Innovation in manufacturing through digital technologies and applications: Thoughts and Reflections on Industry 4.0

The rapid pace of developments in digital technologies offers many opportunities to increase the efficiency, flexibility and sophistication of manufacturing processes; including the potential for easier customisation, lower volumes and rapid changeover of products within the same manufacturing cell or line. A number of initiatives on this theme have been proposed around the world to support national industries under names such as Industry 4.0 (Industrie 4.0 in Germany, Made-in-China in China and Made Smarter in the UK). This book presents an overview of the state of art and upcoming developments in digital technologies pertaining to manufacturing. The starting point is an introduction on Industry 4.0 and its potential for enhancing the manufacturing process. Later on moving to the design of smart (that is digitally driven) business processes which are going to rely on sensing of all relevant parameters, gathering, storing and processing the data from these sensors, using computing power and intelligence at the most appropriate points in the digital workflow including application of edge computing and parallel processing. A key component of this workflow is the application of Artificial Intelligence and particularly techniques in Machine Learning to derive actionable information from this data; be it real-time automated responses such as actuating transducers or informing human operators to follow specified standard operating procedures or providing management data for operational and strategic planning. Further consideration also needs to be given to the properties and behaviours of particular machines that are controlled and materials that are transformed during the manufacturing process and this is sometimes referred to as Operational Technology (OT) as opposed to IT. The digital capture of these properties and behaviours can then be used to define so-called Cyber Physical Systems. Given the power of these digital technologies it is of paramount importance that they operate safely and are not vulnerable to malicious interference. Industry 4.0 brings unprecedented cybersecurity challenges to manufacturing and the overall industrial sector and the case is made here that new codes of practice are needed for the combined Information Technology and Operational Technology worlds, but with a framework that should be native to Industry 4.0. Current computing technologies are also able to go in other directions than supporting the digital ‘sense to action’ process described above. One of these is to use digital technologies to enhance the ability of the human operators who are still essential within the manufacturing process. One such technology, that has recently become accessible for widespread adoption, is Augmented Reality, providing operators with real-time additional information in situ with the machines that they interact with in their workspace in a hands-free mode. Finally, two linked chapters discuss the specific application of digital technologies to High Pressure Die Casting (HDPC) of Magnesium components. Optimizing the HPDC process is a key task for increasing productivity and reducing defective parts and the first chapter provides an overview of the HPDC process with attention to the most common defects and their sources. It does this by first looking at real-time process control mechanisms, understanding the various process variables and assessing their impact on the end product quality. This understanding drives the choice of sensing methods and the associated smart digital workflow to allow real-time control and mitigation of variation in the identified variables. Also, data from this workflow can be captured and used for the design of optimised dies and associated processes

An Improved Switch Migration Decision Algorithm for SDN Load Balancing

Dynamic and Adaptive Load Balancing (DALB) and Controller Adaption and Migration Decision (CAMD) frameworks are the recently developed efficient controller selection frameworks that solved the challenge of load-imbalance in Software-Defined Networking (SDN). While CAMD framework was established to be efficient over DALB framework yet it was not efficient when the incoming-traffic load was elephant flow, hence, leading to a significant reduction in the overall system performance. This study had proposed an Improved Switch Migration Decision Algorithm (ISMDA) that solved the network challenge when the incoming load is elephant flow. The balancing module of the switch migration framework, which runs on each controller, is initiated during the controller load imbalance phase. The improved framework used the controller variance and controller average load status to determine the set of underloaded controllers in the network. The constructed efficient migration model was used to, simultaneously, identify both the migration cost and load-balancing variation for the optimal selection of controller among the set of underloaded controllers. The controller throughput, response time, number of migration space and packet loss were used as the performance comparison metrics. The average controller throughput of ISMDA increased with 7.4% over CAMD framework while average response time of the proposed algorithm improved over CAMD framework with 5.7%. Similarly, the proposed framework had 5.6% average improved migration space over CAMD framework and the packet-loss of ISMDA had average 6.4% performance over the CAMMD framework. It was concluded that ISMDA was efficient over CAMD framework when the incoming traffic load is elephant flow

An Adapted Nondominated Sorting Genetic Algorithm III (NSGA-III) With Repair-Based Operator for Solving Controller Placement Problem in Software-Defined Wide Area Networks

Optimum controller placement in the presence of several conflicting objectives has received significant attention in the Software-Defined Wide Area Network (SD-WAN) deployment. Multi-objective evolutionary algorithms, like Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Particle Swamp Optimization (MOPSO), have proved helpful in solving Controller Placement Problem (CPP) in SD-WAN. However, these algorithms were associated with the challenge of scalability (when there are more than three objectives) for optimization in the SD-WAN. Hence, this study proposed an adapted NSGA-III (A-NSGA-III) to resolve the scalability challenges associated with NSGA-II and MOPSO algorithms in the presence of more than three objectives. This study developed and introduced a repair-based operator into the existing Mechanical Engineering based NSGA-III to propose the A-NSGA-III for optimal controller placement in the SD-WAN. The proposed A-NSGA-III, the NSGA-II and MOPSO algorithms were subjected to evaluation using datasets from Internet2 OS3E WAN topology with six objective functions. The Hypervolume indicator, Percentage Coefficient of Variation (PCV), the percentage difference and the Parallel Coordinate Plots (PCP) confirmed that the proposed A-NSGA-III exhibited high convergence and diversification than the NSGA-II and MOPSO algorithms in the presence of scalability challenge (when the number of objective function exceeded three). The result confirmed that the proposed A-NSGA-III solved the scalability challenges associated with the optimal Controller Placement in the SD-WAN. Hence, A-NSGA-III was recommended over NSGA-II and MOPSO algorithms, subject to the confirmation usage conditions

SymbIoT: Towards An Extensible Blockchain Integration Testbed for IIoT

This paper presents SymbIoT, an extensible hybrid simulation-emulation testbed to investigate the integration of blockchain and distributed ledger technology (DLT) within the Industrial Internet of Things (IIoT) continuum. By adopting a joint software and hardware-based approach, we amalgamate the flexibility of software solutions and the real-world applicability offered by integrating comparable IoT hardware. The versatility of SymbIoT lies in its extensibility, offering flexibility in parameters including consensus algorithms, block size, node count and topology, throughput limitation, and use-case application deployment. SymbIoT facilitates comprehensive empirical studies of blockchain implementations within IIoT, focusing on performance, scalability, and security considerations. The testbed provides a platform for innovative and pragmatic experimentation in blockchain and IIoT integration, holding promise for shaping future applications and solutions in this cross-disciplinary field. We also present results from preliminary experimentation, indicating the applicability of the testbed for IIoT and broader IoT-to-cloud scenarios

Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks

Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN

NOMA based Resource Allocation and Mobility Enhancement Framework for IoT in Next generation Cellular Networks

With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)

Efficient Distribution of Key Chain Commitments for Broadcast Authentication in V2V Communications

Road safety applications such as intersection collision warning, emergency brake warnings, etc., rely on the periodic broadcast of messages by vehicles and roadside infrastructure. PKI-based approaches ensuring the integrity of messages and the legitimacy of the sender are computationally expensive and result in long messages. Approaches based on hashed key chains such as Timed Efficient Stream Loss-tolerant Authentication (TESLA) offer an alternative solution. Because they use symmetric-key cryptography, the messages are shorter and less expensive to verify. However, they bring their own challenges. This paper focuses on one challenge, the problem of distributing key chain commitments required for message verification. We propose and evaluate two techniques, respectively involving periodic broadcast of commitment keys by the vehicles themselves and selective unicasting by a central V2X Application Server (VAS). We find that the VAS-centric solution has advantages over the vehicle-centric solution and a related solution proposed by other researchers

RMCCS: RSSI-based Message Consistency Checking Scheme for V2V Communications

V2V messaging systems enable vehicles to exchange safety related information with each other and support road safety and traffic efficiency applications. The effectiveness of these applications depends on the correctness of the information reported in the V2V messages. Consequently, the possibility that malicious agents may send false information is a major concern. The physical features of a transmission are relatively difficult to fake, and one of the most effective ways to detect lying is to check for consistency of these features with vehicle position information in the message. In this paper, we propose a message consistency checking scheme whereby a vehicle acting independently can utilise the strength and variability of received signals to estimate the distance from a transmitting vehicle without prior knowledge of the environment (building density, traffic conditions, etc.). The distance estimate can then be used to check the correctness of the reported position. We show through simulation that our RMCSS method can detect false information with an accuracy of about 90% for separation distances less than 100m. We believe this is sufficient for the method to be a valuable adjunct to use of digital signatures to establish trust

Detection of JavaScript Injection Eavesdropping on WebRTC communications

WebRTC is a Google-developed project that allows users to communicate directly. It is an open-source tool supported by all major browsers. Since it does not require additional installation steps and provides ultra-low latency streaming, smart city and social network applications such as WhatsApp, Facebook Messenger, and Snapchat use it as the underlying technology on the client-side both on desktop browsers and mobile apps. While the open-source tool is deemed to be secure and despite years of research and security testing, there are still vulnerabilities in the real-time communication application programming interface (API). We show in this paper how eavesdropping can be enabled by exploiting weaknesses and loopholes found in official WebRTC specifications. We demonstrate through real-world implementation how an eavesdropper can intercept WebRTC video calls by installing a malicious code onto the WebRTC webserver. Furthermore, we identify and discuss several, easy to perform, ways to detect wiretapping. Our evaluation shows that several indicators within webrtc-internals API traces can be used to detect anomalous activities, without the need for network monitoring tools

Real-Time Object Detection with Automatic Switching between Single-Board Computers and the Cloud

We present a wireless real-time object detection system utilizing single-board devices, cloud computing platforms and web-streaming. Currently, most inference applications stat- ically perform tasks either on local machines or remote cloud servers. However, devices connected through cellular technolo- gies face volatile network conditions, compromising detection performance. Furthermore, while the limited computing power of single-board computers degrade detection correctness, exces- sive power consumption of machine learning models used for inference reduces operation time. In this paper, we propose a dynamic system that monitors embedded device’s wireless link quality and battery level to decide on detecting objects locally or remotely. The experimental results show that our dynamic offloading approach could reduce devices’ energy usage while achieving high accuracy, real-time object detection. Index Terms—Machine learning, WebRTC, object detection