The blockchain technology is currently penetrating different sides of modern ICT community. Most of the devices involved in blockchain-related processes are specially designed targeting only the mining aspect. At the same time, the use of wearable and mobile devices may also become a part of blockchain operation, especially during the charging time. The paper considers the possibility of using a large number of constrained devices supporting the operation of the blockchain. The utilization of such devices is expected to improve the efficiency of the system and also to attract a more substantial number of users. Authors propose a novel consensus algorithm based on a combination of Proof-of-Work (PoW), Proof-of-Activity (PoA), and Proof-of-Stake (PoS). The paper first overviews the existing strategies and further describes the developed cryptographic primitives used to build a blockchain involving mobile devices. A brief numerical evaluation of the designed system is also provided in the paper.

In this paper we discuss how does the input magnitude data setting influence the behavior of error-reduction algorithm in the case of the one-dimensional discrete phase retrieval problem. We present experimental results related to the convergence or stagnation of the algorithm. We also discuss the issue of the zeros distribution of the solution, when the solution of the problem exists.

Tracking the location of people and their mobile devices creates opportunities for new and exciting ways of interacting with public technology. For instance, users can transfer content from public displays to their mobile device without touching it, because location tracking allows automatic recognition of the target device. However, many uncertainties remain regarding how users feel about interactive displays that track them and their mobile devices, and whether their experiences vary based on the setting. To close this research gap, we conducted a 24-participant user study. Our results suggest that users are largely willing - even excited - to adopt novel location-tracking systems. However, users expect control over when and where they are tracked, and want the system to be transparent about its ownership and data collection. Moreover, the deployment setting plays a much bigger role on people's willingness to use interactive displays when location tracking is involved.

Coarse-grained reconfigurable architectures and other exposed datapath architectures such as transport-triggered architectures come with a high energy efficiency promise for accelerating data oriented workloads. Their main drawback results from the push of complexity from the architecture to the programmer; compiler techniques that allow starting from a higher-level programming language and generate code efficiently to such architectures robustly is still an open research area. In this article we survey the known main sources of challenges and outline a generic processor architecture template that covers the most common architecture variations along with a proposal for a common code generation framework for such challenging architectures.

Most applications and services rely on central authorities. This introduces a single point of failure to the system. The central authority must be trusted to have data stored by the application available at any given time. More importantly, the privacy of the user depends on the service provider capability to keep the data safe. A decentralized system could be a solution to remove the dependency from a central authority. Moreover, due to the rapid growth of mobile device usage, the availability of decentralization must not be limited only to desktop computers. In this work we aim at studying the possibility to use mobile devices as a decentralized file sharing platform without any central authorities. This was done by implementing Asterism, a peer-to-peer file-sharing mobile application based on the Inter-Planetary File System. We validate the results by deploying and measuring the application network usage and power consumption in multiple different devices. Results show that mobile devices can be used to implement a worldwide distributed file sharing network. However, the file sharing application generated large amounts of network traffic even when no files were shared. This was caused by the chattiness of the protocol of the underlying peer-to-peer network. Consequently, constant network traffic prevented the mobile devices from entering to deep sleep mode. Due to this the battery life of the devices was greatly degraded.

Artificial Intelligence (AI) is one of the current emerging technologies. In the history of computing AI has been in the similar role earlier - almost every decade since the 1950s, when the programming language Lisp was invented and used to implement self-modifying applications. The second time that AI was described as one of the frontier technologies was in the 1970s, when Expert Systems (ES) were developed. A decade later AI was again at the forefront when the Japanese government initiated its research and development effort to develop an AI-based computer architecture called the Fifth Generation Computer System (FGCS). Currently in the 2010s, AI is again on the frontier in the form of (self-)learning systems manifesting in robot applications, smart hubs, intelligent data analytics, etc. What is the reason for the cyclic reincarnation of AI? This paper gives a brief description of the history of AI and also answers the question above. The current AI “cycle” has the capability to change the world in many ways. In the context of the CE conference, it is important to understand the changes it will cause in education, the skills expected in different professions, and in society at large.

The present contribution analyzes the performance of non-orthogonal multiple access (NOMA)-based user cooperation with simultaneous wireless information and power transfer (SWIPT). In particular, we consider a two-user NOMA-based cooperative SWIPT scenario, in which the near user acts as a SWIPT-enabled relay that assists the farthest user. In this context, we derive analytic expressions for the pairwise error probability (PEP) of both users assuming the both amplify-and-forward (AF) and decode-and-forward (DF) relay protocols. The derived expressions are expressed in closed-form and have a tractable algebraic representation which renders them convenient to handle both analytically and numerically. In addition to this, we derive a simple asymptotic closed-form expression for the PEP in the high signal-to-noise ratio (SNR) regime which provide useful insights on the impact of the involved parameters on the overall system performance. Capitalizing on this, we subsequently quantify the maximum achievable diversity order of both users. It is shown that numerical and simulation results corroborate the derived analytic expressions. Furthermore, the offered results provide interesting insights into the error rate performance of each user, which are expected to be useful in future designs and deployments of NOMA based SWIPT systems.

The agricultural sector in Finland has been lagging behind in digital development. Development has long been based on increasing production by investing in larger machines. Over the past decade, change has begun to take place in the direction of digitalization. One of the challenges is that different manufacturers are trying to get farmers' data on their own closed cloud services. In the worst case, farmers may lose an overall view of their farms and opportunities for deeper data analysis because their data is located in different services. The goals and previously studied challenges of the 'MIKÄ DATA' project are described in this research. This project will build an intelligent data service for farmers, which is based on the Oskari platform. In the 'Peltodata' service, farmers can see their own field data and many other data sources layer by layer. The project is focused on the study of machine learning techniques to develop harvest yield prediction and find out the correlation between many data sources. The 'Peltodata' service will be ready at the end of 2019.

Software Defined Networking (SDN) is a new networking paradigm in which the control plane and data plane are decoupled. Throughout the recent years, a number of architectures have emerged for protocol-independent packet processing. One such architecture is the Protocol Independent Switch Architecture (PISA). It is a programmable and protocol-independent architecture composed of a number of Match and Action stages. Inside each of these stages is a crossbar to generate the search key and another crossbar to provide the input to the Action Units. In this paper, we design and explore alternative interconnection schemes with the aim of finding the most area- and power-efficient interconnection structure. Moreover, we propose further modifications to the interconnection structure, as a result of which the on-chip area of both match and action crossbars will be reduced by more than 70 % and power dissipation will be reduced by 25.8 % and 23.1 % for match and action crossbars respectively.

Universities are still mainly preparing students for the world, where 'do something useful', i.e. 'do something with your hands' was the main principle and work was done during strictly regulated time. But world has changed and traditional areas of human activity (what also are the main target in University courses) are rapidly diminishing. More important have become virtual products - computer programs, mobile apps, social networks, new types of digital currencies, IOT (voice in your bathroom suggesting to buy the next model of Alexa), video games, interactive TV, virtual reality etc. Most of these new areas are not present in current curricula and there are problems with involving them in curricula - (working) students know (some aspects of) these areas better than many of university teachers, since corresponding knowledge is not yet present in textbooks - it is present only on Internet. The Internet strongly influences both what we teach and how we teach.

One of the biggest problems in managing devices for the Internet of Things (IoT) is the ability for a management server to independently discover and retrieve data models for vendor-specific devices. At the same time, several device management methods also lack methods for device vendors to share their data models in a consistent manner. This paper presents the design and implementation of a repository that can flexibly accommodate many needs with regards to these issues, and allows device vendors to publish semantically similar data models as well as attach meta-data to these models. A Machine-to-Machine (M2M) communication interface also allows a management server to communicate with the repository. We show how these techniques can be used with the Lightweight Machine-to-Machine (LWM2M) standard.

Terahertz (THz) band communications, capable of achieving the theoretical capacity of up to several terabits-per-second, are one of the attractive enablers for beyond 5G wireless networks. THz systems will use extremely directional narrow beams, allowing not only to extend the communication range but also to partially secure the data already at the physical layer. The reason is that, in most cases, the Attacker has to be located within the transmitter beam in order to eavesdrop the message. However, even the use of very narrow beams results in the considerably large area around the receiver, where the Attacker can capture all the data. In this paper, we study how to decrease the message eavesdropping probability by leveraging the inherent multi-path nature of the THz communications. We particularly propose sharing the data transmission over multiple THz propagation paths currently available between the communicating entities. We show that, at a cost of the slightly reduced link capacity, the message eavesdropping probability in the described scheme decreases significantly even when several Attackers operate in a cooperative manner. The proposed solution can be utilized for the transmission of the sensitive data, as well as to secure the key exchange in THz band networks beyond 5G.

Internet-of-things (IoT) objects are expected to exceed 75 billion objects by 2020, and a large part of the expansion is expected to be at a finer granularity than existing silicon-based IoT objects (i.e. tablets and cell phones) can deliver [1]. Currently, placing a room light or a thermostat on the internet for remote control is considered progressive. However, if printed electronics can achieve performance increases, then IoT objects could be affixed to almost anything, such as coffee creamer cartons, cereal boxes, or that missing sock. Each of these IoT objects could be driving a sensor, perhaps position, temperature or pressure, essentially a multitude of applications. In order for IoT objects to emulate a simple postage stamp, with self-powering from energy scavenging and local energy storage, all housed in a non-toxic flexible form factor, advances in solution processable devices need to occur.

Digital predistortion (DPD) has important applications in wireless communication for smart systems, such as, for example, in Internet of Things (IoT) applications for smart cities. DPD is used in wireless communication transmitters to counteract distortions that arise from nonlinearities, such as those related to amplifier characteristics and local oscillator leakage. In this paper, we propose an algorithm-architecture-integrated framework for design and implementation of adaptive DPD systems. The proposed framework provides energy-efficient, real-time DPD performance, and enables efficient reconfiguration of DPD architectures so that communication can be dynamically optimized based on time-varying communication requirements. Our adaptive DPD design framework applies Markov Decision Processes (MDPs) in novel ways to generate optimized runtime control policies for DPD systems. We present a GPU-based adaptive DPD system that is derived using our design framework, and demonstrate its efficiency through extensive experiments.

In this work we propose a framework for improving the performance of any deep neural network that may suffer from vanishing gradients. To address the vanishing gradient issue, we study a framework, where we insert an intermediate output branch after each layer in the computational graph and use the corresponding prediction loss for feeding the gradient to the early layers. The framework-which we name Elastic network-is tested with several well-known networks on CIFAR10 and CIFAR100 datasets, and the experimental results show that the proposed framework improves the accuracy on both shallow networks (e.g., MobileNet) and deep convolutional neural networks (e.g., DenseNet). We also identify the types of networks where the framework does not improve the performance and discuss the reasons. Finally, as a side product, the computational complexity of the resulting networks can be adjusted in an elastic manner by selecting the output branch according to current computational budget.

Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. The current paper addresses the problem of farm detection using low resolution satellite images. In digital agriculture, farm detection has significant role for key applications such as crop yield monitoring. Two main categories of object detection strategies are studied and compared in this paper; First, a two-step semi-supervised methodology is developed using traditional manual feature extraction and modelling techniques; the developed methodology uses the Normalized Difference Moisture Index (NDMI), Grey Level Co-occurrence Matrix (GLCM), 2-D Discrete Cosine Transform (DCT) and morphological features and Support Vector Machine (SVM) for classifier modelling. In the second strategy, high-level features learnt from the massive filter banks of deep Convolutional Neural Networks (CNNs) are utilised. Transfer learning strategies are employed for pretrained Visual Geometry Group Network (VGG-16) networks. Results show the superiority of the high-level features for classification of farm regions.

The performance of physical-layer security of the classic Wyner's wiretap model over Fisher-Snedecor composite fading channels is considered in this work. Specifically, F the main channel (i.e., between the source and the legitimate destination) and the eavesdropper's channel (i.e., between the source and the illegitimate destination) are assumed to experience independent quasi-static Fisher-Snedecor fading conditions, which have been shown to be encountered in realistic wireless transmission scenarios in conventional and emerging communication systems. In this context, exact closed-form expressions for the average secrecy capacity (ASC) and the probability of non-zero secrecy capacity (PNSC) are derived. Additionally, an asymptotic analytical expression for the ASC is presented. The impact of shadowing and multipath fading on the secrecy performance is investigated. Our results show that increasing the fading parameter of the main channel and/or the shadowing parameter of the eavesdropper's channel improves the secrecy performance. The analytical results are compared with Monte-Carlo simulations to validate the analysis.

The Fisher-Snedecor F distribution was recently proposed as an accurate and tractable composite fading model in the context of device-to-device communications. The present work derives the product of the Fisher-Snedecor F composite fading model, which is useful in characterizing fading effects in numerous realistic communication scenarios. To this end, novel analytic expressions are first derived for the probability density function, the cumulative distribution function and the moment of the product of N statistically independent, but not necessarily identically distributed, Fisher-Snedecor F random variables. Capitalizing on these expressions, we derive tractable closed-form expressions for channel quality estimation of the proposed model as well as the corresponding outage probability and average bit error probability for binary modulations. The offered results are corroborated by extensive Monte-Carlo simulation results, which verify the validity of the derived expressions. It is shown that the number of cascaded channels affects considerably the corresponding performance, as a variation of over an order of magnitude is observed across all signal-to-noise ratio regimes.

Markov Decision Processes (MDPs) provide important capabilities for facilitating the dynamic adaptation of hardware and software configurations to the environments in which they operate. However, the use of MDPs in embedded signal processing systems is limited because of the large computational demands for solving this class of system models. This paper presents Sparse Parallel Value Iteration (SPVI), a new algorithm for solving large MDPs on resource-constrained embedded systems that are equipped with mobile GPUs. SPVI leverages recent advances in parallel solving of MDPs and adds sparse linear algebra techniques to significantly outperform the state-of-the-art. The method and its application are described in detail, and demonstrated with case studies that are implemented on an NVIDIA Tegra K1 System On Chip (SoC). The experimental results show execution time improvements in the range of 65 % -78% for several applications. SPVI also lifts restrictions required by other MDP solver approaches, making it more widely compatible with large classes of optimization problems.

Data compression is a common requirement for displaying large amounts of information. The goal is to reduce visual clutter. The approach given in this paper uses an analysis of a data set to construct a visual representation. The visualization is compressed using the address ranges of the memory structure. This method produces a compressed version of the initial visualization, retaining the same information as the original. The presented method has been implemented as a Memory Designer tool for ASIC, FPGA and embedded systems using IP-XACT. The Memory Designer is a user-friendly tool for model based embedded system design, providing access and adjustment of the memory layout from a single view, complementing the 'programmer's view' to the system.

The aim was to study if odors evaporated by an olfactory display prototype can be used to affect participants' cognitive and emotion-related responses to audio-visual stimuli, and whether the display can benefit from objective measurement of the odors. The results showed that odors and videos had significant effects on participants' responses. For instance, odors increased pleasantness ratings especially when the odor was authentic and the video was congurent with odors. The objective measurement of the odors was shown to be useful. The measurement data was classified with 100 % accuracy removing the need to speculate whether the odor presentation apparatus is working properly.

Data collection requires homogenization of the data prior to processing it. This can create a challenge to the companies since data have varicose formats and schemas. This paper discusses the implementation of data collection framework using the PLANTCockpit open source platform, which is an integration platform for business processes. The framework is extended to fetch data from heterogeneous sources and then, allow the user to select the relevant data that matches his/her needs. In addition to this, the extendable framework also makes it possible to select the output data structure based on the user's requirements. Defining such a framework can reduce company's efforts for reshaping and modifying their architectures to handle new challenges posed by the ever-changing data. The framework not only restricts one to collection and transformation, but also provides an option to perform available processing techniques on the transformed data structure. The proposed framework has been tested on a cloud-based platform provided by the Cloud Collaborative Manufacturing Networks (C2NET) project.

There is a trend about the adoption of Knowledge Representation and Reasoning formalisms, such as ontologies, for industrial automation. For example, semantic models are used as knowledge bases that encapsulate different type of information of manufacturing systems, e.g., statuses and capabilities of their cyber and physical resources. Moreover, these models can be updated and accessed during runtime. In this context, models are becoming a critical part of the system infrastructure for both controlling and monitoring activities. However, models tend to be designed for specific purposes and not standardized. This is an issue because the employed formalisms, such as ontologies, emerged in order to bring an engineering tool for commonly classifying, defining, and sharing information. This article proposes the development of modular ontologies based on different parts of the ISA-95 standard for describing the product, process, and resource information of manufacturing systems. In addition, this research work demonstrates a set of semantic rules that may be used for inferring implicit knowledge of the ontology that permits the automatic checking of the required machines to manufacture different product variants.

Industry 4.0 is about the interconnectivity and digitalisation of industrial systems that need to be integrated in order to improve the efficiency of resources and, in turn, processes. Both research and commercial sectors are working towards addressing specific challenges, such as data modelling, collection and processing. A correct manipulation and interpretation of data is critical and, now, more difficult than ever due to the dramatic increment of the amount of data generated at different levels of enterprises. Ultimately, this research work presents a solution, integrated with an existing cloud-based platform, for collecting and processing real-time factory shop floor streams of data. Such solution is an IoT-based development, which consist on both IoT hub and gateway that permit the consumption and communication of device information. The required message exchange is done within state of the art technologies and protocols e.g., MQTT protocol and REST-based interface. The implementation of the solution is demonstrated through an industrial-based scenario.

Cyber-physical Systems (CPS) in industrial manufacturing facilities demand a continuous interaction with different and a large amount of distributed and networked computing nodes, devices and human operators. These systems are critical to ensure the quality of production and the safety of persons working at the shop floor level. Furthermore, this situation is similar in other domains, such as logistics that, in turn, are connected and affect the overall production efficiency. In this context, this article presents some key steps for integrating three pillars of CPS (production line, logistics and facilities) into the current smart manufacturing environments in order to adopt an industrial Cyber-Physical Systems of Systems (CPSoS) paradigm. The approach is focused on the integration in several digital functionalities in a cloud-based platform to allow a real time multiple devices interaction, data analytics/sharing and machine learning-based global reconfiguration to increase the management and optimization capabilities for increasing the quality of facility services, safety and energy efficiency and industrial productivity. Conceptually, isolated systems may enhance their capabilities by accessing to information of other systems. The approach introduces particular vision, main components, potential and challenges of the envisioned CPSoS. In addition, the description of one scenario for realizing the CPSoS vision is presented. The results herein presented will pave the way for the adoption of CPSoS that can be used as a pilot for further research on this emerging topic.

How to measure and train for adaptability has emerged as a priority in military contexts in response to emergent threats and technologies associated with asymmetric warfare. While much research effort has attempted to characterize adaptability in terms of accuracy and response time using traditional executive function cognitive tests, it remains unclear and undefined how adaptability should be measured and thus how simulation-based training should be designed to instigate and modulate adaptable behavior and skills. Adaptable reasoning is well-exemplified in the rescue effort of Apollo 13 by NASA engineers who repurposed available materials available in the spacecraft to retrieve the astronauts safely back to earth. Military leaders have anecdotally referred to adaptability as 'improvised thinking' that repurposes 'blocks of knowledge' to device alternative solutions in response to changes in conditions affecting original tasks while maintaining end-state commander's intent. We review a previous feasibility study that explored the specification of Reusable Modeling Primitives for models and simulation systems building on Dimensional Analysis and Design Structure Matrix for Complexity Management formal methods. This Dimensional Analysis Conceptual Modeling (DACM) paradigm is rooted in science and engineering critical thinking and is consistent with the stated anecdotal premises as it facilitates the objective dimensional decomposition of a problem space to guide the corresponding dimensional composition of possible solutions. Arguably, adaptability also concerns the capability to overcome contradictions, detections, and reductions, which we present in an exemplar addressing the contradiction of increased drag due to increased velocity inherent to torpedoes. We propose that the DACM paradigm may be repurposed as a critical thinking framework for teaching the identification of relevant components in a theater of military operations and how the properties of those components may be repurposed to fashion alternative solutions to tasks involving navigation, call-for-fires, line-of-sight cover, weather and atmospheric effect responses, and others.

This paper describes the core functionality and a proof-of-concept demonstration setup for remote 360 degree stereo virtual reality (VR) gaming. In this end-to-end scheme, the execution of a VR game is off-loaded from an end user device to a cloud edge server in which the executed game is rendered based on user's field of view (FoV) and control actions. Headset and controller feedback is transmitted over the network to the server from which the rendered views of the game are streamed to a user in real-time as encoded HEVC video frames. This approach saves energy and computation load of the end terminals by making use of the latest advancements in network connection speed and quality. In the showcased demonstration, a VR game is run in Unity on a laptop powered by i7 7820HK processor and GTX 1070 GPU. The 360 degree spherical view of the game is rendered and converted to a rectangular frame using equirectangular projection (ERP). The ERP video is sliced vertically and only the FoV is encoded with Kvazaar HEVC encoder in real time and sent over the network in UDP packets. Another laptop is used for playback with a HTC Vive VR headset. Our system can reach an end-to-end latency of 30 ms and bit rate of 20 Mbps for stereo 1080p30 format.

Due to their unconstrained mobility and capability to carry goods or equipment, unmanned aerial vehicles (UAVs) or drones are considered as a part of the fifth-generation (5G) wireless networks and become attractive candidates to carry a base station (BS). As 5G requirements apply to a broad range of uses cases, it is of particular importance to satisfy those during spontaneous and temporary events, such as a marathon or a rural fair. To be able to support these scenarios, mobile operators need to deploy significant radio access resources quickly and on demand. Accordingly, by focusing on 5G cellular networks, we investigate the use of drone-assisted communication, where a drone is equipped with a millimeter-wave (mmWave) BS. Being a key technology for 5G, mmWave is able to facilitate the provisioning of the desired per-user data rates as drones arrive at the service area whenever needed. Therefore, in order to maximize the benefits of mmWave-drone-BS utilization, this paper proposes a methodology for its optimized deployment, which delivers the optimal height, coordinates, and coverage radius of the drone-BS by taking into account the human body blockage effects over a mmWave-specific channel model. Moreover, our methodology is able to maximize the number of offloaded users by satisfying the target signal quality at the cell edge and considering the maximum service capacity of the drone-BS. It was observed that the mmWave-specific features are extremely important to consider when targeting efficient drone-BS utilization and thus should be carefully incorporated into analysis.

This paper presents a novel digital self-interference canceller for inband full-duplex radio transceivers. The proposed digital canceller utilizes a Volterra series with sparse memory to model the residual SI signal, and it can thereby accurately reconstruct the self-interference even under a heavily nonlinear transmitter power amplifier. To the best of our knowledge, this is the first time such a sparse-memory Volterra series has been used to model the self-interference within an inband full-duplex device. The performance of the Volterra-based canceller is evaluated with real-life measurements that incorporate also an active analog canceller. The results show that the novel digital canceller suppresses the SI by 34 dB in the digital domain, outperforming the state- of-the-art memory polynomial-based solution by a margin of 5 dB. The total amount of cancellation is nearly 110 dB with a transmit power of +30 dBm, even though a shared transmit/receive antenna is used. To the best of our knowledge, this is the highest reported cancellation performance for a shared-antenna full-duplex device with such a high transmit power level.

To overcome the limited coverage in traditional wireless sensor networks, mobile crowd sensing (MCS) has emerged as a new sensing paradigm. To achieve longer battery lives of user devices and incentivize human involvement, this paper presents a novel approach that seamlessly integrates MCS with wireless power transfer, named wirelessly powered crowd sensing (WPCS), for supporting crowd sensing with energy consumption and offering rewards as incentives. An optimization problem is formulated to simultaneously maximize the data utility and minimize the energy consumption for service operator, by jointly controlling wireless-power allocation at the access point (AP) as well as sensing-data size, compression ratio, and sensor transmission duration at the mobile sensor (MS). Given the fixed compression ratios, the optimal power allocation policy is shown to have a threshold-based structure with respect to a defined crowd-sensing priority function for each MS. Given fixed sensing-data utilities, the compression policy achieves the optimal compression ratio. Extensive simulations are also presented to verify the efficiency of the contributed mechanisms.

By 2020, unmanned ships such as remotely controlled boats and autonomous vessels would become operational, marking a technological revolution for the maritime industry. Such ships are expected to serve needs ranging from coastal ferries to open sea cargo handling. In this paper we detail the security vulnerabilities of such unmanned ships. The attack surface as well as motivations for attack attempts also are discussed to provide a perspective of how and why attacks are undertaken. Finally defence strategies are proposed as countermeasures.

The evolution of the Internet to an ubiquitous computing environment where massive amounts of devices will be connected. Sharing, receiving and acting upon data has brought in a problem of security. There are as many firmware and software update procedures as there are manufacturers. Therefore it would be good if a common solution could be found. We looked for suitable mechanisms in the past three years, to be used in Internet of Things networks as well as an up and coming research and standardization work. Our findings show that there indeed are good options for firmware update mechanisms that use state-of-The-Art technologies to deliver updates in a secure manner. While not all the mechanisms were specifically targeting deployment scenarios found in the Internet of Things, we still believe the concept of such update mechanism is suitable also for IoT use and thus can be adapted trivially to IoT networks and devices. We also propose a generic four-element model for secure firmware updates.

Close to 100% employment of students and easy access to abundance of information on Internet has essentially changed student's learning practices and their earlier knowledge background, especially on rapidly progressing field of Software Engineering. On workplace they have to use technologies, which are used in practice of their employing enterprise, but often do not understand the scientific and/or technological principles on which these technologies are based. They seek explanations on Internet, but information on Internet is often low quality, one-sided and presented with business targets on mind - to get more users to technologies developed and sold by a business enterprise. Thus university has to explain basic principles of technologies what students already know and have used and correct some popular beliefs, which are supported by software vendors and based their business interests. Non-formal sources of knowledge - workplace training and Internet - do not reduce teacher's task, but force teachers constantly study all new which appears in this field, thus increase teachers workload. Students increasing use of non-formal sources of knowledge imply need for flipping the process - instead of teaching students are set to learn from provided detailed tutorials. Use of Internet and work has made self-study, seeking information from Internet sources very customary for current students, thus such flipping worked very well in a game programming course provided by the first author.

We have focused our paper on the aspects important in adapting an Information System (IS) to the user's cultural background. We are interested both in the factors related to IS development and in the use of IS. Increasingly, ISs are being developed and used in a global context. We have perceived differences in expectations of functionalities, architecture, structural properties, information search practices, web-based system properties, and user interfaces. One conclusion would be that a high quality IS reflects user behavior in its use context. In that case, the system has to model its user one way or another. Until now, the topic has been handled without meaningful effort to model user behavior. Current publications cover a wide variety of rules on how to take into account cultural differences in the IS context. In this paper, our aim is to study the current state-of-the-art of user modeling - modeling the human being as an IS user. We start with general aspects related to the role of the user in IS development and alternatives to adaptable systems. The findings are applicable in the educational context as well. More and more, the use of computers and ISs is becoming an essential part of studies: the use of MOOCs (Massively Open Online Courses) as a part or replacement for traditional face-to-face classes; flipped learning methodology emphasizing the significance of self-learning; and blended learning, including quite often computerized study content. Our focus is on the global context, in which students represent different cultures and the IS is globally available.

Performance measurement tools and techniques have become very significant in today's industries for increasing the efficiency of their processes in order to face the competitive market. The first step towards performance measurement is the real-time monitoring and gathering of the data from the manufacturing system. Applying these performance measurement techniques on real-world industry in a way that is more general and efficient is the next challenge. This paper presents a methodology for implementing the key performance indicators defined in the ISO 22400 standard-Automation systems and integration, Key performance indicators (KPIs) for manufacturing operations management. The proposed methodology is implemented on a multi robot line simulator for measuring its performance at runtime. The approach implements a knowledge-based system within an ontology model which describes the environment, the system and the KPIs. In fact, the KPIs semantic descriptions are based on the data models presented in the Key Performance Indicators Markup Language (KPIML), which is an XML implementation of models developed by the Manufacturing Enterprise Solutions Association (MESA) international organization.

The evolution of industries and their needs towards the implementation of Industry 4.0 based systems has brought both new technological challenges and opportunities. This article proposes the adoption and deployment of cloud robotics at factories to enhance the control and monitoring of processes, such as handling materials multiple assemblies in single cells. The ultimate research objective of this research is the offloading computation and integrating cloud robotics into an industrial scenario. However, the investigation of state of the art techniques, tools and technologies, and the development of functional prototypes is beforehand required. Then, this article presents a small-scale system as a prototype that employs the Google cloud vision API as a resource that, in turn, is used by networked agents for supporting the decision-making in the process of handling material commodities at factory shop floor. The overall concept as well as the interaction between the main actors of the prototype is detailed. Finally, further research directions are discussed.

Transformation of synchronous data flow graphs (SDF) into equivalent homogeneous SDF representations has been extensively applied as a pre-processing stage when mapping signal processing algorithms onto parallel platforms. While this transformation helps fully expose task and data parallelism, it also presents several limitations such as an exponential increase in the number of actors and excessive communication overhead. Partial expansion graphs were introduced to address these limitations for multi-core platforms. However, existing solutions are not well-suited to achieve efficient scheduling on many-core architectures. In this article, we develop a new approach that employs cyclo-static data flow techniques to provide a simple but efficient method of coordinating the data production and consumption in the expanded graphs. We demonstrate the advantage of our approach through experiments on real application models.

Differential modulation has largely re-attracted the attention of academia and industry due to its advantages relating to simple implementation and no need for knowledge of channel state information. The present work analyzes the average bit error rate performance of dual-hop cooperative systems over generalized multipath fading conditions. The considered system is differentially modulated and is assumed to operate based on the amplify-and-forward relaying protocol. Therefore, the main advantage of the considered set up is that it does not require any channel state information neither at the relay nor at the destination nodes. Novel closed-form expressions are derived for the end-to-end error rate under asymmetric generalized multipath fading conditions, which are encountered in realistic wireless communication scenarios. These expressions are subsequently employed in quantifying the effect of generalized fading conditions on the achieved bit error rate performance. It is shown that the impact of multipath fading and shadowing effects is detrimental at both high and low signal-to-noise ratio regimes as the corresponding deviations are often close to an order of magnitude. The incurred difference is also significantly different than the conventional Rayleigh fading conditions, which verifies that accurate channel characterization is of paramount importance in the effective design of conventional and emerging wireless technologies. In addition, it indicates that differential modulation can be a suitable modulation scheme for relay systems, under certain conditions, since it can provide adequate performance at a reduced implementation complexity.

Novel composite fading models were recently proposed based on inverse gamma distributed shadowing conditions. These models were extensively shown to provide remarkable modeling of the simultaneous occurrence of multipath fading and shadowing phenomena in emerging wireless scenarios such as cellular, off-body and vehicle-to-vehicle communications. Furthermore, the algebraic representation of these models is rather tractable, which renders them convenient to handle both analytically and numerically. The present contribution presents the major theoretical and practical characteristics of the η - μ / inverse gamma composite fading model, followed by a thorough ergodic capacity analysis. To this end, novel analytic expressions are derived, which are subsequently used in the evaluation of the corresponding system performance. In this context, the offered results are compared with respective results from cases assuming conventional fading conditions, which leads to the development of numerous insights on the effect of the multipath fading and shadowing severity on the achieved capacity levels. It is expected that these results will be useful in the design of timely and highly demanding wireless technologies, such as wearable, cellular and inter-vehicular communications as well in wireless power transfer based applications in the context of the Internet of Things.

An optimum packet length selection scheme to maximize the throughput of a smart utility network (SUN) is introduced under wireless local area network (WLAN) interference system. The traditional and the investigated segmented packet collision models (PCM) are compared in terms of packet error rate (PER) and maximum achievable throughput. Furthermore, we quantify the impact of minimum mean square error (MMSE) interference mitigation for the SUN in the coexistence of WLAN interfering packets over multipath Rayleigh fading channel. The effect of the distance between the WLAN transmitter and the SUN receiver on the probability of error is also investigated.

Non-orthogonal multiple access (NOMA) has been recently proposed as a viable technology that can potentially improve the spectral efficiency of fifth generation (5G) wireless networks and beyond. However, in practical communication scenarios, transceiver architectures inevitably suffer from radio-frequency (RF) front-end related impairments that can lead to degradation of the overall system performance, with in-phase/quadrature-phase imbalance (IQI) constituting a major impairment in direct-conversion transceivers. In the present work, we quantify the effects of joint transmitter/receiver IQI on the performance of NOMA based multi-carrier (MC) systems under multipath fading conditions. Furthermore, we derive the asymptotic diversity order of the considered MC NOMA set up. Capitalizing on these results, we demonstrate that the effects of IQI differ considerably among NOMA users and depend on the underlying system parameters. For example, it is shown that the first sorted user appears more robust to IQI, which indicates that higher order users are more sensitive to the considered non-negligible impairment.

Design and implementation of smart vision systems often involve the mapping of complex image processing algorithms into efficient, real-time implementations on multicore platforms. In this paper, we describe a novel design tool that is developed to address this important challenge. A key component of the tool is a new approach to hierarchical dataflow scheduling that integrates a global scheduler and multiple local schedulers. The local schedulers are lightweight modules that work independently. The global scheduler interacts with the local schedulers to optimize overall memory usage and execution time. The proposed design tool is demonstrated through a case study involving an image stitching application for large scale microscopy images.

Directional deafness problem is one of the most important challenges in beamforming-based channel access at mmWave frequencies, which is believed to have detrimental effects on system performance in form of excessive delays and significant packet drops. In this paper, we contribute a quantitative analysis of deafness in directional random access systems operating in unlicensed bands by relying on stochastic geometry formulations. We derive a general numerical approach that captures the behavior of deafness probability as well as provide a closed-form solution for a typical sector-shaped antenna model, which is then may be extended to a more realistic two-sector pattern. Finally, employing contemporary IEEE 802.11ad modeling numerology, we illustrate our analysis revealing the importance of deafness-related considerations and their system- level impact.

The use of extremely high frequency (EHF) bands, known as millimeter-wave (mmWave) frequencies, requires densification of cells to maintain system performance at required levels. This may lead to potential increase of interference in practical mmWave networks, thus making it the limiting factor. On the other hand, attractive utilization of dual-polarized antennas may improve over this situation by mitigating some of the interfering components, which can be employed as part of interference control techniques. In this paper, an accurate two-stage ray-based characterization is conducted that models interference-related metrics while taking into account a detailed dual- polarized antenna model. In particular, we confirm that narrower pencil-beam antennas (HPBW = 13) have significant advantages as compared to antennas with relatively narrow beams (HPBW = 20 and HPBW = 50) in the environments with high levels of interference. Additionally, we demonstrate that in the Manhattan grid deployment a transition from interference- to noise-limited regime and back occurs at the cell inter-site distances of under 90 m and over 180 m, respectively.

Recently, new opportunities for utilizing extremely high frequencies have become instrumental in developing fifth-generation(5G) mobile technology. The use of highly directional antennas in millimeter-wave (mmWave) bands poses an important question of whether two- dimensional modeling suffices to capture the resulting system performance. Accounting for the effects of human body blockage by mmWave transmissions, in this work we compare the performance of the conventional two-dimensional and the proposed three- dimensional modeling. With our stochastic geometry based approach, we consider the aggregate interference and signal-to- interference ratio (SIR) to be the main metrics of interest. Both counterpart models attempt to capture the inherent behavior of 5G mmWave systems by incorporating the effects of human body blockage and antenna directivity. We thus deliver a realistic numerical assessment by comparing the three-dimensional modeling with its two-dimensional projection to reveal the resulting discrepancy.

Current forecasts predict that the Industrial Internet of Things (IIoT) will comprise about 10 billion devices by 2020. Because of its unique and novel demands, the emerging concepts of Industry 4.0 and SmartGrid networks have recently been coined and are now well established in the technical speech. In this work, we propose a newly developed multi-platform software tool aimed at testing the capabilities of Wireless MBus (WM-Bus) networks via simulating sensor-like behavior in uni-directional communication with a remote data concentrator. Building on our developed machine-type communication gateway (MTCG) able to receive WM-Bus data, we extend the set of features and introduce new machine-type communication device (MTCD) capable of emulating the corresponding data transmissions. As utility companies lack these features, our software implementation and hardware design open the door to initial verification of WM-Bus-based data transmissions for them without the need for investing into expensive development and certification of smart meters, where WM-Bus is utilized for data transmissions.

In this paper, we present a new software tool, called HTGS Model-based Engine (HMBE), for the design and implementation of multicore signal processing applications. HMBE provides complementary capabilities to HTGS (Hybrid Task Graph Scheduler), which is a recently-introduced software tool for implementing scalable workflows for high performance computing applications. HMBE integrates advanced design optimization techniques provided in HTGS with model-based approaches that are founded on dataflow principles. Such integration contributes to (a) making the application of HTGS more systematic and less time consuming, (b) incorporating additional dataflow-based optimization capabilities with HTGS optimizations, and (c) automating significant parts of the HTGS-based design process. In this paper, we present HMBE with an emphasis on novel dynamic scheduling techniques that are developed as part of the tool. We demonstrate the utility of HMBE through a case study involving an image stitching application for large scale microscopy images.

In this paper, we propose a novel framework, called Hierarchical MDP framework for Compact System-level Modeling (HMCSM), for design and implementation of adaptive embedded signal processing systems. The HMCSM framework applies Markov decision processes (MDPs) to enable autonomous adaptation of embedded signal processing under multidimensional constraints and optimization objectives. The framework integrates automated, MDP-based generation of optimal reconfiguration policies, dataflow-based application modeling, and implementation of embedded control software that carries out the generated reconfiguration policies. HMCSM systematically decomposes a complex, monolithic MDP into a set of separate MDPs that are connected hierarchically, and that operate more efficiently through such a modularized structure. We demonstrate the effectiveness of our new MDP-based system design framework through experiments with an adaptive wireless communications receiver.

The main target of this paper is to perform the multidimensional analysis of multipath propagation at higher frequencies i.e. 15 GHz and 28 GHz, using 'sAGA' a 3D ray tracing tool. A real world outdoor Line of Sight (LOS) microcellular environment from the Yokusuka city of Japan is considered for the analysis. The simulation data acquired from the 3D ray tracing tool includes the received signal strength, power angular spectrum and the power delay profile. The different propagation mechanisms were closely analyzed. The simulation results show the difference of propagation at two frequencies i.e. 15 GHz and 28 GHz and draw a special attention on the impact of diffuse scattering at 28 GHz. In a simple outdoor microcellular environment with a valid LOS link between the transmitter and a receiver, a path loss difference of around 5.7 dB was found between 15 GHz and 28 GHz frequency of operation. However, the propagation loss at higher frequency can be compensated by using the antenna with narrow beamwidth and larger gain.

The main target of this research work is to study the provision of indoor service (coverage) using outdoor base stations at higher frequencies i.e. 10 GHz in the context of a single building scenario. In an outdoor to indoor propagation, an angular wall loss model is used in the General Building Penetration (GBP) model for estimating the additional loss at the intercept point of the building exterior wall. A novel angular wall loss model based on a separate incidence angle in azimuth and elevation plane is proposed in this paper. In the second part of this study, an Extended Building Penetration (EBP) model is proposed, and the performance of EBP model is compared with the GBP model. In EBP model, the additional fifth path known as the 'Direct path' is proposed to be included in the GBP model. Based on the evaluation results, the impact of the direct path is found significant for the indoor users having the same or closed by height as that of the height of the transmitter.

This paper investigates the feasibility of a radio access system with a self-backhauling access node under full-duplex and half-duplex operation modes. In particular, after making certain simplifying assumptions, closed-form solutions for the feasibility conditions of such a radio access system are derived for both of the considered operation modes. Furthermore, the analysis incorporates given quality of service (QoS) constraints for the system, defined in terms of minimum data rates. The numerical results show that the full-duplex scheme outperforms the corresponding half-duplex scheme under most circumstances, both in terms of the highest achievable rates and the highest tolerable path losses, when given the same QoS target. However, this requires a certain amount of self-interference attenuation in the access node. Performing a similar feasibility analysis without any simplifications in the system model is an important future work item.

In-band full-duplex (FD) operation can be regarded as one of the greatest discoveries in civilian/commercial wireless communications so far in this century. The concept is significant because it can as much as double the spectral efficiency of wireless data transmission by exploiting the new-found capability for simultaneous transmission and reception (STAR) that is facilitated by advanced self-interference cancellation (SIC) techniques. As the first of its kind, this paper surveys the prospects of exploiting the emerging FD radio technology in military communication applications as well. In addition to spectrally efficient two-way data transmission, the STAR capability could give a major technical advantage for armed forces by allowing their radio transceivers to conduct electronic warfare at the same time when they are also receiving or transmitting information signals at the same frequency band. After providing a detailed introduction to FD transceiver architectures and SIC requirements in military communications, this paper outlines and analyzes some potential defensive and offensive applications of the STAR capability.