Fog computing is promising for Industrial Internet of Things (IIoT) which are typically large-scale, time-critical, and geo-distributed. However, it needs to address challenges such as scalability and heterogeneity in application provisioning with diverse and strict Quality-of-Service (QoS) requirements from IIoT devices. In this paper, a fog service orchestrator, Q-FSO, is proposed for IIoT application provisioning in which request satisfaction is maximized. With a two-level QoS model that considers key performance metrics for IIoT, including availability, reliability, response time, and cost, Q-FSO can realize large-scale service orchestration by exploiting the advantages of fog computing. Further with a harmonious local QoS assignment model, Q-FSO solves the workflow construction problem by multiple independent local optimization tasks, one for each stage, for the concurrent requests. Each local optimization task can be reduced from the multiple multidimensional knapsack (MMKP) problem. We then propose two practical MMKP heuristic algorithms, namely, Incremental Similarity Matching (ISM) and Greedy Multiple Matching (GMM), to tackle the local QoS assignment problem with polynomial time complexity. The scalability issue in orchestration is then handled by a decentralized workflow construction protocol. The simulation results validate their applicability, demonstrating that Q-FSO is indeed an efficient and effective IIoT application provision mechanism with significant performance benefits in terms of service processing throughput, resource utilization, and speed of service orchestration.

IoT data economy is an emerging concept since sensing data are widely applied in various IoT services. To maintain the stability of data economic system, this paper proposes the Reliable IoT Data Economic System (RIDES) to provide IoT data a reliable, automatic, and intelligent trading environment. Applying the smart contract technique, IoT data in RIDES can be automatically traded without human intervention. As the trading procedure is performed based on blockchain, all transactions can be protected against tampering. Also, the RIDES provides the Smart Decision Making Module for users to develop practical pricing strategies. Thus, a seller can derive a price with the highest expected revenue for its data and a buyer can purchase its desired data referring to cost-performance ratio or data owner's trustworthiness. Even if malicious sellers cheat buyers, the latter can invoke trade dispute procedure to refund their payment, which makes the trading environment more reliable. As the blockchain-based operations usually cost much, the RIDES adopts the edge computing architecture to alleviate the burdens of IoT devices. Finally, the RIDES is implemented in IoT environments. The case study demonstrates that the transactions on IoT data can be performed correctly by RIDES.

Internet of Things (IoT) is an emerging network technology applied to provide various services in our daily life. Generally, IoT environments are composed of numerous heterogeneous devices with constrained resource. The limited capability of IoT devices makes it impractical to perform traditional security mechanisms, and thus IoT services are usually vulnerable to all kinds of security threats, such as impersonation and forgery attacks. Moreover, the inflexible protection provided by these security mechanisms leads to inefficiency because different services haves diverse requirements. To provide IoT services suitable security protection, Multi-graph Zero-knowledge-based Authentication System (M-ZAS), which is not only light-weight but also high-adaptive, is proposed. Compared to traditional authentication mechanisms as well as other Zero-knowledge-proof (ZKP) methods such as GMW-ZKP, M-ZAS provides higher performance and better security protection. In addition, M-ZAS has lower transmission overheads than GMW-ZKP does. Considering relevant contexts as parameters, M-ZAS provides adaptive protection to fulfill what users actually need. Experiment results show that M-ZAS is 3 times faster than GMW-ZKP and even 7 times than traditional authentication mechanisms in IoT devices. Also, M-ZAS reduces 3 times network traffic than GMW-ZKP. Thus, the proposed M-ZAS is the most practical authentication system in IoT environments.

Motion capture (mocap) data normally used to produce a three-dimensional (3D) mesh animation is exploited for watermarking. Copyright protection of the 3D mesh animation obtained is not guaranteed since identical joints in the mocap data are not distinguished easily. A novel robust blind watermarking method is proposed for the copyright protection of 3D mesh animations in the paper. At the beginning, the shape diameter function (SDF) method segments one frame of a 3D animation into several meaningful parts. A watermark is then embedded into these parts by adjusting the geodesic distances. Vertex trajectories are further exploited to modify the geodesic distribution of residual frames to be the same as that of the watermarked frame. Our experimental result demonstrates that the proposed method is able to resist the noise addition, similarity transformation, quantization, cropping, simplification, smoothing, and resampling attacks.

A novel feature region selection method for robust digital image watermarking is proposed in this paper. This method aims to select a nonoverlapping feature region set, which has the greatest robustness against various attacks and can preserve image quality as much as possible after watermarked. It first performs a simulated attacking procedure using some predefined attacks to evaluate the robustness of every candidate feature region. According to the evaluation results, it then adopts a track-with-pruning procedure to search a minimal primary feature set which can resist the most predefined attacks. In order to enhance its resistance to undefined attacks under the constraint of preserving image quality, the primary feature set is then extended by adding into some auxiliary feature regions. This work is formulated as a multidimensional knapsack problem and solved by a genetic algorithm based approach. The experimental results for StirMark attacks on some benchmark images support our expectation that the primary feature set can resist all the predefined attacks and its extension can enhance the robustness against undefined attacks. Comparing with some well-known feature-based methods, the proposed method exhibits better performance in robust digital watermarking.

Pose editing, changing the poses of three-dimensional (3D) mesh model, frequently results in de-synchronization effect which most 3D watermarking methods are sensitive to and normally regard it as an attack. A novel pose-invariant blind watermarking algorithm for 3D triangular mesh model is proposed in this paper to resist the pose changing attack. In this algorithm, the geodesic distance of each vertex pair on the 3D mesh model to be watermarked is calculated, and then this model is segmented into meaningful pose-invariant components based on the calculation result. Each meaningful component is further divided into non-overlapping cells according to the distribution of geodesic distances from its vertices to the cutting boundary. Finally the defined watermark is embedded into these cells by adjusting the mean value of geodesic distances in order to achieve the invariance of pose changing, and later detection follows the same concept. Our experimental result demonstrates that the proposed algorithm has the ability to resist the attacks of pose changing, similarity transformations, vertex reordering, local deformation, cropping, simplification, additive random noise, and smoothing.

In this paper, a feature-based robust digital image watermarking algorithm is proposed to achieve the goal of image authentication and protection simultaneously. The Hessian-Affine feature detector is at first adopted to extract characteristic regions of an image in the proposed watermark embedding scheme. Then the copyright watermark is embedded into these characteristic regions according to the local orientation of each pixel. Moreover, the remainder regions are applied for image authentication by using block-wise fragile watermarking method. Similarly, the proposed watermark detection scheme follows the above procedure to extract the copyright watermark and the authentic information independently and blindly from watermarked images. Various attacks are also applied to the watermarked images in order to examine the robustness of our algorithm. The experimental results in this paper show that the proposed watermarking algorithm can resist most removal and geometric attacks. Besides, changes or modifications of an image will be reflected in our hidden watermarks.