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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

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.

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.

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.

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.

We introduce a new approach for Search-and-Rescue Operations (SAROs) to search for survivors after large-scale disasters, assuming the wireless communication network cells are partially operational and exploiting the recent trend of using Unmanned Aerial Vehicles (UAVs) as a part of the network. These SAROs are based on the idea that almost all survivors have their own wireless mobile devices, called User Equipments (UEs), which serve as human-based sensors on the ground. Our approach is aimed at accounting for limited UE battery power while providing critical information to first responders: 1) generate immediate crisis maps for the disaster-impacted areas, 2) provide vital information about where the majority of survivors are clustered/crowded, and 3) prioritize the impacted areas to identify regions that urgently need communication coverage.

The use of Substation Communication Networks (SCN) in power distribution centres of thermal power plants for protection, control and monitoring is increasing as industry’s confidence grows in the application of the IEC-61850 based Substation Automation Systems (SAS). IEC-61508, which is the standard for functional safety, addresses Electrical/Electronic/Programmable Electronic (E/E/PE) safety based technologies in so far as safety lifecycle as well as reliability and verification of safety related systems are concerned. It follows therefore, that Substation Communication Networks (SCN), as well as associated substation equipment that forms part of the overall safety system should satisfy the requirements of functional safety standard IEC-61508. In this paper, Substation Automation System standard (SAS) IEC-61850, Substation Communication Networks (SCNs) as well as dependability evaluation methods are reviewed and discussed.

Software systems are increasingly being used in business or mission critical scenarios, where the presence of certain types of software defects, i.e., bugs, may result in catastrophic consequences (e.g., financial losses or even the loss of human lives). To deploy systems in which we can rely on, it is vital to understand the types of defects that tend to affect such systems. This allows developers to take proper action, such as adapting the development process or redirecting testing efforts (e.g., using a certain set of testing techniques, or focusing on certain parts of the system). Orthogonal Defect Classification (ODC) has emerged as a popular method for classifying software defects, but it requires one or more experts to categorize each defect in a quite complex and time-consuming process. In this paper, we evaluate the use of machine learning algorithms (k-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Nearest Centroid, Random Forest and Recurrent Neural Networks) for automatic classification of software defects using ODC, based on unstructured textual bug reports. Experimental results reveal the difficulties in automatically classifying certain ODC attributes solely using reports, but also suggest that the overall classification accuracy may be improved in most of the cases, if larger datasets are used.

Accurate maintenance decision making is important for military equipment. Extremely demanding situations like limited time availability for maintenance during the war escalate the possibility of human errors in the maintenance of such equipment. Human errors in maintenance negatively impact the life of the systems. Human Reliability Analysis (HRA) methodologies have evolved to systematically quantify the human error in terms of Human Error Probability (HEP). However, the exact effect of the human error on the life of the component is unknown yet. In the presence of the diverse operating profiles for military equipment, estimating such effects becomes a complex and mathematically challenging problem to be handled by the conventional statistical techniques. This paper presents a machine learning approach to estimate the residual life of a component by incorporating the effect of human error in maintenance. Based on the nature of the maintenance data, a decision tree based boosted ensemble machine learning model is developed which predicts the Remaining Useful Life (RUL) of the component while considering error induced by maintenance personnel during its maintenance. The developed model is illustrated in the decision-making of replacement of a component in a mission critical military system in pre-mission maintenance break.

Breast Cancer is among the most widespread forms of cancer that affects hundreds of thousands of women across the world each year. Early detection of this cancer can really help, in that it cannot only make the treatment easier, but also significantly improve survival chances. It is thus imperative that the machine learning algorithm being deployed for real world use be reliable and accurate. This paper not only compared the performance of several classifiers on the given dataset on breast cancer from UCI with data from close to 600 cases, but also used the Recurrent Neural Network (RNN) for classification, which to the best of the authors' knowledge has never been used before for breast cancer classification. Also, the performances of these classifiers are compared with the same already implemented before in this domain and discuss about the improvements made in the proposed work. The proposed experimental results showed that, RNN has yielded best performance with the highest accuracy of 98.49%, followed by the linear kernel SVM classifier with an accuracy of 98.24% and XGBoost at 97.36%. This research work has trained and tested a total of 9 classifiers and analyzed their performance by considering accuracy as the key metric. Although, to the best of the authors' knowledge, the Recurrent Neural Network has not been implemented in this domain before and is thus novel. Since our domain is mission critical, the proposed research work highly emphasizes on accuracy, while ensuring that the every classifier is trained and tested accurately.

Agent‐based models (ABMs) are a powerful class of computational models widely used to simulate complex phenomena in many different application areas. However, one of the most critical aspects, poorly investigated in the literature, regards an important step of the model credibility assessment: solution verification. This study overcomes this limitation by proposing a general verification framework for ABMs that aims at evaluating the numerical errors associated with the model. A step‐by‐step procedure, which consists of two main verification studies (deterministic and stochastic model verification), is described in detail and applied to a specific mission critical scenario: the quantification of the numerical approximation error for UISS‐TB, an ABM of the human immune system developed to predict the progression of pulmonary tuberculosis. Results provide indications on the possibility to use the proposed model verification workflow to systematically identify and quantify numerical approximation errors associated with UISS‐TB and, in general, with any other ABMs., A general solution verification framework for agent‐based models (ABMs) has been proposed. The procedure has been applied to a specific mission critical scenario: the quantification of the numerical approximation error for UISS‐TB an ABM of the human immune system. The proposed approach proved to be affective at identifying and quantifying different sources of errors related to deterministic and stochastic aspects of the model.

Wireless sensor networks (WSN) has been extensively used in many real time wireless sensor networks applications. Due to limitations of hardware resources and restricted communication capabilities of sensor nodes, it is very challenging to use wireless sensor networks in real time data transmission. Data collection and routing is the main issue in such applications. To enhance the performance under such real time transmission scenario, it is essential to make the protocol intelligent to choose the appropriate path with change in network scenario. Now a days, many machine learning and deep learning algorithms are used for improving the real time data transmissions in WSNs. From the survey on existing methods, it is clear that using machine learning makes the computational methods more reliable, powerful and economical. This paper proposes a machine learning based Medium Access Control (MAC) protocol to handle real time traffic in wireless sensor networks. To deal with the limitations of WSN in real time application, the proposed scheme can help to increase the performance of time-critical wireless sensor network applications. Simulation results authorize our work and confirm the accuracy of the proposed ML-MAC protocol strategy is higher than the existing work.

the rapid evolution in web applications and software development has not witnessed a similar pace in development opposite what has taken place in the security and cybersecurity arenas. Web applications security has become a significant issue since insecure applications and software undermine various areas including finance, health care, defense, and other mission critical infrastructures. Web application vulnerability results from misconfiguration, flaws in the design, implementation, operation, or management at the different levels of a web application (input side, output side, or both sides). This taxonomy paper studies web vulnerabilities, their impact on users’ infrastructures, and their detection tools (static, dynamic, or hybrid) and mitigation mechanisms at different software architecture levels.

Sensor cloud is an integral component for smart computing infrastructure. Cloud servers are largely used to store and process sensor data. For mission critical applications use of only wireless sensor network results in provisioning of service in a small area and the use of a long distant remote cloud servers increase delay that degrades the Quality of Service. Further, geospatial information differs over regions. Thus storing and processing the data of all regions inside the cloud data centres may not be efficient with respect to response time (latency), energy consumption etc., which are crucial factors for mission critical applications. To overcome these limitations, we propose multi-sensor geo-fog paradigm. We consider defense sector in our work as mission critical application. For energy optimized services with minimal delay fog computing has been used, where the intermediate devices process the data. The proposed paradigm will offer fast and energy-efficient processing of defense related sensor and geospatial data. A mathematical model of the paradigm is developed. The sensor and geospatial data processing and analysis take place inside the fog device. If abnormality is detected in the data or emergency situation occurs, then shortest path to the victim region is determined using intelligent K* heuristic search algorithm. The simulation results demonstrate that the proposed fog based network scenario reduces energy consumption, average jitter and average delay by 12–15%, 10–14% and 9–11% respectively than the cloud based network. The simulation results demonstrate that saving about 20% of resources increases the performance for priority user whereas the resource availability for the normal users is not compromised.

Modern real-time systems tend to be mixed-critical, in the sense that they integrate on the same computational platform applications at different levels of criticality (e.g., safety critical and mission critical). Scheduling of such systems is a popular topic in literature due to the complexity and importance of the problem. In this paper we propose two algorithms for job scheduling in mixed critical systems: mixed criticality earliest deadline first (MCEDF) and mixed critical priority improvement (MCPI). MCEDF is a single processor algorithm that theoretically dominates state-of-the-art fixed-priority algorithm own criticality based priority (OCBP), while having a better computational complexity. The dominance is achieved by profiting from a common extension of fixed-priority online policy to mixed criticality. MCPI is a multiprocessor algorithm that supports dependency constraints. Experiments show good schedulability results. Also we formally prove that both MCEDF and MCPI are optimal in a particular class of algorithms.

Various studies have suggested enhancing the performance of large-scale systems, such as mission critical communication systems (MCCSs). However, few have modelled and evaluated the performance of such systems in a way that targets overall system performance in real time. Moreover, it is not enough to define the Key Performance Indicators (KPIs) for a system without using them for system performance measurement and performance evaluation. The Session Initiation Protocol (SIP) and IP Multimedia Subsystem (IMS) both have a set of KPIs, such as the registration process delay, that can be used to measure and thus optimize overall system performance. This article articulates different options for system simulation and evaluation. The registration process affects performance and reflects the overall system performance. The article shows how the registration process is delayed and how the overall system scalability are negatively impacted by system overload.

Random-access channels (RACHs) are designed to establish a connection between the user equipments (UEs) and the network. However, the current long-term evolution (LTE) standard has limitations in providing RACH resources to massive UE connections. If numerous UEs send a connection request simultaneously, it results in severe collisions and significant access delays that degrade system performance. There has been a lot of previous research into controlling this overload; however, there are no proposals to resolve RACH overload issues for mission-critical high-priority (MCHP) UEs in coexisting LTE-based public safety (PS-LTE) and LTE-based marine (LTE-M) networks. Thus, immense interest and practical research are urgently required to resolve the UE initial access problem during the random-access procedure, most importantly when MCHP users exist. In this paper, we propose an efficient mission-critical user priority-based random-access scheme for coexisting PS-LTE and LTE-M networks. Since PS-LTE users have mission-critical service requirements, we give higher priority to PS-LTE UEs when allocating RACH resources in the contention-based random-access (RA) procedure. Our proposed scheme efficiently assigns RA preambles to MCHP UEs in order to avoid preamble collisions when multiple UEs try to access coexisting PS-LTE and LTE-M networks. In this paper, the performance of the proposed scheme is analyzed and evaluated based on the number of successful RACH attempts, the number of collisions, and the access delay. The simulation results show the effectiveness of the proposed scheme. Compared with a conventional random-access scheme, the proposed scheme performs remarkably well by improving the number of successful RACH attempts, reducing the number of collisions, and minimizing the access delay to coexisting PS-LTE and LTE-M networks.

Machine Learning (ML) algorithms and Artificial Intelligence (AI) are now regarded as very useful for data-driven applications including resilient multi-domain operations. However, ML algorithms and AI systems can be controlled, dodged, biased, and misled through flawed learning models and input data, they need robust security features and trust. Furthermore, ML algorithms and AI systems add challenges when we have (unlabeled/labeled) sparse/small data or big data for training and evaluation. It is very important to design, evaluate and test ML algorithms and AI systems that produce reliable, robust, trustworthy, explainable, and fair/unbiased outcomes to make them acceptable and reliable in mission critical multi-domain operations. ML algorithms rely on data and work on the principle of ``Garbage In, Garbage Out," which means that if the input data to learning model is corrupted or compromised, the outcomes of the ML/AI would not be optimal, reliable and trustworthy.

Mission-critical sensors and sensor networks (MC-SSN) have been applied to missions such as battlefield, border patrol, search and rescue, critical structure monitoring, and surveillance. To support critical missions, sensors and sensor networks need to be flexible and interactive and continuously work despite limited bandwidth, intermittent connectivity, and with a large number of devices on the network. Sometimes, humans will be the elements within mission-critical sensors and sensor networks that are most vulnerable to deception, and humans will be handicapped when they are concerned about the received information from the network is not trustworthy, even if their concern is misplaced. In MCSSN, the advantages of linking multiple electronic support measures and electronic attack assets to achieve improved capabilities across a networked mission-critical force have yet to be quantified. Algorithms are sought for fused and/or coherent cross-platform radio frequency (RF) sensing. The MC-SSN algorithms should be capable of utilizing RF returns from multiple aspects in time-coordinated sensors and sensor networks. Such adaptation, management, and reorganization of information sources, devices, and networks must be accomplished almost autonomously in order to avoid imposing additional burdens on the humans, and without much reliance on support and maintenance services. Moreover, humans, under extreme cognitive and physical stress, will be strongly challenged by the massive complexity of the MC-SSN and the information it will provide and carry. Advances in technologies that capitalize on the benefits of the MC-SSN have to assist humans in making effective use of this massive, complex, confusing, and potentially deceptive ocean of information while taking into account the ever-changing mission. New approaches and low-complexity algorithms are expected to enable MC-SSN to automatically manage and effect risk and uncertainty in a highly deceptive, mixed cooperative/adversarial, information-centric environment.

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our Machine Learning Technology Readiness Levels (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics.

Wireless Sensor Networks (WSNs) are employed in a range of applications these days and can be employed in even additional applications within the future. However, WSNs seems to be not as much reliable as the wired networks. With increasing range of applications particularly within the real-time WSN developments need extremely reliable and time-constrained data transmission schemes. In real-time scenario, like industrial automation, environmental monitoring, etc., the network must assure the guaranteed and timeliness delivery of data packets to the sink node. In light of the kind of use, it is fundamental to allow various degrees of Quality of Service (QoS) in WSNs. This paper heighted, QoS prerequisites for delay bounded WSNs applications. Some existing QoS aware agreements to help these applications are examined with application-specific benefits and limitations. Moreover, in such applications, researchers worked on providing dedicated MAC scheduling as well as QoS aware routing based on application area and requirements. This paper also discussed some machine learning approaches used to attain the quality of service requirements in WSNs considering parameters Energy, delay, and trustworthiness. A machine learning based algorithm will help to reduce the average delay of critical packets.

An increasing interest is growing around the idea of microservices and the promise of improving scalability when compared to monolithic systems. Several companies are evaluating pros and cons of a complex migration. In particular, financial institutions are positioned in a difficult situation due to the economic climate and the appearance of agile competitors that can navigate in a more flexible legal framework and started their business since day one with more agile architectures and without being bounded to outdated technological standard. In this paper, we present a real world case study in order to demonstrate how scalability is positively affected by re-implementing a monolithic architecture (MA) into a microservices architecture (MSA). The case study is based on the FX Core system, a mission critical system of Danske Bank, the largest bank in Denmark and one of the leading financial institutions in Northern Europe. The technical problem that has been addressed and solved in this paper is the identification of a repeatable migration process that can be used to convert a real world Monolithic architecture into a Microservices architecture in the specific setting of financial domain, typically characterized by legacy systems and batch-based processing on heterogeneous data sources.

We live in a world full of data that are expanding at astonishing rates. Internet of Things (IoT) is one of the most significant disruptive emerging technologies, harboring the potential for massive societal impact. IoT is expected to enable mass participation of end users (individuals) in mission critical services (energy, mobility, legal, and democratic stability). Smart objects are not only drivers for change in terms of content and applications. Given their ability to potentially change in function because they can be digitally enhanced and “upgraded,” they may acquire disruptive power that could lead to serious consequences.

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.

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.

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.

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.

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.

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.

Nowadays, various computing services are often hosted on cloud platforms for their availability and cost effectiveness. However, such services are frequently exposed to vulnerabilities. Therefore, many countermeasures have been invented to defend against software hacking. At the same time, more complicated attacking techniques have been created. Among them, code-reuse attacks are still an effective means of abusing software vulnerabilities. Although state-of-the-art address space layout randomization (ASLR) runtime-based solutions provide a robust way to mitigate code-reuse attacks, they have fundamental limitations; for example, the need for system modifications, and the need for recompiling source codes or restarting processes. These limitations are not appropriate for mission-critical services because a seamless operation is very important. In this paper, we propose a novel ASLR technique to provide memory rerandomization without interrupting the process execution. In addition, we describe its implementation and evaluate the results. In summary, our method provides a lightweight and seamless ASLR for critical service applications.

Recently, two terms, namely Big Data and Internet of Things (IoT) have gained popularity individually. However, their interconnections are not fully explored and understood. It is expected that the fusion of Big Data and IoT would create many complex systems for smart cities. While the data from IoT lies in Big Data, the scale of operations are completely different in terms of providing the required real-time analytics for such smart systems. Even though NoSQL databases and other next generation solutions could be deployed to achieve real-time responses, the major security challenges need to be understood as mission critical and sensitive data intertwines Big Data and IoT. In this paper, we identify the security challenges shared by the closely-knit Big Data and IoT in three main risk areas: i) NoSQL security vulnerabilities, ii) mobile IoT (M-IoT) security and privacy constraints and iii) encryption key security threats. We perform a comparative study of security vulnerabilities of NoSQL databases and identify the security and privacy constraints of M-IoT networks. Encryption key attacks for resource constrained IoT devices are also illustrated mathematically. Overall, this paper explores new research directions in these prime areas of security and privacy that would result in solution opportunities for a meaningful fusion of Big Data and IoT for a smart environment.

The rapid evolution of technology in our society has brought great advantages, but at the same time it has increased cybersecurity threats. At the forefront of these threats is the proliferation of malware from traditional computing platforms to the rapidly expanding Internet-of-things. Our research focuses on the development of a malware detection system that strives for early detection as a means of mitigating the effects of the malware’s execution. The proposed scheme consists of a dual-stage detector providing malware detection for compromised devices in order to mitigate the devices malicious behavior. Furthermore, the framework analyzes task structure features as well as the system calls and memory access patterns made by a process to determine its validity and integrity. The proposed scheme uses all three approaches applying an ensemble technique to detect malware. In our work we evaluate these three malware detection strategies to determine their effectiveness and performance.

With the emergence of the demand for railway multimedia services, the railway mobile communication system has begun to develop rapidly toward long-term evolution for railway (LTE-R). We analyze the railway video service requirements, compare the standardization processes of the 3rd Generation Partnership Project (3GPP) and International Union of Railways (UIC), and find that there is currently no solution for the rail-specific video services. Based on the above reasons, this paper designs and develops a video call scheme using Function Number (FN) for the railway system to enable railway multimedia services. First of all, the feasibility of the Mission Critical (MC) Service to the railway is discussed. Secondly, Mission Critical System (MCS) in LTE-R is introduced. Then, the Mission Critical Video (MCVideo) software architecture is designed, and the FN-based video service procedure is proposed in this paper, including FN registration and FN-based MCVideo call. Finally, the potential of the proposed scheme is verified on the laboratory static testbed. The results show that the proposed video call scheme has good performance.

New applications such as remote surgery and connected cars, which are being touted as use cases for 5G and beyond, are mission-critical. As such, communications infrastructure needs to support and enforce stringent and guaranteed levels of service before such applications can take off. However, from an operator's perspective, it can be difficult to provide uniformly high levels of service over long durations or large regions. As network conditions change over time, or when a mobile end point goes to regions with poor coverage, it may be difficult for the operator to support previously agreed upon service agreements that are too stringent. Second, from a consumer's perspective, purchasing a stringent service level agreement with an operator can also be expensive. Finally, failures in mission critical applications can lead to disasters, so infrastructure should support assignment of liabilities when a guaranteed service level is reneged upon - this is a difficult problem because both the operator and the customer have an incentive to lay the blame on each other to avoid liabilities of poor service.To address the above problems, we propose AJIT, an architecture that allows creating fine-grained short-term contracts between operator and consumer. AJIT uses smart contracts to allow dynamically changing service levels so that more expensive and stringent levels of service need only be requested by a customer for short durations when the application needs it, and operator agrees to the SLA only when the infrastructure is able to support the demand. Second, AJIT uses trusted enclaves to do the accounting of packet deliveries such that neither the customer requesting guaranteed service levels for mission-critical applications, nor the operator providing the infrastructure support, can cheat.

The well-known Internet of Things (IoT) is recently being considered for critical missions, such as search and rescue, surveillance, and border patrol. One of the most critical issues that these applications are currently facing is how to correctly distinguish between human and animal targets in a cost-effective way. In this paper, we present a relatively low-cost, but robust approach that uses a combination of device-free sensing (DFS) and machine-learning technologies to tackle this issue. In order to validate the feasibility of the presented approach, a variety of data is collected in a cornfield using impulse-radio ultra-wideband (IR-UWB) transceivers. These data are then used to investigate the influence of different statistical properties of the radio-frequency (RF) signal on the accuracy of human/animal target classification. Based on the probability density function of different statistical properties, two distinguishing features for target classification are found, namely, standard deviation and root mean spread delay spread. Using them, the impact on the classification accuracy due to different classifiers, number of training samples, and different values of signal-to-noise ratio is extensively verified. Even with the worst case, the classification accuracy of the system is still better than 91% in terms of distinguishing between human and animal targets (including goats and dogs), which indicates that the presented approach has a great potential to be deployed in the near future.

Internet of Things is coming to critical missions such as battlefield, border patrol, search and rescue, critical structure monitoring and surveillance, etc. Internet of Things (IoT) in Critical Missions or Internet of Mission-Critical Things (IoMCT) is propelled by the convergence of sensing, communication, computing, and control. To support IoMCT, the mission-critical networks will need to be flexible and interactive, and still work despite limited bandwidth, intermittent connectivity and with a large number of devices on the network. The focus of IoMCT is to improve surveillance utilizing a network, not fusion of disparate sensor products. Such adaptation, management, and re-organization of information sources, devices, and networks must be accomplished almost entirely autonomously, in order to avoid imposing additional burdens on the humans, and without much reliance on support and maintenance services.

MCPTX (Mission Critical Push to X) is catering the need of group communication in different kind of emergency situations. Mission Critical (MC) operations in the civil domain comprise of activities handled by police officers, fire fighters, Search & Rescue teams, Medical Emergency support personnel, Civic Protection etc. There are low priority and high priority users. For Low priority user, it is always a problem to transmit the voice on priority under critical situation. In this paper, authors have proposed a method to support auxiliary voice from lower priority participants during MCPTX group call.

The big data software stack based on Apache Spark and Hadoop has become mission critical in many enterprises. Performance of Spark and Hadoop jobs depends on a large number of configuration settings. The manual tuning procedure is expensive and brittle. There have been efforts to develop online and off-line automatic tuning approaches to make the big data stack more autonomic, but many researchers noted that it is important to tune only when truly necessary because many parameter searches can reduce rather than enhance performance. Autonomic systems need to be able to accurately detect important changes in workload characteristics, predict future workload characteristics, and use this information to pro-actively optimise resource allocation and frequency of parameter searches. This paper presents the first study focusing on workload change detection, change classification and workload forecasting in big data workloads. We demonstrate 99% accuracy for workload change detection, 90% accuracy for workload and workload transition classification, and up to 96% accuracy for future workload type prediction on Spark and Hadoop job flows simulated using popular big data benchmarks. Our method does not rely on past workload history for workload type prediction.

Hardware-based TPM provides hardware-backed security solutions and a root of trust for various mission critical applications. However, hardware-based TPM has several intrinsic problems such as extremely low performance, off-chip security vulnerability, and a lack of incident response agility. In the upcoming Quantum computing era, it is critical to provide Quantum-Resistant (QR) cryptography functions without harming performance. Unfortunately, hardware-based TPM's rigid hardware and software architecture model makes it extremely difficult for hardware-based TPM to transition to accommodate future QR cryptographic systems. On the other hand, software-based TPMs (e.g., firmware-based TPM) provide a CPU-based, on-chip security solution. They utilize low-level on-chip primitives offered by chipsets such as ARM TrustZone or Intel Software Guard Extensions (SGX) to build a system with a high-level of trust computing environment. A software-based TPM solution provides higher performance, on-chip security, and incident response agility. However, it is lacking in hardware-backed protection and several vital features such as secure key storage, robustness against side-channel attacks, true random number generation, among others. In addition, its implementation is highly dependent on low-level primitives provided by each hardware vendor, which makes it difficult for it to be provided as a generalized solution. In this paper, we propose hybrid-TPM (hTPM), which fully utilizes the advantages of a hardware-based TPM and diminishes a hardware-based TPM's weaknesses through software-based TPM solutions inside a secure container, e.g., Virtualization-Based Security (VBS).We implemented hTPM as a fully dual mode TPM, i.e., giving end-users full control in choosing between a hardware TPM mode and a software TPM mode based on their needs. We performed and will provide a full risk analysis of the proposed hTPM to show how to best overcome security challenges in realizing hTPM. Finally, we provide a performance analysis of our proposal to show the drastic improvements in cryptographic operations.

Informational needs of agricultural consultants are increasingly complex. Advising farmers on the appropriate measures for optimizing cropping yields demands access to custom data archives and analytics tools. In line with the increasing number of archives, the expertise required of consultants goes beyond the capabilities of these non-technical agri-specialists. These end users have diverse ad-hoc query needs and require tools that provide simple access to distributed data silos and easy ways to integrate relevant information. In this article, the authors report on a pilot deployment of Semantic Automated Discovery and Integration (SADI) Web services for the federation and computation of agricultural data. A registry of 9 SADI Web services was deployed to expose data from a variety of different data resources in support of a defined set of query needs. The authors demonstrate that the deployment of these services facilitates the ad-hoc creation and execution of mission critical workflows targeting use cases in agricultural operations management. Using HYDRA, a semantic query engine for SADI Web services with a custom built graphical user interface, agricultural consultants can identify optimal crop varieties, and compute profit margins of each variety using a complex cost model.

The set of outline and guidelines as well as tools for automation to ensure continuity of business-continuity in the lifecycle of mission critical systems are considered. This complex is called the Life Cycle Supporting System (LCSS). The aim of the system is to reduce the risk level of the realization of critical errors in the system and application software throughout the life cycle of mission critical system, reducing operational risks and total cost of ownership of mission critical system. LCSS in the ISO / IEC / IEEE 15288 terms is enabling system. LCSS is created for life cycle of mission critical system support in the organization-owner.

Encryption has protected the Internet for some time now and it has come to raise user trust on the otherwise unsecure Internet. However, recent years have seen the use of robust encryption as stepping stone for cyber-criminal activities. Ransomware has not escaped the headlines even as it has attacked almost every sector of the society using a myriad of infection vectors. Mission critical data has been held to ransom and victims have had to part away with millions of dollars. The advent of the anonymous Bitcoin network has made matters worse where it’s been virtually infeasible to trace the perpetrators. In this paper, we endeavor to perform dynamic analysis of WannaCry ransomware samples based on malwarefree infection vectors. Further, we perform reverse-engineering to dissect the ransomware code for further analysis. Results show that despite the use of resilient encryption, the ransomware like other families in the wild uses the same attack structure and cryptographic primitives. Our analysis leads us to the conclusion that this ransomware strain isn't as complex as previously reported. This detailed practical analysis tries to raise awareness to the business community on the realities and importance of IT security whilst hinting on prevention, recovery and the limitations thereof.

In the evolving world of the Internet of Things, the security of any piece of electronic equipment can easily come into question. Traditional electronics are being replaced with upgraded "smart" alternatives that are deployed worldwide as embedded systems in connected cars, autonomous aircraft, wireless medical devices, and control systems. The desire to maintain a constant connection to these systems and share data, monitor operation, and diagnose and resolve performance issues has driven designers and developers to adopt new technologies to support the task. Each of these pieces of technology, whether just the next wirelessenabled gadget or mission critical equipment, offers an attack surface that may be taken advantage of and successfully exploited.

We devised a decision support system (DSS) for the weakly formalized problems of information protection and the provision of cybersecurity at the informatization objects. The system is based on the models that describe the tasks of information safety and cyberprotection in the conceptual and functional aspects. We described the process of compiling a knowledge base of DSS for the circumstances related to the detection of hard-to-explain attributes of anomalies and attacks. The DSS "Decision Support System of Management protection of information – DMSSCIS", which we designed, makes it possible to improve understanding of the analyzed situations that occur in the process of cyberprotection of mission critical computer systems. While tested at the enterprises, it was established that the "DMSSCIS" system enabled effective visualization and interpretation of results of current assessment of the revealed hard-to-explain attributes of anomalies and cyberattacks, as well as allowed us to describe current situation in the course of multistage targeted cyberattacks. It was established that the application of DSS "DMSSCIS" in the interaction with other systems for the intelligent recognition of illegitimate interference in the computer systems operations made it possible to improve efficiency of decision making on information security. While testing, it was found that the application of the "DMSSCIS" system allowed reducing the time required to inform persons, responsible for cybersecurity, about the incidents by 6.9–7.2 times.

Public protection and disaster relief (PPDR) situations are becoming increasingly common due to the rapid urbanization of our society. Among these, harsh weather conditions and harmful human activity create challenges for reliable operation of mobile communication infrastructures, which calls for immediate action. The wireless networks of today may not be ready to accommodate emergency scenarios as they have not been optimized for PPDR contexts historically. In this work, we first review the important use cases, challenges, and requirements in the context of next-generation mobile networking for PPDR applications. We argue that many emerging services may be supported by the novel communication technology operating in millimeter-wave (mmWave) spectrum. Against this background, we contribute a new analytical model to characterize session continuity and other service-level performance indicators in cases when the communication system is susceptible to sudden intermissions caused by emergency situations. publishedVersion

Mission-critical Internet of Things (MC-IoT) systems involve heterogeneous things from both the digital and physical worlds. They run applications whose failure might cause significant and possibly dramatic consequences, such as interruption of public services, significant business losses, and deterioration of enterprise operations. These applications require not only high availability, reliability, safety, and security but also regulatory compliance, scalability, and serviceability. At the same time, they're exposed to various facets of uncertainty, spanning from software and hardware variability to mission planning and execution in possibly unforeseeable environments. Model-driven engineering can potentially meet these challenges and better enable the adoption of MC-IoT systems.

Today's power grid has so many challenges in terms of centralized power generation, limited flow of information, limited support for distribution, poor management of peak loads and power disruptions. Due to these limitations several organizations are working on Smart grid. Smart grid consists of numerous kind of heterogeneous devices that increase the complexity and inefficiency. To cope with heterogeneity and provide interoperability among the communication of these devices, middleware is considered to be the best approach. There are so many middlewares that have been proposed so far but Data Distribution Service (DDS) middleware provides high level of reliability and efficiency by addressing more performance metrics and several QoS policies especially in real time and mission critical applications. We have considered Smart grid standard ANSI C12.19 based DDS deployment in transmission and consumption sides. Data structures are obtained for topics formation over RTI Connext to establish communication and to conduct experimental study for the analysis of interoperability and other performance metrics to prove that DDS is better solution for Smart grid data interoperability and high reliability.