Nowadays, network slicing (NS) technology has gained widespread adoption within Internet of Things (IoT) systems to meet diverse customized requirements. In the NS based IoT systems, the detection of equipment failures necessitates comprehensive equipment monitoring, which leads to significant resource utilization, particularly within large-scale IoT ecosystems. Thus, the imperative task of reducing failure rates while optimizing monitoring costs has emerged. In this paper, we propose a monitor application function (MAF) based dynamic dormancy monitoring mechanism for the novel NS-IoT system, which is based on a network data analysis function (NWDAF) framework defined in Rel-17. Within the NS-IoT system, all nodes are organized into groups, and multiple MAFs are deployed to monitor each group of nodes. We also propose a dormancy monitor mechanism to mitigate the monitoring energy consumption by placing the MAFs, which is monitoring non-failure devices, in a dormant state. We propose a reinforcement learning based PPO algorithm to guide the dynamic dormancy of MAFs. Simulation results demonstrate that our dynamic dormancy strategy maximizes energy conservation, while proposed algorithm outperforms alternatives in terms of efficiency and stability.

Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for active volcano monitoring. This paper studies the problem of picking arrival times of primary waves (i.e., P-phases) received by seismic sensors, one of the most critical tasks in volcano monitoring. Two fundamental challenges must be addressed. First, it is virtually impossible to download the real-time high-frequency seismic data to a central station for P-phase picking due to limited wireless network bandwidth. Second, accurate P-phase picking is inherently computation-intensive, and is thus prohibitive for many low-power sensor platforms. To address these challenges, we propose a new P-phase picking approach for hierarchical volcano monitoring WSNs where a large number of inexpensive sensors are used to collect fine-grained, real-time seismic signals while a small number of powerful coordinator nodes process collected data and pick accurate P-phases. We develop a suite of new in-network signal processing algorithms for accurate P-phase picking, including lightweight signal pre-processing at sensors, sensor selection at coordinators as well as signal compression and reconstruction algorithms. Testbed experiments and extensive simulations based on real data collected from a volcano show that our approach achieves accurate P-phase picking while only 16% of the sensor data are transmitted.

To address the drawbacks of continuous partial differential equations, a diffusion method based on spectral graph theory and random walk with restart kernel is proposed, which uses non-subsampled contourlet transform to capture the geometric feature of image. Specifically, a new graph weighting function is constructed based on the geometric feature. Moreover, a second-order random walk with restart kernel was generated. The derivation shows that the proposed method is equivalent to the denoising methods based on partial differential equations. The simulation results demonstrate that the proposed method can effectively reduce Gaussian noise and preserve image edge with superior performance compared with other graph-based partial differential equation methods.