Augmented intelligence (AuI) is a concept that combines human intelligence (HI) and artificial intelligence (AI) to leverage their respective strengths. While AI typically aims to replace humans, AuI integrates humans into machines, recognizing their irreplaceable role. Meanwhile, human-in-the-loop reinforcement learning (HITL-RL) is a semisupervised algorithm that integrates humans into the traditional reinforcement learning (RL) algorithm, enabling autonomous agents to gather inputs from both humans and environments, learn, and select optimal actions across various environments. Both AuI and HITL-RL are still in their infancy. Based on AuI, we propose and investigate three separate concept designs for HITL-RL:HI-AI,AI-HI, andparallel-HI-and-AIapproaches, each differing in the order of HI and AI involvement in decision making. The literature on AuI and HITL-RL offers insights into integrating HI into existing concept designs. A preliminary study in an Atari game offers insights for future research directions. Simulation results show that human involvement maintains RL convergence and improves system stability, while achieving approximately similar average scores to traditional$Q$-learning in the game. Future research directions are proposed to encourage further investigation in this area.

Traffic forecasting is an important part of Intelligent Transportation System (ITS). The traffic prediction results provide data support for traffic guidance and traffic information release system. Research on traffic forecasting is of great significance to alleviate traffic congestion and increase traffic flow. However, the road network is complex and dynamic. The accuracy of multi-step traffic forecasting decreases rapidly with the increase of time steps, and other performances such as RMSE, MAE, ACC and R2are getting worse and worse, which seriously affects the prediction results. Therefore, we proposes a traffic prediction model based on two-stage stacked graph convolution network ( ED2GCN ) , in which EDGCN is used in both encoder and decoder stages. EDGCN includes a GRU and two stacked AGCN modules. A single AGCN module is composed of GCN, GLU and Attention mechanism. In order to capture the spatial dependence between distant points, two AGCNS are superimposed by residual connection to form a deep AGCN structure. In this paper, PEMS08 data set is used for experimental verification. The experimental results show that the prediction effect of ED2GCN proposed in this paper is better than that of the baselines. Among them, RMSE, MAE, ACC and R2are optimized by at least 7.8%, 10.8%, 0.9% and 0.7% respectively compared with A3T-GCN.

Existing static grid resource scheduling algorithms, which are limited to minimizing the makespan, cannot meet the needs of resource scheduling required by cloud computing. Current cloud infrastructure solutions provide operational support at the level of resource infrastructure only. When hardware resources form the virtual resource pool, virtual machines are deployed for use transparently. Considering the competing characteristics of multi-tenant environments in cloud computing, this paper proposes a cloud resource allocation model based on an imperfect information Stackelberg game (CSAM-IISG) using a hidden Markov model (HMM) in a cloud computing environment. CSAM-IISG was shown to increase the profit of both the resource supplier and the applicant. Firstly, we used the HMM to predict the service provider's current bid using the historical resources based on demand. Through predicting the bid dynamically, an imperfect information Stackelberg game (IISG) was established. The IISG motivates service providers to choose the optimal bidding strategy according to the overall utility, achieving maximum profits. Based on the unit prices of different types of resources, a resource allocation model is proposed to guarantee optimal gains for the infrastructure supplier. The proposed resource allocation model can support synchronous allocation for both multi-service providers and various resources. The simulation results demonstrated that the predicted price was close to the actual transaction price, which was lower than the actual value in the game model. The proposed model was shown to increase the profits of service providers and infrastructure suppliers simultaneously.

Advertisements(Ads) dissemination in Vehicular Ad Hoc Networks (VANETs) is a promising application. It not only provides benefits to customers but also brings commercial profits to merchants. However, little previous work has focused on the ads dissemination problem to maximize the overall profit. In this paper, we target the ads dissemination problem and design two algorithms for ads delivery. We first propose a Spread Utility Based Delivery Algorithm based on the knowledge derived from the empirical study of vehicle traces. Based on the first algorithm, an Optimized Ads Delivery Algorithm is designed, which considers consumers' interests and their mobilities. At last, we conduct extensive simulation, and the simulation results indicate that our algorithms can achieve a higher delivery rate and effective rate than current algorithms in most cases. Additionally, when the consumer interest is considered, our algorithm can achieve a tradeoff between the delivery rate and visiting probability.

Cooperative communication has been proven effective in enhancing the performance of wireless networks. In this paper, we propose an adaptive multi-relay selection with power allocation mechanism to offer energy fairness at each node for a cooperative network. Unlike traditional approaches where all nodes are considered to transmit in a collaborative manner, we explicitly consider the situation where each node exhibits some degree of selfishness behavior. By introducing novel concepts of the selfishness index and utility function which denote node's benefit from cooperation, we show that the proposed cooperation scheme can well balance the energy consumption among nodes as well as maximize the network lifetime. Theoretical analysis and extensive simulation results are supplemented to show advantages of the proposed approach in power saving and power allocation fairness.

Links in Challenged Networks, particularly in Delay Tolerance Networks, are mostly disconnected due to the mobility, fallibility and scarcity of nodes. Owing to its intermittent connectivity, there exists substantive uncertainty, in which fuzziness and randomness are the most important and fundamental. In this paper, in virtue of the relationship analysis between fuzziness and randomness, we discuss the uncertainty in Challenged Networks. An approach is proposed to describe the uncertainty reasoning by means of the intrinsically uncertainty on fuzzy reasoning and modeling the uncertain states on probability. Certainty theory supports the protocol design in a flexible and adaptive manner. Modeling associates fuzziness with randomness is a cross-discipline which provides a basis for the artificial intelligence of both logic and image thinking with uncertainty.

Tolerant network (DTN) is a kind of network with high latency and frequent disconnection. To guarantee the success of delivering a message to the destination and to make time cost as low as possible, routers in this kind of network (DTN) need to multicopy the message and store it longer than routers in other kinds of network. This paper focuses on the relation and reciprocity between routing and buffer efficiency in DTN. We implement a variant of epidemic routing, called surround routing. This kind of routing performs better than epidemic routing in buffer- limited conditions since buffer space is efficiently used.

In virtue of its widespread application scenarios, especially in battlefield communication, disaster rescue, satellite and deep-space transmission, Delay-disruption Tolerant Network (hereinafter referred to as DTN) has become one of the most attractive focuses in research for future Internet. Due to the intermittent connectivity, mobility of nodes, signal interference, jamming in DTN, there exist substantive uncertainty which will cause potential security hazard and degradation of performance (including average latency, delivery ratio, etc.). So there are challenges in security and reliability for DTN. In our paper, from the perspective of the uncertainty reduction in DTN, we propose a routing strategy of multi-hop forwarding based on potential energy hierarchy. We also introduce the concept of integration of routing & switching, and propose a multi-port switching fabric to schedule data in accordance with the multi-hop routing. In terms of theory, entropy demonstrate this strategy can provide the dependability and stability of security routing in DTN.