The emergence of edge computing has witnessed a fast growing volume of data on edge devices belonging to different stakeholders which, however, cannot be shared among them due to lack of trust. By exploiting blockchain's non-repudiation and non-tampering properties that enable trust, we develop a blockchain-based big data sharing framework to support various applications across resource-limited edges. In particular, we devise a number of novel resource-efficient techniques for the framework: (1) the PoC (Proof-of-Collaboration) based consensus mechanism with low computation complexity which is especially beneficial to the edge devices with low computation capacity, (2) the blockchain transaction filtering and offloading scheme that can significantly reduce the storage overhead, and (3) new types of blockchain transaction (i.e., Express Transaction) and block (i.e., Hollow Block) to enhance the communication efficiency. Extensive experiments are conducted and the results demonstrate the superior performance of our proposal.

Underwater acoustic sensor networks (UASNs) are used extensively in activities such as underwater data collection and water pollution detection. A UASN consists of acoustic sensors that use batteries as their power supply. Because of the complex underwater environments in which UASNs are employed, replacing these batteries is difficult. Prolonging the battery life of UASNs by reducing their energy consumption (improving their energy efficiency) is one means of mitigating this problem. This paper proposes an energy-balanced unequal layering clustering (EULC) algorithm that improves the energy efficiency of acoustic sensors. The EULC algorithm designs UASNs with unequal layering based on node depth, providing a solution to the “hot spot” issue through the construction of clusters of varying sizes within the same layer. Simulation results show that the EULC algorithm effectively balances the energy in UASN nodes and thereby prolongs network lifetime.

Virtual reality (VR) is an important landing scenario for 5G and a key enabling technology for the digital economy, and the core challenge it faces is how to ensure real-time interaction and content distribution efficiency. Existing solutions rely on adaptive delivery, overall content edge caching, or asynchronous rendering in the cloud, with drawbacks such as high caching and computing costs and difficulty in ensuring real time. This article is oriented to content generation and synchronization of virtual reality, and aims at realizing intelligent transmission of VR content with high efficiency and low delay guarantee, and carries out research on cloud-edge-end collaborative mechanisms. First, for AI-aided VR content generation, a cloud-edge-end collaborative service architecture is constructed to support independent encoding and distribution of background and interactive content generation. Second, to address the issue that the MEC node with limited cache space cannot meet the dynamical demands of VR user, a cloud-edge collaborative caching strategy based on graph neural networks is proposed to achieve optimal caching and updating of background content, in which the background content is first cached MEC node according to the content request with node sharing scheme, then the content is updated according to a minimal cost update algorithm based on the graph neural networks (GNN)-aided request prediction. Finally, the proposed algorithm is simulated and tested, the simulation results show that the proposed caching and update algorithm achieve better quality of experiment (QoE) and higher cache hit ratio compared with comparison algorithms.

Beam-hopping scheduling technology is a crucial technology for improving the performance of resource-constrained multibeam satellite (MBS) systems. However, it is challenging to dynamically match the communication demands of the terrestrial cells with the beam transmission capacity. Almost all state-of-art beam-hopping scheduling approaches only consider a fixed beam coverage radius, which always results in a waste of transmission resources. In this letter, we propose a deep reinforcement learning-based algorithm to jointly optimize the beam-hopping scheduling and coverage control (called DeepBeam) which flexibly uses three degrees of freedom of time, space and beam coverage radius. In addition, to reduce the computational complexity and accelerate the training of DeepBeam, we first train a single model for one of the beams and then use this trained model for each beam in a polling manner to determine the cell to be illuminated and the beam coverage. Furthermore, the simulation results illustrate that the proposed DeepBeam can improve not only the throughput of MBS but also reduce the packet loss probability.

The underwater acoustic sensor network (UASN) is a specific deployment of Internet-of-Things (IoT) technology in the underwater environment, since energy constraints limit the lifetime of UASNs, effectively balancing the energy consumption of acoustic sensor nodes in UASNs is important to maximize the amount of information collected and to prolong the network lifetime. Node clustering is widely regarded as one of the most important energy-efficient schemes for UASNs. However, most existing clustering schemes focus on the cooperation-based election of cluster headers (CHs) in a centralized manner. Due to the limited energy capacity, acoustic sensor nodes are designed to save their own energy, hindering the realization of such cooperation. To address this issue in this article, game theory is applied to UASNs to balance network energy consumption and model acoustic sensor nodes as rational and selfish players. Specifically, a game-theory-based clustering (GTC) scheme for UASNs is developed. In the CH election phase, each node makes a decision in pursuit of a greater payoff based on the Nash equilibrium. An incentive mechanism is invented to induce nodes to make more beneficial collective decisions and plays a role in the CH rotation to effectively balance the energy consumption. Meanwhile, the network area is divided into nonuniform sectors to ensure the energy consumption of the CH is more evenly distributed. Simulation results show that the proposed GTC scheme can effectively balance network energy consumption and extend the network lifetime.

Due to the explosive growth in the scale of low Earth orbit (LEO) satellite networks, such as Starlink, and the extremely high mobility and limited network resources of single LEO satellites, designing dynamic routing has become a promising way to improving satellite communication performance. Most of the existing approaches derive the routing policies in a centralized paradigm, which often suffer from the high cost of collecting the global routing information and the high computation complexity in large-scale networks. Therefore, this letter proposes a spatial location aided fully-distributed routing algorithm for large-scale satellite network with minimizing the average delivery time. First, we model a partially observable Markov decision process (POMDP) for each satellite, where only the spatial location, queuing state and estimated residual propagation delay (ERPD) of one-hop adjacent satellites need to be communicated. Second, a novel Fully Distributed dynamic Routing algorithm based on Multi-Agent deep Reinforcement Learning (FDR-MARL) is proposed to derive the optimal routing strategy. Finally, extensive experiments are carried out to verify the effectiveness and advantages of our proposed approach under large-scale LEO satellite networks.