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The Internet of Things (IoT) suffers from a profound lack of trust between central gateways and sensors, e.g., gateways suspect sensors of flooding malicious packets, and vice versa, sensors suspect gateways of manipulating traffic data. One important reason for the mistrust is the asymmetry of a centralized network organization. A Decentralized Autonomous Organization (DAO) can establish a trustful and symmetric network with the blockchain. However, it is a vacant area for IoT networks to build trust between gateways and sensors within the DAO. In this paper, we firstly propose a trustful and secure IoT Network DAO solution (NetDAO) to mitigate the data manipulation and the malicious flooding packets. In particular, the NetDAO has a security rating algorithm to assign a reputation value for each entity in the network. Based on this, each entity can mitigate the malicious flooding packets using a proof-of-reputation packet-forwarding mechanism. In addition, the NetDAO stores traffic data using the blockchain to mitigate the data manipulation. The experimental results show that the NetDAO effectively mitigates malicious flooding packets and costs 1 s for ∼480 entities to complete the rating algorithm.

Different from previous generations of communication technology, 5G has tailored several modes especially for industrial applications, such as Ultra-Reliable Low-Latency Communications (URLLC) and Massive Machine Type Communications (mMTC). The industrial private 5G networks require high performance of latency, bandwidth, and reliability, while the deployment environment is usually complicated, causing network problems difficult to identify. This poses a challenge to the operation and maintenance (O&M) of private 5G networks. It is needed to quickly diagnose or predict faults based on high-dimensional data of networks and services to reduce the impact of network faults on services. This paper proposes a ConvAE-Latency model for anomaly detection, which enhances the correlation between target indicators and hidden features by multi-target learning. Meanwhile, transfer learning is applied for anomaly prediction in the proposed LstmAE-TL model to solve the problem of unbalanced samples. Based on the China Telecom data platform, the proposed models are deployed and tested in an Automated Guided Vehicles (AGVs) application scenario. The results have been improved compared to existing research.

Quantum Key Distribution (QKD) is a promising paradigm for Internet of Things (IoT) networks against eavesdropping attacks. However, classical quantum-based mechanisms are overweight and expensive for resource-constrained IoT devices. That is, the devices need to frequently exchange with the QKD controller via an out-band quantum channel. In this paper, we propose a novel Quantum-based Secure and Lightweight Transmission (QSLT) mechanism to ease the overweight pain for IoT devices against eavesdropping. Particularly, the mechanism predistributes quantum keys into IoT devices with SIM cards. Using one of the keys, QSLT encrypts or decrypts IoT sensitive data. It is noting that an in-band key-selection method is used to negotiate the session key between two different devices. For example, on one IoT device, the in-band method inserts a key-selection field at the end of the encrypted data to indicate the key’s sequence number. After another device receives the data, QSLT extracts the key-selection field and decrypts the data with the selected quantum key stored locally. We implement the proposed mechanism and evaluate its security and transmission performances. Experimental results show that QSLT can transmit IoT data with a lower delay while guaranteeing the security performance. Besides, QSLT also decreases power usage by approximately 58.77% compared with state of the art mechanisms.