Unmanned aerial vehicle (UAV) swarm networking and collaboration have significant prospects in both civilian and military applications, due to its remarkable properties in cooperative efficiency, reduced risks, and operational cost. Traditional algorithms have challenging issues of high computational complexity and low efficiency in UAV swarm networking and collaboration, while computational intelligence (CI) has attracted increasing attention since it has advantages in solving complex optimization problems. The networking of UAV swarms serves as an essential foundation for collaboration, and intelligent collaboration is a crucial means of enhancing the performance of UAV swarm systems. To date, extensive CI-based algorithms have been proposed to improve the networking and collaboration capabilities of UAV swarms, and several relevant surveys have also been presented. However, existing surveys either review networking or collaboration. To the best of our knowledge, there is no survey that simultaneously concentrates on CI-based UAV swarm networking and collaboration. In this survey, we provide a comprehensive overview of CI-based networking and collaboration algorithms from six typical aspects including channel access, network routing, cooperative task assignment, cooperative path planning, cooperative search, and cooperative jamming. More importantly, to help researchers choose appropriate algorithms to satisfy the requirements of different missions, we classify CI-based algorithms into four categories, namely heuristic behavior search-based algorithms, policy design-based algorithms, policy learning-based algorithms, and hybrid algorithms. Finally, we discuss open issues and future directions that may influence future research on UAV swarm intelligence networking and collaboration. This review may provide new insights and valuable references for researchers in this field.

Integrated sensing, communication, and computation (ISCC) will become a key enabler for automation applications. However, since the performance region of ISCC has a higher dimension than those of traditional wireless networks, existing schedulers typically fail to simultaneously meet the heterogeneous requests in ISCC. In this work, we propose a novel wireless scheduling architecture to explore the coordination gains of sensing, communication, and computation from a perspective of joint optimization. Specifically, we first construct an implementation framework of ISCC by combining the mobile edge computing paradigm with the integrated sensing and communication technology, where the inherent tradeoff between sensing, communication, and computation performance is characterized. Next, a joint device association and subchannel assignment problem is formulated to capture the network externalities induced by resource competition among mobile devices with multi-functional requirements. Due to its intractability, we then reformulate it in the matching theoretical manner. To obtain a mutually satisfactory solution under externalities, an iterative matching algorithm is developed by introducing pairwise stability and proved to be convergent and stable. The extensive simulations elucidate the significant superiority of our proposed scheme over those externality-unaware wireless schedulers.

Data service has been considered as one the most prominent characteristics for Industrial Internet of Things (IIoT). This paper studies how to design an optimal computing manner for a general IIoT system. On the theory end, we analyze the relationship between the data processing and the energy consumption through investigating the content correlation of the captured data. Importantly, we derive an exact expression for the performance of IIoT by combining computation with intelligence. On the application end, we design an efficient way to obtain a threshold by approximating the performances of different computing manners, and show how to apply it to practical IIoT applications. We believe that the proposed computation rules hold great significance for the IIoT designer, that is, it is better to use distributed computing manner when the content correlation is high, otherwise, centralized computing manner is better.

This context-aware middleware system facilitates diverse multimedia services in heterogeneous network environments by combining an adaptive service provisioning middleware framework with a context-aware multimedia middleware framework.

The objective of the study is to explore an effective way for providing students with the appropriate learning resources in the remote education scenario. Artificial intelligence (AI) technology and educational psychology theory are applied for designing a personalized online learning resource recommendation scheme to improve students' learning outcomes. First, according to educational psychology, students' learning ability can be obtained by analyzing their learning behaviors. Their identities can be classified into three main groups. Then, features of learning resources such as difficulty degree are extracted, and a LinUCB-based learning resource recommendation algorithm is proposed. In this algorithm, a personalized exploration coefficient is carefully constructed according to student's ability and attention scores. It can adaptively adjust the ratio of exploration and exploitation during recommendation. Finally, experiments are conducted for evaluating the superior performance of the proposed scheme. The experimental results show that the proposed recommendation scheme can find appropriate learning resources which will match the student's ability and satisfy the student's personalized demands. Meanwhile, by comparing with existing state-of-the-art recommendation schemes, the proposed scheme can achieve accurate recommendations, so as to provide students with the most suitable online learning resources and reduce the risk brought by exploration. Therefore, the proposed scheme can not only control the difficulty degree of learning resources within the student's ability but also encourage their potential by providing suitable learning resources.

Cross-modal applications that elaborately integrate audio, video, and haptic streams will become the mainstream of the eHealth systems. However, existing stream schedulers usually fail to simultaneously meet the cross-modal transmission requests in terms of low latency, high reliability, high throughput, and low complexity. To circumvent this dilemma, this paper proposes a general cross-modal stream scheduling scheme by fully taking advantage of the characteristics of different modal streams and their underlying temporal, spatial, and semantic relevance. Specifically, we first propose a hierarchical stream category framework, in which the transmission priority of the modal stream instead of the data flow can be flexibly settled. Next, we design a series of modal-aware stream scheduling schemes by jointly making use of the network slice and mobile edge computing to achieve the tradeoff among the various metrics. Importantly, the transmission strategy can be adjusted adaptively to realize the optimal resource allocation. Subsequently, we analyze the relationship among the user experience, multi-modal impact, and stream scheduling through investigating the interacted impacts among the different modal streams, then develop a user experience based scheduling switch strategy to improve the application generality and reduce the performance fluctuation. Numerical objective and subjective results demonstrate the efficiency of the proposed cross-modal scheduling scheme.