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During the New Space era small countries are becoming important players in the space business. While space activities are rapidly increasing globally, it is important to make operations in a sustainable and safe way in order to preserve satellite services for future generations. Unfortunately, the sustainability aspect has been largely overlooked in the existing surveys on space technologies. As a result, in this survey paper, we discuss the multi-layer networking approaches in the 6G era specifically from the sustainability perspective. Moreover, our comprehensive survey includes aspects of some interesting industrial, proprietary, and standardization views. We review the most important regulations and international guidelines and revisit a three-dimensional architecture vision to support the sustainability target for a variety of application areas. We then classify and discuss space safety paradigms that are important sustainability enablers of future satellite communications. These include space traffic management, debris detection, environmental impacts, spectrum sharing, and cyber security aspects. The paper also discusses advances towards a planned European connectivity constellation that could become a third flagship infrastructure along with the Galileo and Copernicus systems. Finally, we define potential research directions into the 2030s.

Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.

Publisher Copyright: Author In this paper, we describe how the up-to-date state of a digital twin, and its corresponding simulation model, can be used as a fitness function of an evolutionary algorithm for optimizing a large-scale industrial process. An ICT architecture is presented for solving the computational challenges that arise when the fitness function evaluation takes considerable amount of time. Parallel computation of the fitness function in a cloud computing environment is proposed and the evolutionary algorithm is connected to the computational environment using the Function-as-a-Service approach. A case-study was conducted on the district heating network of Espoo, the second largest city in Finland. The study shows that the architecture is suited for optimizing the operating costs of the large district heating network, with over 800 km of water pipes and over 14 heat producers, reaching a cost-saving of an average of 2%, and up-to 4%, over the current industrial state-of-the-art method in use at the city of Espoo.

Effective, long-lasting Industrial IoT (IIoT) solutions start with short-term gains and progressively mature with added capabilities and value. The heterogeneous nature of IIoT devices and services suggests frequent changes in resource requirements for different services, applications, and use cases. With such unpredictability, resource orchestration can be quite complicated even in basic use cases and almost impossible to handle in some extensively dynamic use cases. In this paper, we propose SDRM; an SDN-enabled Resource Management scheme. This novel orchestration methodology automatically computes the optimal resource allocation for different IIoT network models and dynamically adjust assigned resources based on predefined constraints to ensure Service Level Agreement (SLA). The proposed approach models resource allocation as a Constraint Satisfaction Problem (CSP) where optimality is based on the solution of a predefined Satisfiability (SAT) problem. This model supports centralized management of all resources using a software defined approach. Such resources include memory, power, bandwidth, and edge-cloud resources. SDRM aims at accelerating efficient resource orchestration through dynamic workload balancing and edge-cloud resource utilization, thereby reducing the cost of IIoT system deployment and improving the overall ROI for adopting IIoT solutions. We model our resource allocation approach on SAVILE ROW using ESSENSE PRIME modeling language, we then implement the network model on CloudSimSDN and PureEdgeSim. We present a detailed analysis of the system architecture and the key technologies of the model. We finally demonstrate the efficiency of the model by presenting experimental results from a prototype system. Our test results show an extremely low solver time ranging from 0.47 ms to 0.5 ms for nodes ranging from 100 to 500 nodes. With edge-cloud collaboration, our results show about 4 percent improvement in overall task success rates.

Optimal management of demand-side flexibility in buildings is important for properly integrating large amounts of intermittent generation from windmills and photovoltaics. This paper proposes a novel Energy Management Agent (EMA) concept that can optimize building’s energy costs with respect to external prices while at the same time allow building’s flexibility to be used via explicit demand response. The EMA combines Artificial Neural Networks (ANN) and model predictive control for modelling and optimization of building’s flexibility. It continuously manages building’s flexibility with respect to external prices and provides forecasts of the load and available flexibility for a defined time window. A proof-of-concept (PoC) of the EMA is implemented for controlling a heat pump in an apartment, located in Oulu, Finland. Two ANN-based models were implemented for modelling the energy consumption of the heat pump and the indoor temperature of the apartment. Monte Carlo Tree Search based planning and control was implemented for finding optimal control policies with the ANNs. The EMA PoC was evaluated in 16-week period between 11 November 2019 - 1 March, 2020. When compared to a fixed setpoint control strategy, the EMA achieved 14.8 % lower costs under Nord Pool spot prices for Finland. At the same time, it was also able to accurately follow the 24h load plans (NRMSE was 0.050) and activate the offered flexibilities (NRMSE was 0.074).

The communication infrastructure is most likely to be damaged after a major disaster occurred, which would lead to further chaos in the disaster stricken area. Modern rescue activities heavily rely on the wireless communications, such as safety status report, disrupted area monitoring, evacuation instruction, rescue coordination, etc. Large amount of data generated from victims, sensors and responders must be delivered and processed in a fast and reliable way, even when the normal communication infrastructure is degraded or destroyed. To this end, reconstructing the post-disaster network by deploying MDRU (Movable and Deployable Resource Unit) and relay unit at edge is a very promising solution. However, the optimal wireless access control in this heterogeneous hastily formed network is extremely challenging, due to the frequent varying environment and the lack of statistics information in advance in post-disaster scenarios. In this paper, we propose a learning based wireless access control approach for edge-aided disaster response network. More specifically, we model the wireless access control procedure as a discrete-time single agent Markov decision process, and solve the problem by exploiting deep reinforcement learning technique. By extensive simulation results, we show that the proposed mechanism significantly outperforms the baseline schemes in terms of delay and packet drop rate.

Economic load dispatch (ELD) in power system problems involves scheduling the power generating units to minimize cost and satisfy system constraints. Although previous works propose solutions to reduce CO2 emission and production cost, an optimal allocation needs to be considered on both cost and emission—leading to combined economic and emission dispatch (CEED). Metaheuristic optimization algorithms perform relatively well on ELD problems. The gradient-based optimizer (GBO) is a new metaheuristic algorithm inspired by Newton’s method that integrates both the gradient search rule and local escaping operator. The GBO maintains a good balance between exploration and exploitation. Also, the possibility of the GBO getting stuck in local optima and premature convergence is rare. This paper tests the performance of GBO in solving ELD and CEED problems. We test the performance of GBO on ELD for various scenarios such as ELD with transmission losses, CEED and CEED with valve point effect. The experimental results revealed that GBO has been obtained better results compared to eight other metaheuristic algorithms such as Slime mould algorithm (SMA), Elephant herding optimization (EHO), Monarch butterfly optimization (MBO), Moth search algorithm (MSA), Earthworm optimization algorithm (EWA), Artificial Bee Colony (ABC) Algorithm, Tunicate Swarm Algorithm (TSA) and Chimp Optimization Algorithm (ChOA). Therefore, the simulation results showed the competitive performance of GBO as compared to other benchmark algorithms.

Industry 4.0 encompasses a promise of a new industrial revolution in terms of providing secure, intelligent, autonomous and self-adaptive industrial IoT (IIoT) networks. Key industrial applications and systems will be significantly more complex due to the involvement of the vast number of different devices and diverse nature of various stakeholders and service providers. These complex industrial processes, services and applications also have strict requirements in terms of performance — latency in particular — and resource-efficiency, together with high standards for security and trust. In this context, Blockchain and Edge Computing emerge as prominent technologies to address the mentioned essential requirements and to further strengthen the rise of the new era of digitization. The Edge computing paradigm ensures low latency services for IIoT applications while optimizing the network usage, whereas Blockchain provides a decentralized way for ensuring data integrity, trust and security. In this paper, we propose a “BlockEdge” framework that combines these two enabling technologies to address some of the critical issues faced by the current IIoT networks. We verify the feasibility of our approach by evaluating the performance and resource-efficiency of BlockEdge in terms of latency, power consumption and network usage, through simulations against non-Blockchain solution.

Echocardiogram (echo) is the earliest and the primary tool for identifying regional wall motion abnormalities (RWMA) in order to diagnose myocardial infarction (MI) or commonly known as heart attack. This paper proposes a novel approach, Active Polynomials, which can accurately and robustly estimate the global motion of the Left Ventricular (LV) wall from any echo in a robust and accurate way. The proposed algorithm quantifies the true wall motion occurring in LV wall segments so as to assist cardiologists diagnose early signs of an acute MI. It further enables medical experts to gain an enhanced visualization capability of echo images through color-coded segments along with their 'maximum motion displacement' plots helping them to better assess wall motion and LV Ejection-Fraction (LVEF). The outputs of the method can further help echo-technicians to assess and improve the quality of the echocardiogram recording. A major contribution of this study is the first public echo database collection composed by physicians at the Hamad Medical Corporation Hospital in Qatar. The so-called HMC-QU database will serve as the benchmark for the forthcoming relevant studies. The results over HMC-QU dataset show that the proposed approach can achieve 87.94% accuracy, 92.86% sensitivity and 87.64% precision in MI detection even though the echo quality is quite poor and the temporal resolution is low.

Digitalization is strongly entering new application domains around the world, thus also entering people’s everyday lives. Digitalization enables creating new kinds of businesses, but it also changes the way services, applications and data are provided and consumed. We carried out interviews with company representatives acting in the energy domain to receive up-to-date information about the companies’ awareness of digitalization, and the required changes they have identified in their future business scenarios. The interviewed companies were aware of the change pressure caused by digitalization, and especially the data exchange with other domain actors, predictions using data, new data-based services and data-based decision making were identified as key issues. However, currently there is a lack of digital infrastructure enabling the actors in the energy domain to perform data-based co-operation. The purpose of this paper is to identify the requirements for an energy data ecosystem that enables the companies in the energy domain to reach their business goals and to co-operate in data-based business. The initial goal is to identify the future tasks and goals of energy domain actors and define the type of co-operation environment, including knowledge management models and supporting services, required to support them. The research was implemented with industry actors in the energy domain that represent several different roles in energy markets, and the results of the research were also validated among the industry.

Before the implementation of a solution, it is cost-efficient and practical to be able to evaluate and analyze the expected value of use cases. Especially, this is emphasized in blockchain (BC) use cases, which typically have a wide business ecosystem and possibly disruptive business models. This paper presents two BC use case value evaluations and results. The IoT use cases were selected from two different industry segments: telecommunications-oriented 5G network slice brokering and the energy industry-related internal electricity allocation in a housing society. The use case value was assessed by applying a resource configuration framework and 4C - commerce, context, content, and connection - business model typology against BC and smart contracts characteristics and capabilities. The results derived from the data collected from the expert workshops proved the expected value of the use cases, and in general, the feasibility of BC technology for facilitating various value-creating resource configuration processes was shown. Furthermore, the resource configuration framework proved to be a valuable theoretical approach for analyzing and developing also the BC-enabled novel use cases and business models. According to the findings, further development of the framework is proposed with an introduced novel decentralized resource configuration prototype that can replace predominant platform-based business models.

As communication and computation systems become more complex and target at higher performance, the fundamental limits of nature can be expected to constrain their development and optimization. This calls for intelligent use of basic resources, that is, materials, energy, information, time, frequency, and space. We present a multidisciplinary and historical review on the body of knowledge that can be applied in researching such intelligent and resource-efficient systems. We review general system theory, decision theory, control theory, computer science, and communication theory. While multidisciplinarity has been recognized important, there are no earlier reviews covering all these five disciplines. Based on the review, we build a chronology of intelligent systems and identify connections between the disciplines. Optimization, decision-making, open- and closed-loop control, hierarchy, and degree of centralization turn out as recurring themes in these disciplines, which have converged to similar solutions that are based on remote control, automation, autonomy, and self-organization. We use future wireless networks as an example to illustrate the open questions and how they can be addressed by applying multidisciplinary knowledge. This paper can help researchers to use knowledge outside their own field and avoid repeating the work done already. The resulting consolidated view can speed up research and is especially important when the fundamental limits of nature are approached and new insights are required to overcome the challenges. The general, long-standing problem to be tackled is multiobjective optimization with autonomous and distributed decision-making in an uncertain, dynamic, and nonlinear environment where the objectives are mutually conflicting.

It is widely acknowledged that the forthcoming 5G architecture will be highly heterogeneous and deployed with high degree of density. These changes over the current 4G bring many challenges on how to achieve an efficient operation from the network management perspective. In this paper, we introduce revolutionary vision of the future 5G wireless networks, in which operating the wireless networks is no longer limited by hardware or even software. Specifically, by the idea of virtualizing the wireless networks, which has recently gained increasing attention, we introduce the everything-as-a-service (XaaS) taxonomy to light the way towards designing the service-oriented wireless networks. The concepts and challenges along with the research opportunities for realizing XaaS in wireless networks are overviewed and discussed.

The explosive growth of mobile date traffic and ubiquitous mobile services cause an high energy consumption in mobile devices with limited energy supplies, which has become a bottleneck for deploying device-to-device (D2D) communication. Simultaneous wireless information and power transfer (SWIPT), which enables mobile devices to harvest energy from the radio frequency signals, has emerged as a promising solution to improve the energy efficiency (EE) performance. In this paper, we address joint power control and spectrum resource allocation problem in SWIPT-based energy-harvesting D2D underlay networks. First, we formulate joint optimization problem as a 2-D matching between D2D pairs and cellular user equipments (CUEs), and propose a preference establishment algorithm based on Dinkelbach method and Lagrange dual decomposition. Second, we propose an energy-efficient stable matching algorithm by exploring the Gale-Shapley algorithm, which is able to maximize the EE performance of D2D pairs and the amount of energy harvested by CUEs simultaneously. Third, we provide in-depth theoretical analysis of the proposed matching algorithm in terms of stability, optimality, and complexity. Simulation results demonstrate that the proposed algorithm can bring significant EE performance gains compared with some heuristic algorithms.

Simulation has become an established technique to support the design of complex, mechatronic or cyber-physical systems. Ideally, simulations should already be performed at an early design phase before high-cost design commitments are made, and the recent advances in the digitalization of design information open possibilities for automatic generation of simulation models. However, high-fidelity simulation model building depends on accurate data. In particular, first-principles models are desirable source information for simulations, but such models generally are not available at an early design stage. This paper investigates the automatic generation of first-principles 3-D models for piping intensive systems based on design information that is available at an early design stage, namely piping & instrumentation diagrams (P&ID). An algorithm is presented for the generation of such 3-D models based on machine-readable P&ID information. The main focus of the algorithm is the automatic generation of feasible pipelines into the 3-D models, so that the model has sufficient information, which can be exploited in further work to automatically generate high fidelity first-principles thermo-hydraulic simulations.

The use of freely available online data is rapidly increasing, as companies have detected the possibilities and the value of these data in their businesses. In particular, data from social media are seen as interesting as they can, when properly treated, assist in achieving customer insight into business decision making. However, the unstructured and uncertain nature of this kind of big data presents a new kind of challenge: how to evaluate the quality of data and manage the value of data within a big data architecture? This paper contributes to addressing this challenge by introducing a new architectural solution to evaluate and manage the quality of social media data in each processing phase of the big data pipeline. The proposed solution improves business decision making by providing real-time, validated data for the user. The solution is validated with an industrial case example, in which the customer insight is extracted from social media data in order to determine the customer satisfaction regarding the quality of a product.

Software development is facing new challenges as a result of evolution toward integration and collaboration-based service engineering, which embody high degrees of dynamism both at design time and run-time. Short times-to-market require cost reduction by maximizing software reuse. Openness for new innovations presumes a flexible development platform and fast software engineering practices. User satisfaction assumes situation-based applications of high quality. The main contribution of this paper is the piecemeal service engineering (PSE) approach developed for and tested in application development for smart spaces. The intent of PSE is to maximize the reuse of existing knowledge of business and design practices and existing technical assets in the development of new smart-space applications. Business knowledge is mostly informal and domain-dependent, but architectural knowledge is generic, at least semiformal, and represented in principles, ontologies, patterns, and rules that together form a reusable architectural knowledge base for fast smart-space application development. The PSE facilitates the incremental development of intelligent applications by supporting abstraction, aggregation, and adaptability in smart-space development.

During the New Space era small countries are becoming important players in the space business. While space activities are rapidly increasing globally, it is important to make operations in a sustainable and safe way in order to preserve satellite services for future generations. Unfortunately, the sustainability aspect has been largely overlooked in the existing surveys on space technologies. As a result, in this survey paper, we discuss the multi-layer networking approaches in the 6G era specifically from the sustainability perspective. Moreover, our comprehensive survey includes aspects of some interesting industrial, proprietary, and standardization views. We review the most important regulations and international guidelines and revisit a three-dimensional architecture vision to support the sustainability target for a variety of application areas. We then classify and discuss space safety paradigms that are important sustainability enablers of future satellite communications. These include space traffic management, debris detection, environmental impacts, spectrum sharing, and cyber security aspects. The paper also discusses advances towards a planned European connectivity constellation that could become a third flagship infrastructure along with the Galileo and Copernicus systems. Finally, we define potential research directions into the 2030s.