Situational awareness (SA) is defined as the perception of entities in the environment, comprehension of their meaning, and projection of their status in near future. From an Air Force perspective, SA refers to the capability to comprehend and project the current and future disposition of red and blue aircraft and surface threats within an airspace. In this article, we propose a model for SA and dynamic decision-making that incorporates artificial intelligence and dynamic data-driven application systems to adapt measurements and resources in accordance with changing situations. We discuss measurement of SA and the challenges associated with quantification of SA. We then elaborate a plethora of techniques and technologies that help improve SA ranging from different modes of intelligence gathering to artificial intelligence to automated vision systems. We then present different application domains of SA including battlefield, gray zone warfare, military- and air-base, homeland security and defense, and critical infrastructure. Finally, we conclude the article with insights into the future of SA.

While Internet of Things (IoT) technology has been widely recognized as an essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today’s AC is the centralized authorization server, which can cause a performance bottleneck or be the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable effective protection for devices, services and information in large-scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registration, propagation, and revocation of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI nodes) and more powerful computing devices (i.e., laptops) and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems.

The fast development of the Internet of Things (IoT) and deep learning enables learning useful patterns from the massive amount of collected data with sporadic nodes in IoT systems. Federated learning has received increasing attention in distributed machine learning where only intermediate parameters are exchanged with training samples that resided at local nodes. Nonetheless, most of the existing federated learning schemes assume a homogeneous distribution of data. The assumption, however, does not apply to IoT systems because of the heterogeneity of the IoT architecture. The nonindependent and identical distribution (non-IID) property in data volume and statistical distribution of IoT nodes can impact the performance of an aggregated global model that fits all nodes. Existing federated learning solutions for non-IID data sets either have to train additional models or require extra data exchange to check the node distribution. However, due to resource constraints in IoT systems, these approaches will increase the burden on limited computation capacity and cause network overhead. To address the issue, in this article, a novel weight-similarity-based client clustering (WSCC) approach is proposed, in which clients are split into different groups based on their data set distributions. An affinity-propagation-based method with the cosine distance of the client’s weight parameters is designed to iteratively and automatically determine dynamic clusters. The proposed approach is ideal for IoT systems since there are no auxiliary models and extra data transmissions are needed. Through the theoretical convergence analysis and empirical results, we show that our proposed WSCC scheme outperforms the representative federated learning schemes under different non-IID settings, achieving up to 20% improvements in accuracy.

Artificial intelligence (AI), owing to recent breakthroughs in deep learning, has revolutionized applications and services in almost all technology domains including aerospace. AI and deep learning rely on huge amounts of training data that are mostly generated at the network edge by Internet of Things (IoT) devices and sensors. Bringing the sensed data from the edge of a distributed network to a centralized cloud is often infeasible because of the massive data volume, limited network bandwidth, and real-time application constraints. Consequently, there is a desire to push AI frontiers to the network edge toward utilizing the enormous amount of data generated by IoT devices near the data source. The merger of edge computing and AI has engendered a new discipline, that is, AI at the edge or edge intelligence. To help AI make sense of gigantic data at the network edge, data fusion is of paramount significance and goes hand in hand with AI. This article focuses on data fusion and AI at the edge. In this article, we propose a framework for data fusion and AI processing at the edge. We then provide a comparative discussion of different data fusion and AI models and architectures. We discuss multiple levels of fusion and different types of AI, and how different types of AI align with different levels of fusion. We then highlight the benefits of combining data fusion with AI at the edge. The methods of AI and data fusion at the edge detailed in this article are applicable to many application domains including aerospace systems. We evaluate the effectiveness of combined data fusion and AI at the edge using convolutional neural network models and multiple hardware platforms suitable for edge computing. Experimental results reveal that combining AI with data fusion can impart a speedup of 9.8× while reducing energy consumption up to 88.5% over AI without data fusion. Furthermore, results demonstrate that data fusion either maintains or improves the accuracy of AI in most cases. For our experiments, data fusion imparts a maximum improvement of 15.8% in accuracy to AI.

The metaverse concept has been evolving from static, pre-rendered virtual environments to a new frontier: the real-time metaverse. This survey paper explores the emerging field of real-time metaverse technologies, which enable the continuous integration of dynamic, real-world data into immersive virtual environments. We examine the key technologies driving this evolution, including advanced sensor systems (LiDAR, radar, cameras), artificial intelligence (AI) models for data interpretation, fast data fusion algorithms, and edge computing with 5G networks for low-latency data transmission. This paper reveals how these technologies are orchestrated to achieve near-instantaneous synchronization between physical and virtual worlds, a defining characteristic that distinguishes the real-time metaverse from its traditional counterparts. The survey provides a comprehensive insight into the technical challenges and discusses solutions to realize responsive dynamic virtual environments. The potential applications and impact of real-time metaverse technologies across various fields are considered, including live entertainment, remote collaboration, dynamic simulations, and urban planning with digital twins. By synthesizing current research and identifying future directions, this survey provides a foundation for understanding and advancing the rapidly evolving landscape of real-time metaverse technologies, contributing to the growing body of knowledge on immersive digital experiences and setting the stage for further innovations in the Metaverse transformative field.