Cloud-edge collaboration, as an emerging computing paradigm, aims to solve the shortcomings of remote transmission of conventional cloud computing. More precisely, it combines the powerful resource service capability of cloud computing with the advantages of low latency and relatively low energy consumption of edge computing to achieve the goal of optimization of various applications. However, with the rapid growth of computation-intensive industrial tasks, the overload problem of edge networks is becoming increasingly serious. Prior studies usually assume that the real-time state of edge resources has been known when selecting the offloading strategy so as to classify and execute tasks, but do not consider the fragmentation and heterogeneity features of edge computing resources. In light of these, we first generalize and model the computing resources of the edge nodes uniformly and then propose new heterogeneous task classification and recognition methods empowered by edge intelligence. We conduct intensive experiments to justify that our proposed design can minimize the data transmission delay caused by repeated computational tasks while saving energy consumption.

Surface inspection of industrial equipment defection plays a vital role in real production. Traditional inspection routines require a large number of inspection workers, which not only affects production efficiency but also leads to unreliable results. Computer vision-based detection approaches, e.g., using the deep learning method, have shown great potential in this trend. Specifically, the semantic segmentation algorithm based on Convolutional Neural Network (CNN) can extract relatively complete feature information. And the Transformer, which emerged from the field of Natural Language Processing (NLP), also performs well in maintaining and transmitting semantic information. In light of these, we propose to design a segmentation model called eSwin-UNet, i.e., enhanced Swin-UNet, that leverages the advantages of the CNN and Transformer. It uses multi-scale information fusion to better integrate the feature information in the CNN and Transformer branches. Moreover, it also utilizes deep supervision and makes two branches for collaborative training to further improve accuracy. By testing with the MVTec ITODD dataset, Fl-Score and Jaccard achieve results of 0.7891 and 0.6516 respectively, which outperform most current models.

The Industrial Internet of Things (IIoT) is deemed a promising direction to drive a new industrial revolution. However, due to the isolation of the existing OT network and IT network, the requirements of low latency, low jitter, and high reliability for transmission and processing of industrial time-sensitive tasks data traffic in IIoT scenarios with strong dynamic and complex topology face a series of non-trivial challenges. In this paper, we propose a hierarchical computing network collaboration architecture for IIoT based on edge/fog computing. Our architecture is built upon the Time-Sensitive Networking (TSN) to flexibly support different requirements of large-scale industrial production applications by constructing the hierarchical computing network collaboration domain, combined with an improved Cyclic Queuing and Forwarding (CQF) scheduling shaper mechanism. We tackle the critical problems of architecture design by presenting three essential components. Moreover, we build and implement our simulation testbed based on Omnet++, and evaluate our design.

As a popular privacy-preserving model training technique, Federated Learning (FL) enables multiple end-devices to collaboratively train Deep Neural Network (DNN) models without exposing local privately-owned data. According to the FL paradigm, resource-constrained end-devices in IoT should perform model training which is computation-intensive, whereas the edge server occupied with powerful computation capability only performs model aggregation. Due to the above unbalanced computation pattern, IoT-oriented FL is time-consuming and inefficient. In order to alleviate the computation burden of end-devices, recent countermeasures introduce the edge server to assist end-devices in model training. However, existing works neither efficiently address the computation heterogeneity across end-devices nor reduce the leakage risk of data privacy. To this end, we propose a Federated Synergy Learning (FedSyL) paradigm which innovatively strikes a balance between training efficiency and data leakage risk. We explore the complicated relationship between the local training latency and multi-dimensional training configurations, and design a uniform training latency prediction method by applying the polynomial quadratic regression analysis. Additionally, we design the optimal model offloading strategy with the consideration of resource limitation and computation heterogeneity of end-devices, so as to accurately assign capability=matched device-side sub-models for heterogeneous end-devices. We implement FedSyL on a real test-bed comprising multiple heterogeneous end-devices. Experimental results demonstrate the superiority of FedSyL on training efficiency and privacy protection.

Anti-piracy is fundamentally a procedure that relies on collecting data from the open anonymous population, so how to incentivize credible reporting is a question at the center of the problem. Industrial alliances and companies are running anti-piracy incentive campaigns, but their effectiveness is publicly questioned due to the lack of transparency. We believe that full transparency of a campaign is necessary to truly incentivize people. It means that every role, e.g., content owner, licensee of the content, or every person in the open population, can understand the mechanism and be assured about its execution without trusting any single role. We see this as a distributed system problem. In this paper, we present Argus, a fully transparent incentive system for anti-piracy campaigns. The groundwork of Argus is to formulate the objectives for fully transparent incentive mechanisms, which securely and comprehensively consolidate the different interests of all roles. These objectives form the core of the Argus design, highlighted by our innovations about a Sybil-proof incentive function, a commit-and-reveal scheme, and an oblivious transfer scheme. In the implementation, we overcome a set of unavoidable obstacles to ensure security despite full transparency. Moreover, we effectively optimize several cryptographic operations so that the cost for a piracy reporting is reduced to an equivalent cost of sending about 14 ETH-transfer transactions to run on the public Ethereum network, which would otherwise correspond to thousands of transactions. With the security and practicality of Argus, we hope real-world anti-piracy campaigns will be truly effective by shifting to a fully transparent incentive mechanism.

Blockchain is multi-centralized, immutable and traceable, thus is very suitable for distributed storage, privacy and security management in IoTs. However, most existing researches focus on the integration of public blockchain and IoTs. In fact, problems such as slow consensus, low transmission throughput, and completely open storage on the public blockchain are intolerable in IoT scenarios. Although consortium blockchain represented by Hyperledger Fabric has improved the transmission rate, its data security completely relies on the PKI-based certificate mechanism, resulting in transmission inefficiency and privacy leakage. In this paper, a key-derived Controllable Lightweight Secure Certificateless Signature (CLS2) algorithm is proposed to significantly improve the transmission efficiency and keep similar computation overhead of consortium blockchain. Compared with the existing certificateless signatures, CLS2achieves more secure transactions, whose controllable anonymity and key-derived mechanism not only prevents public key replacement attacks and forged signature attacks, but also supports hierarchical privacy protection. Armed with CLS2, we design a consortium blockchain security architecture based on Hyper-ledger Fabric and edge computing. To the best of our knowledge, this is the first implementation of certificateless signature in consortium blockchain. We formally prove the security of our schemes in the random oracle model. Specifically, the security of the proposed scheme is reduced to the Elliptic curve discrete logarithm problem (ECDLP). Security analysis and experiments in IoT scenarios verify the feasibility and effectiveness of CLS2.

In distributed Internet of Things (IoTs), channel hopping (CH) is an effective scheme for neighbor nodes to achieve blind rendezvous over common available channels and to establish communication links. When nodes are unaware of each other's local clocks and the global channels and have no pre-assigned CH strategies or identifiers (IDs), it is particularly challenging to guarantee blind rendezvous within a finite period of time, which has not been solved yet by using only one radio. In this paper, we propose a novel quaternary-encoding-based CH (QECH) algorithm to tackle the above issue. The QECH algorithm encodes a randomly selected channel into a quaternary string according to the 6B/8B encoding. We also append a common prefix string as well as the randomly selected channel before the quaternary string to guarantee overlaps in the asynchronous scenario. For all kinds of quaternary digits, we construct four mutually co-prime numbers to enumerate all possible combinations of the common available channels. We theoretically analyze the deterministic rendezvous principle and the upper bounded rendezvous latency of the QECH algorithm. We also verify the effectiveness of the QECH algorithm through extensive simulations. Evaluation results show the superiority of the QECH algorithm in terms of rendezvous latency.