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Wireless sensor networks (WSNs) play a very important role in providing real-time data access for Big Data and Internet of Things applications. However, the open deployment, energy constraint, and lack of centralized administration make WSNs very vulnerable to various kinds of malicious attacks. In WSNs identifying malicious sensor devices and eliminating their sensed information plays a very important role for mission critical applications. Standard cryptography and authentication schemes cannot be directly used in WSNs because of the resource constraint nature of sensor devices. Thus, energy efficient and low latency methodology is required for minimizing the impact of malicious sensor devices. This paper presents a secure and load balanced routing (SLBR) scheme for heterogeneous clustered based WSNs. SLBR presents a better trust-based security metric that overcomes the problem when sensors keep oscillating from good to bad state and vice versa, and also SLBR balances load among CH. Thus, aids in achieving better security, packet transmission, and energy efficiency performance. Experiments are conducted to evaluate the performance of proposed SLBR model over existing trust-based routing model namely Exponential Cat Swarm Optimization (ECSO). The result attained shows SLBR model attains better performance than ECSO in terms of energy efficiency (i.e., network lifetime considering first sensor device death and total sensor device death), communication overhead, throughput, packet processing latency, malicious sensor device misclassification rate and identification.

Fog computing is becoming a vital component for Internet of things (IoT) applications, acting as its computational engine. Mission-critical IoT applications are highly sensitive to latency, which depends on the physical location of the cloud server. Fog nodes of varying response rates are available to the cloud service provider (CSP) and it is faced with a challenge of forwarding the sequentially received IoT data to one of the fog nodes for processing. Since the arrival times and nature of requests is random, it is important to optimally classify the requests in real-time and allocate available virtual machine instances (VMIs) at the fog nodes to provide a high QoE to the users and consequently generate higher revenues for the CSP. In this paper, we use a pricing policy based on the QoE of the applications as a result of the allocation and obtain an optimal dynamic allocation rule based on the statistical information of the computational requests. The developed solution is statistically optimal, dynamic, and implementable in real-time as opposed to other static matching schemes in the literature. The performance of the proposed framework has been evaluated using simulations and the results show significant improvement as compared with benchmark schemes., IEEE Transactions on Mobile Computing 2020

In recent years, a high connectivity demand started being experienced in wireless communication. Practically, everyone and everything needs to be connected because of the huge variety of applications existing today. This is a challenging situation for terrestrial telecommunications infrastructure that they cannot address on their own. Therefore, the 3rd Generation Partnership Project (3GPP) started in 2017 to study the integration of satellites as a part of the 5G ecosystem involving both cellular and satellite stakeholders. The substantial value added by satellites as part of the access technology mix for 5G is now becoming clear, especially for mission critical and other applications where ubiquitous coverage is crucial. For example, NTN can broaden service delivery to unserved or underserved areas, by complementing and extending terrestrial networks (Liolis, K., Geurtz, A., Sperber, R. et al. (2018). Use cases and scenarios of 5G integrated satellite‐terrestrial networks for enhanced mobile broadband: the SaT5G approach. International Journal of Satellite Communications and Networking 37: 91–112; Hofmann, M. (2020). Satellite communication in the age of 5G. Journal of ICT Standardization 8(3). https://journals.riverpublishers.com/index.php/JICTS/article/view/4327/3093 ).In this chapter, we will elaborate on the current standardization of NTN in 5G and further detail the architecture and research challenges toward 6G‐NTN.

Mission-critical IT systems are utilizing closed source software (CSS) mainly due to reasons related to “quality assurance” and “continued support” despite much better benefits of using Open Source...

Due to recent advancements in smart city traffic and transport monitoring industry 4.0 applications. Flying Ad-Hoc Networks (FANETs) ability to cover geographically large areas, makes it a suitable technology to address the challenges faced during remote areas traffic monitoring. The implementation of drone based FANETs have several advantages in remote traffic monitoring, including free air-to-air drone assisted communication zone and smart surveillance and security. The drone-based FANETs can be deployed within minutes without requiring physical infrastructure, making it suitable for mission critical applications in several areas of interests. Here a drone-based FANETs application for smart city remote traffic monitoring is presented while addressing several challenges including coverage of larger geographical area and data communication links between FANETs nodes. A FANET-inspired enhanced ACO algorithm that easily coped with drone assisted technology of FANETs is proposed to cover the large areas. Simulation results are presented to compare the proposed technique against different network lifetime and number of received packets. The presented results show that the proposed technique perform better compared to other state-of-the-art techniques.

In recent times, Wireless Sensor Network (WSN) technology has received significant attention owing to its numerous applications in various mission critical services, such as healthcare, military monitoring, public safety systems, and forest monitoring. However, sensor node’s low computational power and inadequate advancements in battery design hinder resource-rigorous applications from fully exploiting WSN capabilities. The trend of shifting computations and storage to remote clouds offers the opportunity to integrate WSNs into the cloud to mitigate their limitations. The Sensor Cloud augments WSN capabilities of remote sensing and monitoring while exploiting resource-rich cloud infrastructure. To date, numerous solutions have been proposed to handle the integration concerns of WSNs with the cloud. However, a comprehensive study that covers various Sensor Cloud aspects in terms of architecture, network dynamics, heterogeneity, communication patterns, data management, and security, is still lacking. To fill this gap, state-of-the-art sensor cloud integration frameworks are analyzed in this article. Moreover, a detailed thematic taxonomy is presented to classify existing sensor cloud frameworks while focusing in service oriented and client server architectures. Related features and critical aspects of existing frameworks are inspected through a qualitative analysis based on the parameters selected from the literature and extracted from the taxonomy. Several research opportunities in this research domain are suggested to assist researchers in identifying and designing optimal resource sensor cloud integration frameworks to host emerging applications.

Mobile edge computing (MEC) is a promising technique to alleviate the energy limitation of Internet of Things (IoT) devices, as it can offload local computing tasks to the edge server through a cellular network. By leveraging extreme value theory (EVT), this work proposes a priority-differentiated offloading strategy that takes into account the stringent quality of service (QoS) requirements of mission-critical services and green resource allocation. Particularly, Lyapunov optimization is first introduced to derive an upper-bound queue minimization problem with the consideration of energy consumption and task priority. The peaks-over-thresholds (POT) model is then applied to evaluate the stationery status and cooperate with Wolf-PHC learning to optimize resource allocation. Finally, simulation results verify that the proposed offloading policy performs well in terms of its energy-saving capability while satisfying different demands of mission-critical IoT services.

The ubiquitous presence of data driven technologies that move information from the edge of the Industrial Internet of Things (IIoT) to the cloud for advanced computation and back to the edge for action are pushing wireless connections to the limit. Under these conditions optimizing WIFI Received Signal Strength Intensity (RSSI) can improve data management, computational workflows, and geolocation accuracy while reducing energy consumption in order to minimize charging and computational resource requirements at the edge. Ensuring connectivity for these mission critical processes will require detailed knowledge (either measured or simulated) of the state of the electromagnetic fields in advanced manufacturing scenarios. Simulation has the advantage of developing more scalable solutions to this characterization problem but comes at a very high computational cost that may not be possible on edge devices with limited computational resources. In order to reduce the time and resource cost of achieving real time simulations with low computing specification edge devices, we propose creating a novel method that exploits the notion of information channels to create efficient Convolutional Neural Networks (CNNs) capable of determining the RSSI given a completely new geometry (never used in training) where objects or obstacles (walls, machines, tables, etc.) and their respective location, size and reflectivity indices, along with the antenna location are completely random.

Mission-critical networks, which for example can be found in autonomous cars and avionics, are complex systems with a multitude of interconnected embedded nodes and various service demands. Their resilience against failures and attacks is a crucial property and has to be already considered in their design phase. In this paper, we introduce a novel approach for optimal joint service allocation and routing, leveraging virtualized embedded devices and shared backup capacity for the fault-tolerant design of mission-critical networks. This approach operates in phases utilizing multiple optimization models. Furthermore, we propose a new heuristic that ensures resource efficiency and fault-tolerance against single node and link failures as pre-requisite for resilience. Our experiments for different application scenarios indicate that our heuristic achieves results close to the optimum and provides 50% of capacity gain compared to a dedicated capacity protection scheme. Moreover, our heuristic ensures fault-tolerance against at least 90% of all potential single node failures.

Cellular network design must incorporate disaster response and repair scenarios. Requirements for high reliability and low latency often fail to incorporate network survivability for mission critical services. This paper defines a practical modeling approach using a Markov chain Matrix Exponential (ME) model. Transient and the steady state representations of system repair models, namely, fast and slow (i.e., crew-based) repairs for networks consisting of a multiple repair crews have been analyzed. Failures are exponentially modeled as per common practice, but ME distributions describe the more complex recovery processes. The model used in this paper is evaluated for varying number of repair crews, base stations, repair models and squared coefficient of variation values. This model is scalable for larger networks, calculates the restoration and network availability times, consists of asymptotic approximations to estimate network availability and determines the optimal number repair crews required. This ME model shows how survivable networks can be designed based on controlling numbers of crews, times taken for individual repair stages in the repair process, and the balance between fast and slow repairs.

Real Time Mission Critical Communication (RTMCC) in emergency situations can include real-time video and audio calls between peers and first responders all occurring simultaneously. RTMCC also requires secure end-to-end (E2E) group communication (GC) sessions against potential security threats during such incidents. In this paper, we explore all aspects of the possible methods that are suitable for a software implementation of for session key change during GC in E2E encryption of RTMCC. Later, we introduce our Entropy Service concept, which can be very effective in secure E2E RTMCC sessions. The proposed method ensures E2E security in real-time communication systems while allowing very fast session key change for clients involved in an RTMCC session with a computational complexity of 𝒪(1). Our experimental results show that the proposed Entropy Service can reduce total time by 99.6% and 99.2%, the idle time by 99.4% and 98.99%, and the number of messages by 51.4% and 35.33% compared to the key refreshing and hash methods, respectively, when the number of users in the system increases to 45. These results show that both communication and computation complexity are significantly reduced with the proposed RTMCC session key change.

Cyber-Physical Systems (CPS) often involve complex networks of interconnected software and hardware components that are logically combined to achieve a common goal or mission; for example, keeping a plane in the air or providing energy to a city. Failures in these components may jeopardise the mission of the system. Therefore, identifying the minimal set of critical CPS components that is most likely to fail, and prevent the global system from accomplishing its mission, becomes essential to ensure reliability. In this article, we present a novel approach to identifying the Most Likely Mission-critical Component Set (MLMCS) using AND/OR dependency graphs enriched with independent failure probabilities. We address the MLMCS problem as a Maximum Satisfiability (MaxSAT) problem. We translate probabilities into a negative logarithmic space to linearise the problem within MaxSAT. The experimental results conducted with our open source tool LDA4CPS indicate that the approach is both effective and efficient. We also present a case study on complex aircraft systems that shows the feasibility of our approach and its applicability to mission-critical cyber-physical systems. Finally, we present two MLMCS-based security applications focused on system hardening and forensic investigations.

With people paying more attention to marine resources, the Internet of Things (IoT) has been extended to underwater, promoting the development of the Internet of Underwater Things (IoUT). Various compelling IoUT applications bring a new age to maritime activities. However, some mis-sion-critical maritime activities, including ocean earthquake forecasting, underwater navigation, and so on, pose a substantial challenge on existing IoUT architectures and relevant techniques. Therefore, in this article, to empower these implacable maritime activities, we conceive the concept of mission-critical IoUT and highlight its key features and challenges. Furthermore, to satisfy the stringent requirements of mission-critical IoUT, we propose a future maritime network architecture and machine-learning-aided key techniques in terms of information sensing, transmission, and processing. Moreover, we present our recent research on reliable and low-latency underwater information transmission. Finally, we provide the open issues and potential research trends for future mission-critical IoUT.

In recent years, the number of objects connected to the internet have significantly increased. Increasing the number of connected devices to the internet is transforming today’s Internet of Things (IoT) into massive IoT of the future. It is predicted that, in a few years, a high communication and computation capacity will be required to meet the demands of massive IoT devices and applications requiring data sharing and processing. 5G and beyond mobile networks are expected to fulfill a part of these requirements by providing a data rate of up to terabits per second. It will be a key enabler to support massive IoT and emerging mission critical applications with strict delay constraints. On the other hand, the next generation of software-defined networking (SDN) with emerging cloudrelated technologies (e.g., fog and edge computing) can play an important role in supporting and implementing the above-mentioned applications. This paper sets out the potential opportunities and important challenges that must be addressed in considering options for using SDN in hybrid cloud-fog systems to support 5G and beyond-enabled applications.

Sensor node location information is critical in many Wireless Sensor Network (WSN) applications with random sensor node deployment. In such applications, node localization using a faster area exploration mechanism is needed to initiate precise sensing and communication. In this paper, faster area exploration approach for mobility-assisted localization scheme is proposed for mission-critical WSN applications. Each Mobile Anchor (MA) node embedded with a localization module moves in the sensor field in a coordinated manner while localizing the deployed static nodes. The proposed area exploration scheme is implemented using a hybrid of max-gain approach and cost-utility based frontier (HMF) approach. The paper addresses the issue of sparse anchor node condition during the localization process. The simulation results obtained using Cooja simulator shows that the proposed mobility-assisted localization scheme results in accurate localization with minimum delay. The proof-of-concept of the proposed scheme is demonstrated using Berkeley static nodes and custom designed MA nodes.

IoT supports a spectrum of applications, each of which has certain specific requirements. For instance, mission critical applications cannot tolerate delay in data transmission however simple monitoring applications are delay tolerant. The lifetime and performance of IoT sensor networks depend on the metric/constraint (ETX, Energy etc.) selected for routing path, data size and quality of service required. The selection of metric /constraint dependent Objective function (OF) in RPL provides a range of solutions for IoT applications. However, state-of-art solutions mainly focus on single metric/constraint resulting in poor performance of protocol. To understand the protocol behavior for different metrics (single and combined) a complete evaluation of RPL over important performance parameters is needed. Researchers have proposed several routing algorithms which are application specific and do not define a generic parent selection process. We require a structured algorithm for Minimum Rank Hysteresis Objective Function (MRHOF) applicable to majority of IoT applications. In this paper we have proposed a generalized algorithm for MRHOF along with routing path cost evaluation which defines the complete parent selection process. Further, comparative analysis of different RPL OFs has been done to identify suitable OF for enhanced RPL performance. Performance evaluation parameters have been extended to PDR, power consumption, hop count, average ETX, Rt metric and inter packet time, for different network size and link quality. Results are obtained using Cooja simulator of Contiki. RPL with combined metric provide 24% higher PDR, 28% lower power consumption and 39% lower inter-packet time as compared to RPL with single metric.

Forward operating bases of the Armed Forces are located in remote areas where grid connection is normally not available and if available are vulnerable to outages due to vagaries of weather or action by adversary. Microgrids are generally more efficient and may provide electric power storage for emergency supply of electricity to mission critical equipment like surveillance systems, sensors, communication, command and control systems of such forward bases. Several small independent microgrids can also be interconnected together to satisfy the energy requirements of the remote military areas. Networking of several self-supervised microgrids is being developed with the aim to enhance efficiency, reliability and resiliency as well as the steadiness of power systems. In this work, an interconnection of microgrids at three remote bases of the armed forces is presented and simulated. The proposed power co-ordination strategy is designed in such a way that during peace time operation it tries to minimize the overall requirement of fossil fuel for generators supplying the critical loads and in case of outages, it can tackle the emergency situation by automatically routing the power to critical loads from other operational bases. The entire system has been simulated and verified using MATLAB 2018.

Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems’ environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems’ security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach’s feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems’ constituents to learn about security threats in their environments collaboratively. © 2022 by the authors. open access

Predictive maintenance is an invaluable tool to preserve the health of mission critical assets while minimizing the operational costs of scheduled intervention. Artificial intelligence techniques have been shown to be effective at treating large volumes of data, such as the ones collected by the sensors typically present in equipment. In this work, we aim to identify and summarize existing publications in the field of predictive maintenance that explore machine learning and deep learning algorithms to improve the performance of failure classification and detection. We show a significant upward trend in the use of deep learning methods of sensor data collected by mission critical assets for early failure detection to assist predictive maintenance schedules. We also identify aspects that require further investigation in future works, regarding exploration of life support systems for supercomputing assets and standardization of performance metrics.

Mission-critical Communication Systems that are adaptable for use with the latest generation of multimedia services are crucial for system users. To determine the set of requirements that need to be hardcoded into such systems, a clear distinction between mission-critical and non-mission-critical systems is required. Moreover, the users of services provided by such systems are very different to those of current mobile commercial communication systems. These differences give rise to a set of challenges that need addressing to facilitate migration from existing systems to those now being proposed. One such challenge relates to the performance of the IP Multimedia Subsystem (IMS) registration process. This is a crucial consideration for mission-critical systems, particularly in large-scale systems where thousands or even millions of users may seek to access the system in disaster scenarios. This paper presents an evaluation of IMS and Session Initiation Protocol (SIP) performance metrics and Key Performance Indicators (KPIs). Moreover, it articulates a proposed study that will seek to address some of the challenges identified.

Edge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems, and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission-critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss, and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer non-linear programming problem, a decomposition into sub-problems is performed and a two-stage heuristic algorithm is proposed. Simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queuing and end-to-end delay., 17 pages, has been submitted to EURASIP JWCN, major revision

Appropriate battery selection is a major design decision regarding the fast growth of battery-operated devices like space rovers, wireless sensor network nodes, rescue robots, and so on. Many such systems are mission critical, where estimation of the battery depletion time has an important role in the design efficiency with regard to the mission time. Accurate characterization of the system power usage pattern is essential for such an estimation. The following complexities exist: (1) The system behavior changes during interaction with the physical world, (2) the power consumption varies as the runtime progresses, (3) the total delivered battery charge has non-linear dependency on the power variability, and (4) design-time exhaustive study about runtime execution paths is almost impossible. This article presents an analytical method to first characterize the power variability of a given embedded program modeled by a directed acyclic graph, concerning the first and the second complexities. To include the third complexity, however, the concept of Worst-case Power Consumption Trace (WPCT) is proposed toward the worst-case scenario in terms of charge depletion for a given battery. A polynomial algorithm is also presented to construct WPCT and use it to estimate a tight lower bound for the system energy depletion time, i.e., its failure time, avoiding an exhaustive study. Comparisons between the analytical and simulation results reveal less than 3.4% of error in the bound estimations for the considered setups.

This workshop aims at identifying, examining, structuring and sharing educational resources and approaches to support the process of teaching/learning Human-Computer Interaction (HCI) Engineering. The broadening of the range of available interaction technologies and their applications, many times in safety and mission critical areas, to novel and less understood application domains, brings the question of how to address this ever-changing nature in university curricula usually static. Beyond, as these technologies are taught in diverse curricula (ranging from Human Factors and psychology to hardcore computer science), we are interested in what the best approaches and best practices are to integrate HCI Engineering topics in the curricula of programs in software engineering, computer science, human-computer interaction, psychology, design, etc. The workshop is proposed on behalf of the IFIP Working Groups 2.7/13.4 on User Interface Engineering and 13.1 on Education in HCI and HCI Curricula.

Immunization of mission-critical facilities such as hospitals and first responders against power outages is crucial for the operators due to their significant value of the lost load, affecting citizens’ lives. This paper proposes a novel evaluating framework which enables facility operators to efficiently size and optimally dispatch their behind-the-meter energy storage systems (BTM-ESS) for resiliency purposes during grid emergencies. The proposed framework, formulated as a mixed integer linear programming model, aids facility operators to quantify the impacts of various BTM-ESSs on resilience enhancement where the Avoided Loss of Load (ALOL) is incorporated as the resilience indicator. BTM-ESS is assumed to be operated in both standalone and coupled with solar photovoltaic (PV) as an onside backup generation which is a viable energy solution for more prolonged power outages. The proposed model is developed on a probabilistic energy procurement model, aiming to minimize the facility’s total operation cost. The uncertainty of power outages is characterized by a set of a large number of scenarios generated by the brute-force enumeration method. Additionally, to analyze the impacts of facilities’ behaviors on the BTM-ESS evaluation procedure, a set of 24 facilities from different end use sectors with various functionalities are simulated by employing our in-house-developed building simulator, which is a physics-based simulation tool. Finally, the practicality of the proposed evaluating framework is investigated through two case studies where both short and long-duration grid outages are examined based on the historical outage data adopted from New Jersey, USA. The simulation results reveal that a BTM-ESS with 4 hours discharge duration that is sized at rated power equal to 50% or more of the facility’s peak load generates sufficient resilience benefits for most of the 24 representative facilities in case of short-duration power outages.

LISA Pathfinder is a technology demonstrator space mission, aimed at testing key technologies for detecting gravitational waves in space. The mission is the precursor of LISA, the first space gravitational waves observatory, whose launch is scheduled for 2034. The LISA Pathfinder scientific payload includes two gravitational reference sensors (GRSs), each one containing a test mass (TM), which is the sensing body of the experiment. A mission critical task is to set each TM into a pure geodesic motion, i.e. guaranteeing an extremely low acceleration noise in the sub-Hertz frequency bandwidth. The grabbing positioning and release mechanism (GPRM), responsible for the injection of the TM into a geodesic trajectory, was widely tested on ground, with the limitations imposed by the 1-g environment. The experiments showed that the mechanism, working in its nominal conditions, is capable of releasing the TM into free-fall fulfilling the very strict constraint imposed on the TM residual velocity, in order to allow its capture on behalf of the electrostatic actuation. However, the first in-flight releases produced unexpected residual velocity components, for both the TMs. Moreover, all the residual velocity components were greater than maximum value set by the requirements. The main suspect is that unexpected contacts took place between the TM and the surroundings bodies. As a consequence, ad hoc manual release procedures had to be adopted for the few following injections performed during the nominal mission. These procedures still resulted in non compliant TM states which were captured only after impacts. However, such procedures seem not practicable for LISA, both for the limited repeatability of the system and for the unmanageable time lag of the telemetry/telecommand signals (about 4400 s). For this reason, at the end of the mission, the GPRM was deeply tested in-flight, performing a large number of releases, according to different strategies. The tests were carried out in order to understand the unexpected dynamics and limit its effects on the final injection. Some risk mitigation maneuvers have been tested aimed at minimizing the vibration of the system at the release and improving the alignment between the mechanism and the TM. However, no overall optimal release strategy to be implemented in LISA could be found, because the two GPRMs behaved differently., This work has been made possible by the LISA Pathfinder mission, which is part of the space-science programme of the European Space Agency.The French contribution has been supported by the CNES (Accord Specific de projet CNES 1316634/CNRS 103747), the CNRS, the Observatoire de Paris and the University Paris-Diderot.E. Plagnol and H. Inchauspé would also like to acknowledge the financial support of the UnivEarthS Labex program at Sorbonne Paris Cité (ANR-10-LABX-0023 and ANR-11-IDEX-0005-02).The Albert-Einstein-Institut acknowledges the support of the German Space Agency, DLR. The work is supported by the Federal Ministry for Economic Affairs and Energy based on a resolution of the German Bundestag (FKZ 50OQ0501 and FKZ 50OQ1601).The Italian contribution has been supported by Agenzia Spaziale Italiana and Istituto Nazionale di Fisica Nucleare.The Spanish contribution has been supported by contracts AYA2010-15709 (MICINN), ESP2013-47637-P, and ESP2015-67234-P (MINECO).M. Nofrarias acknowledges support from Fundacion General CSIC (Programa ComFuturo).F. Rivas acknowledges an FPI contract (MINECO). The Swiss contribution acknowledges the support of the Swiss Space Office (SSO) via the PRODEX Programme of ESA. L. Ferraioli is supported by the Swiss National Science Foundation.The UK groups wish to acknowledge support from the United Kingdom Space Agency (UKSA), the University of Glasgow, the University of Birmingham,Imperial College, and the Scottish Universities Physics Alliance (SUPA). J. I. Thorpe and J. Slutsky acknowledge the support of the US National Aeronautics and Space Administration (NASA).

Machine Learning (ML) applications are growing in an unprecedented scale. The development of easy-to-use machine-learning application frameworks has enabled the development of advanced artificial intelligence (AI) applications with only a few lines of self-explanatory code. As a result, ML-based AI is becoming approachable by mainstream developers and small businesses. However, the deployment of ML algorithms for remote high throughput ML task execution, involving complex data-processing pipelines can still be challenging, especially with respect to production ML use cases. To cope with this issue, in this paper we propose a novel system architecture that enables Algorithm-agnostic, Scalable ML (ASML) task execution for high throughput applications. It aims to provide an answer to the research question of how to design and implement an abstraction framework, suitable for the deployment of end-to-end ML pipelines in a generic and standard way. The proposed ASML architecture manages horizontal scaling, task scheduling, reporting, monitoring and execution of multi-client ML tasks using modular, extensible components that abstract the execution details of the underlying algorithms. Experiments in the context of obstacle detection and recognition, as well as in the context of abnormality detection in medical image streams, demonstrate its capacity for parallel, mission critical, task execution.

Flexible Gastrointestinal Endoscopy is a commonly performed procedure about the globe as either a diagnostic or therapeutic tool in the treatment of GI Disease. In the United States, it has been estimated that over 20 million endoscopic procedures are performed annually. The reprocessing of an endoscope is an extremely detailed and multi-stepped process which is required after each procedure to render a contaminated instrument safe for reuse. Endoscopes are rather intricate instruments constructed with multiple long narrow internal channels with right angles. In addition to their complex construction, endoscopes must transverse an environment which bear high and diverse level of microbial population and organic matter. Due to this architectural structure, successful endoscope reprocessing is built upon a foundation of 9 primary steps which must be meticulously performed in concert with the Manufacturer's Instructions for Use as well as guidelines from professional organization to mitigate the potential of the transmission of Endoscope Associated Infections. The 9 pillars of effective endoscope reprocessing are: point of use precleaning, leakage testing, manual cleaning, rinse after cleaning, inspection, high level disinfection, liquid chemical sterilization or sterilization, rinse after high level disinfection or Liquid Cemical Sterilization, drying and storage. completing these steps each and every time an endoscope is processed is mission critical in the prevention of lapses and breaches in the reprocessing of endoscopes. Table 1 a well-trained and highly engaged reprocessing staff is also necessary to carry out these duties in accordance with strict institutional oversight of reprocessing protocols. While it is possible to gain insight into the general reprocessing practices throughout the nation through the use of surveys and interviews of endoscopists and nurses, due to the anonymity of these tools, it is often difficult to isolate specific facilities to gain a deeper appreciation of their practice unless the activities of these facilities has been published. Several topics and concepts related to opportunities to enhance reprocessing will be explored in this writing to evaluate and augment endoscope reprocessing with in the facility.

When targeting mission critical applications, the design of the electronic actuation systems needs to consider many requirements and constraints not typical in standard industrial applications. One of these is tolerance to faults, as the unplanned shutdown of a critical subsystem, if not handled correctly, could lead to financial harm, environmental disaster, or even loss of life. One way this can be avoided is through the design of an electric drive systems based on multi-phase machines that can keep operating, albeit with degraded performance, in a partial configuration under fault conditions. Distributed architectures are uniquely suited to meet these challenges, by providing a large degree of isolation between the various components. This paper presents a system architecture suitable for scalable and high-performance fault tolerant machine drive systems. the effectiveness of this system is demonstrated through theoretical analysis and experimental verification on a six-phase machine.

We propose an unmanned aerial vehicle (UAV) communications scheme with spectrum-sharing mechanism to provide mission-critical services such as disaster recovery and public safety. Specifically, the UAVs can serve as flying base stations to provide extended network coverage for the affected area under spectrum-sharing cognitive radio networks (CRNs). To cope with the effects of network destruction in a disaster, we propose a real-time optimisation framework for resource allocation (e.g., power and number of UAVs) for CRNs assisted by UAV relays. The proposed optimisation scheme aims at optimising the network throughput of primary and secondary networks under the stringent constraint of maximum tolerable interference impinged on the primary users. We also propose a deep neural network (DNN) model to significantly reduce the execution time under real-time solution of mixed-integer UAV deployment problems. For both primary and secondary networks, our real-time optimisation algorithms impose low computational complexity, hence, have a low execution time in solving throughput optimisation problems, which demonstrates the benefit of our approached proposed for spectrum-sharing UAV-assisted mission-critical services.

The Publisher's final version can be found by following the DOI link. Open access article. System monitors are applications used to monitor other systems (often mission critical) and take corrective actions upon a system failure. Rather than reactively take action after a failure, the potential of fuzzy logic to anticipate and proactively take corrective actions is explored here. Failures adversely affect a system’s non-functional qualities (e.g., availability, reliability, and usability) and may result in a variety of losses such as data, productivity, or safety losses. The detection and prevention of failures necessarily improves a critical system’s non-functional qualities and avoids losses. The paper is self-contained and reviews set and logic theory, fuzzy inference systems (FIS), explores parameterization, and tests the neighborhood of rule thresholds to evaluate the potential for anticipating failures. Results demonstrate detectable gradients in FIS state spaces and means fuzzy logic based system monitors can anticipate rule violations or system failures.

Applications and technologies of the Internet of Things are in high demand with the increase of network devices. With the development of technologies such as 5G, machine learning, edge computing, and Industry 4.0, the Internet of Things has evolved. This survey article discusses the evolution of the Internet of Things and presents the vision for Internet of Things 2.0. The Internet of Things 2.0 development is discussed across seven major fields. These fields are machine learning intelligence, mission critical communication, scalability, energy harvesting-based energy sustainability, interoperability, user friendly IoT, and security. Other than these major fields, the architectural development of the Internet of Things and major types of applications are also reviewed. Finally, this article ends with the vision and current limitations of the Internet of Things in future network environments.

The introduction of IEC-61850 digital-based Substation Automation System (SAS) eases implementation of elaborate schemes; however, its reliability and availability continue to be investigated for executing mission-critical applications. Independent repairable multi-channel systems with voting capability such as ‘one-out-of-two’ tripping schemes are often used for critical safety-related functions because the individual scheme channels hardly ever fail simultaneously. The system configuration enables the scheme to self-reconfigure when a link failure occurs in one of the channels, as well as being repairable with no interruption to the overall mission. This paper reviews the reliability and availability of evaluation methods to highlight their advantages and disadvantages. Structure-function modelling, as well as Markov process incorporating Systems Thinking and Mathematical Expectation are used to model the reliability of IEC-61850 based SCN to demonstrate the shortcomings of combinatorial methods in the evaluation of mission-critical systems where diagnostic capabilities of the systems and imperfect repairs should be considered. It is evident from the results of the study that system diagnostic capability and repair efficiency cannot be ignored for mission-critical applications for the reason that the simplifying assumptions of combinatorial analysis methods greatly over-state the reliability and availability performance of the system as observed from the results.