This paper addresses the challenges of topology control, multi-hop routing, and link mode selection in optical-acoustic hybrid underwater wireless sensor networks (OA-UWSNs) by proposing a joint optimization framework that integrates hierarchical reinforcement learning (HRL) with an improved non-dominated sorting genetic algorithm (NSGA-III+). The OA-UWSN is formulated as a multi-objective graph optimization problem subject to connectivity, mode allocation, and reliability constraints. At the topology and link layers, the HRL-based approach employs a deep deterministic policy gradient (DDPG) algorithm at the upper level to assign connectivity weights to network links and incorporates a minimal connectivity restoration mechanism to construct feasible subgraphs. At the lower level, Q-learning is adopted to enable adaptive selection between acoustic and optical transmission modes for each link. For the routing layer, NSGA-III+ is developed to achieve multi-objective route optimization, leveraging segmented path encoding, a dynamically weighted fitness function, and a multidimensional crowding adjustment strategy to enhance both the convergence rate and the diversity of the solution set. Simulation results demonstrate that the proposed HRL+NSGA-III+ framework consistently outperforms existing baseline methods in terms of convergence speed, energy efficiency, latency reduction, and link quality, and exhibits robust performance across various network scales.

With the increasing number of terminal devices in the Ocean of Things (OoT), it is necessary to apply the OoT mobile edge computing (MEC) paradigm to low-Earth orbit (LEO) satellites. The aim is to support the operation of compute-intensive OoT services with LEO satellite assistance. To address the proliferation of computing services in OoT, this article proposes a satellite-assisted task offloading and resource allocation (STORA) approach for OoT edge computing, which includes a generalized framework for three-layer MEC systems in space, on the surface, and underwater. First, the MEC system energy minimization problem is described as mixed integer-nonlinear programming (MINLP) and divided into two subproblems: 1) task offloading and 2) resource allocation. Second, the task offloading subproblem is modeled as a Markov decision process (MDP). The proposed adaptive deep deterministic policy gradient (A-DDPG) algorithm jointly optimizes the offloading policy and offloading volume. In A-DDPG, a soft network update method with an adaptive updating coefficient ensures stable network updates while achieving fast convergence. Finally, the resource allocation is decomposed into a joint optimization problem involving buoy and satellite computational resources, which is shown to be convex. The Lagrange multiplier method is used to optimize the buoy-satellite resource allocation problem while also balancing edge computational load across servers. The experimental results show that STORA can reduce network energy consumption by 17.8%, increase network lifetime by 24.4%, and lower network latency by 11.5%.

To build the new generation of ubiquitous communication and service integration networks with “network omnipresence and computing ubiquitous,” it is urgent to improve the in-orbit computing ability of low earth orbit (LEO) constellation networks and develop intelligent technology for satellite–ground collaborative edge computing. Communication tasks between ground nodes and satellites are increasing, but the satellite-to-ground spectrum resources are limited. The reasonable application of channels determines the performance of the network, which in turn affects the users’ experience. An outstanding issue is how to allocate channels rationally and control the power of data transmission to reduce co-channel interference and minimize system overhead effectively. This article studies multiuser computation offloading for low earth orbit (LEO) constellation networks under dynamic environment, wherein the system overhead is minimized by joint offloading strategy and power optimization. First, we propose a generic network architecture for computation offloading of LEO constellation networks under the dynamic environment. Then, from a game-theoretic perspective, we model the overhead minimization problem as a potential game and prove that the Nash equilibrium (NE) minimizes the system overhead. After that, to reach the NE, we design the Synchronous log-linear learning-based power control algorithm and joint offloading strategy and power optimization algorithm based on SLA (JOPAS), and prove the convergence of the algorithms. Finally, the effectiveness of the proposed algorithm is verified through extensive simulations and comparisons with benchmark algorithms, and the proposed algorithm achieves near-optimal performance.

Delay/disruption-tolerant networking (DTN) is a networking technology conceived to manage opportunistic connections with no consistent end-to-end link connectivity which is common in both terrestrial and space communication environments. DTN is recognized as a baseline technology for implementing deep-space networks. Considered as the primary transport protocol of DTN in space, Licklider transmission protocol (LTP) is expected to provide reliable data delivery service in a challenging networking environment regardless of presence of random link disruptions and/or extremely long propagation delays. The National Aeronautics and Space Administration (NASA) has implemented the use of DTN protocols on the International Space Station (ISS) for data delivery to the earth ground station. However, little work has been done in studying the performance of LTP for reliable data/file delivery in such a communication environment, especially in presence of link disruption. There is a lack of a solid performance evaluation of LTP for its use in the space station communications. In this paper, an analytical framework is presented to evaluate the performance of LTP for reliable file delivery between the space station and the ground stations with a focus on the effect of link disruption, which may occur either over the downlink or over the uplink. The effect of data loss due to channel error is also integrated. Realistic data block transmission experiments using a PC-based experimental infrastructure are conducted to validate the analytical models.

Non-terrestrial networks (NTNs) using flying objects such as satellites play key roles in the next-generation wireless system (6G). The NTN links with long propagation delay and random packet losses pose a great challenge to the performance of Transmission Control Protocol (TCP), which many Internet applications rely on. Performance enhancing proxy (PEP) is an easy-to-deploy approach for improving TCP's performance. In this paper, we design and implement a novel PEP calledPEPescwhich has two distinctive features. First, it featuresretransmission-freeloss recovery, using an adaptive packet-level forward erasure correction method called streaming coding (SC). Second, as packet losses are recovered by SC, the congestion control problem is simplified to rate control and local acknowledgement between entities based on bandwidth estimation. Based on a queueing theoretic analysis of the design, we carefully devise a protocol and implement PEPesc as an open-source application. Extensive evaluations show that PEPesc can achieve much higherandsmoother goodput than the canonical TCP variants and than other existing open-source PEPs in applications includingiperfand HTTP-based adaptive streaming, and achieves similar performance in web browsing. Finally, we also present a deployment case over a real-world geostationary satellite link.

This paper introduces a protection mechanism for complete recovery from multi-link failures in Low Earth Orbit (LEO) satellite networks. Inter-satellite links (ISLs) may break down frequently due to the interruption of wireless channels, the high-speed movement of satellites and the long distance for communication. As a proactive scheme for recovery, IP Fast Reroute (IPFRR) has been used to achieve fast rerouting via pre-calculating backup paths. Although IPFRR has effectively addressed the problem of single-link failures, the recovery from multi-link failures still needs further studies, especially on aspects such as recovery rate, computation complexity and storage cost. For better survivability of LEO satellite networks, we develop an IPFRR mechanism for recovering from multi-link failures. This new mechanism, called Grid Bypass Routing (GBR), assigns two addresses to every network device – a normal address and a protection address. When failures occur, routers will choose the protection address to deliver packets via alternative paths. We develop a technique to compute backup paths and prove that GBR can guarantee complete recovery. We evaluate GBR in the simulation, and the result shows that the advantage of its computational cost, forwarding table size and recovery rate makes GBR a more efficient and easily manageable IPFRR solution to link failures in LEO satellite networks.

Internet of Space Things (IoST), which extends the concept of Internet of Things (IoT) to space, has emerged as a new paradigm for offering monitoring/reconnaissance, in-space backhaul, and cyber-physical integration services. As Low Earth Orbit (LEO) satellites are increasingly deployed for global Internet services, Mobile Edge Computing (MEC) is being introduced into satellite networks to provision computing services by placing edge servers on satellites. Nevertheless, it is a nontrivial and unexplored task to efficiently choose edge server deployment locations from a large number of satellites. In this paper, we address this issue in detail towards average response delay minimization considering propagation delay, forwarding delay, and service delay. In particular, we formulate the dynamic server placement problem as well as the static server placement problem, and devise a genetic algorithm-based heuristic approach to solve them. Simulation results compare the two placement strategies with two benchmarks and demonstrate the performance of our genetic algorithm-based approach. Furthermore, a comparison between the dynamic placement and the static placement is investigated.

Space communications are characterized by link intermittent connectivity and long link propagation delay. The use of bundle protocol (BP) in space communications has been in debate. An analytical understanding of its performance for reliable data delivery in the presence of link intermittent connectivity, especially with a lengthy link disruption, is quickly needed. In this paper, analytical models are built to estimate the number of transmission rounds (or efforts) caused by the link disruption and the resulting delay in bundle delivery of BP for applications in space communication networks. The models are validated by conducting bundle delivery experiments using a testbed.

Delay/disruption tolerant networking (DTN) was proposed for reliable data transfer despite frequent link disruptions and long propagation delay that are typical of the deep-space communication environment. DTN communications rely heavily on its core bundle protocol (BP) for reliable data delivery. To date, little work has been seen in theoretical analysis of the performance of BP in deep-space communications. In particular, an analytical understanding of the performance of BP for reliable data delivery in a deep-space communication environment in the presence of link disruptions is quickly needed. In this paper, we present a study of the effect of link disruption on transmission performance of BP, in both analytical and experimental manners, in a deep-space communications system characterized by link disruptions accompanied by an extremely long propagation delay and lossy data links. For the first time, an analytical framework is built to estimate the effect of link disruption on the total bundle delivery time of BP depending on its starting time with respect to bundle delivery. The analytical models are validated by running experiments using a test bed.

In recent years, Industrial Internet of Things (IIoT) has attracted growing attention from both academia and industry. Meanwhile, when traditional wireless sensor networks are applied to complex industrial field with high requirements for real time and robustness, how to design an efficient and practical cross-layer transmission mechanism needs to be fully investigated. In this paper, we propose a Q-learning-based dynamic spectrum access method for IIoT by introducing cognitive self-learning technical solution to solve the difficulty of distributed and ordered self-accessing for unlicensed terminals. We first devise a simplified MAC access protocol for unlicensed users to use single available channel. Then, a Q-learning-based multi-channels access scheme is raised for the unlicensed users migrating to other lower cells. The channel with most Q value will be considered to be selected. Every mobile terminals store and update their own channel lists due to distributed network mode and non-perfect sensing ability. Numerical results are provided to evaluate the performances of our proposed method on dynamic spectrum access in IIoT. Our proposed method outperforms the traditional simplified accessing methods without self-learning capability on channel usage rate and conflict probability.

With the rapid development of modern satellite communications, broadband satellite services are experiencing a period of remarkable growth in both the number of users and the available bandwidth. More efficient spectrum management schemes require deeper investigation in order to meet the ever-increasing demand for broadband spectrum. In this paper, we propose a band allocation method for multibeam satellite systems by introducing a market-driven pricing mechanism. Instead of adopting static and fixed band selling, we consider a satellite network operator that utilizes the mode of price bargaining to trade the unused band with terrestrial network operators. By applying market-based mechanism to support satellite spectrum allocation, higher spectrum efficiency can be attained in order for satellite systems to meet the increasing demands for satellite bandwidth at an affordable cost. Besides, for the one-to-many bargaining case without terrestrial operator involved in, a differential spectrum pricing solution is devised to address heterogeneous users' spectrum preferences. In a typical price bargaining model, market participants (i.e., terrestrial network operators) are assumed to know exactly their needs dynamically, which is hard to achieve in near real-time; thus, our approach approximates it with a sub-optimal estimation on the network operators' benefit threshold. To be specific, we obtain the optimal pricing at every round of bargaining by predicting the overall benefits of terrestrial network operators and reaching the Nash equilibrium. Essential discussions and proofs for the pricing rationality are provided. Numerical results are given to evaluate the impact of the pricing scheme on the profits of satellite systems.

Millimetre‐wave propagation is a promising broadband transmission technology for future fifth generation mobile communication systems. For a vector orthogonal frequency division multiplexing system, the authors investigate the millimetre‐wave propagation through a sparse multipath channel in a sense that it has a large time delay spread but with only a few non‐zero taps. By exploiting the sparse nature of millimetre‐wave channel, any sparse multipath channel can be characterised by the multipath delays and their corresponding channel coefficients. They first study an ideal case that the pilot signals are transmitted through a sparse channel without noise, and an exactly sparse inverse fast Fourier transform (SIFFT) algorithm is performed to estimate the non‐zero channel taps with reduced complexity. Then, they consider a more practical scenario that the pilot signals through a sparse channel with noise interference, and an approximately SIFFT algorithm is employed to estimate the effective channel taps, while the remaining small coefficients interfered by noise can be wiped out. Through numerical analysis, they demonstrate that the proposed SIFFT algorithms can reduce the computational complexity while keeping the root mean squared error of channel estimation at a low level.

Bundle protocol (BP) is the main protocol of delay/disruption-tolerant networking which is developed to provide reliable data delivery services in a stressed communications environment. A typical application of BP is for data delivery in a challenging deep-space communication environment. However, little work has been done in analyzing the performance of BP in deep-space communications, especially in presence of highly channel-rate asymmetry. In this paper, we present analytical modeling of the transmission performance of BP over deep-space communication channels characterized by highly asymmetric channel rates. The model is built to estimate the expected file delivery time (and goodput) of the protocol for reliable data delivery in a deep-space transmission scenario. The model is validated by conducting data transfer experiments using a PC-based experimental test bed.

The increasingly extensive availability of location- acquisition technologies (such as GPS and GSM networks) and mobile computing techniques have generated a lot of spatial-temporal trajectory data which represents the mobility of diversification of moving objects such as people, vehicles, and animals. This brings new opportunities to understand the movement behaviors of moving objects in mobile social networks, which can become valuable for location-based services (LBS). However, the explosive growth of users' trajectory data has brought a lot of troubles to online real-time processing. In this paper, we focus on this direction and develop a complete data-driven framework involving real-time user mobility pattern modeling and a novel user similarity measurement based on both spatial and temporal information. Through experimental evaluation, it is verified that the proposed mobility pattern modeling method and similarity measurement can deliver excellent performance.

Based on the well known Lenstra Lenstra Lovász (LLL) algorithm, we propose a possible swap LLL algorithm (P-SLLL) for lattice reduction aided (LRA) MIMO detection in this paper. The reduction process of the new algorithm is modified by searching for the next column swap through the whole basis, instead of the sequential implementation in the original LLL algorithm. Two different searching criteria are proposed, i.e. the random selection criterion and the optimal swap selection criterion. Comparing to the LLL algorithm, the PSLLL algorithm enjoys fast termination property and lower computational complexity, which can benefit practical hardware implementation. Simulation results prove our analysis and show that PSLLL aided linear MIMO detectors achieve the same performance as the LLL aided methods.