In this paper, we propose a multi-dimensional resource allocation scheme for Internet of Remote Things (IoRT) data collection in a high altitude platform (HAP)-assisted unmanned aerial vehicle (UAV) network. Considering the quality of service (QoS) requirements of delay-sensitive IoRT data, we propose a UAV-HAP double relay data transmission mode to reduce the transmission delay for delay-sensitive data. Since the resources of the UAV are limited, we jointly optimize communications, computing and storage resources to maximize the utility of the considered system. Due to the high dimensionality of the solution space, we design a Twin Delayed Deep Deterministic policy gradient-based multi-dimensional resource allocation (TD3-MDRA) algorithm to find the optimal resource allocation strategy. Extensive simulation results are presented to demonstrate the superior performance of TD3-MDRA for IoRT data collection with delay and resource constraints.

With the fierce global competition on satellite networks, the building of satellite constellations grows explosively. Sharply increasing on-orbit data will face the challenge of satellite-ground data transmission. GEO satellites become the top choice for satellite data relay due to their stable satellite-ground link. Most existing spectrum resource management for GEO relays is equipment-oriented and benefit priority, which may lead to a waste of spectrum resources. In this paper, we propose a real-time task-oriented resource allocation strategy for GEO relay systems. We model the spectrum allocation problem as a distributed non-cooperative Stackelberg game process. We prove that when both sides of the game pursue the maximization of personal revenue, the system will enter a Nash equilibrium state, whereas spectrum resources are not fully used. Based on the maximization of individual utilities (U-prior) and spectrum utilization (S-prior) methods, we design an adaptive dual-mode pricing mode to maximize the spectrum resources within a certain loss of revenue. The simulation results show that the S-prior and U-prior have better performance than the baseline method and existing optimization methods. Our proposed dual-mode strategy is making more throughputs and has less delay with little loss of utility values than that of individual utility maximization.

Existing satellite constellations typically lack on-orbit processing capabilities, and on-orbit data backhaul face to reliability and effiency challenges. Tiansuan constellation is with the capability of providing satellite edge computing services, which can support complex data coding transmission methods. To effectively support effective data backhaul for existing satellite constellations, we propose a reliable data transmission service in Tiansuan constellation. The service adopts the adaptive coding transmission strategy to adjust the optimal size of the encoded data packet according to real-time satellite network conditions. An adaptive encoding transmission approach is proposed to reduce end-to-end delay. Experimental results based on the ground simulation platform demonstrate that the proposed method can effectively reduce end-to-end delay and improve effective data transmission rate.

Geosynchronous Earth Orbit (GEO) satellites, which can relay image data for Low Earth Orbit (LEO) satellites, play an important role in remote sensing. With the development of satellite technologies, the significantly improved computation capabilities of GEO satellites have enabled space service computing, through which GEO satellites can provide data processing services before forwarding to reduce the quantity of transmitted data. In the presence of multiple LEO satellites, how to make effective use of limited communication and computation resources in GEO satellites has become crucial. At present, the research on satellite resource management typically focuses on either communication or computation resources. Existing resource management algorithms are usually of slow convergence speed, which limits their applicability in real-time remote sensing scenarios. Therefore, we propose an aggregated resource management method for remote sensing applications. We first propose models for transmission tasks and processing tasks of remote sensing images. Then we formulate the aggregated resource management for satellite edge computing as a hybrid Stackelberg game and simplify the problem to speed up its convergence speed. Then we propose a distributed resource management algorithm to determine the optimal strategies. Simulation results show that the proposed method can quickly obtain the optimal resource allocation strategy and outperforms typical dynamic iterative algorithms in terms of service quantity and throughput.

Industrial Internet of Things (IIoT) is a new stage for traditional industry to achieve intelligent development. However, the problems of limited resource, communication congestion, and capacity-constrained batteries have emerged due to massive wireless sensing devices (WSDs). With the recent advent of the wireless power transfer (WPT) technique and the development of edge computing technology, the IIoT pays much attention to the interconnected, instant, and high-intelligent system. In this article, we first consider a three-layer architecture to describe the IIoT environment and we propose a resource allocation strategy aiming at maximizing the residual energy of WSDs. The optimization of residual energy is formulated as a mixed-integer nonconvex programming NP hard problem, by constraining the offloading decision, power allocation, computing resource allocation, and time allocation. In addition, an improved hybrid whale optimization algorithm (IHWOA) is proposed to search for the approximate optimal solution. Rapid convergent and stable efficient solution is obtained through simulation experiments. Finally, numerical results prove that the proposed solution achieves good performance.

With the rapid development of Internet of Things (IoT), more and more applications focus on the detection of unmanned areas. With the assistance of unmanned aerial vehicle (UAV), IoT devices are able to access the network via aerial base stations. These UAV-assisted IoT applications still face security and energy challenges. The open environment of IoT applications makes the application easy to encounter external invasion. Limited energy of UAV results in the limited lifetime of network access. To address these challenges, researches on IoT security and energy efficiency are becoming hotspots. Nevertheless, in the UAV continuous coverage scenario, there is still an enormous potential to improve the security and efficiency of data collection in IoT applications. In this article, blockchain is introduced into the scene of UAV-assisted IoT, and a data collection system considering security and energy efficiency is proposed. In this system, UAV, as an edge data collection node, provides a long-term network access for IoT devices through regular cruises with recharging. By forwarding data and recording transactions, UAVs get charging coins as rewards. UAVs use charging coins to exchange charging time. UAV swarm builds distributed ledgers based on blockchain to resist the invasion of malicious UAV. In order to reduce energy consumption, this article designs an adaptive linear prediction algorithm. Through this algorithm, IoT devices upload prediction model instead of original data to greatly reduce in-network transmissions. Simulation results show that the proposed system can effectively improve the security and efficiency of data collection.

The past few years have witnessed a wide deployment of low earth orbit (LEO) satellites communications and networking. With the explosive growth of new businesses, satellite network is expected to provide global coverage and high bandwidth availability service. Toward this end, Multipath TCP(MPTCP) is a promising transport protocol to use in LEO satellites networks. MPTCP can not only achieve seamless handover, but also enhance throughput by using multiple paths transmission mechanism. However, following the improvement of the performance and scalability, it also brings unprecedented challenges for congestion control of multiple sub-flows. Especially, currently works on the congestion control largely relies on a manual process which presents a poor performance in the high-dynamic complexity network environment. Inspired by the recent success of applying machine learning in many challenging control decision domains, such as video game, self-driving, we employ deep deterministic policy gradient for learning the optimal congestion control strategies by interacting with the underlying network environment. Some simulation results demonstrated the effectiveness and feasibility of our architecture and algorithms.

Past few years have witnessed the compelling applications of the remote sensing satellites in our daily life, ranging from the weather forecast to military surveillance. Due to the computation and power constraints, the LEO satellites have to download the remote sensing data to the ground stations for further processing. However, the long-distance transmission and the ionospheric interference is problematic for supporting the latency-sensitive remote sensing services, such as hotspot detection, hotspot tracing. As a remedy, in this paper, the space stations are introduced to offload the computation task of the remote sensing satellites to reduce the transmission delay. With the space station joining in, a three-tier intelligent remote sensing satellites operation system is constructed. In order to perform well, we study the computation resource allocation strategies in this three-tier system. We model the resource management and pricing problems among three players as a Stackelberg game, where the space stations act as the leaders, the ground stations as the followers, and the LEO satellites as the sub-followers. For searching the Nash equilibrium of this game, we apply ’WoLF-PHC’ algorithm for learning the optimal resource management strategies. In addition, some simulation results are presented to demonstrate the feasibility and performance of our architecture and algorithm.

The past few years have witnessed a wide deployment of software defined networks facilitating a separation of the control plane from the forwarding plane. However, the work on the control plane largely relies on a manual process in configuring forwarding strategies. To address this issue, this paper presents NetworkAI, an intelligent architecture for self-learning control strategies in software defined networking networks. NetworkAI employs deep reinforcement learning and incorporates network monitoring technologies, such as the in-band network telemetry to dynamically generate control policies and produces a near optimal decision. Simulation results demonstrated the effectiveness of NetworkAI.