Compressed sensing (CS) can substantially enhance the transmission efficiency of wireless sensor networks (WSNs). To tackle the difficulties of high transmission delay and reconstruction failure caused by compressible measurements loss, this article proposes a measurements–residuals collaboration sparse reconstruction (MCSR). First, the acquisition node performs embedded compressed sampling (ECS) to improve transmission efficiency. The measurements obtained from the ECS are transmitted wirelessly to the gateway node, and can be random lost owing to unstable communication link. In addition, sensing matrix adaptive matching is proposed to address the mismatch between the dimensions of the missing measurements and the dimensions of the sensing matrix resulting in a failure of the reconstruction, providing a basis for subsequent effective reconstruction. Moreover, learning dictionary-based split Bregman iteration (SBI-LD) sparse reconstruction algorithm is adopted to realize the initial signal reconstruction based on the effective measurements obtained from wireless transmission. Furthermore, based on the initial reconstruction signal, the learning dictionary-based residuals reconstruction algorithm is proposed to obtain the reconstructed signal of the measurement residuals. Finally, the experimental results demonstrate that the proposed algorithm achieves higher reconstruction accuracy, compared with other popular methods. This provides a solution of great significance for efficient and reliable mechanical vibration monitoring in WSN.

The edge recognition of mechanical faults using deep learning models requires the deployment and operation of neural networks, which consume a large amount of memory. However, edge devices have limited memory. To address the problem, a neural network micromemory control strategy is proposed. This strategy addresses the memory resource constraints through process quantization and byte indexing while maintaining the model structure and accuracy. First, the neural network parameters with Gaussian and uniform distributions are tested and corrected to achieve process quantization. Then, a parameter memory control is implemented to achieve low-memory deployment and runtime. Subsequently, a bytes indexing is proposed for parameter location reading, enabling low-memory process inversed quantization. Finally, temporary variables is computed through micro memory overlay. This memory control method using process quantization and bytes indexing not only allows for ultra-low memory deployment and operation of neural networks but also improves test accuracy. It achieves over 97% test accuracy and an edge computation speed of over 200 KB/s with a maximum of 3.89 KB of runtime memory.

In wireless sensor networks (WSNs) for mechanical vibration monitoring systems, the network topology is initially in equilibrium. However, it progressively unbalances over time owing to the high sample frequency and large amount of data transmitted by each node. This can result in different remaining energy levels of each node and shorten the system lifetime. Therefore, dynamic adjustment of the topology is crucial to balance the networks in a lifetime cycle. This article proposes a time–space incentives control algorithm to dynamically improve the topology equilibrium for WSNs used in mechanical vibration monitoring systems. In the proposed algorithm, multiple relevant parameters that influence the network topology are designed and adopted. Two calculation methods involving expert experience and the data driver approach are devised to determine the parameter weights. A static evaluation model is constructed to measure the performance of the network topology in the space dimension. Based on this model, the dynamic topological equilibrium control algorithm and strategy are proposed considering time–space incentives. Finally, the transmission power of each node is changed by considering the comprehensive evaluation value, and the network topology is dynamically adjusted. The proposed algorithm constitutes an advanced approach because it comprehensively integrates the various parameters affecting the equilibrium of the network topology in the time and space dimensions. The experimental results indicate that the time–space incentives model contributes significantly to the dynamic improvement of the network topology equilibrium.

This article proposes a novel edge collaborative compressed sensing for mechanical vibration monitoring (MVM) to address the severe shortage of storage and computational resources and long delay in transmitting massive vibration data in wireless sensor networks (WSN) for MVM. It first combines compressed sensing and edge computing (EC) into a WSN to effectively solve the above-mentioned problems. Based on the self-developed acquisition node (AN) and EC node (ECN), the proposed approach is implemented in the AN and ECN, respectively. This enhances the acquisition efficiency and computing capacity of the WSN. Meanwhile, the sparse pattern of a mechanical fault signal is analyzed. In addition, the adaptive parameter adjustment mechanism for the alternating direction of multiplier method based on Anderson acceleration is proposed to generate a convex optimization algorithm with fast convergence to realize data reconstruction in the ECN. The results of comprehensive experiments demonstrate that the proposed method can reduce the transmission data by 70%, while maintaining high-precision bearing fault feature detection, compared with the Shannon sampling theory. Furthermore, the acquisition and transmission efficiencies are improved significantly. This provides a potential solution for practical engineering applications.