Emergency logistics is of great significance for supply security in emergencies. As a crucial component of strategic material reserve and allocation, emergency logistics is characterized by high levels of safety and efficacy. The security of the IoT-based emergency logistics system cannot be overstated, considering the potential damage caused by malicious accessors. Authentication is an essential means of system security. Existing certificateless authentication protocols are mostly based on bilinear pairings, which cannot match the objectives of emergency logistics networks for lightweight deployment and fast authentication of massive nodes. In this paper, we propose a lightweight certificateless authentication protocol (CL-LAP) without bilinear pairings. The security is determined by the discrete logarithm problem on elliptic curves. Nonlinear pairings guarantee energy efficiency and minimum computation cost. In addition, we use batch verification to reduce the authentication cost and tackle the problem of quick authentication in broadcast messages. We conduct a security analysis on the proposed CL-LAP under the random oracle model to demonstrate that the proposed scheme can withstand common security threats and provide the necessary security for the perception layer. The performance analysis indicates that the suggested scheme is less complex and more efficient than similar schemes with the same level of security.

The ubiquitous 5G-enable industrial Internet of Things interconnects a great number of intelligent sensors and actors. Network management becomes challenging due to massive traffic data generated by industrial equipment. However, the conventional single traffic factor is insufficient for the increasingly complicated network engineering tasks due to the poor representation capability. Besides, the insecure equipment with open communication access easily brings irregular network fluctuations to network traffic which interferes with the primary traffic factor. The simple and interfered traffic factor decreases the network management efficiency and misleads the operators. Motivated by that, we construct a comprehensive tensor model representing multi-dimension traffic factors to describe the network traffic beneficial characteristics. Meanwhile, an adaptive and generic low-rank tensor recovery (AG-LRTR) algorithm in the tensor singular value decomposition (t-SVD) framework is proposed for denoising. For effective tensor recovery, the alternating direction method of multipliers is employed to theoretically solve the partial augmented Lagrangian function of our objective with a closed-form solution. Numerical experiments on both synthetic data and real-world traffic data in IIoT validate that our proposed algorithm outperforms other state-of-the-art of tensor recovery algorithms.

Aiming at utilizing artificial neural networks to enhance intelligent filtering for interfered wireless communication signal in harsh environments, a new method named convolutional neural filtering is designed and presented in this paper. This method is based on model-driven deep learning princeple, by analyzing the theoretical connection between the filter model and the convolutional neural layer, it attempts to use one-dimensional convolution kernels to learn a matched or bandpass filter. Moreover, the model introduces a kernel-wise attention mechanism between different convolution kernels to selectively emphasize informative filters. The results show that in terms of interference and noise suppression for received wireless signal, the filtering method has highlighted dynamic adaptability to variation of signals and interference, and it also reveals that the performance is affected by the initialization parameters and the number of convolution kernels. Based on this method an embeddable filtering unit fully based on neural network is provided, which can be easily integrated into a deep learning network targeting such as wireless signal detection and recognition applications, avoiding complex preprocessing for end-to-end wireless signal learning.

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