Efficient topology management in future 6G networks is a fundamental challenge for dynamic network creation based on location services, where each autonomous sub-network can be tailored to specific application scenarios. This paper studies the performance of a novel topology change management system in a 6G network dynamically organized into autonomous sub-networks. We propose and analyze an algorithm for intelligent prediction of topology changes and compare it with a monitoring-based approach. A case study on edge video distribution, aligned with 3GPP and ETSI MEC (Multi-access Edge Computing) standards, demonstrates the system's practical relevance. The proposed topology change prediction algorithm optimizes and selects the best machine learning models based on the scenario under study. For link change scenario, the results show that ANN demonstrates the best performance in identifying cases with no changes, slightly outperforming random forest and XGBoost. For user mobility scenario, XGBoost is more efficient in learning patterns for topology change prediction. In terms of cost efficiency, our ML-based approach represents a significantly cost-effective alternative to traditional monitoring approaches.

Computing in the Network (COIN) has the potential to reduce the data traffic and thus the end-to-end latencies for data-rich services. Existing COIN studies have neglected the impact of the size of the data unit that the network nodes compute on. However, similar to the impact of the protocol data unit (packet) size in conventional store-and-forward packet-switching networks, the Computing Data Unit (CDU) size is an elementary parameter that strongly influences the COIN dynamics. We model the end-to-end service time consisting of the network transport delays (for data transmission and link propagation), the loading delays of the data into the computing units, and the computing delays in the network nodes. We derive the optimal CDU size that minimizes the end-to-end service time with gradient descent. We evaluate the impact of the CDU sizing on the amount of data transmitted over the network links and the end-to-end service time for computing the convolutional neural network (CNN) based Yoho and a Deep Neural Network (DNN) based Multi-Layer Perceptron (MLP). We distribute the Yoho and MLP neural modules over up to five network nodes. Our emulation evaluations indicate that COIN strongly reduces the amount of network traffic after the first few computing nodes. Also, the CDU size optimization has a strong impact on the end-to-end service time; whereby, CDU sizes that are too small or too large can double the service time. Our emulations validate that our gradient descent minimization correctly identifies the optimal CDU size.

Acoustic data from the Industrial Internet of Things (IIoT) are widely used in anomaly detection because audio information reflects richer internal statuses of monitored working machines than the video does. Since multiple acoustic data sources interfere with each other by nature, source data estimation is a prerequisite of subsequent anomaly detection. Existing schemes often use a centralized manner to separate full data on a remote node in clouds. However, such a centralized manner may delay reactions to anomalies due to data transmission delay and the complexity of solving data separation problems. This article shows that the data separation phase can be substantially accelerated with an in-network computing approach. The key idea is to offload data processing jobs to intermediate network nodes along the forwarding path. We first propose a distributed algorithm so that the data separation jobs can be done in a progressive manner; likewise, we modify the forwarding layer in order to eliminate hop-by-hop data transmission delay that hurts the performance of using in-network computing. We further derive theoretical upper and lower bounds of the required number of intermediate nodes that achieve the maximum acceleration. We also implement our proposed solution in a full-stack network emulator. Based on an open and professional data set, evaluation results justify the feasibility and advantages of our idea with nearly 32.18% acceleration on total processing time. This work exemplifies the convergence of IIoT, edge, and clouds.

The increasingly difficult challenges in managing big data are inciting for revolutionary and fundamental techniques, as well as for technologies that can handle the complexity and the never-before-seen volume of data. Our goal is to enable high-density industrial networks to transmit and store production-related information efficiently and confidentially while retaining any existing network infrastructure. As the deployment of sensors leads to both the benefits and the challenges of industrial big data, our Bitteiler solution represents a means to reduce the data volume at the early stages of the data cycle to curb its impact on communications and storage systems. Bitteiler is a plug-and-play solution that allows customers to leverage smart IoT solutions to the fullest to achieve an industry 4.0-ready production process that is both efficient and secure. Our practical demonstration provides a hands-on experience with the Bitteiler technology – including in-network compression and coding – using a number of live IoT devices that can be manipulated by the audience.

This demonstration focuses on in-network computing as an enabler for low-latency Industrial Internet of Things (IIoT) applications, such as audio source separation for anomaly detection. By demonstrating a specific industrial application, we show that our method Progressive ICA (pICA), improves accuracy and reduces overall service latency progressively. The idea is to parallelize data transmission and processing along a multi-hop path consisting of in-network computing nodes. The audience can experience the benefits of the novel concept of in-network computing by interacting with the demonstration remotely via the Internet or in person.

Applying renewable energy in a smart micro grid (MG) is increasingly receiving attention to reduce greenhouse gas emissions. However, the mismatch between supply and demand hinders the realization of this process. With the widespread use of plug-in electric vehicles (EVs) and the development of emerging mobile edge cloud (MEC), intelligent energy optimization becomes a way to address the challenge. Therefore, in this paper, we propose a novel two-stage approach based on deep learning (DL) to reduce overall energy cost for sharing EVs integrated MG by forecasting its state and optimizing EVs scheduling. Our simulation results show that the joint design of forecasting and optimization reduces the overall energy consumption and the payment to the external grid.

Modern manufacturing is now deeply integrating new technologies such as 5G, Internet-of-things (IoT), and cloud/edge computing to shape manufacturing to a new level – Smart Factory. Autonomic anomaly detection (e.g., malfunctioning machines and hazard situations) in a factory hall is on the list and expects to be realized with massive IoT sensor deployments. In this paper, we consider acoustic data-based anomaly detection, which is widely used in factories because sound information reflects richer internal states while videos cannot; besides, the capital investment of an audio system is more economically friendly. However, a unique challenge of using audio data is that sounds are mixed when collecting thus source data separation is inevitable. A traditional way transfers audio data all to a centralized point for separation. Nevertheless, such a centralized manner (i.e., data transferring and then analyzing) may delay prompt reactions to critical anomalies. We demonstrate that this job can be transformed into an in-network processing scheme and thus further accelerated. Specifically, we propose a progressive processing scheme where data separation jobs are distributed as microservices on intermediate nodes in parallel with data forwarding. Therefore, collected audio data can be separated 43.75% faster with even less total computing resources. This solution is comprehensively evaluated with numerical simulations, compared with benchmark solutions, and results justify its advantages.

Blind Source Separation (BSS) for time-sensitive applications in the Internet of Things (IoT) results in a tradeoff between separation speed and accuracy. Data extraction has been widely employed recently to solve this problem. Although the introduction of current data extraction methods reduces the required time for separation, it is at the expense of separation quality. In this paper, we propose Adaptive extraction-based Independent Component Analysis (AeICA) to address these limitations. Specifically, the speed of separation is improved by using the extracted subset of the available data without affecting the overall separation accuracy, which we demonstrate through extensive numerical evaluations. In particular, AeICA reduces the total separation time by 50% to 75%, compared to the most remarkable related work.

In time-sensitive applications within industry 4.0, e.g. anomaly detection and human-in-the-loop, the data generated by multiple sources should be quickly separated to give the applications more time to make decisions and ultimately improve production performance. In this paper, we propose a Component-dependent Independent Component Analysis (CdICA) method that can separate multiple randomly mixed signals into independent source signals faster, for further data analysis in time-sensitive applications. Based on the Independent Component Analysis (ICA) algorithm, we first generate an initial separation matrix relying on the known mixture components, so that the separation speed of the traditional ICA can be increased. Our simulative results show that the CdICA method reduces the separation time by 55% to 83% compared to the most notable related work called FastICA and meanwhile it does not diminish the accuracy of the separation.

Virtualization of services in factory production allows achieving higher service reliability and smaller cost of industrial equipment. The demonstrator shows Mobile Edge Cloud (MEC) implementation for services such as path planning and movement control of a robot arm, collaboration between two robot arms, and remote control of a robot arm. The audience can participate in the demonstrator through guiding a robot arm remotely using a wireless controller. The MEC resources required for such virtualized services are shown in terms of traffic (throughput and inter-arrival time) and server load.

This proposal for demonstration focuses on network slicing as an enabler for flexible and efficient Industrial IoT (IIoT) networks. We show that by using network slicing in a novel three-layer architecture, self-organization and flexibility, as well as maximization of network efficiency can be achieved in IIoT networks. A practical scenario of conditional monitoring is demonstrated by means of distributing sensor devices on site to engage the audience.

Conditional monitoring for industrial IoT often uses acoustic signals for non-invasive anomaly detection. The acoustic sensors capture the mixed sound of several working machines, which should be separated in per-machine components for further analysis. The accuracy can be in part improved by installing redundant acoustic sensors. However, this would increase the amount of the transmitted data. In this paper, we propose a joint application of (i) Blind Source Separation (BSS) to separate the mixed sound of several working machines, and (ii) Compressed Sensing (CS) for reducing the amount of data transmitted over the network for partially correlated data sources. We also propose a set of key performance indicators to evaluate the whole system. Our simulation results, performed using the FastICA and CVXPY libraries, show that our solution provides a well balance between the amount of transmitted data and the separation quality. In other words, it optimizes the network throughput for the given value of desired separation quality.

Factory automation and production are currently undergoing massive changes, and 5G is considered being a key enabler. In this paper, we state uses cases for using 5G in the factory of the future, which are motivated by actual needs of the industry partners of the “5Gang” consortium. Based on these use cases and the ones by 3GPP, a 5G system architecture for the factory of the future is proposed. It is set in relation to existing architectural frameworks.