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Ledarskap har en stor roll för att skapa och leda organisationer framåt. Ett av dessa ledarskap är det transformativa ledarskapet som har en grund i öppenhet och delaktighet. Det som arbetet vilar på är hur det transformativa ledarskapet motiverar anställda på arbetsplatsen. Uppsatsen har en litteraturöversikt för att fånga kunskapsläget i dagsläget. Studien har som frågeställning hur det transformativa ledarskapet påverkar anställdas motivation och tillfredsställelse i en organisation som fokuserar på produktion. I arbetet går vi igenom hur den kvantitativa studien genomfördes samt hur det empiriska materialet samlas in. I metoden gör vi reflektioner om risker samt etiska förhållningssätt. Totalt skickades 150 enkäter ut och 101 svar samlades in. Den data som samlades in presenteras i resultatdelen för att sedan analyseras i följande kapitel. I analysdelen går vi igenom vad varje hypotes som ställs inom studien betyder och hur korrelationen mellan de svar som vi fick in ser ut. I analysen försöker vi förklara varför resultatet ser ut som det gör och varför eventuella korrelationer inte är så starka som de kanske borde vara. I sista kapitlet presenteras slutsatsen samt ett svar på frågeställningen som visar på att det transformativa ledarskapet används i organisationen med fokus på produktion men att det kanske finns andra egenskaper som påverkar ledarskapet som inte kunde identifieras i det här arbetet.

The rise of technologies such as 6G networks, edge computing, and the Industrial Internet has led to a dramatic increase in the amount of data that needs to be transmitted over heterogeneous integrated networks. The resources of embedded devices limit the ability of the Industrial Internet to transmit data. While the multipath transmission mechanism can mitigate data transmission issues of low reliability and low real-time performance from the network-level perspective. As the complexity of industry applications increases, however, the phenomenon that the high-quality data transmission is subject to the influence of the underlying layer is becoming increasingly apparent. The paper aims to explores the possibility of multipath transmission protocol running on a real-time kernel from the perspective of the operating system, as there is a lack of research and reports in this area. Based on RT-Preempt, a real-time system RT-Linux suitable for the "NXP i.MX6Q" ARM integrated board has been proposed, which replaces the native Linux kernel to optimize and enhance its real-time performance. As described in the experiment part, the original standard Linux system OR-Linux and the new RT-Linux are tested with single-threaded and multi-threaded load experiments, respectively. The results of the analysis show that this paper provides a way of validating the trial data and ensuring its accuracy using the lognormal distribution model, which is a statistical distribution used to model variables that are positive and skewed to the right. The RT-Linux scheme has better real-time performance and is more stable than the OR-Linux scheme after real-time processing, showing the viability of the scheme.

This paper is focused on multiple supervised machine learning algorithms' performance evaluation in terms of classification accuracy and speed for the blind frequency bands classification into three occupancy classes: white, gray, and black spaces for potential implementation in cognitive radio application. Training and validation data sets consisting of 180 000 samples, including 60 000 samples per class, have been collected in the controlled experiment. Data samples have been generated using a hardware signal generator and recorded on the receiver's front end as the time-domain complex signals. Gray space data samples contain one, two, or three signals modulated into 2FSK, BPSK, or QPSK with symbol rates 10, 100, or 1000 kSymbol/s. White space data samples contain no own generated signals. Black space data samples contain two signals with the symbol rate of 22.5 MSymbol/s and offset +14 MHz and -14 MHz from the central frequency occupying the entire observation band. Training and validation of twenty supervised machine learning algorithms have been performed offline in the Matlab Classification Learner application using the collected data set. Fine decision trees have demonstrated the highest classification accuracy of 87.8 %, the observed classification speed of 630000 Objects/s is also higher than the required 2000 Objects/s. Medium decision trees and ensemble boosted trees have demonstrated 87.5 % and 87.7 % accuracy and classification speeds of 950000 and 230000 Objects/s respectively. Therefore, ensemble boosted trees, and fine and medium decision trees have been selected for the deployment on the target radio application in the scope of future work.

In order to facilitate deterministic behavior; industrial real-time communication stacks need another design than non-real-time communication stacks typically found in e.g., Internet of Things and Cloud solutions. We propose a flexible stack design that enable code reuse between testbeds and simulators, as well as how stack layers are driven and prioritized. The design can be generalized and used for non-real-time bare-metal solutions like battery powered Internet of Things. Our approach aims at extending the use of simulation during development of industrial systems in order to find logical errors and wrong assumptions earlier in the development. Conducted and evaluated experiments show that the proposed solutions are able to extend the use of simulation during development of real-time communication software. This is achieved by reusing the same code on an industrial testbed and in a discrete event simulator. In addition, the experiments show that the stack design is generalizable and enable reuse with other non-real-time embedded systems. The contribution consists of a set of building blocks for real-time systems that enable control over the system timing when executing on a simulation host while reusing the source code from an industrial testbed. Overall, this will improve the engineering situation, with respect to code reuse, flexibility and debugging.

This study aims to investigate whether chance can beat an actively managed equity fund during a ten-year period on the Swedish stock market. Since the stock market consists of fierce competition among investors, the EMH would suggest that stock price movements should not be far from reflecting all available information. Due to the highly unexpected nature of new information, such as news, price fluctuations should follow a random walk. Therefore a highly competent investor, e.g. a fund manager, should not be able to consistently outperform an investor basing his investments on a blindfolded monkey throwing darts at a stock list. The result shows that, on average, randomly generated portfolios of stocks, annually rebalanced during a ten-year period, outperformed the mean fund in return as well as alpha. However, quite expectedly there’s a larger spread of the returns, entailing a larger standard deviation, yet there is a fairly similar Sharpe ratio in comparison to the funds.

Under the vision of industry 4.0, industrial networks are expected to accommodate a large amount of aggregated traffic of both operation and information technologies to enable the integration of innovative services and new applications. In this respect, guaranteeing the uninterrupted operation of the installed systems is an indisputable condition for network management. Network measurement and performance monitoring of the underlying communication states can provide invaluable insight for safeguarding the system performance by estimating required and available resources for flexible integration without risking network interruption or degrading network performance. In this work, we propose a data-driven in-band telemetry method to monitor the aggregated traffic of the network at the switch level. The method learns and models the communication states by local network-level measurement of communication intensity. The approximated model parameters provide information for network management for prognostic purposes and congestion avoidance resource planning when integrating new applications. Applying the method also addresses the consequence of telemetry data overhead on QoS since the transmission of telemetry packets can be done based on the current state of the network. The monitoring at the switch level is a step towards the Network-AI for future industrial networks.

This paper is focused on the performance evaluation of nine supervised machine learning algorithms in terms of classification accuracy applied to perform two radio scene analysis tasks: 1. blind binary frequency band occupancy classification: vacant or occupied; 2. interference type classification: sine wave interference, or modulated signal or additive white Gaussian noise (AWGN) for the frequency hopping spread spectrum cognitive radio application. Twenty-nine features derived from the time-, frequency-domain and RSSI, have been used as classification inputs to the evaluated machine learning classifiers. Classifiers training and validation have been performed offline in Matlab Classification Learner and Neural Networks applications using four data sets, generated in the controlled experiment, covering both classification tasks in AWGN and mixed channel propagation conditions (AWGN and Rician fading). Data samples have been generated using a hardware signal generator and recorded on the target application receivers' front end as the time-domain complex signals. The highest classification accuracy of 98.71 % has been demonstrated by Feed Forward Neural Network (FFNN) for the binary occupancy classification in K-fold validation for the mixed data set containing both AWGN and Rician fading channel samples. For the interference type classification, FFNN has demonstrated classification accuracy of 99.82 % for K-fold validation and 99.71 % for hold-out validation. FFNN has been concluded as an acceptable algorithm for further adaptation and embedded deployment on our target radio application for both binary classification between occupied or vacant frequency bands and interference type classification.

The Industrial Internet of Things (IIoT) has revolutionized businesses by changing the way data are used to make products and services more efficient, reliable, and profitable. To achieve the improvement goals, the IIoT must guarantee the real-time performance of industrial applications such as motion control, by providing stringent quality of service (QoS) assurances for their (industrial applications) communication networks. An application or service may malfunction without adequate network QoS, resulting in potential product failures. Since an acceptable end-to-end delay and low jitter or packet delay variation (PDV) are closely related to quality of service (QoS), their impact is significant in ensuring the real-time performance of industrial applications. Although a communication network topology ensures certain jitter levels, its real-life performance is affected by dynamic traffic due to the changing number of devices, services, and applications present in the communication network. Hence, it is essential to study the jitter experienced by real-time traffic in the presence of background traffic and how it can be maintained within the limits to ensure a certain level of QoS. This paper presents a probabilistic network calculus approach that uses moment-generating functions to analyze the delay and PDV incurred by the traffic flows of interest in a wired packet switched multi-stage network. The presented work derives closed-form, end-to-end, probabilistic performance bounds for delay and PDV for several servers in series in the presence of background traffic. The PDV analysis conducted with the help of a Markovian traffic model for background traffic showed that the parameters from the background traffic significantly impact PDV and that PDV can be maintained under the limits by controlling the shape of the background traffic. For the studied configurations, the model parameters can change the PDV bound from 1 ms to 100 ms. The results indicated the possibility

This paper is focused on the blind symbol rate estimation for the digital FSK modulated signals, based on the classification between three symbol rate classes: 10, 100, and 1000 KSymbol/second using the scalogram images obtained from continuous wavelet transform with Morse wavelet. Pretrained deep learning AlexNet has been transfer learned to classify between symbol rate classes. Training, testing, and validation data sets have been composed of the artificial data generated using Bernoulli binary random signal generator modulated into FSK signal corrupted by additive white Gaussian noise (AWGN) noise with SNR ranging from 1 to 30 dB. Training and validation data sets have been augmented to obtain twice more extensive data set i.e 1800 scalogram images, compared to the original size of 900 samples. The average classification accuracy during validation has reached 99.7 % and during testing 100 % and 96.3 % for the data sets with SNR 25–30 dB and 20–25 dB respectively. The proposed algorithm has been compared with cyclostationary and has shown improved classification accuracy, especially in conditions of low SNR.

This paper is focused on the blind vacant frequency channels detection in 56 MHz observation band divided into 56 channels (1 MHz each) implemented on in-house developed hardware based on the AD9364 transceiver operating in automatic gain control (AGC) mode. Vacant channel detection has been modeled as a binary hypothesis testing problem. Three signal detection algorithms widely used in the literature including energy detection, wavelets, and cyclostationary have been tested and evaluated for potential use in our target application. Primary, offline testing has been performed in the Matlab environment using the data samples captured on the target receiver's front end as a time-domain complex signal. Data samples containing one, two, or three signals generated by hardware signal generator and modulated into 2FSK, BPSK, or QPSK with symbol rate 10, 100, or 1000 kSymbol/s. The highest accuracy of 91.0 % has been observed in the offline detection for continuous wavelet transform, while energy detection has demonstrated 86.4 % accuracy. Cyclostationary detection has shown no distinguishable difference in the spectrum correlation values calculated for the AWGN noise sample and the sample containing BPSK and 2FSK modulated signals. Energy detection and discrete wavelet transform have been implemented on our target hardware and tested in the office environment in conditions that could be approximated by AWGN channel. Test sequences containing one or two signals have been generated by the signal generator and received and processed by our target radio node. Discrete wavelet transform has demonstrated 85.73 % and energy detection 85.25 % accuracy in real-time testing. © 2022 IEEE.

The industrial network infrastructures are transforming to a horizontal architecture to enable data availability for advanced applications and enhance flexibility for integrating new tech-nologies. The uninterrupted operation of the legacy systems needs to be ensured by safeguarding their requirements in network configuration and resource management. Network traffic modeling is essential in understanding the ongoing communication for resource estimation and configuration management. The presented work proposes a two-step approach for modeling aggregated traffic classes of brownfield installation. It first detects the repeated work-cycles and then aims to identify the operational states to profile their characteristics. The performance and influence of the approach are evaluated and validated in two experimental setups with data collected from an industrial plant in operation. The comparative results show that the proposed method successfully captures the temporal and spatial dynamics of the network traffic for characterization of various communication states in the operational work-cycles.

Recent advances in the industrial internet of things (IIoT) and cyber–physical systems drive Industry 4.0 and have led to remote monitoring and control applications that require factories to be connected to remote sites over wide area networks (WAN). The adequate performance of remote applications depends on the use of a clock synchronization scheme. Packet delay variations adversely impact the clock synchronization performance. This impact is significant in WAN as it comprises wired and wireless segments belonging to public and private networks, and such heterogeneity results in inconsistent delays. Highly accurate, hardware–based time synchronization solutions, global positioning system (GPS), and precision time protocol (PTP) are not preferred in WAN due to cost, environmental effects, hardware failure modes, and reliability issues. As a software–based network time protocol (NTP) overcomes these challenges but lacks accuracy, the authors propose a software–based clock synchronization method, called CoSiWiNeT, based on the random sample consensus (RANSAC) algorithm that uses an iterative technique to estimate a correct offset from observed noisy data. To evaluate the algorithm’s performance, measurements captured in a WAN deployed within two cities were used in the simulation. The results show that the performance of the new algorithm matches well with NTP and state–of–the–art methods in good network conditions; however, it outperforms them in degrading network scenarios., Future Industrial Networks

Most evaluations of industrial real-time software are conducted on real embedded systems. The use of simulators that provides easily reproducible evaluations is often limited, due to different levels of abstraction, e.g., programming languages and run-time contexts. This paper extends previous work on a flexible task design, enabling tasks to be agnostic to run-time context, with evaluations conducted on bare-metal and real-time operating systems. Based on the same design and experiments we extend the proof-of-concept implementation in a discrete event simulation context, executing on a Windows based simulation host. Our experiments show that the flexible task design can be driven in a simulation run-time context, and still support typical industrial constructs. The result indicates that improved code reuse between discrete event simulators and industrial embedded systems is feasible.

Industrial real-time software is commonly evaluated on real embedded systems, while simulators are less used, since the abstraction level and purpose vary with for example, different programming languages and run-time contexts. This paper extends, applies, and evaluates previous work on a flexible task design that improve code reuse between discrete event simulators and embedded real-time systems. The paper focuses on two parts: (i) The performance cost of the proposed design in practice, by comparing the flexible task design with a traditional threaded approach. (ii) The potential of an alternative way to support legacy code in combination with discrete event simulation. The experiments indicate an almost negligible performance cost with respect to the real-time behavior, i.e., latency and jitter, while enabling improved code reuse between discrete event simulation and industrial embedded real-time systems.

Modulation type classification is a part of waveform estimation required to employ spectrum sharing scenarios like dynamic spectrum access that allow more efficient spectrum utilization. In this work multiple classification features, feature extraction, and classification algorithms for modulation type classification have been studied and compared in terms of classification speed and accuracy to suggest the optimal algorithm for deployment on our target application hardware. The training and validation of the machine learning classifiers have been performed using artificial data. The possibility to use instantaneous values of the time domain signal has shown acceptable performance for the binary classification between BPSK and 2FSK: Both ensemble boosted trees with 30 decision trees learners trained using AdaBoost sampling and fine decision trees have shown optimal performance in terms of both an average classification accuracy (86.3 % and 86.0 %) and classification speed (120 0000 objects per second) for additive white gaussian noise (AWGN) channel with signal-to-noise ratio (SNR) ranging between 1 and 30 dB. However, for the classification between five modulation classes demonstrated average classification accuracy has reached only 78.1 % in validation. Statistical features: Mean, Standard Deviation, Kurtosis, Skewness, Median Absolute Deviation, Root-Mean-Square level, Zero Crossing Rate, Interquartile Range and 75th Percentile derived from the wavelet transform of the received signal observed during 100 and 500 microseconds were studied using fractional factorial design to determine the features with the highest effect on the response variables: classification accuracy and speed for the additive white gaussian noise and Rician line of sight multipath channel. The highest classification speed of 170 000 objects/second and 100 % classification accuracy has been demonstrated by fine decision trees using as an input Kurtosis derived from the wavelet coefficients der, Export Date: 5 March 2021; Article; Correspondence Address: Valieva, I.; School of Innovation, Sweden; email: inna.valieva@mdh.se

The network infrastructures in the future industrial networks need to accommodate, manage and guarantee performance to meet the converged Internet technology (IT) and operational technology (OT) traffics requirements. The pace of IT-OT networks development has been slow despite their considered benefits in optimizing the performance and enhancing information flows. The hindering factors vary from general challenges in performance management of the diverse traffic for green-field configuration to lack of outlines for evolving from brown-fields to the converged network. Focusing on the brown-field, this study provides additional insight into a brown-field characteristic to set a baseline that enables the subsequent step development towards the future’s expected converged networks. The case study highlights differences between real-world network behavior and the common assumptions for analyzing the network traffic covered in the literature. Considering the unsatisfactory performance of the existing methods for characterization of brownfield traffic, a performance and dynamics mixture measurement is proposed. The proposed method takes both IT and OT traffic into consideration and reduces the complexity, and consequently improves the flexibility, of performance and configuration management of the brown-field.

Recent advances in industrial internet of things~(IIoT) and cyber-physical systems drive Industry 4.0 and lead to advanced applications. The adequate performance of time-critical automation applications depends on a clock synchronization scheme used by control systems. Network packet delay variations adversely impact the clock synchronization performance. The impact is significant in industrial sites, where software and hardware resources heavily contribute to delay variations, and where harsh environmental conditions interfere with communication network dynamics. While existing time synchronization methods for IIoT devices, e.g., Simple Network Time Protocol~(SNTP), provide adequate synchronization in good operating conditions, their performance degrades significantly with deteriorating network conditions. To overcome this issue, we propose a scalable, software-based, lightweight clock synchronization method, called CoSiNeT, for IIoT devices that maintains precise synchronization performance in a wide range of operating conditions. We have conducted measurements in local network deployments such as home and a university campus in order to evaluate the proposed algorithm performance. The results show that CoSiNeT matches well with SNTP and state-of-the-art method in good network conditions in terms of accuracy and precision; however, it outperforms them in degrading network scenarios. In our measurements, in fair network conditions, CoSiNeT improves synchronization performance by 23% and 25% compared to SNTP and state-of-the-art method. In the case of poor network conditions, it improves performance by 43% and 26%, respectively., Future Industrial Networks

Industrial control systems have strict requirements for time-sensitive applications and clock synchronization services. Performance of such applications is adversely impacted by packet delays and jitters. The impact is especially critical in process industries due to harsh environmental conditions. In this paper, we analyze delays and jitters to assess the performance of time-sensitive applications. To this end, we captured and analyzed round trip delay data retrieved from a paper factory. Analysis shows that a sub-millisecond level average delays and the jitters derived from the observed data are sufficient to meet the minimum 10ms update frequency required for most critical control applications. Moreover, the filtered delay variations at the end devices are less than the recommended 150us, which guarantees an adequate time synchronization accuracy in the factory network. Besides, this analysis can provide significant insights into performance bottlenecks for factory applications., Future Industrial Networks

There are many initiatives and technologies working towards implementing factories of the future. One consensus is that the classical hierarchical automation system design needs to be flattened while supporting the functionality of both Operation Technology (OT) and Information Technology (IT) within the same network infrastructure. To achieve the goal of IT/OT convergence in process automation, an evolutionary transition is preferred. Challenges are foreseen during the transition, mainly caused by the traditional automation architecture, and the main challenge is to identify the gap between the current and future network architectures. To address the challenges, in this paper, we describe one desired future scenario for process automation and carry out traffic measurements from a pulp and paper mill. The measured traffic is further analyzed, which reveals representative traffic characteristics in the process automation. Finally, the key challenges and future directions towards a system architecture for factories of the future are presented.

The Internet of Things (IoT) is rapidly becoming an integral component of the industrial market in areas such as automation and analytics, giving rise to what is termed as the Industrial IoT (IIoT). The IIoT promises innovative business models in various industrial domains by providing ubiquitous connectivity, efficient data analytics tools, and better decision support systems for a better market competitiveness. However, IIoT deployments are vulnerable to a variety of security threats at various levels of the connectivity and communications infrastructure. The complex nature of the IIoT infrastructure means that availability, confidentiality and integrity are difficult to guarantee, leading to a potential distrust in the network operations and concerns of loss of critical infrastructure, compromised safety of network end-users and privacy breaches on sensitive information. This work attempts to look at the requirements currently specified for a secure IIoT ecosystem in industry standards, such as Industrial Internet Consortium (IIC) and OpenFog Consortium, and to what extent current IIoT connectivity protocols and platforms hold up to the standards with regard to security and privacy. The paper also discusses possible future research directions to enhance the security, privacy and safety of the IIoT.

Next Generation Industrial IoT (NIIT)

Synchronization is essential for correct and consistent operation of automation systems. Synchronized devices accurately time-stamp the events and enable timely communication of messages over a communication network. In absence of a common time base, critical functions of automation systems cannot be carried out in a safe fashion. Unsynchronized systems may lead to malfunctions such as false alarms, wrong decisions and erroneous outcomes resulting into serious showstopper for plant operations. Despite technical advances in synchronization, industrial automation systems have lagged compared to telecommunication and financial services in utilization of latest synchronization technology. Thus, there is a need to investigate the adoption of synchronization in industrial networks, its current state and implementation problems. We carried out an extensive literature search in a structured way to study the evolution of synchronization in automation systems. We also investigated today's industrial automation systems and their network topologies to get insight into the synchronization techniques and mechanisms being used. As an outcome of study, the paper highlights the challenges related to synchronization in existing automation networks that need to be addressed in the immediate and short-term future.

Time synchronization is essential for the correct and consistent operation of automation systems. An inaccurate analysis being a consequence of improper synchronization, can affect automation functions, e.g., by producing false commands and warnings. Industrial systems are evolving from the rigid automation pyramid to a flexible and reconfigurable architecture due to market evolution. The new trends in Cyber-Physical-Systems (CPS), Industry 4.0, and Internet of Things (IoT) are enabling this evolution. Citing a need to understand the future synchronization requirements, this paper envisions the architecture, communication network, and applications of future automation systems. Built on this vision, the paper derives the future needs of synchronization and analyzes them with state-of-art synchronization means. Based on the analysis, we envision the future of synchronization systems for automation systems.

Wireless sensors and networks are used only occasionally in current control loops in the process industry. With rapid developments in embedded and highperformance computing, wireless communication, and cloud technology, drastic changes in the architecture and operation of industrial automation systems seem more likely than ever. These changes are driven by ever-growing demands on production quality and flexibility. However, as discussed in "Summary," there are several research obstacles to overcome. The radio communication environment in the process industry is often troublesome, as the environment is frequently cluttered with large metal objects, moving machines and vehicles, and processes emitting radio disturbances [1], [2]. The successful deployment of a wireless control system in such an environment requires careful design of communication links and network protocols as well as robust and reconfigurable control algorithms.

In this paper, we consider a wireless energy harvesting network consisting of one hybrid access point (HAP) having multiple antennas, and multiple sensor nodes each equipped with a single antenna. In contrast to conventional uplink wireless networks, the sensor nodes in the considered network have no embedded energy supply. They need to recharge the energy from the wireless signals broadcasted by the HAP in order to communicate. Based on the point-to-point and multipoints-to-point model, we propose two medium access control protocols, namely harvesting at the header of timeslot (HHT) and harvesting at the dedicated timeslot (HDT), in which the sensor nodes harvest energy from the HAP in the downlink, and then transform its stored packet into bit streams to send to the HAP in the uplink. Considering a deadline for each packet, the cumulative distribution functions of packet transmission time of the proposed protocols are derived for the selection combining and maximal ratio combining (MRC) techniques at the HAP. Subsequently, analytical expressions for the packet timeout probability and system reliability are obtained to analyze the performance of proposed protocols. Analytical results are validated by numerical simulations. The impacts of the system parameters, such as energy harvesting efficiency coefficient, sensor positions, transmit signal-to-noise ratio, and the length of energy harvesting time on the packet timeout probability and the system reliability are extensively investigated. Our results show that the performance of the HDT protocol outperforms the one using the HHT protocol, and the HDT protocol with the MRC technique has the best performance and it can be a potential solution to enhance the reliability for wireless sensor networks., SafeCOP - Safe Cooperating Cyber-Physical Systems using Wireless Communication

In this paper the potential of improving channel utilization by signal modulation type classification based on machine learning algorithms has been studied. The classification has been performed between two popular digital modulations: BPSK and FSK in target application. Classification was based on three features available on a popular software defined radio transceiver AD9361: In-phase and quadrature components of the digital time domain signal and signal-to-noise ratio (SNR), measured as RSSI value. Data used for network training, validation and testing was generated by the Simulink model consisting mainly of modulator, transceiver AD9361 and AWGN to generate the signal with SNR ranging from 1 to 30 dB. Twenty-three supervised machine learning algorithms including K-nearest neighbor, Support Vector Machines, Decision Trees and Ensembles have been studied, evaluated and verified against the target application's requirements in terms of classification accuracy and speed. The highest average classification accuracy of 86.9% was achieved by Support Vector Machines with Fine Gaussian kernel, however with demonstrated classification speed of 790 objects per second it was considered unable to meet target application's real-time operation requirement of 2000 objects per second. Fine Decision Trees and Ensemble Boosted Trees have shown optimal performance in terms of both reaching classification speed of 1200000 objects per second and average classification accuracy of 86.0% and 86.3% respectively. Classification accuracy has been also studied as a function of SNR to determine the most accurate classifier for each SNR level. At the target application's demodulation threshold of 12 dB 87.0% classification accuracy has been observed for the Fine Decision Trees, 87.5% for both Fine Gaussian SVM and Coarse KNN. At SNR higher than 27 dB Fine Trees, Coarse KNN have reached 97.5% classification accuracy. The effects of data set size and number of classification features on classi

TIMELINESS, SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)

Industrial wireless sensor and actuator networks (IWSANs) offer significant advantages to industria lautomation. However, high-reliability demands and hard communication deadlines pose challenges to its practical applications. To achieve this goal, flooding is considered as a promising approach due to multipath diversity and simplicity. In this paper, an enhanced version of REALFLOW, a flooding-based routing protocol for IWSANs is presented. Compared to the original REALFLOW, network management and network stability are improved. REALFLOW is compared with four other flooding protocols via simulations. The simulation results show that REALFLOW has better performance in terms of reliability and consecutive transmission errors when considering deadlines. Compared with normal flooding, REALFLOW achieves comparable reliability performance with decreased redundancy. Measurements from a prototype implementation conducted in an industrial manufacturing workshop reveal that high-reliability and low-application failure rates can be achieved, giving more confidence in providing reliable wireless sensing and actuating for industrial automation., ASIS, TIMELINESS, SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)

With the growing interest in adoption of wireless technologies in industrial automation and the continuous search for new revenue streams new players are entering the arena in order to make their business grow further. One of the emerging wireless technologies aiming to support industrial automation applications is 5G, targeting anything from extreme throughput (>10 Gbit/s) to extreme low latency (< 1 ms) to ultra high reliability (>99.999%). In this article we intend to discuss the potential and challenges of adopting 5G in real industrial environments and give a more balanced picture compared to previous articles mainly written by telecom researchers and vendors as a way to promote their technology. Specifically, this article will discuss and provide some real industrial requirements, describe the main technical features of 5G and try to assess what applications it will support that are not by already supported by existing technologies. In the end, the success of 5G will depend on appealing business models and scalability, i.e., whether or not the same equipment can be deployed worldwide without any changes and spectrum rules, and the migration paths beyond 5G for reasonable business risks., TIMELINESS, SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)

The Internet of Things (IoT) is captivating the society because of its potential to rapidly transform businesses and people’s lives. It is widely believed that IoT will also transform the industrial automation business in terms of improved productivity, less cost, flexibility, and increased revenues. Hence, there are some challenges that needs to be addressed when IoT is introduced to the industrial automation domain. This paperaims to present realistic requirements and highlights identified challenges such as security, interoperability, deterministic and low latency communication, and how the required availability (uptime) can be kept. Moreover, the paper also point out the need of standardization and sustainable business models. The conclusion is that introducing IoT devices and connecting them directly to cloud services is not straightforward for process automation., M2M Communication, Timeliness, SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)

Clock synchronization is a core asset to protect when securing cyber-physical systems with a time-triggered architecture. One of the most challenging attacks to protect against is a delay attack, where an adversary delays one of the synchronization messages, making node offset calculations incorrect for keeping clocks synchronized. One way to detect a breach of clock synchronization is by monitoring the offsets calculated in a node according to the clock synchronization algorithm. The analysis in this work assumes that the distributed nodes need to share the same notion of time and for this reason, uses the IEEE 1588 standard. Using this approach, a monitor needs to make a decision about if and when a node is under attack, in which case rules and methods for decision making should be put in place. There are many aspects to consider when setting thresholds for the monitored values in order to make such a decision. In this work we conduct an analysis of monitor indicators and an investigation of their applicability. Further, we identify dependencies within the proposed monitoring approach and conduct a sensitivity analysis of the parameters needed to make a decision about a system being under attack. The analysis outcomes allow to identify important parameters to consider while thresholding indicators and enables a greater generality in their applicability., READY - Research Environment for Advancing Low Latency Internet, SafeCOP - Safe Cooperating Cyber-Physical Systems using Wireless Communication

Clock synchronization is one of the most essential assets for distributed real-time systems, as sensing, control and actuation require synchronized communication to meet real-time deadlines. We propose a distributed monitoring method to detect if an adversary is interfering with the clock synchronization protocol. The monitor uses certain network indicators and a set of rules to decide about switching between Normal, Quarantine or Attack Detected states. Further, we propose a way to define thresholds for decision-making based on theoretical analysis of the indicator values influenced by an attack. In addition, we formulate the problem of adversary influence detection in the network as a detection theory problem and use it to derive an additional indicator for the network monitor. Finally, we analyze the time chase between the monitor and an adversary to investigate which factors influence the final outcome., SafeCOP - Safe Cooperating Cyber-Physical Systems using Wireless Communication