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.

In this letter, we propose a reconfigurable 1-bit reflectarray antenna (RAA ) with a novel varactor diode mounted multi-via mushroom-type (VDMM ) structure for heterogeneous networks (HetNets ). Specifically, its reflection phase and operating frequency band are controlled by changing a reverse bias voltage applied to a varactor diode mounted on the unit cell. Moreover, the proposed VDMM structure reduces frequency dependency in each operating frequency band by introducing multiple vias into the metasurface. As a result, the 1-bit RAA with the proposed VDMM structure can adaptively control the reflection direction and operating frequency band.

In the Industrial Internet of Things (IIoT), vision-based industrial detection technology is crucial in the production process and can be used in many smart manufacturing applications, such as automated production control and Non-Destructive Evaluation (NDE). To enable timely and accurate decision-making, the network must transmit product status information to the server under stringent requirements of ultra-reliability and low latency. However, traditional pixel-centric industrial image transmission consumes additional bandwidth, and existing deep learning-based semantic communication systems rely on costly manual annotations. To overcome these limitations, this paper proposes a novel object-centric semantic communication framework based on improved slot attention for Multiple-Input Multiple-Output (MIMO) transmission in a 6G smart manufacturing scenario. First, we propose an improved slot attention method based on unsupervised learning for real-world manufacturing image datasets. The proposed method decouples complex industrial images into different object instances, each corresponding to an independent semantic component slot, effectively isolating task-related visual targets from redundant backgrounds. Furthermore, we propose a priority-based semantic transmission strategy. By quantifying the task-relevant importance of each semantic slot and jointly matching MIMO sub-channels, our method optimizes industrial image transmission streams, ensuring the reliable transmission of the important semantic information. Extensive simulation results demonstrate that the proposed framework significantly enhances communication transmission efficiency. Even under constrained bandwidth ratios and a low Signal-to-Noise Ratio (SNR), our framework achieves superior visual reconstruction quality and improves the Peak Signal-to-Noise Ratio (PSNR) by 4.25 dB compared to existing benchmarks.