Autonomous exploration in unknown environments is a fundamental capability for intelligent mobile IoT systems, especially in scenarios where prior environmental information is unavailable. In such settings, mobile IoT devices are required to achieve safe and efficient full-area coverage based solely on onboard sensing and limited computational resources. However, the unpredictable and continuously changing nature of unknown environments poses significant challenges to adaptive and collision-free exploration, particularly for resource-constrained mobile IoT devices. To address these challenges, we propose an autonomous full-area exploration algorithm for mobile IoT devices based on reward strategy cloning. Specifically, a state representation method using color mapping is designed to improve the information intensity of input state in full-area coverage exploration missions. Simultaneously, to address the issue of sparse rewards in full-area exploration missions, we construct an intensive reward shaping function that integrates exploration rewards, collision penalties, and incentives for exploring frontier trends. Furthermore, a lightweight exploration model that maps state to action reward is designed for mobile IoT devices with limited computing power and storage resources. Moreover, we propose a reward-sensitive dynamic ϵ-greedy strategy that adaptively balances exploration and exploitation based on real-time performance trends. Finally, empirical results demonstrate the robustness of the proposed algorithm in exploring various complexities and dynamic environments. In particular, the computational complexity of the proposed exploration model is significantly reduced compared to other models.

Integrated sensing and communication (ISAC) is emerging as a key technology and research focus for future vehicular communication networks (VCN). It achieves efficient reuse of wireless infrastructure and spectrum resources through the collaborative design of sensing and communication functionalities. However, this integration leads to inevitable mutual interference, and the complexity of channel conditions further complicates the coordination of these functionalities. This paper primarily focuses on the joint sensing-communication performance analysis of ISAC-enabled VCN. Specifically, we model the spatial distribution of roads using a Poisson line process, while the locations of vehicles and roadside units (RSUs) are represented by two one-dimensional Poisson point processes. We characterize the dynamic interference distribution caused by RSUs during the sensing and communication phases and calculate the probability of successful perception (PSP) for a typical pair. Furthermore, for this typical pair, we meticulously derive the communication coverage probability based on the derived PSP for such a pair. To provide a detailed analysis of the interaction between communication and sensing functionalities, we evaluate their trade-off relationship and derive the joint probability of ISAC coverage. Moreover, we perform comprehensive simulations to verify the theoretical results. Additionally, the numerical results demonstrate how different parameters impact the network performance, providing guidance for network deployment and resource allocation under certain performance requirements.

With the ubiquitous deployment of mobile sensors and smart devices, the scope of Internet of things (IoT) has extended to the space of mobile networks, where IoT terminals are moving around instead of being fixed in buildings, ground infrastructures, etc. In this paper, we consider such mobile Internet of things (MIoT), and propose an autonomous rate control (RC) scheme for the uplink transmission from MIoT terminals to access stations. A deep reinforcement learning (DRL) based approach is designed to capture the channel variations of the link and to improve the effectiveness of the rate selection for each egress frame. Extensive simulations are conducted for MIoT terminals including vehicles and UAVs and show significant throughput performance improvement comparing with traditional methods, as well as the robustness and scalability of the DRL-RC algorithm. The proposed DRL-RC can provide inspirations for efficient and scalable link adaptation schemes for MIoT terminals.