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Stream processing lies in the backbone of modern businesses, being employed for mission critical applications such as real-time fraud detection, car-trip fare calculations, traffic management, and stock trading. Large-scale applications are executed by scale-out stream processing systems on thousands of long-lived operators, which are subject to failures. Recovering from failures fast and consistently are both top priorities, yet they are only partly satisfied by existing fault tolerance methods due to the strong assumptions these make. In particular, prior solutions fail to address consistency in the presence of nondeterminism, such as calls to external services, asynchronous timers and processing-time windows. This paper describes Clonos, a fault tolerance approach that achieves fast, local operator recovery with exactly-once guarantees and high availability by instantly switching to passive standby operators. Clonos enforces causally consistent recovery, including output deduplication, by tracking nondeterminism within the system through causal logging. To implement Clonos we re-engineered many of the internal subsystems of a state of the art stream processor. We evaluate Clonos' overhead and recovery on the Nexmark benchmark against Apache Flink. Clonos achieves instant recovery with negligible overhead and, unlike previous work, does not make assumptions on the deterministic nature of operators.

Despite fueling a once-in-a-century downturn of global business activity in spring 2020, the pandemic was more a tailwind than an impediment for the development of digital and platform Business Designs. This chapter introduces a scientific framework for such Business Designs by defining the four mission critical elements of platform firms, by describing five distinct platform patterns and by discussing the financial and strategic characteristics of platforms. Based on this framework, three novel scientific concepts to assess, design, and evaluate platform Business Designs are introduced: The 10C Business Design enables the strategic analysis of existing and innovation of new business models by defining the ten mission-critical architectural cornerstones of a digital firm. The eight platform value levers, as second strategic tool, assess a platform’s strength. The latter is defined by a platform’s scalability, its dominance within a defined ecosystem, and its value creation potential. The Reverse Discounted Cash Flow (DCF) pairs strategic diagnostics with financial valuation. It builds upon the well-known Discounted Cash Flow concept of corporate valuation but applies it upside down. The valuation’s origin is the steady-state description of a distinct Business Design embedded in a “picture-of-the-future” scenario. Open image in new window

New mission-critical machine-type communication is one of the central design aspects of 5G communications. But there are numerous challenges and explicit requirements for developing a successful mission-critical communication system and industrial applications. Reliability and delay optimization are the two most crucial among them. Achieving reliability is influenced by several difficulties, including but not limited to fading, mobility, and external interference. Achieving reliability may cost one of the most critical features of mission-critical communication, which is a delay. This paper discusses possible strategies to achieve reliability in a mission-critical network. We want to derive a 5G design that enables mission-critical communication under a unified system air interface and architecture with other 5G technology components.

5G will lay the foundations for the mainstream broadband wireless technology of the next decade, a leverage toward ensuring the efficiency, effectiveness and adaptability of everyday high-demanding operations, such as those in Public Protection and Disaster Relief. The ITU considers LTE-Advanced systems and 5G as a mission critical PPDR technology able to address the needs of MC intelligence, providing support for MC voice, data and video services as an IMT radio interface. In this paper, we introduce 5G-EPICENTRE, an innovation action funded by the EC under the Horizon 2020 research framework, which aims to deliver an open, federated 5G end-to-end experimentation platform specifically tailored to the needs of PPDR software solutions. The envisioned platform will allow SMEs and developers a lower entry barrier to the PPDR market, enabling them to build-up and experiment with their solutions in a cost effective way. The 5G-EPICENTRE platform will be based on an open Service Oriented Architecture and will accommodate open access to 5G networks' resources, acting this way as an open source repository for PPDR 5G Network Applications (NetApps). The purpose of the federated platform is to provide sufficient resources to cover the entire range of the 3 ITU-defined service types (i.e. eMBB, mMTC and URLLC) and to deliver secure interoperability capabilities beyond vendor-specific implementation. © 2021 IEEE.

As the 5G technologies start to become a reality in telecommunication networks, more services and applications are designed to take advantage of the new features that 5G technology is offering. Additionally, several vertical sectors are using advanced applications in order to improve their performances. One important vertical is the Mission Critical Services (MCS) sector, which could significantly exploit 5G networks. When an emergency event occurs, such as a strong earthquake or a flood, the network traffic is proved to be rapidly increased. At the same time, the first responders need all the available resources in order to offer their services efficiently. In a situation like the one described which is extremely demanding and the available resources should be used as a priority by the first responders, the existing 4G network does not seem to be sufficient. It must be ensured that the first responders could be interconnected in a reliable network, which will provide a low latency and ultra-high throughput transmission being able to support all the advanced equipment and devices (UAVs, robots, AMRs, augmented reality and virtual reality glasses, etc.) that the first responders need. These requirements are satisfied by 5G networks. The 5G network architecture has been designed and implemented based on a new approach. The 5G network architecture that was designed and implemented for the needs of FASTER project and the advantages that this architecture offers to the first responders is presented in this paper.

There has recently been a call for revisiting the effect of technology on guest experience in hotels. Technology has been gradually and usually applied to the hospitality industry. Hence, the present study identifies the effects of the agreement of the guest room technology adoption the perspective of hotel guests on their choice of the hotel. A questionnaire survey was adopted to collect data from guests in the four and five stars' hotels in Luxor and Aswan governorates to investigate the impacts of individual-level importance on the technology acceptance in guests' rooms. This study aims to investigate the special effects of advanced technology in hotels' rooms on guest loyalty. This study observed in-room technologies and identified the ones realized to be mission critical for the hotel guests. It also determined the differences in guest empowerment technology preferences and expectations across generations and travel frequency. Moreover, it investigated whether the quality of in-room technologies impressions guests’ decision in choosing a hotel. The data were collected through personal meetings with the guests, hotels, as well as Google drive, hotels mails and social media. (500) questionnaires were distributed, only (428) samples were correctly ready to be analyzed. Important finding of this study was that a plurality of respondents informed that the availability of new guest-room technologies would favorably impact their decision to choose a hotel.

This paper explores a low-cost method to bridge analog public safety Land mobile radio (LMR) systems into a standards based Long Term Evolution (LTE) MCPTT system. Design goals included robustness, cost, and the creation of a system that closely conforms to released and future standards. A proof of concept prototype that successfully bridges an analog LMR system with a standards compliant LTE based MCPTT system was developed and tested. The results and design details will be covered in this paper.

International audience; Euclid, an ESA mission designed to characterise dark energy and dark matter, passed its Mission Critical Design Review in November 2018. It was demonstrated that the project is ready to start integration and test of the main systems, and that it has the ability to fulfil its top-level mission requirements. In addition, based on the performances at M-CDR, the scientific community has verified that the science requirements can be achieved for the Weak Lensing and Galaxy Clustering dark energy probes, namely a dark energy Figure of Merit of 400 and a 2% accuracy in the growth factor exponent gamma. We present the status of the main elements of the Euclid mission in the light of the demanding high optical performance which is the essential design driver is the to meet the scientific requirements. We include the space segment comprising of a service module and payload module hosting the telescope and its two scientific instruments, and the ground segment, which encompasses the operational and science ground segment. The elements for the scientific success of the mission for a timely release of the data are shortly presented: the processing and calibration of the data, and the design of the sky survey. Euclid is presently on schedule for a launch in September 2022.

In this chapter we present a compact overview of the 3GPP designed New Radio (NR) system with emphasis on the Radio Access Network (RAN) part, and related innovations that enable mission critical communication for Industrial Internet of Things (IIoT) including the use of 5G seamlessly in an Industrial Ethernet scenario. We focus on Ultra-Reliable Low-Latency Communication (URLLC), including its evolution towards enhanced URLLC (eURLLC), and Time-Sensitive Communications (TSC). The chapter is organized by first shortly introducing the addressed IIoT use cases and requirements addressed by the NR (e)URLLC and TSC enablers. To understand how 5G enables wireline-like performance while retaining the flexibility of wireless deployment, we then provide a brief overview of the NR RAN protocol stack and architecture options followed by a summary of main enablers for (e)URLLC in 3GPP NR Release-15 and 16. Then, the seamless integration of 5G into an IIoT factory setup with TSC is explained and main enablers introduced in 3GPP NR Release-16 are introduced. Finally, the chapter is closed with an outlook of further enhancements being considered for Release-17 standardization. Throughout the chapter, we provide pointers to the most relevant 3GPP Technical Reports (TRs) and Technical Specifications (TS), as well as selected publications where more details can be found.

Within the military, cognitive readiness is essential to ensure the warfighter can return to highly demanding combat training and deployment operations. The warfighter must be able to make split second decisions and adapt to new tools and environments. After a traumatic brain injury, clinicians helping the warfighter must have techniques that address warfighter cognitive readiness. Current rehabilitation for cognitive complaints used in military medicine are modeled after civilian therapies which focus on remediating moderate to severe impairment through building compensatory strategies. This traditional approach to cognitive rehabilitation does not translate well to mild traumatic brain injury (mTBI) where impairments are subtle, nor does it meet the needs of our warfighters in deployed and combat training environments. Challenging our current methods is critical in adapting to the needs of this highly valued population to ensure that our warfighters are able to carry out mission critical decision making. Here we present a review of our best current practices for cognitive rehabilitation, describe the limitations our traditional approaches impose for mTBI in military personnel, and present an alternative treatment called Strategic Memory Advanced Reasoning Training (SMART) that can be adopted through a randomized clinical trial design. We propose directly comparing traditional treatment approaches with a novel cognitive rehabilitation strategy which has been well validated outside of the military setting. Procedures were developed to execute this clinical trial in a way that is most relevant to the study population by establishing ecologically valid outcome metrics.

Software engineering for autonomous robot (SE4AR) is an emerging interdisciplinary field with the aims to support the development, running and evolution of software systems for autonomous robot - a safety and mission critical cyber-physical system. Such field has recently gained increasing attentions from both academia and industry and made rapid progresses in the past years. However, many challenges pose on the SE4AR due to the specific features and complexities of autonomous robot software (ARS) and several open problems should be tackled in the future researches. This paper aims to present a comprehensive investigation on the researches and practices of SE4AR. Our contributions are three-fold as follows: an in-depth analysis on the development challenges, a systematic review on the current progresses, and an open discussion of weaknesses in current researches and opportunities in future researches.

The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based cyber-physical applications, and develops a scheduling solution to mitigate its effect. In general, priority inversion occurs in real-time systems when computations that are of lower priority are performed together with or ahead of those that are of higher priority.1 In current machine intelligence software, significant priority inversion occurs on the path from perception to decision-making, where the execution of underlying neural network algorithms does not differentiate between critical and less critical data. We describe a scheduling framework to resolve this problem, and demonstrate that it improves the system’s ability to react to critical inputs, while at the same time reducing platform cost.

Providing predictable performance to tenants is mission critical for network hypervisors. As a hypervisor acts as an intermediary between tenants controllers and the physical infrastructure, its resources (e.g., CPU, RAM) should be provisioned and allocated carefully. Initially, we demonstrate that state-of-the-art CPU prediction approaches are not suitable for provisioning network hypervisor CPU resources, since they predict only the mean CPU utilization. However, provisioning the resources with a mean value can significantly degrade the forwarding performance of a network hypervisor. In this article, we present a novel approach which provisions network hypervisor CPU resources efficiently, while avoiding performance degradation. We take three steps to achieve our goal: (i) conducting a profound measurement campaign to determine what is the minimum amount of CPU resources that needs to be allocated to a network hypervisor in order to have no performance degradation; (ii) revealing the key properties of virtual networks that affect the CPU utilization; (iii) designing a precise CPU prediction model. Using randomly generated virtual networks and arbitrary physical topologies, we show that our prediction model exhibits an average relative error of around 4%. Further, our evaluations indicate that provisioning the CPU resources of a network hypervisor based on the proposed prediction model does not degrade the hypervisor forwarding performance. Utilizing our approach, network operators can minimize their resources consumption while still providing predictable and undegraded forwarding performance to tenants.

One of the common problems faced by Telecommunication (TLC) companies is the lack of power supply, usually for those appliances with scarce chances of grid connection often placed in remote zones. This issue is more and more critical if the radio network has the specific task of guaranteeing the so-called &ldquo, mission-critical communications&rdquo, This manuscript aims to propose and assess a viable solution to optimize the power supply and maintenance operations required to assure the proper functionality in such critical and remote sites. In particular, the main goals are defining a method to select the critical sites in an extensive and composite radio system and designing the hybrid power system in a way to improve the service availability and technical-economic benefits of the whole mission-critical TLC system. Finally, the proposed method and related procedures are tested and validated in a real scenario.

Mission-critical machine type communication (MC-MTC) systems in which machines communicate to perform various tasks such as coordination, sensing, and actuation, require stringent requirements of ultra-reliable and low latency communications (URLLC). Edge computing being an integral part of future wireless networks, provides services that support URLLC applications. In this paper, we use the edge computing approach and present a statistical learning-based dynamic retransmission mechanism. The proposed approach meets the desired latency-reliability criterion in MC-MTC networks employing framed ALOHA. The maximum number of retransmissions Nr under a given latency-reliability constraint is learned statistically by the devices from the history of their previous transmissions and shared with the base station. Simulations are performed in MATLAB to evaluate a framed-ALOHA system's performance in which an active device can have only one successful transmission in one round composed of (Nr + 1) frames, and the performance is compared with the diversity transmission-based framed-ALOHA.

Integrity of location data from smart-phones is essential in several location-dependent applications. Unfortunately it is quite easy to spoof location data on smart phones. Therefore, most mission critical services such as road-side assistance, use location aggregators to supply verified location with user permission. This verified location is purchased from cellular carriers and user permission is required to comply with governmental privacy laws. However, cellular carriers, not aggregators are bound by privacy regulation. Therefore, when there is a breach of confidentiality, as it happened recently in the US, it is the cellular carriers' who were held responsible and must take corrective actions. In this paper, we present a mechanism to obtain location certificates using LTE positioning protocols so that it is possible for cellular carriers to (a) obtain verifiable consent from the mobile user before their location is released, (b) be able to directly provide verified location as a service to businesses rather than selling customer data to third party location aggregators, and (c) enable mobile users to store self-certifiable proofs of their own location to reuse as needed. With this mechanism in place, people can have greater control over their privacy as location-dependent services are assured of integrity.

Exceptions and errors occurring within mission critical applications due to hardware failures have a high cost. With the emerging Next Generation Platforms (NGPs), the rate of hardware failures will likely increase. Therefore, designing our applications to be resilient is a critical concern in order to retain the reliability of results while meeting the constraints on power budgets. In this paper, we discuss software resilience in AMTs at both local and distributed scale. We choose HPX to prototype our resiliency designs. We implement two resiliency APIs that we expose to the application developers, namely task replication and task replay. Task replication repeats a task n-times and executes them asynchronously. Task replay reschedules a task up to n-times until a valid output is returned. Furthermore, we expose algorithm based fault tolerance (ABFT) using user provided predicates (e.g., checksums) to validate the returned results. We benchmark the resiliency scheme for both synthetic and real world applications at local and distributed scale and show that most of the added execution time arises from the replay, replication or data movement of the tasks and not the boilerplate code added to achieve resilience.

Mission Critical Video (MCVideo) service has been introduced into the Long Term Evolution for Railway (LTE-R) system to realize railway video trunking communication. According to the test results, the reliability of the existing end-to-end access process of MCVideo cannot satisfy the requirements of the railway video trunking communications. In this paper, the reliability of end-to-end access process of the MCVideo is investigated. The end-to-end access process system is modeled with Deterministic and Stochastic Petri nets (DSPNs), and then the probability of successful access is analyzed based on DSPN model. With the analysis results, the dual-module communications and retransmission strategies are proposed to optimize reliability of MCVideo service. Extensive simulation results are conducted to verify the reliability of the end-to-end access process with dual-module communications and retransmission, and it is shown that the probability of successful access can achieve the QoS requirements of LTE-R.

Smart connected LED streetlights are emerging as an important infrastructure that can support basic lighting control services as well as a wide range of current and future smart city applications and services. Each smart streetlight is turned into multi-sensor-equipped smart node, a sensor ‘hub’ node, capable of capturing and transmitting/receiving real-time data (digitally controllable nodes). A smart LED has sensors embedded into and connectivity to the cloud. This paper assesses the feasibility and quantifies the performance of commercial point-to point (P2P) 4G LTE cellular networks when used to provide robust connectivity between a massive number of smart streetlight hub nodes and the cloud. Each smart streetlight hub node is assumed to be running simultaneously few basic lighting control services as well as smart city services and applications, including mission-critical with strict latency and reliability requirements, with particular emphasis on HD IP video surveillance cameras.

Data center risk assessment must provide an understanding of the risks that the mission critical facility is exposed to. This paper proposes a novel framework for data center site risk assessment, as an important tool for data center due diligence. The proposed methodology is aimed at standardizing a process to help quantify and prioritize external risks to enable comparisons. It consists of three steps: risk identification, risk analysis, and risk evaluation. The risk analysis incorporates the infrastructure resiliency rating and a data center site risk metric to quantify the weighted risk level, as well as criteria based on standards, best practices and expert knowledge. Based on the results from the risk assessment, the risk level may be treated, to adjust it to the desired level. The risk assessment is a way to better communicate and understand risks associated to the data center location, and help evaluate mitigation strategies.

In this research, we propose a velocity-adaptive contention window (CW) control method that reduces the maximum delay under dynamic interference on automobiles with IEEE802.11 wireless LAN communication. The method is developed for mission-critical communications for tiny periodic data. In the proposed method, the average backoff time (random wait time) is reduced as the vehicle velocity decreases, and this is done to reduce the maximum delay in situations where the influence of interference is large. Additionally, increasing the average contention window at the time of retransmission is prohibited, and the window size is fixed. Developing the proposed method, we focused on the condition in which, the slower the velocity of the vehicle is, the smaller the distance to the surrounding vehicles (and therefore the greater the amount of interference). Furthermore, we did not focus on the fact that the CW size is not optimal; instead, we focused on interference as the main cause of retransmission. This can reduce the delay determined by the number of retransmissions (the number of transmission failures) and the backoff time. To research the effect of the proposed method, we evaluated the performance of a sensor network in a vehicle using a model that causes interference when other vehicles pass near the vehicle at various velocities. The effectiveness of the proposed method was clarified by comparing the conventional method with fixed control for interference and the proposed method with control according to vehicle velocity.

This paper reviews the provisions of 3rd Generation Partnership Project (3GPP) Release 16 as it affects Industrial Internet of Things and mission critical communications with a view to see the implementation possibilities and challenges. The goal is to give a useful insight into the expectations of beyond 5G or 6G system with respect to IIoT. 3GPP completed the Release 16 version of its specifications in July 2020 a little behind the target of March 2020. The reality is that the growth of the Internet-of Things (IoT) has resulted in an explosion in the use of IoT devices leading to the prediction that IoT devices will exceed 25 billion in 2020 and may exceed 75 billion in 2025. This explosion has placed the direction of Release 16 on new features for eURLLC (enhanced Ultra-Reliable Low Latency Communication) and Industrial Internet of Things, including Time Sensitive Networks, enhanced location services and support for non public networks. The paper adopted short review of major works available on ieee Xplore between 1993-2020. The result of the research showed that the following are open research issues: Security for industrial IoT beyond 5G, Supporting technologies for industrial IoT beyond 5G, and the need for more testbed research for industrial IoT beyond 5G.

Programmability is one of the key features of fifth generation (5G) system which enables flexibility in provisioning customized services. 5G supports a wide range of mission critical use cases with challenging requirements of ultrahigh reliability and availability, and low latency. These use cases can benefit from programmability of quality of service (QoS) as the connectivity requirements can vary across different mission critical scenarios. In this paper, an application programming interface (API), that allows third party mission critical applications to initiate sessions with specified QoS, is presented. The API exposes session control capabilities at the network edge utilizing the infrastructure of Multi-access Edge Computing. The proposed API is described by typical use cases, information flows and data types. The API feasibility is demonstrated by session state models and performance evaluation.

Mission Critical (MC) communications are key to effective Public Protection and Risk Reduction (PPRR) actions. The 3GPP standards include the definition of MC applications and services in an architectural framework compatible with current (LTE) and future (5G) mobile networks. In this paper we report an experimental activity where MC communication services are implemented in a fully virtualized environment, being deployed and tested in a multi-domain network slicing architecture compliant with the ETSI NFV MANO specifications. The level of automation in service deployment and the slice isolation features are demonstrated, in line with the 5G approach of separation between control and data plane, showing the benefits in terms of application performance and management flexibility.

Mission-critical communication (MCC) enables and supports operations by providing reliable connectivity and interoperability, facilitating operational continuity and allows mission-performers to focus on mission goals and objectives. Any communication technology used in isolation may fail. For instance, land mobile radio (LMR) networks may provide poor coverage to tactical "push-to-talk" radio devices within stone buildings, while cellular devices may not satisfy strict performance criteria (e.g. setup and response time). An alternative approach would be dynamic orchestration of such hybrid communication networks, which also involve Bluetooth, cellular, and cloud-based networking technologies. We propose an integrated approach that accounts for situation, location, and connectivity considerations to enhance MCC network availability, and mission-performers’ connectivity and readiness, by harnessing communication, location, and situational awareness in networking technologies, applications, and users. This framework provides a holistic cyber-physical perspective on the problem. Our approach is useful in various real-life applications for operational connectivity of first responders, e.g. when breaking into a scene of an emergency, in which LMR coverage is expected to deteriorate significantly.

Cloud computing is reaching its limits when it comes to mission-critical applications. Edge computing, on the other hand, attempts to place computing capabilities closer to the user equipment device, minimizing response time and increasing reliability. In order to take full advantage of the edge and to offer a broad service coverage, a wide range of mission-critical applications use mobile edge computing. However, one of the challenges that affects reliability in mobile edge computing is the border problem. The border problem typically occurs when the mobile user equipment crosses the border between two points of access. During this condition, the response time increases and becomes less predictable, which severely hurts the reliability of mission-critical applications. This work tackles this problem by developing a sliding-window approach, which is to maintain multiple connections between a user equipment and several edge node servers. The sliding-window reduces the adverse effects produced by the border problem in mobile edge computing, ensuring a more predictable response time. The proposed approach is implemented in the Street-Edge Hazard Notification System (SEHNS), a mission-critical application in the domain of automotive safety that disseminates hazards information among intelligent vehicles in real-time. Initially, an analysis of the border problem cases is carried out. Then, the performance of SEHNS is examined in each of the cases, with and without sliding-window. Finally, the approach is implemented in a realistic scenario where two vehicles follow each other along a road with several hazards. Results show that the sliding-window approach reduces the worst case response time by about 50%.

Reliability, a crucial requirement in any modern microprocessor, assures correct execution over its lifetime. As mission critical components are becoming common in commodity systems, e.g., control of autonomous cars, the demand for reliable processing continues to grow. The latest process technologies have aggravated the situation by causing microprocessors to be highly vulnerable to reliability concerns. This paper examines the asymmetric aging phenomenon, which is a major reliability worry in advanced process nodes. In this phenomenon, logical elements and memory cells suffer from unequal timing degradation over their lifetimes and, consequently, raise reliability concerns. Thus far, most studies approached asymmetric aging from a circuit or physical design viewpoint, but these solutions were quite limited and suboptimal. In this paper, we introduce an asymmetric aging-aware microarchitecture that aims to reduce the phenomenon's impact. The study is mainly focused on the following subsystems: execution units, register files and memory hierarchy. Our experiments indicate that the proposed solutions incur minimal overhead while significantly mitigating asymmetric aging stress.

With the emerging commercial deployments of 5G Network, the Mobile Network Operators (MNOs) will be able to offer more significant network capabilities such as efficient ultra-reliable and low latency communications (URLLC), higher bandwidth for data transfer with enhanced Mobile Broadband (eMBB), and massive capacity to connect devices with massive Internet of Things (mIoT). MNOs are making huge investments to setup 5G Networks with an anticipation of growth in their revenues. However, MNOs may not see the return of investment through end user consumer base alone and may need additional revenue streams. Thus, 5G system has been designed to have advanced built-in features such as network slicing and edge computing, considering requirements from enterprise segments or vertical industries including healthcare, automotive, smart factories, mission critical communications, etc. To enable more rapid deployment of vertical services, the 5G deployments require fostering innovation at the application layer. With the growing needs of verticals industries, the operators are now focusing on enabling standards for vertical applications in 3rd Generation Partnership Project (3GPP), a global standards forum for 5G. In this paper, the authors explain the Service Enabler Architecture Layer (SEAL) standard for 5G verticals and the services offered by the enabler layer. The paper also elaborates how the operators can leverage the SEAL to quickly develop and deploy vertical applications over the 5G Network.

Many mission critical devices are increasing with upcoming 5G network to fulfill a low latency for a real time network service on smart factory, autonomous vehicle, etc. Distributed cloud computing system also has a key role to execute the various mobile devices, because, an edge computing is the nearest from the mobile devices to provide low latency and computation energy consumption. In this paper, we consider the autonomous vehicles with video live streaming services. Especially, the vehicles require a low transmission delay as within 10 ms. To reduce a latency with low energy consumption, we propose a service chaining offloading decision with a deep reinforcement learning. We split tasks of the vehicle per service function blocks which have their own role. So it can do partial offloading and user association in a On-Device Edge of the vehicle and in the SBS at the same time. We can get results that service chaining offloading decision gives more optimal energy consumption with low-latency to autonomous vehicle users.

Fifth generation (5G) mobile networks possess capabilities to address the strong requirements of mission critical communications for low latency, very high reliability and availability. Further improvement of network performance can be achieved by applying Multi-access Edge Computing (MEC) paradigm for distributing the computing and storage capabilities at the network edge. In this paper, we propose a new mobile edge service which provides notifications about session related events to applications and enables applications to determine the further session handling. The service, which follows Representational State Transfer style, is described by its resources and supported methods. The service performance is evaluated by emulation.

The infrastructure of communication networks is vulnerable to emergencies or natural catastrophes such as earthquakes, tsunamis, among others. When these emergencies arise, the vast majority of components are affected, hindering communication after the disaster. Both the first organizations to respond (civil defense, fire, and police) and the population in general therefore need an alternative communication system that allows knowing reliable information in real-time. For this reason, in this article, we present a prototype of mission-critical communication using cognitive radio networks (CRN) implemented in software-defined radio devices (SDR), which works on the AM and FM bands. The results show that the cognitive protocol can be implemented in the SDRs and allows the transmission of mission-critical messages in these bands, protecting the integrity of the primary users that are still operating through an energy detector.

Distributed computing systems often consist of hundreds of nodes (machines), executing tasks with different resource requirements. Efficient resource provisioning and task scheduling in such systems are non-trivial and require close monitoring and accurate forecasting of the state of the system, specifically resource utilisation at its constituent machines. Two challenges present themselves towards these objectives.First, collecting monitoring data entails substantial communication overhead. This overhead can be prohibitively high, especially in networks where bandwidth is limited. Second, forecasting models to predict resource utilisation should be accurate and also need to exhibit high inference speed. Mission critical scheduling and resource allocation algorithms use these predictions and rely on their immediate availability.To address the first challenge, we present a communication-efficient data collection mechanism. Resource utilisation data is collected at the individual machines in the system and transmitted to a central controller in batches. Each batch is processed by an adaptive data-reduction algorithm based on Fourier transforms and truncation in the frequency domain. We show that the proposed mechanism leads to a significant reduction in communication overhead while incurring only minimal error and adhering to accuracy guarantees. To address the second challenge, we propose a deep learning architecture using complex Gated Recurrent Units to forecast resource utilisation. This architecture is directly integrated with the above data collection mechanism to improve inference speed of the presented forecasting model. Using two real-world datasets, we demonstrate the effectiveness of our approach, both in terms of forecasting accuracy and inference speed.Our approach resolves several challenges encountered in resource provisioning frameworks and can also be generically applied to other forecasting problems.

Software faults are dangerous. Software systems are often essential to a business operation or organization, and failures in such systems cause disruption of some goal-directed activity (mission critical). Faults in safety-critical systems may result in death, loss of property, or environmental harm. Run-time faults are the most damaging as they are not always detectable during the testing process. Detecting faults before they occur gives the designers a brief inner view of the possible failure and their frequency of appearance. This helps in focused testing and saves time during the software development. Prediction models have the ability to differentiate between various patterns. This article showcases the effectiveness of cross-validation in design and development of a neural network for software fault detection.

Lately, drones have seen significant usage in multipurpose services, most prominently in emergency and critical communications. Drones rely on wireless communication technologies to assist the delivery of ‘Mission Critical’ (MC) services such as telemedicine, precision agriculture and public safety. However, to realize their potential in these areas, drones need to demonstrate ubiquitous coverage, seamless mobility, high reliability and enhanced security in emergency scenarios. The defacto solution to these requirements is anticipated to be 5G-based cellular connectivity, which facilitates reliability by enabling a ‘network of networks.’ To assess the potential of the above solution, this paper outlines an MC drone use case and relates it to the advances in the 5G network infrastructure to establish a reference network architecture design for reliable drone communication. Within the proposed reference framework, a super-resolution localization technique to increase communication system reliability through drone-tracking has been considered. Our analysis emphasizes the need to further parameterize and integrate the super resolution localization algorithms to help continuously track the presence of drones in the future deployments of 5G mission critical networking technologies.

Professional Mobile Radios (PMR) are very successful systems that are used for the mission critical communications during the last 25 years and have been trusted by a lot of Protection and Disaster Relief (PPDR) services globally. PMR systems refer to narrowband technologies such as TETRA, P-25 and Tetrapol which they meet the PPDR demands. The application of these standards in wide geographical areas with low population density, are expensive solutions for operational usage, to replace the analogue PMR systems that are still used by lots of PPDR Services. Amongst the upper successful professional PMR systems, the Digital Mobile Radio (DMR) trunking system, constitutes a very attractive and cost efficient solution for mission critical communications. Due to this economical technical balance DMR trunking technology seems to be capable of replacing the current analog radio communications, used by Hellenic PPDR Services. This study offers a realistic approach of radio coverage and also an accurate method for capacity planning for mission critical communications using the DMR standard. As a Service we choose the Fire Brigade due to its wider areas of action compared to the other Services. Moreover, we apply our investigation on the Attica region, which has the most incidents per year in all Hellenic territory. A result of this research is the investigation of geographic radio coverage using the current infrastructure sites and the examination of the target for 95% coverage that is the requirement for PPDR services. Furthermore, with the aim of a realistic scenario for PPDR services that we study, using real statistical data we estimate the capacity needs in traffic channels concluding that 44 DMR base stations and a spectrum about 1,100 Mhz are needed for our approach.

International audience; Though the requirements are well defined and established, the adoption of radios for mission critical applications is lagging due to the fuzzy transmission characteristics of the wireless medium. To enable automation and safety applications, in particular for Machine-to-Machine traffic, strict reliability and availability demands must be met end-to-end. The Reliable and Available Wireless (RAW) Working Group (WG) aims specifically at enabling such guarantees by leveraging end-to-end Layer 3 services to compensate for the lossiness of the lower layers. In this letter, we present the Operation, Administration and Management (OAM) requirements to support industrial requirements. We detail first the operation challenge, the tools to verify that the network works properly, and to detect possibly faults. Then, we explain why we need to administrate the network to know in real time the state and the resources provisioned. Finally, we detail the mechanisms to make the network reliable and fault-tolerant.

Internet of Things (IoT) technologies have enabled Cyber-Physical Systems (CPS) to become fully interconnected. This connectivity however has radically changed their threat landscape. Existing risk assessment methodologies often fail to identify various attack paths that stem from the new connectivity/functionality features of IoT-enabled CPS. Even worse, due to their inherent characteristics, IoT systems are usually the weakest link in the security chain and thus many attacks utilize IoT technologies as their key enabler. In this paper we review risk assessment methodologies for IoT-enabled CPS. In addition, based on our previous work (Stellios et al. in IEEE Commun Surv Tutor 20:3453–3495, 2018, [47]) on modeling IoT-enabled cyberattacks, we present a high-level risk assessment approach, specifically suited for IoT-enabled CPS. The mail goal is to enable an assessor to identify and assess non-obvious (indirect or subliminal) attack paths introduced by IoT technologies, that usually target mission critical components of an CPS.

This study focuses on the impact of Additive Manufacturing on the responsiveness, efficiency and sustainability of the spare parts supply chain of armed forces during military and humanitarian missions. These spare parts are slow movers, high tech and highly specific. As a consequence their demand is notoriously variable and difficult to forecast. At the same time they are mission critical. A systematic literature review, desk research of different cases, as well as semi-structured interviews with internal and external stakeholders within the Royal Netherlands Army demonstrate that Additive Manufacturing can reduce lead times, waste, energy use, spare parts inventories, as well as improve system readiness and sustainability of armed forces, specifically during military and humanitarian missions abroad. However, challenges must be met in particular related to energy use, local collaboration, (out-)sourcing, security and intellectual properties. This paper connects Additive Manufacturing and the growing importance of sustainability with the need for well-prepared operational armed forces and outlines an agenda for future research regarding mission based supply chains.

Reliable communication is essential to alleviate incidents and escalate rescue operations. However, wireless communication is very challenging in underground mine due to irregular confined shapes and rough environments. A recent wireless standard LoRa (Long Range) is promising in mine environments because of its ultralow power consumption, long range, and deep penetration capabilities. In underground mine, sensors are deployed for continuous monitoring the working environments as well as tracking objects and miners. Therefore, different types of traffic are generated with different QoS requirements. LoRaWAN, the standardized medium access control (MAC) protocol is based on pure ALOHA that can not meet the requirements of mission-critical communications. The mission-critical applications require very low latency and high reliability. In this paper, we evaluate the performance of LoRa and LoRaWAN technologies in an underground mine in the presence of different kinds of traffic; and subsequently we propose redundant retransmission aided adaptive latency reduction protocol for low latency communication. In this protocol the ACK-TIMEOUT is adjusted based on the air time of the previous uplink transmission and the contention stage. Simulation results demonstrate that the proposed protocol significantly improves the performance of the system and outperforms LoRaWAN in terms of data extraction ratio (DER) and average transmission delay.

The most frequent problem detected by the companies in the telecommunications sector is the loss of power supply in site, usually located in remote zones. This is more delicate when the radio network has the specific objective to guarantee “mission critical” communications. The object of this study is to propose and evaluate a way to decrease the number of maintenance operations necessary to restore the correct functioning, aiming at previously defined critic and strategic sites in a wide and complex radio network. This to obtain an increased availability, longer continuity and technical and economic benefits. Finally, the developed methods and the algorithms are applied to a real “mission critical” case study.

Deep neural networks (DNNs) are now starting to emerge in mission critical applications including autonomous vehicles and precision medicine. An important question is the dependability of DNNs and trustworthiness of their predictions. Considering the irreparable damage that can be caused by mispredictions, assessment of their potential misbehavior is necessary for safe deployment. In this paper, we first show the deficiency of current confidence-based methods as reliability measurement, and assess the effectiveness of traditional architecture reliability methods such as modular redundancy (MR). Then, we propose PolygraphMR and show that the combination of input preprocessing, smarter decision policies, and inclusion of prediction confidences can substantially improve the effectiveness of MR for DNNs. Next, we show how to prohibit explosive growth in the cost of MR by the help of reduced-precision designs and staged activations. Across six benchmarks, PolygraphMR detects an average of 33.5% of the baseline mispredictions with less than 2x overhead.

For over a decade now, Amazon Web Services (AWS) has offered its spare capacity at a discounted price in the form of EC2 spot instances. This discount comes at the price of variable pricing and sudden instance termination. In this paper, we present a machine-learning solution to one of the challenges when using AWS Spot Instances, namely the termination of the instance on short notice. Our system, Spot Instance Management System (SimS), can effectively manage spot instances and keep up the availability at the desired level using 100-tree Random Forest Regression model. By using a risk assessment mechanism and proactive actions, SimS assures a three-nines SLA using AWS spot instances with lower running costs on workloads for a major European financial institution.

Dear Editor, I am writing to comment on the recent article, ‘Making communication and resolution programmes mission critical in healthcare organisations’, authored by Gallagher et al .1 When implementing a communication and resolution programme (CRP) or disclosure programmes as they are also referred to, there are two key factors to keep in mind: (1) how to market or sell the programme to stakeholders and (2) designing and implementing the programme for long-term success. The authors of this paper seem to negate or dismiss the most important marketing tool for CRP/disclosure programmes: litigation reduction and cost savings for insurers and healthcare organisations. They stated, …

The 3rd generation partnership project (3GPP) standard has introduced vehicle to everything (V2X) communi-cation in Long Term Evolution (LTE) to pave the way for future intelligent transport solutions. V2X communication envisions to support a diverse range of use cases for e.g. cooperative collision avoidance, infotainment with stringent quality of service (QoS) requirements. The QoS requirements range from ultra-reliable low latency to high data rates depending on the supported application. This paper presents a QoS aware decentralized resource allocation for V2X communication based on a deep reinforcement learning (DRL) framework. The proposed scheme incorporates the independent QoS parameter, i.e. priority associated to each V2X message, that reflects the latency required in both user equipment (UE) and the base station. The goal of the approach is to maximize the throughput of all vehicle to infrastructure (V2I) links while meeting the latency constraints of vehicle to vehicle (V2V) links associated to the respective priority. A performance evaluation of the algorithm is conducted based on system level simulations for both urban and highway scenarios. The results show that incorporating the QoS parameter (i.e. priority) pertaining to the type of service supported is crucial in order to meet the latency requirements of the mission critical V2X applications.

Organisations tailor agile methods to fit their specific needs. There are two recognised approaches to agile method tailoring: Contingency Factors and Method Engineering. Spotify has developed its own agile culture, which has become influential among agile proponents. The Spotify model has formed the basis of agile methods used in numerous other organisations. We have identified a lack of research into the tailoring approach used in the Spotify model. In order to explore How practitioners do achieve agile tailoring using the Spotify model, a longitudinal embedded case study over 21 months was conducted. A very large-scale FinTech organisation using the Spotify model was selected for the study. We conducted 14 semi-structured interviews and direct observations of agile practices. A grounded theory method was adopted to analyse the data. We discovered a novel approach to agile method tailoring, which we call "Heterogeneous Tailoring". This approach is characterised by two key features. Firstly, each autonomous cross-functional squad is empowered to select and tailor its own development method. This key feature is the main focus of this paper. Secondly, each squad is aligned to common product development goals. The practitioners in our study report benefits of this approach such as improved creativity and productivity, whilst risks of divergence from shared development objectives are mitigated through alignment practices. We contribute in this study by introducing a new third approach to agile tailoring (i.e., "Heterogeneous Tailoring"). This, in turn, complements the tailoring approaches by including Contingency Factors, Method Engineering, and Heterogeneous Tailoring.

As one of the main scenarios of 5G, Ultra Reliable Low Latency Communication (URLLC) plays an important role in Industrial Internet of Things (IIoT) for smart factories. At present, most researches on URLLC use Shannon capacity for infinite blocklength, which is not suitable for short packet transmission adopted by URLLC. In this paper, we consider the resource allocation of mission-critical service in the smart factory, where IIoT devices receive the control information from both base station (BS) and relay. The purpose of this paper is to jointly optimize the power allocation, the position of relay, and the blocklength with finite blocklength information theory to minimize the decoding error rate of the device. This paper proposes a low-complexity algorithm that effectively solves this optimization problem. Simulation results show that relay- assisted transmission and optimized resource allocation can significantly reduce the error rate.