Routing decisions made by unmanned aerial vehicle (UAV) swarms are affected by complex and dynamically changing topologies. A centralized routing algorithm imposes the entire computational burden on one module, and the high data dimensionality renders computation burdensome. In this letter, we develop a multi-agent reinforcement learning-based routing algorithm for a UAV swarm. The UAVs are trained in a data-driven manner to make distributed routing decisions. Factors that include channel quality, UAV movement, UAV overhead, and the extent of neighbor variation are incorporated into link quality assessment. Long short-term memory is used to improve the Actor and Critic networks, and more information on temporal continuity is added to facilitate adaptation to the dynamically changing environment. Simulations show that the proposed routing algorithm reduces data transmission delay and enhances the transmission rate compared with traditional routing algorithms.

Routing protocols do not respond quickly to environmental changes due to the high mobility of nodes in the Flying Ad Hoc Network (FANET), to obtain reliable transmission links. This paper proposes an adaptive link maintenance method based on deep reinforcement learning (DRL-MLsA), which can dynamically adjust the time interval of broadcasting Hello packets. This method can cope with the highly dynamic network environment, and adapt to both active routing and table-driven routing protocols. The method considers the channel model of the signal and investigates the impact of UAV communication range on link maintenance. We can get an agent by perceiving the degree of changes in the number of neighbors in a dynamic environment. The optimal broadcast cycle was obtained to maximize the energy of the node to send and receive task data. We substituted the single-output network model with a competitive network to overcome the reward overestimation problem, which also improves the convergence speed of the algorithm. Simulation results showed that DRL-MLsA can reduce the communication overhead for link maintenance, while at the same time increase the throughput of the network and decrease the packet loss of transmission.

A variety of independent research activities have recently been undertaken to explore the feasibility of incorporating social networking principles into the Internet of Things solutions. The resulting model, called the Social Internet of Things, has the potential to be more powerful and competitive in supporting new IoT applications and networking services. This paper's main contribution is in sentiment analysis, which aims to predict aspect sentiments to improve the making of automated decisions and communication between associates in the social internet of things. In recent years, to analyze sentiment polarity at a subtle level, sentiment classification has become a primetime attraction. Current approaches commonly use the Long-Short Term Memory network to figure aspects and contexts separately. Usually, they perform sentiment classification using simple attention mechanisms and avoiding the bilateral information between sentences and their corresponding aspects. Therefore, the results are not satisfactory. This manuscript intends to develop a new Bidirectional gated recurrent unit model by depending on natural language processing for fully-featured mining to perform the aspect-level sentiment classification task. Our proposed model uses the Bidirectional gated recurrent unit network to acquire the dependency-based semantic analysis of sentences and their corresponding terms compared to earlier work. At the same time, it proposes a method to learn the sentiment polarity of terms in sentences. To check out our model's achievements, we perform several experiments on datasets, namely, (LAPTOP, RESTUARANT, and TWITTER). Our experiment results demonstrate that our model has achieved compelling performance and efficiency improvements in aspect sentiment classification compared with several existing models.

Recent advancements in protocol design and optimization for Flying Ad-hoc Networks have shown significant progress. However, the existing centralized optimization control algorithms pose conflicts with the distributed nature of these systems. To address this challenge, this paper proposes a novel distributed routing optimization algorithm based on multi-agent reinforcement learning, integrated with an adaptive multi-mode routing framework incorporating multi-protocol cooperation mechanisms. The algorithm adopts the Decentralized Execution Multi-Agent Deep Deterministic Policy Gradient (DE-MADDPG) algorithm framework, enabling individual unmanned aerial vehicles to directly adjust the protocols and protocol parameters of the current node based solely on local network information. This optimization enhances network structure and overall performance without considering the transmission delay of control signals. The ns3-gym simulation platform is employed for performance evaluation, comparing the proposed algorithm with Deep Q-Network (DQN), Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and other algorithms. The experimental results demonstrate that compared to the routing algorithm optimized based on DQN, the proposed algorithm exhibits the best overall performance, with a 26.31% reduction in energy consumption and a 19.69% decrease in end-to-end delay.

Network coding based multipath transmission method has been shown to improve the reliability and throughput of end-to-end data delivery for ad hoc networks. However, when it is adapted to flying ad hoc networks (FANETs), multipath establishment and maintenance scheme should be elaborately designed due to frequently local topology changes during UAVs’ formation flying. This letter proposes a network-coding-aware multipath routing protocol NCAnt. It achieves reliable end-to-end transmission by maximizing recoding opportunities based on the ant colony optimization (ACO). Moreover, the positive feedback mechanism of ant colony pheromone is utilized to reduce the routing reconstruction cost under frequent local topology changes. Simulation results show NCAnt can provide up to 50% additional coding opportunities and achieves lower path recovery delay than baseline schemes.

An intelligent cache replacement policy suitable for picture archiving and communication systems (PACS) was proposed in this work. By combining the logistic regression (LR) algorithm with the classic least recently used (LRU) cache replacement policy, we have created a new intelligent cache replacement policy called LR-LRU. The LR-LRU policy is unlike conventional cache replacement policies, which are solely dependent on the intrinsic properties of the cached items. Our PACS-oriented LR-LRU algorithm identifies the variables that affect file access probabilities by mining medical data. The LR algorithm is then used to model the future access probabilities of the cached items, thus improving cache performance. In addition,.1-regularization was used to reduce the absolute values of the variables’f coefficients. This screens some variables that have little influence on the model by causing their coefficients to approach zero, which achieves the effect of screening the variables. Finally, a simulation experiment was performed using the trace-driven simulation method. It was shown that the.1-regularized LR model is superior to the LR and.2-regularized LR models. The LR-LRU cache algorithm significantly improves PACS cache performance when compared to conventional cache replacement policies like LRU, LFU, SIZE, GDF, and GDSF.