5G networks will provide opportunities for the creation of new services, for new business models, and for new players to enter the mobile market. The networks will support efficient and cost-effective launch of a multitude of services, tailored for different vertical markets having varying service and security requirements, and involving a large number of actors. Key technology concepts are network slicing and network softwarisation, including network function virtualisation and software-defined networking. The presented security architecture builds upon concepts from the 3G and 4G security architectures but extends and enhances them to cover the new 5G environment. It comprises a toolbox for security relevant modelling of the systems, a set of security design principles, and a set of security functions and mechanisms to implement the security controls needed to achieve stated security objectives. In a smart city use case setting, we illustrate its utility; we examine the high-level security aspects stemming from the deployment of large numbers of IoT devices and network softwarisation.

With the emergence of cyber-physical systems (CPSs) in utility systems like electricity, water, and gas networks, data collection has become more prevalent. While data collection in these systems has numerous advantages, it also raises concerns about privacy as it can potentially reveal sensitive information about users. To address this issue, we propose a Bayesian approach to control the adversarial inference and mitigate the physical-layer privacy problem in CPSs. Specifically, we develop a control strategy for the worst-case scenario where an adversary has perfect knowledge of the user’s control strategy. For finite state-space problems, we derive the fixed-point Bellman’s equation for an optimal stationary strategy and discuss a few practical approaches to solve it using optimization-based control design. Addressing the computational complexity, we propose a reinforcement learning approach based on the Actor-Critic architecture. To also support smart meter privacy research, we present a publicly accessible ’Co-LivEn’ dataset with comprehensive electrical measurements of appliances in a co-living household. Using this dataset, we benchmark the proposed reinforcement learning approach. The results demonstrate its effectiveness in reducing privacy leakage. Our work provides valuable insights and practical solutions for managing adversarial inference in cyber-physical systems, with a particular focus on enhancing privacy in smart meter applications. 

Communication networks are becoming increasingly important in military operations, with task fields like time-critical targeting, covert special operations, command and control, training, and logistics, all relying heavily on the communication network and its services. On the other hand, commercial communication has dramatically transformed our society and the way we communicate. The newest network mode at present, 5G and beyond (5GB), is characterized by high speed, low latency, high reliability, and high communication density. Although the use of 5GB commercial networks for defense agencies can offer greater flexibility and efficiency, they also face a new challenge that requires high standards of network protection and harsh working conditions and environments. In this paper, we discuss the significance of communication networks in several potential military applications, particularly for warfare, training/drilling, logistics, and special mission-specific stations. We present the communication trends adopted in military applications. Then, we open up various 5GB key performance indexes and their use cases for the military communication systems. We also elaborate on unique challenges of the military communication networks that are unlikely to be resolved via commercial 5GB research. The various 5GB enabling technologies for military communication systems are discussed. Lastly, we present and analyze 5GB new radio for the private military communication under C-band.

The neutral host network (NHN) is a new self-contained network envisioned by fifth generation (5G) of cellular networks, which offers wireless connection to its subscribers from a variety of service providers, including both conventional mobile network operators and non-conventional service providers. The NHN infrastructure, which is operated and maintained by a third neutral party, is rented or leased to network operators looking to scale up their network capacities and coverage in a cost-effective way. This paper highlights NHN as an emerging communication technology for private networks and discuss its opportunities and challenges in realizing multi-tenanted space such as factory, hospitals, stadiums, and universities. The paper also investigates the current state of the art in NHN and elaborates on the underlying enabling technologies for the NHN. Lastly, an efficient radio access network (RAN) slicing scheme based on the multi-arm bandit approach has been proposed to allocate radio resources to various slices, which maximizes resource utilization while guaranteeing the availability of resources to meet the capacity needs of each multi-tenanted operator. The simulation results show that the proposed Thompson's sampling (TS)-based approach performs best in finding the optimal RAN slice for all the operators. 

For most modern data centres, it is of high value to select practical methods for improving energy efficiency and reducing energy waste. IT-equipment and cooling systems are the two most significant energy consumers in data centres, thus the energy efficiency of any data centre mainly relies on the energy efficiency of its computational and cooling systems. Existing techniques of optimising the energy usage of both these systems have to be compared. However, such experiments cannot be conducted in real plants as they may harm the electronic equipment. This paper proposes a modelling toolbox which enables building models of data centres of any scale and configuration with relative ease. The toolbox is implemented as a set of building blocks which model individual components of a typical data centre, such as processors, local fans, servers, units of cooling systems, it provides methods of adjusting the internal parameters of the building blocks, as well as contains constructors utilising the building blocks for building models of data centre systems of different levels from server to the server room. The data centre model is meant to accurate estimating the energy consumption as well as the evolution of the temperature of all computational nodes and the air temperature inside the data centre. The constructed model capable of substitute for the real data centre at examining the performance of different energy-saving strategies in dynamic mode: the model provides information about data centre operating states at each time point (as model outputs) and takes values of adjustable parameters as the control signals from system implementing energy-saving algorithm (as model inputs). For Module 1 of the SICS ICE data centre located in Luleå, Sweden, the model was constructed from the building blocks. After adjusting the internal parameters of the building blocks, the model demonstrated the behaviour quite close to real data from the SICS ICE data centre. Therefore the model is applicable to use as a substitute for the real data centre. Some examples of using the model for testing energy-saving strategies are presented at the end of the paper.

Over the past few decades, Industrial Control Systems (ICS) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. Although a few commonly used datasets may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. This paper introduces the ’ICS-Flow’ dataset, which offers network data and process state variables logs for supervised and unsupervised ML-based IDS assessment. The network data includes normal and anomalous network packets and flows captured from simulated ICS components and emulated networks, where the anomalies were applied to the system through various cyberattacks. We also proposed an open-source tool, ’ICSFlowGenerator,’ for generating network flow parameters from Raw network packets. The final dataset comprises over 25,000,000 raw network packets, network flow records, and process variable logs. The paper describes the methodology used to collect and label the dataset and provides a detailed data analysis. Finally, we implement several ML models, including the decision tree, random forest, and artificial neural network to detect anomalies and attacks, demonstrating that our dataset can be used effectively for training intrusion detection ML models.

In this work we develop a rewarding framework that can be used to enhance existing crowd-sensing applications. Although a core requirement of such systems is user engagement, people may be reluctant to participate because sensitive information about them may be leaked or inferred from submitted data. The use of monetary rewards can help incentivize participation, thereby increasing not only the amount but also the quality of sensed data. Our framework allows users to submit data and obtain Bitcoin payments in a privacy-preserving manner, preventing curious providers from linking the data or the payments back to the user. At the same time, it prevents malicious user behavior such as double-redeeming attempts, where a user tries to obtain rewards for multiple submissions of the same data. More importantly, it ensures the fairness of the exchange in a completely trustless manner; by relying on the Blockchain, the trust placed on third parties in traditional fair exchange protocols is eliminated. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, only the simplest of Blockchain transactions are used as opposed to prior works that are based on the use of more complex smart contracts.

This paper presents an accurate analytical method for calculating the leakage inductance of shell-type E-core transformers with rectangular windings. For this purpose, first, an expression for calculating the leakage inductance per unit length inside the core window considering the core walls as the flux-normal boundary condition is derived. Then, a new accurate method for determining the Mean Length of Turns (MLT) based on the total stored energy is presented. The MLT is needed for the leakage inductance calculation using 2-D methods. By dividing the MLT into three partial lengths and calculating the corresponding leakage inductances using three different core window arrangements, the effect of core structure on the total leakage inductance is considered. The method is verified by 3-D FEM simulations as well as the leakage inductance measurements on two different fabricated transformer prototypes. The superiority of the method is also confirmed by comparisons with the previous analytical approaches. The proposed method enables the leakage inductance calculation with an error less than 1%, compared to the 3-D FEM results. Using the presented method, the leakage inductance calculations can be performed rapidly and accurately in the design stage without the need for time-consuming 3-D FEM simulations. CCBYNCND

The leakage field in shell-type transformers is strongly affected by the boundary conditions introduced by the core walls and thus the effect of the core should be considered properly in the leakage inductance calculation. In this paper, a new method for accurate calculation of the leakage inductance of shell-type multi core-segment transformers with circular windings is presented. For this purpose, first, the expressions for self and mutual inductances are derived in cylindrical coordinates considering the core walls as the flux-normal boundary condition. Then, a new approach is proposed for calculating the leakage inductance considering the number and dimensions of the used core segments. The method is developed at first for single and double core-segment transformers (known also as E-core and U-core transformers) and then adopted for shell-type segmented-core transformers. The method is verified by 3-D FEM simulations. The comparisons with the previous analytical methods demonstrate the superiority of the proposed method. A transformer prototype has been built and verification tests have been conducted. The comparisons show that the leakage inductance can be estimated with an error less than 1%, demonstrating a very high accuracy with the proposed method.

Communication security of an Internet-of-Things (IoT) product depends on the variety of protocols employed throughout its lifetime. The underlying low-power radio communication technologies impose constraints on maximum transmission units and data rates. Surpassing maximum transmission unit thresholds has an important effect on the efficiency of the solution: transmitting multiple fragments over low-power IoT radio technologies is often prohibitively expensive. Furthermore, IoT communication paradigms such as one-to-many require novel solutions to support the applications executing on constrained devices. Over the last decade, the Internet Engineering Task Force (IETF) has been working through its various Working Groups on defining lightweight protocols for Internet-of-Things use cases. “Lightweight” refers to the minimal processing overhead, memory footprint and number of bytes in the air, compared to the protocol counterparts used for non-constrained devices in the Internet. This article overviews the standardization efforts in the IETF on lightweight communication security protocols. It introduces EDHOC, a key exchange protocol, OSCORE and Group OSCORE, application data protection protocols adapted for securing IoT applications. The article additionally highlights the design considerations taken into account during the design of these protocols, an aspect not present in the standards documents. Finally, we present an evaluation of these protocols in terms of the message sizes and compare with the non-constrained counterpart, the (D)TLS protocol. We demonstrate that the novel key exchange protocol EDHOC achieves ×5 reduction over DTLS 1.3 authenticated with pre-shared keys in terms of total number of bytes transmitted over the air, while keeping the benefits of authentication with asymmetric credentials.

Gear rolling is a forming process to produce gear wheels by plastic deformation. The advantage of the process is to eliminate the chip formation during production and also to improve the product properties since the non-metallic inclusions will be oriented along the cog surface and not perpendicular to it. The method has been developed in the past years for gear production for automobile application with modules up to 3 mm. The successful application of gear rolling in those cases raises the question regarding the feasibility of using cold rolling to manufacture gears with larger modules which can be used for heavy vehicles. In this paper, a gear wheel with normal module of 4 mm has been studied in order to investigate if such large modules can be manufactured by gear rolling. One of the issues in rolling of gears is the design of the blank geometry in order to obtain the right gear geometry after the rolling process. Blank shape modifications are necessary to control and to reduce the undesired shape deviations caused by the large plastic deformations in rolling. The blank modifications also help the process designer to control the forming force and torque. In this paper, the process has been modeled by finite element simulation and the influence of different blanks has been simulated. The validity of the FE model has been checked through several experiments. Both the numerical and experimental results revealed favorable blank modifications to apply for further developments of the gear rolling process.

The vulnerabilities in deployed IoT devices are a threat to critical infrastructure and user privacy. There is ample ongoing research and efforts to produce devices that are secure-by-design. However, these efforts are still far from translation into actual deployments. To address this, worldwide efforts towards IoT device and software certification have accelerated as a potential solution, including UK’s IoT assurance program, EU Cybersecurity Act and the US executive order 14028. In EU, the Cybersecurity Act was launched in 2019 which initiated the European cybersecurity certification framework for Internet and Communications Technology (ICT). The heterogeneity of the IoT landscape with devices ranging from industrial to consumer, makes it challenging to incorporate IoT devices in the certification framework or introduce a European cybersecurity certification scheme solely for IoT. This paper analyses the cybersecurity certification prospects for IoT devices and also places article 54 of the EU Cybersecurity Act in an international perspective. We conducted a comparative study of existing IoT certification schemes to identify potential gaps and extract requirements of a candidate IoT device security certification scheme. We also propose an approach that can be used as a template to instantiate an EU cybersecurity certification scheme for IoT devices. In the proposed template, we identify IoT-critical elements from the article 54 of the Cybersecurity Act. We also evaluate the proposed template using the ENISA qualification system for cybersecurity certification schemes and show its qualification on all criteria. 

Engaging in non-driving related tasks (NDRTs) while driving can be considered distracting and safety detrimental. However, with the introduction of highly automated driving systems that relieve drivers from driving, more NDRTs will be feasible. In fact, many car manufacturers emphasize that one of the main advantages with automated cars is that it “frees up time” for other activities while on the move. This paper investigates how well drivers are able to engage in an NDRT while in automated driving mode (i.e., SAE Level 4) in real traffic, via a Wizard of Oz platform. The NDRT was designed to be visually and cognitively demanding and require manual interaction. The results show that the drivers’ attention to a great extent shifted from the road ahead towards the NDRT. Participants could perform the NDRT equally well as when in an office (e.g. correct answers, time to completion), showing that the performance did not deteriorate when in the automated vehicle. Yet, many participants indicated that they noted and reacted to environmental changes and sudden changes in vehicle motion. Participants were also surprised by their own ability to, with ease, disconnect from driving. The presented study extends previous research by identifying that drivers to a high extent are able to engage in an NDTR while in automated mode in real traffic. This is promising for future of automated cars ability to “free up time” and enable drivers to engage in non-driving related activities.

Driven by the increasing number of connected vehicles and related services, powerful communication and computation capabilities are needed for vehicular communications, especially for real-time and safety-related applications. A cellular network consists of radio access technologies, including the current long-term evolution (LTE), the LTE advanced, and the forthcoming 5th generation mobile communication systems. It covers large areas and has the ability to provide high data rate and low latency communication services to mobile users. It is considered the most promising access technology to support real-time vehicular communications. Meanwhile, fog is an emerging architecture for computing, storage, and networking, in which fog nodes can be deployed at base stations to deliver cloud services close to vehicular users. In fog computing-enabled cellular networks, mobility is one of the most critical challenges for vehicular communications to maintain the service continuity and to satisfy the stringent service requirements, especially when the computing and storage resources are limited at the fog nodes. Service migration, relocating services from one fog server to another in a dynamic manner, has been proposed as an effective solution to the mobility problem. To support service migration, both computation and communication techniques need to be considered. Given the importance of protocol design to support the mobility of the vehicles and maintain high network performance, in this paper, we investigate the service migration in the fog computing-enabled cellular networks. We propose a quality-of-service aware scheme based on the existing handover procedures to support the real-time vehicular services. A case study based on a realistic vehicle mobility pattern for Luxembourg scenario is carried out, where the proposed scheme, as well as the benchmarks, are compared by analyzing latency and reliability as well as migration cost.

Traditional reliability evaluation of lighting sources often assesses only 50% of a lamp’s volume, which can lead to performance disparities and misapplications due to their limited reflection of real-world scenarios. To address the limitations, it is essential to adopt advanced asset management approaches that enhance awareness and provide a more comprehensive evaluation framework. This paper delves into the nuances of human-centric and integrative lighting asset management in Swedish public libraries, employing a qualitative field study to ascertain the alignment of current practices with these advanced lighting principles. Expanding library services to 20 high-latitude locations (>55° N) in Sweden, our research employed field observations, stakeholder interviews, and questionnaires, coupled with a thorough gap analysis, to understand the current landscape and stakeholder perceptions. Our findings reveal a dichotomy between the existing conditions of library lighting and the stakeholders’ experiences and expectations. Despite the intention to create conducive environments, there is a clear disconnect, with overt problems and covert challenges affecting user satisfaction and efficacy of lighting management. Managers, staff, and users reported varied concerns, including eye strain and discomfort, indicative of substantial room for improvement. The study advocates for a paradigm shift in not only lighting asset management but also reliability evaluation of lighting sources, moving toward continuous improvement, and enhanced awareness and training on human-centric and integrative lighting principles.

Quantum key distribution (QKD) is regarded as an alternative to traditional cryptography methods for securing data communication by quantum mechanics rather than computational complexity. Towards the massive deployment of QKD, embedding it with the telecommunication system is crucially important. Homogenous optical multi-core fibers (MCFs) compatible with spatial division multiplexing (SDM) are essential components for the next-generation optical communication infrastructure, which provides a big potential for co-existence of optical telecommunication systems and QKD. However, the QKD channel is extremely vulnerable due to the fact that the quantum states can be annihilated by noise during signal propagation. Thus, investigation of telecom compatibility for QKD co-existing with high-speed classical communication in SDM transmission media is needed. In this paper, we present analytical models of the noise sources in QKD links over heterogeneous MCFs. Spontaneous Raman scattering and inter-core crosstalk are experimentally characterized over spans of MCFs with different refractive index profiles, emulating shared telecom traffic conditions. Lower bounds for the secret key rates and quantum bit error rate (QBER) due to different core/wavelength allocation are obtained to validate intra- and inter-core co-existence of QKD and classical telecommunication

Current trends strongly indicate a transition towards large-scale programmable networks with virtual network functions. In such a setting, deployment of distributed control planes will be vital for guaranteed service availability and performance. Moreover, deployment strategies need to be completed quickly in order to respond flexibly to varying network conditions. We propose an effective optimization approach that automatically decides on the needed number of controllers, their locations, control regions, and traffic routes into a plan which fulfills control flow reliability and routability requirements, including bandwidth and delay bounds. The approach is also fast: The algorithms for bandwidth and delay bounds can reduce the running time at the level of 50x and 500x, respectively, compared to state-of-the-art and direct solvers such as CPLEX. Altogether, our results indicate that computing a deployment plan adhering to predetermined performance requirements over network topologies of various sizes can be produced in seconds and minutes, rather than hours and days. Such fast allocation of resources that guarantees reliable connectivity and service quality is fundamental for elastic and efficient use of network resources.

Anomaly-based In-Vehicle Intrusion Detection System (IV-IDS) is one of the protection mechanisms to detect cyber attacks on automotive vehicles. Using artificial intelligence (AI) for anomaly detection to thwart cyber attacks is promising but suffers from generating false alarms and making decisions that are hard to interpret. Consequently, this issue leads to uncertainty and distrust towards such IDS design unless it can explain its behavior, e.g., by using eXplainable AI (XAI). In this paper, we consider the XAI-powered design of such an IV-IDS using CAN bus data from a public dataset, named 'Survival'. Novel features are engineered, and a Deep Neural Network (DNN) is trained over the dataset. A visualization-based explanation, 'VisExp', is created to explain the behavior of the AI-based IV-IDS, which is evaluated by experts in a survey, in relation to a rule-based explanation. Our results show that experts' trust in the AI-based IV-IDS is significantly increased when they are provided with VisExp (more so than the rule-based explanation). These findings confirm the effect, and by extension the need, of explainability in automated systems, and VisExp, being a source of increased explainability, shows promise in helping involved parties gain trust in such systems. 

This paper investigates the interplay of cloud computing, fog computing, and Internet of Things (IoT) in control applications targeting the automation industry. In this context, a prototype is developed to explore the use of IoT devices that communicate with a cloud-based controller, i.e., the controller is offloaded to cloud or fog. Several experiments are performed to investigate the consequences of having a cloud server between the end device and the controller. The experiments are performed while considering arbitrary jitter and delays, i.e., they can be smaller than, equal to, or greater than the sampling period. This paper also applies mitigation mechanisms to deal with the delays and jitter that are caused by the networks when the controller is offloaded to the fog or cloud.

Smart city digital twins can provide useful insights by making effective use of multidisciplinary urban data from diverse sources. Whilst these insights provide new information that helps cities in decision making, verifying the authenticity, integrity, traceability and data ownership across various functional units have become critical characteristics to ensure the data is from an authentic and trustworthy source. However, these characteristics are rarely considered in a digital twin ecosystem. In this research we introduce a novel framework, namely, 'SIGNED: Smart cIty diGital twiN vErifiable Data framework' that is designed on the basis of data ownership, selective disclosure and verifiability principles. Using Verifiable Credentials, SIGNED ensures digital twin data are verifiably authentic i.e., it covers provenance, transparency, and reliability through verifiable presentation. A proof of concept is designed and evaluated based on a smart water management use case to demonstrate the effectiveness of SIGNED in securing verifiable exchange of digital twin data across multiple functional units. The proof-of-concept demonstrates that SIGNED successfully allows the exchange of data in a trusted and verifiable manner at negligible performance cost, thus enhancing security and alleviating privacy issues when sharing data between various functional units in a smart city. 

In this paper, a complete operating cycle (OC) description is developed for heavy-duty vehicles traveling long distances in the region of Västra Götaland, Sweden. Variation amongst road transport missions is accounted for using a collection of stochastic models. These are parametrized from log data for all the influential road parameters that may affect the energy performance of heavy trucks, including topography, curvature, speed limits, and stop signs. The statistical properties of the developed OC description are investigated numerically by considering some composite variables, condensing the salient information about the road characteristics, and inspired by two existing classification systems. Two examples are adduced to illustrate the potential of the OC format, which enables ease of classification and detailed simulation of energy efficiency for individual vehicles, with application in vehicle design optimization and selection, production planning, and predictive maintenance. In particular, for the track used in the first example, a Volvo FH13 equipped with a diesel engine, simulation results indicate mean CO2 emissions of around 1700 g km-1, with a standard deviation of 360 g km-1; in the second example, dealing with electrical fleet sizing, the optimal proportion shows a predominance of tractor-semitrailer vehicles (70%) equipping 4 motors and 11 battery packs.

When dealing with customers, original equipment manufacturers (OEMs) classify vehicular usage by resorting to simplified, often colloquial, descriptions that allow for a rough understanding of the operating conditions and the user's needs. In this way, the information retrieved from the customers is exploited to guide their choices in terms of vehicle design and configuration, based on the characteristics of the transport application, labeled using intuitive metrics. However, a common problem in this context is the absence of any formal connection to lower levels of representation that might effectively be used to assess vehicular energy performance in simulation, or for design optimization using mathematical algorithms. Indeed, both processes require more accurate modeling of the surroundings, including exhaustive information about the local road, weather, and traffic conditions. Therefore, starting with a detailed statistical description of the environment, this paper proposes a method for mathematical classification of transport missions and applications within the theoretical framework of the operating cycle (OC). The approach discussed in the paper combines a collection of statistical models structured hierarchically, called a stochastic operating cycle (sOC), with a bird's-eye view description of the operating environment. The latter postulates the existence of different classes, which are representative of the usage and whose definition is based on simple metrics and thresholds expressed mathematically in terms of statistical measures. Algebraic expressions, called operating classes in the paper, are derived analytically for all the stochastic models presented. This establishes a connection between the two levels of representation, enabling to simulate longitudinal vehicle dynamics in virtual environments generated based on the characteristics of the intended application, using log data collected from vehicles and/or information provided by customers. Additionally, the relationships between the two descriptions are formalized using elementary probability operators, allowing for an intuitive characterization of a transport operation. An example is adduced to illustrate a possible application of the proposed method, using six sOCs parametrized from log data collected during real-world missions. The proposed approach may facilitate the interaction between OEMs, customers, and road operators, allowing for planning of maintenance, and optimization of transport missions, components, and configurations using standard procedures and routines. 

Embedded software is at the core of current and future telecommunication, automotive, multimedia, and industrial automation systems. The success of practically any industrial application depends on the embedded software system's dependability, and one method to verify the dependability of a system is testing its robustness. The motivation behind this paper is to provide a knowledge base of the state of the practice in robustness testing of embedded software systems and to compare this to the state of the art. We have gathered the information on the state of the practice in robustness testing from seven different industrial domains (telecommunication, automotive, multimedia, critical infrastructure, aerospace, consumer products, and banking) by conducting 13 semi-structured interviews. We investigate the different aspects of robustness testing, such as the general view of robustness, relation to requirements engineering and design, test execution, failures, and tools. We highlight knowledge from the state of the practice of robustness testing of embedded software systems. We found different robustness testing practices that have not been previously described. This paper shows that the state of the practice, when it comes to robustness testing, differs between organizations and is quite different from the state of the art described in the scientific literature. For example, methods commonly described in the literature (e.g., the fuzzy approach) are not used in the organizations we studied. Instead, the interviewees described several ad hoc approaches that take specific scenarios into account (e.g., power failure or overload). Other differences we found concern the classification of robustness failures, the hypothesized root causes of robustness failures, and the types of tools used for robustness testing. This paper is a first step in capturing the state of the practice of robustness testing of embedded software systems. The results can be used by both researchers and practitioners. Researchers can use our findings to understand the gap between the state of the art and the state of the practice and develop their studies to fill this gap. Practitioners can also learn from this knowledge base regarding how they can improve their practice and acquire other practices.

Social Internet of Things (SIoT) is a specialized form of adaptive networks, where any change in one node can affect other nodes in the network. Millions of devices, including vehicles, buildings, healthcare, industries, and several other types of devices, share and exchange sensitive data among each other. Interconnection of these devices changes the paradigms of the world around us and puts a great effect on the overall society. The number of nodes, types of devices, layout of the overall network, and sensitivity of the data adds more and more complexity in the domain of SIoT. In this paper, our contribution is twofold, first, we provide a brief and comprehensive overview of the social aspects of the IoT by selecting the most recent papers to investigate the complexity, adaptivity, and other social aspects of the IoT and their implications. The main aim of this paper is to analyze the benefits of IoT to the society, social acceptance of IoTs, and social connectivity of different age groups. Second, we forecast the future trends and predict the world in the year 2020. We provide a detailed analysis by using several different data tables to present the future trends of different aspects of SIoT. With this survey, we aim to provide an easy and concise view of different challenges in implementing SIoT and how it will affect the future smart cities and overall impact on the society.

The popularity of immersive multimedia content is prevalent and the consumption of 360° videos is increasing rapidly in varied domains. The broadcast of such content in cellular networks will be challenging in terms of dynamic content adaptation and efficient resource allocation to serve heterogeneous consumers. In this work, we propose an intelligent immersive new radio multimedia broadcast multicast system (NR-MBMS), I2MB, for next-generation cellular networks. I2MB intelligently forecasts the users' viewing angle and the 360° video tiles to be broadcast beforehand using long short-term memory network. We define broadcast areas by using modified K-means clustering. The complex multivariable optimization problem that integrates efficient adaptive 360-degree video encoding and tiled broadcast using optimized transmission parameters is defined as as a Markov decision process (MDP). In a dense urban scenario with a large MBSFN (multimedia broadcast multicast service single frequency network) synchronization area, the state and action space dimensionality is very high, in which the solution is obtained by using deep deterministic policy gradient (DDPG) algorithm. I2MB incorporates deep reinforcement learning based radio resource allocation (modulation-coding scheme and frequency-time resource blocks) and tiled video encoding to maximize the viewport video quality experienced by the broadcast mobile users. I2MB provides improved immersive video broadcast streaming quality while serving a higher number of mobile users. Adaptive encoding of 360° video tiles and radio resource allocation are performed based on users' forecasted viewing angle, spatial distribution, channel conditions, and service request. The performance evaluation of our proposed scheme, I2MB, shows considerable gains in viewport quality (46.83%) and number of users served (30.52%), over a recent state-of-the-art method VRCAST. 

The evolution and popular adaptation of drone technology in diverse applications has necessitated advancement of UAV communication framework. UAVs inherently support features like mobility, flexibility, adaptive altitude, which make them a preferable option for dynamic surveillance of remote locations. Multiple UAVs can cooperatively work to accomplish surveillance missions more efficiently. However, the intermittent network connectivity and the limited onboard energy storage impose a great challenge on UAV-assisted remote surveillance. This paper presents an Energy-efficient Collaborative Multi-UAV Surveillance (ECMS) system for surveillance of inaccessible regions. The system employs an optimal Multi-UAV Collaborative Monocular Vision (MCMV) topology to facilitate the surveillance with zero blind spot using minimum number of drones. We also propose an application-aware Multi-Path Weighted Load-balancing (MWL) routing protocol for handling congestion by distributing traffic among all available resources in UAV network and adaptively selecting the of source datarate (i.e. switching video resolution). The simulation results demonstrate that the proposed surveillance system achieves coverage with lesser number of UAVs compared to the existing systems. It also achieves higher throughput, higher packet-delivery ratio, higher residual energy of UAVs, and lower end-to-end delay. 

A promising solution to meet the demands on accurate positioning and real-time situational awareness in future intelligent transportation systems (ITSs) is cooperative positioning, where vehicles share sensor information over the wireless channel. However, the sensing and communication technologies required for this will be gradually introduced into the market, and it is, therefore, important to understand what performance we can expect from cooperative positioning systems as we transition to a more modern vehicle fleet. In this paper, we study what effects a gradual market penetration has on cooperative positioning applications, through a Fisher information analysis. The simulation results indicate that solely introducing a small fraction of automated vehicles with high-end sensors significantly improves the positioning quality but is not enough to meet the stringent demands posed by safety critical ITS applications. Furthermore, we find that retrofitting vehicles with low-cost satellite navigation receivers and communication have marginal impact when the positioning requirements are stringent and that the longitudinal road position can be estimated more accurately than lateral. © 2013 IEEE.

Intersection management of automated vehicles relies on wireless communication, whereby communication resources should be allocated to vehicles while maintaining safety. We present a collision-aware resource allocation (CARA) strategy for coordination of automated and connected vehicles by a centralized intersection manager. The proposed strategy is based on a self-triggered approach and proactively reduces the risk of channel congestion by only assigning communication resources to vehicles that are in critical configurations, i.e., when there is a risk for a future collision. Compared with collision-agnostic communication strategies, typically considered for automated intersection management, the CARA strategy aims to bridge the gap between control, sensing, and communication. It is shown to significantly reduce the required amount of communication (albeit with a slight increase in the control cost), without compromising safety. Furthermore, control cost can be reduced by allowing more frequent communication, which we demonstrate through a trade-off analysis between control performance and communication load. Hence, CARA can operate in communication-limited scenarios, but also be modified for scenarios where the control cost is of primary interest.

The main goal of software testing is to detect as many hidden bugs as possible in the final software product before release. Generally, a software product is tested by executing a set of test cases, which can be performed manually or automatically. The number of test cases which are required to test a software product depends on several parameters such as the product type, size, and complexity. Executing all test cases with no particular order can lead to waste of time and resources. Test optimization can provide a partial solution for saving time and resources which can lead to the final software product being released earlier. In this regard, test case selection, prioritization, and scheduling can be considered as possible solutions for test optimization. Most of the companies do not provide direct support for ranking test cases on their own servers. In this paper, we introduce, apply, and evaluate sOrTES as our decision support system for manual integration of test scheduling. sOrTES is a Python-based supportive tool which schedules manual integration test cases which are written in a natural language text. The feasibility of sOrTES is studied by an empirical evaluation which has been performed on a railway use-case at Bombardier Transportation, Sweden. The empirical evaluation indicates that around 40 % of testing failure can be avoided by using the proposed execution schedules by sOrTES, which leads to an increase in the requirements coverage of up to 9.6%. 

The article analyzes the total cost of ownership (TCO) of 5G fronthauling solutions based on analog and digital radio-over-fiber (RoF) architectures in cloud radio access networks (C-RANs). The capital and operational expenditures (CAPEX, OPEX) are assessed, for a 10-year period, considering three different RoF techniques: intermediate frequency analog RoF (IF-A-RoF), digital signal processing (DSP) assisted analog RoF (DSP-A-RoF), and digital RoF (D-RoF) based on the common public radio interface (CPRI) specifications. The greenfield deployment scenario under exam includes both fiber trenching (FT) and fiber leasing (FL) options. The TCO is assessed while varying (i) the number of aggregated subcarriers, (ii) the number of three-sector antennas located at the base station, and (iii) the mean fiber-hop length. The comparison highlights the significance that subcarrier aggregation has on the cost efficiency of the analog RoF solutions. In addition, the analysis details the contribution of each cost category to the overall CAPEX and OPEX values. The obtained results indicate that subcarrier aggregation via DSP results in high cost efficiency for a mobile fronthaul network, while a CPRI-based architecture together with FL brings the highest OPEX value. 

Aiming at addressing environmental challenges, large data centers, such as Facebook, Google, and Yahoo, are increasing share of green power in their daily energy consumption. Such trends drive research into new directions, e.g., sustainable data centers. The research often relies on expressive models that provide sufficient details, however, practical to re-use and expand. There is a lack of available data center models that capture internal operating states of the facility from the CPU to the cooling tower. It is a challenge to develop a model that allows to describe complete data center of any scale including its connection to the grid. This paper proposes such a model building on the existing work. The challenge was to put the pieces of data center together and model behavior of each element so that interdependencies between components and parameters and operating states are captured correctly and in sufficient details. The proposed model was used in the project 'Data center microgrid integration' and proven to be adequate and important to support such study.