Indoor/Outdoor (IO) detection (IOD) using Wi-Fi- and smartphone-based technologies is in high demand and interest in both the industrial and research fields. This paper proposes a novel and effective hybrid IOD (HIOD) approach for detecting a smartphone user's IO location. The HIOD approach uses signals received from both Wi-Fi and GPS as well as the latest positioning technologies such as multilateration, fingerprinting and machine learning. This paper proposes and implements two-level signal-to-noise ratio (SNR) threshold parameters for the first time, which are specifically derived from GPS signals through 42 empirical tests at seven test sites with adequate environmental factors considered. Using the newly derived IOD threshold parameters and a set of IO detection rules, the HIOD approach is then tested at 20 test points (TPs) in a city canyon area, where most of the TPs are under semi-indoor or semi-outdoor conditions. The final test results show that a 100% IOD rate is achieved.

With the evolution of the 5th generation (5G) and the advent of the 6th generation (6G) mobile communication technologies, the consumer Internet of Things (CIoT) has emerged as a critical enabler for ubiquitous information exchange across diverse applications. The integration of sensing and communication (ISAC) offers significant potential to enhance communication performance by leveraging multimodal heterogeneous data collected through sensors in CIoT systems. This paper presents a comprehensive discussion on multimodal heterogeneous data-based environment sensing and its integration with communication in CIoT systems. We provide an overview of multimodal ISAC in CIoT, addressing the need for advanced environment understanding and optimization of network performance. Furthermore, we introduce a novel ISAC framework that integrates semantic environmental mapping, beam management, and resource allocation to improve communication efficiency. The concept of a semantic environment map (SEM) is proposed, which fuses multimodal sensing data for precise environment recognition and adaptive communication strategies. Additionally, we identify key future trends and challenges, including performance evaluation, multimodal data transmission, and security, and then propose a roadmap for future research. This paper aims to bridge the gap between multimodal sensing and communication in CIoT systems, offering insights into integrating advanced technologies such as artificial intelligence (AI) and 6G for smarter, more connected consumer applications.

To address the challenges of complex low-altitude flight environments for UAVs, where numerous obstacles often lead to GPS signal obstruction and multipath effects, this study proposes an integrated inertial navigation and GPS positioning approach for coordinated cross-path planning in drone swarms. The methodology involves the following: (1) discretizing continuous 3D airspace into grid cells using occupancy grid mapping to construct an environmental model; (2) analyzing dynamic flight characteristics through attitude angle variations in a 3D Cartesian coordinate system; and (3) implementing collaborative state updates and global positioning through fused inertial–GPS navigation. By incorporating Cramér–Rao lower bound optimization, the system achieves effective cross-path planning for drone formations. Experimental results demonstrate a 98.35% mission success rate with inter-drone navigation time differences maintained below 0.5 s, confirming the method’s effectiveness in enabling synchronized swarm operations while maintaining safe distances during cooperative monitoring and low-altitude flight missions. This approach demonstrates significant advantages in coordinated cross-path planning for UAV clusters.