due tolack of contact opportunities and limited on‐board storage space. In this study, therefore, a novel back‐tracing partition directed on‐path caching distributionmechanism (BPDM) is proposed for the file distribution in the hybrid LEOconstellation and terrestrial network. In the proposed strategy, a group offeasible on‐path cache nodes is iteratively selected by utilising awell‐designed cross‐timeslot graph, as well as a collaborative cached contentplacement strategy, called as multiple regions cooperative cache algorithm, bycarefully considering diversified popularity of target files. As a result, theproposed BPDM could efficiently reduce redundant transmissions of content accessfor different users by fetching objective file mainly from limited quantities ofintermediate caching nodes. Through the simulation results, the proposed methodcan obviously decrease the holistic overheads and access delay compared with theminimum spanning tree algorithm and Network Central Location (NCL) nodeselection metric.

In a satellite disruption/delay‐tolerant network, bundle delivery is obviously affected by time‐varying parameters, i.e. bit error rate and propagation latency, due to constantly changing distance and connectivity between paired orbital nodes. The authors proposed a Markov decision‐based optimisation approach for bundle size, which could efficiently improve the expected time of delivery over a dynamic two‐hop inter‐satellite link. In particular, a group of optimal bundle sizes are adaptively selected according to current distance‐dependent channel parameters, leading to a full utilisation on intermediate node's memory. The simulation results verified the proposed method under different conditions with comparison.