The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Especially, some common algorithms in IIoT technology, such as deep models, strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this article, we conduct a comprehensive survey on the opportunities, applications, and challenges of differential privacy in IIoT. We first review related papers on IIoT and privacy protection, respectively. Then, we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up research on industrial differential privacy.

This paper suggests a multimodal multi-objective evolutionary algorithm with dual clustering in decision and objective spaces. One clustering is run in decision space to gather nearby solutions, which will classify solutions into multiple local clusters. Non-dominated solutions within each local cluster are first selected to maintain local Pareto sets, and the remaining ones with good convergence in objective space are also selected, which will form a temporary population with more than N solu-tions (N is the population size). After that, a second clustering is run in objective space for this temporary population to get N final clusters with good diversity in objective space. Finally, a pruning process is repeatedly run on the above clusters until each cluster has only one solution, which removes the most crowded solution in decision space from the most crowded cluster in objective space each time. This way, the clustering in decision space can distinguish all Pareto sets and avoid the loss of local Pareto sets,while that in objective space can maintain diversity in objective space. When solving all the benchmark problems from the competition of multimodal multi-objective optimization in IEEE Congress on Evolutionary Computation 2019, the experi-ments validate our advantages to maintain diversity in both objective and decision spaces.

The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications. However, the presence of rogue IoT devices has exposed the IoT to untold risks with severe consequences. The first step in securing the IoT is detecting rogue IoT devices and identifying legitimate ones. Conventional approaches use cryptographic mechanisms to authenticate and verify legitimate devices’ identities. However, cryptographic protocols are not available in many systems. Meanwhile, these methods are less effective when legitimate devices can be exploited or encryption keys are disclosed. Therefore, noncryptographic IoT-device identification and rogue device detection become efficient solutions to secure existing systems and will provide additional protection to systems with cryptographic protocols. Noncryptographic approaches require more effort and are not yet adequately investigated. In this article, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators. We classify the IoT-device identification and detection into four categories: 1) device-specific pattern recognition; 2) deep learning-enabled device identification; 3) unsupervised device identification; and 4) abnormal device detection. Meanwhile, we discuss various ML-related enabling technologies for this purpose. These enabling technologies include learning algorithms, feature engineering on network traffic traces and wireless signals, incremental learning, and abnormality detection.

With the exponentially growth in the number of Internet of Things (IoT) devices, the cloud-centric computing paradigm can hardly meet the increasingly high requirements for low-latency, high-bandwidth, easily available and more intelligent services. Therefore, a distributed and decentralized computing architecture is imperative, where edge-centric computing, such as fog computing and mist computing, is recently proposed. Edge-centric computing resources can be managed locally and personally rather than being administered by a remote centralized third-party. However, security and privacy issues are the main challenges due to the absence of trust between the IoT devices and edge computing nodes. A blockchain, as a decentralized, trustless and immutable public ledger, can well solve the trust-absence issue. In this paper, we first elaborate on the security and privacy issues of edge computing-enabled IoT, and then present the key characteristics of blockchains, which make blockchains well-suited for the edge-centric IoT scenarios. Furthermore, we propose a general framework for blockchain-based edge computing-enable IoT scenarios that specifies the step-by-step procedure of a single transaction between an IoT end and an edge computing node. In addition, we design a smart contract within a private blockchain network that exploits the-state-of-the-art machine learning algorithm, Asynchronous Advantage Actor-Critic (A3C), to allocate the edge computing resources, which exemplifies how artificial intelligence (AI) can be combined with blockchains. We further discuss the benefits of the convergence of AI and blockchains. Finally, simulation results are presented.

With wide adoption of Internet of Things across the world, the IoT devices are facing more and more intensive computation task nowadays. However, the IoT devices are usually limited by their computing capability and battery lifetime. Mobile Edge Computing provides new opportunities for developments of IoT, since edge computing servers which are close to devices can provide more powerful computing resources. The IoT devices can offload the intensive computing tasks to edge computing servers, while saving their own computing resources and reducing energy consumption. However, the benefits come at the cost of higher latency, mainly due to additional transmission time, and it may be unacceptable for many IoT applications. In this paper, we try to find a trade-off between energy consumption and latency, in order to satisfy user demands of various IoT applications. We formalize the problem into a constrained multi-objective optimization problem and find the optimal solutions by a modified fast and elitist nondominated sorting genetic algorithm (NSGA-II). To improve performance of the algorithm, we propose a novel problem-specific encoding scheme and genetic operators in the proposed modified NSGA-II. We also conduct extensive simulation experiments to evaluate the proposed algorithm and its sensitivity under certain major parameters. The experimental results show that the proposed algorithm can find a large number of optimal solutions to adjust the corresponding offloading decision according to the real-world situation.