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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Network programmability is a key feature of fifth generation (5G) system which, in combination with cloud-based services, can support many use cases, including mission critical and healthcare communications. Programmability enables flexibility in customization of service connectivity. Multi-access Edge Computing (MEC) services and applications are enablers for network programmability. In this paper, MEC capabilities for programmability of multiparty multimedia call control at the network edge are studied. Multiparty video calls are one of the key applications of 5G, and are efficient way to exchange ideas, knowledge, expertise, information, and so on. The paper presents an approach to design MEC Application Programming Interfaces (APIs) which enable third party applications to create multiparty multimedia sessions and dynamically manage session participations. The API functionality is described by required information and message flows. The paper specifies the proposed MEC API with data model. Feasibility study includes modelling and formal validation of multiparty session state models supported by the network and mobile edge application. The latency injected by the API is evaluated by emulation.

In this letter, we investigate Mission Critical Push-to-Talk (MCPTT) communications used by emergency first responders that are currently migrating from legacy trunked digital Land Mobile Radio (LMR) systems to Fourth Generation (4G) Long Term Evolution (LTE) networks. Following the Key Performance Indicators (KPIs) proposed by the Third Generation Partnership Project (3GPP) for performance evaluation of public safety networks. We provide a comparative study for the two networks, and highlight the trade-offs as a function of the respective system parameters.

Ultra-reliable low latency communication (URLLC) is one of three primary use cases in the fifth-generation (5G) networks, and its research is still in its infancy due to its stringent and conflicting requirements in terms of extremely high reliability and low latency. To reduce latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and higher decoding error probability. In this case, conventional resource allocation based on Shannon capacity achieved with infinite blocklength codes is not optimal. Security is another critical issue in mission-critical internet of things (IoT) communications, and physical-layer security is a promising technique that can ensure the confidentiality for wireless communications as no additional channel uses are needed for the key exchange as in the conventional upper-layer cryptography method. This paper is the first work to study the resource allocation for a secure mission-critical IoT communication system with URLLC. Specifically, we adopt the security capacity formula under finite blocklength and consider two optimization problems: weighted throughput maximization problem and total transmit power minimization problem. Each optimization problem is non-convex and challenging to solve, and we develop efficient methods to solve each optimization problem. Simulation results confirm the fast convergence speed of our proposed algorithm and demonstrate the performance advantages over the existing benchmark algorithms.

Mission-critical Internet of Things (MC-IoT) will play a vital role in remote healthcare, haptic interaction, and industrial automation. On the one hand, in such application fields, haptic applications have become more critical. Benefitting from the development of the fifth-generation (5G) wireless communication networks and the technological advances of Internet of Things (IoT), the tactile Internet (TI), which provides control communications through the transmission of touch and actuation in real time, has been envisioned as a promising enabler of MC-IoT services. On the other hand, different MC-IoT services could have diverse requirements. This requires a flexible network architecture for enabling different MC-IoT services. Network function virtualization (NFV) is a promising method to tackle this issue. To provide MC-IoT services flexibly, in this article, a 5G network architecture based on the NFV technology is designed to support the implementation of the TI. Moreover, a utility function model is proposed for the performance evaluation of the 5G NFV-based TI. Considering the just-noticeable difference (JND) in the human perception and the corresponding network costs on providing MC-IoT services, a human perception-based TI utility optimization (ACTION) algorithm is developed to optimize the utility for 5G NFV-based TI. The simulation results indicate that the maximum utility achieved under the proposed ACTION algorithm is improved by 35.4% to the network slice requests (NSRs) implementation algorithm.

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.

In recent years, the emergence of flying ad hoc networks (FANETs) with multiple unmanned aerial vehicles (UAVs) has made it possible to effectively perform not only the far-off missions but also assorted complex missions. In this article, we consider a mission-critical FANET to perform given missions using multiple UAVs, taking into account a dynamic environment with a time-varying network topology. To effectively operate the mission-critical FANET, we study the joint mission assignment and topology management problem aiming at maximizing the weighted sum of mission and network performances, while guaranteeing end-to-end communications between mission-performing UAVs and their corresponding ground control stations, inter-UAV safety distance maintenance, and other mission-related constraints. To address this problem, we first develop three algorithms: one is to construct a mission-critical FANET from scratch, and the others are to manage the network topology and to switch UAV roles between mission performing and data relaying in response to the changes in the network topology. Then, we develop a dynamic mission-critical FANET operation algorithm incorporating the three algorithms with a few rules, by which the mission-critical FANET can be effectively managed and operated with reasonable computational complexity in the dynamic environment. Through simulation results, we show that our proposed algorithm works well in the dynamic environment while satisfying the constraints, and that its performance is not only superior to the existing algorithms but also close to the optimal performance.

Some energy consumers require power on an anytime, all-year-round basis with a high level of certainty, including defense installations, isolated communities, and some industrial processes. For the...

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.

Rapidly evolving, the Internet of Things imposes new challenges for the developers of wireless networks. Various critical infrastructure monitoring scenarios require fast and reliable alert delivery. In such systems, multiple sensors are entrusted to react to the same emergency event. Thus, it is enough to receive an alert message from any of these sensors. However, such a message shall be reliably delivered as soon as possible. The recently published Wi-Fi HaLow standard defines the Restricted Access Window (RAW) mechanism that coordinates transmissions of numerous devices. Thus, it can improve reliability and reduce delays. The paper is the first to study the usage of RAW in a scenario of emergency alerts, where the alert shall be received from at least one sensor. The paper presents an easy-to-calculate mathematical model of alert delivery with RAW. The model allows dynamic online reconfiguration of RAW parameters to select such parameters that minimize consumed channel timeshare while providing satisfactory reliability and delivery delay for an alert. Intensive performance evaluation shows that the RAW is fruitful for mission-critical data delivery in the considered scenario.

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.

The LTE (Long-Term Evolution) eMBMS (evolved Multimedia Broadcast Multicast Service) technology employed in MBSFNs (Multicast Broadcast Single Frequency Networks) has been shown to be capable of considerably increasing the capacity of serving public safety users under group communication scenarios. However, due to its own limitations, a more fine-tuned scheduling approach is needed in order to fully utilize the strengths of eMBMS multicast. In this work, we first identify and analyze several overlooked challenges for scheduling multicast traffic in MBSFNs. Then we develop an effective and efficient dynamic scheduling algorithm for eMBMS multicast in time and frequency varying channel. The proposed algorithm leverages the advantages of both multicast and unicast schemes via user grouping; and through extensive simulations, is shown to be capable of significantly enhancing the mission critical performances under both best effort and guaranteed bit rate delivery models. We also show the resiliency of our proposed algorithm by applying it onto various network and user deployment scenarios. Our consistent results further prove that LTE eMBMS in MBSFN is a key solution in overcoming limitations in near future public safety networks.

Mission-critical MAC protocol is an attractive research area since past few years as the wireless sensor networks have developed their utility in mission-critical applications like border s...

Deep learning methods have been extensively studied and have been proven to be very useful in multiple fields of technology. This paper presents a deep learning approach to optically detect hidden hardware trojans in the manufacturing and assembly phase of printed circuit boards to secure electronic supply chains. Trojans can serve as backdoors of accessing on chip data, can potentially alter functioning and in some cases may even deny intended service of the chip. Apart from consumer electronics, printed circuit boards are used in mission critical applications like military and space equipment. Security compromise or data theft can have severe impact and thus demand research attention. The advantage of the proposed method is that it can be implemented in a manufacturing environment with limited training data. It can also provide better coverage in detection of hardware trojans over traditional methods. Image recognition algorithms need to have deeper penetration inside the training layers for recognizing physical variations of image patches. However, traditional network architectures often face vanishing gradient problem when the network layers are added. This hampers the overall accuracy of the network. To solve this a Residual network with multiple layers is used in this article. The ResNet34 algorithm can identify manufacturing tolerances and can differentiate between a manufacturing defect and a hardware trojan. The ResNet operates on the fundamental principle of learning from the residual of the output of preceding layer. In the degradation issue, it is observed that, a shallower network performs better than deeper network. However, this is with the downside of lower accuracy. Thus, a skip connection is made to provide an alternative path for the gradient to skip forward the training of few layers and add in multiple repeating blocks to achieve higher accuracy and lower training times. Implementation of this method can bolster automated optical inspection setup used to detect manufacturing variances on a printed circuit board. The results show a 98.5% accuracy in optically detecting trojans by this method and can help cut down redundancy of physically testing each board. The research results also provide a new consideration of hardware trojan benchmarking and its effect on optical detection.

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.

The Paris Agreement’s Mission Innovation initiative to accelerate government spending on clean energy research is currently succeeding in its quest to support carbon mitigation. It should be renewed for an additional five years, with increased ambition, and changed to better integrate the private sector.

Vehicle-to-everything (V2X) communications provide cars with unprecedented connected services. Merging with the cellular-based Internet of Things (IoT) technology, i.e., Narrowband-IoT (NB-IoT), V2X can further enhance its potential. Some limitations exist in Vehicle-to-IoT (V2IoT) communications, e.g., different bands between V2X and IoT, lack of NB sidelink, and low-reception rate of NB-IoT devices. In this letter, we propose a novel V2IoT communication system to support direct communications via the LTE band or the NB. For IoT devices, the enhanced discontinuous reception configuration adaptation to reflect the mission critical characteristics of vehicular communications is proposed. Also, for vehicles, efficient utilization of the LTE band and the NB resources is provided using the common preambles.

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.

This paper addresses an uplink (UL) scheduling issue that arises in the midst of the recent trend in applying the commercial long term evolution (LTE) technology to public safety. Mission critical push-to-talk (MCPTT), which specifies a group communication service over LTE in public-safety scenarios, needs to satisfy the requirements of extremely short latency for its control procedures. We herein propose a practical UL-scheduling scheme, which can be feasible in the LTE system to shorten the time for the initial UL transmissions. Through the empirical assessment conducted on our LTE-based MCPTT testbed, the proposed scheme is validated to reduce the control plane latency with the little increment of implementation complexity.

Energy efficient wireless sensor networks play a very important role in mission critical applications, as in these applications it is difficult to deploy and change the nodes frequently or to change their battery. Some of the mission critical applications are healthcare applications, environmental monitoring and control applications (Tsunami alert, earthquake alert, volcanic eruption prone areas) and surveillance applications. These applications require quick response time along with power management, so that the energy efficiency can be achieved without having latency beyond a critical threshold limit. Several MAC protocols have been proposed by researchers with a preview to enhance energy efficiency but hey failed to fulfill the need of mission critical applications. An in-depth survey analysis has helped in exploring several MAC protocols for their compatibility towards mission critical scenarios. In this paper we propose a novel energy efficient contention based ADMC-MAC protocol. This protocol is more adaptable to mission critical applications by improving the data delivery performance based on traffic conditions considering the queue size of the node and also have improved energy efficient performance. This protocol overcomes the lacunas of standard MAC protocols. Two algorithms have been proposed in this protocol. The first algorithm, which is priority based, select the node as cluster head with high number of packets in queue and having maximum energy among the neighboring nodes forming virtual cluster. The second algorithm uses regression technique for deciding the duty cycle of selected node based on traffic conditions and residual energy of the node. Only the selected nodes take part in transmission of packets. So the packets overloading at intermediate nodes is reduced. This helps in increasing the overall efficiency of the system by enhancing energy saving up to 71.452% as compare to S-MAC protocol in mission critical scenarios. The packet delivery ratio is enhanced by 15.198 percent in mission critical scenarios along with considerable improvement in throughput. S-MAC is found to be the most appropriate existing MAC protocol which can be enhanced for mission critical scenarios. So the proposed protocol is enhanced S-MAC protocol implemented in NS-2.35 and the results are compared with standard S-MAC protocol and latest MC-MAC mission critical MAC protocol. It is found that the proposed protocol ADMC-MAC outperforms the latest MC-MAC protocol in terms of energy saving and also provides efficient data delivery performance.

The paper presents a robust QoS centric routing protocol for mission-critical communication over mobile Wireless Sensor Networks (CL-mWSN) that exploits dynamic network states from the different layers of the IEEE 802.15.4 protocol stack to make the routing decision. The CL-mWSN protocol exploits three key layers: application layer, network layer and MAC layer. It exhibits proactive network and node table management, service differentiation, fair resource scheduling and congestion detection, avoidance at the network layer, as well as dynamic link quality estimation and packet injection rate estimation at the MAC layer to assess its candidature as the best forwarding node for QoS-centric mission-critical communication. Simulation reveals that the proposed routing model exhibits higher throughput, minimum loss and deadline miss ratio that augments QoS provision in mobile WSNs.

Until recently, reliability was not considered to be a major design concern for circuit designers, except in the case of space and mission critical applications. However, the aggressive scaling of CMOS devices has significantly affected their reliable operation. Several schemes have been used for mitigating the scaling effects and maintaining the reliability above a certain threshold. Many of these schemes rely on incorporating different types of redundancy at the device, gate, and system level, which inevitably affect the area, power, and delay parameters. To optimize the tradeoff between these conflicting parameters, an accurate and efficient reliability EDA tool is needed. Such a tool would help circuit designers compare different reliability schemes and select the one that achieves the target reliability margins while having minimum impact on the other design parameters. However, the enormous size and the complexity of today's logic circuits make accurate calculation of the circuit's reliability a very challenging and time-consuming process. This paper introduces a novel, accurate, and efficient algorithm for circuit reliability estimation. The algorithm improves accuracy by taking the effect of reconvergent fan-out nodes into consideration, while improving efficiency by implementing a multithreaded approach for node evaluation. Simulation results show that the proposed algorithm is efficient and more accurate than other reliability estimation algorithms currently proposed in the literature.

In pervasive Internet of Things (IoT) applications, the use of short packets is expected to meet the stringent latency requirement in ultra-reliable low-latency communications; however, the incurred security issues and the impact of finite blocklength coding on the physical-layer security have not been well understood. This paper comprehensively investigates the performance of secure short-packet communications in a mission-critical IoT system with an external multi-antenna eavesdropper. An analytical framework is proposed to approximate the average achievable secrecy throughput of the system with finite blocklength coding. To gain more insight, a simple case with a single-antenna access point (AP) is considered first, in which the secrecy throughput is approximated in a closed form. Based on that result, the optimal blocklengths to maximize the secrecy throughput with and without the reliability and latency constraints, respectively, are derived. For the case with a multi-antenna AP, following the proposed analytical framework, closed-form approximations for the secrecy throughput are obtained under both beamforming and artificial-noise-aided transmission schemes. Numerical results verify the accuracy of the proposed approximations and illustrate the impact of the system parameters on the tradeoff between transmission latency and reliability under the secrecy constraint., 14 pages, 8 figures, accepted for publication in the IEEE Transactions on Wireless Communications

Fault diagnosis and failure prognosis of power electronics systems are essential for various mission critical applications, where safety, continuity, and survivability of the system are of paramount importance. With the recent advances in power device failure precursor identification and lifetime estimation, it is possible to extend the lifetime of the power devices, and thereby converters through secondary control schemes. The essence of the lifetime extension strategies relies on either reducing the power loss on the degraded device(s) and shifting it to other parts, or decreasing the total power loss of the converter. As the power converters are optimized for efficiency and total harmonic distortion (THD) requirements, these secondary control strategies can result in operations above the design limits. This paper proposes a hybrid secondary lifetime extension control scheme for three-phase inverters based on the identified failure precursors, which dynamically changes the modulation scheme and adjusts the switching frequency. In the content of this paper, the tradeoff between the THD and achievable lifetime extension is addressed, and a control algorithm is proposed, which maximizes the lifetime with feasible lowest THD.

Cloud data centers consume huge amounts of electrical energy which results in an increased operational cost, decreased system reliability and carbon dioxide footprints. Thus, it is highly important to develop scheduling strategy to reduce energy consumption. Dynamic voltage and frequency scaling (DVFS) has been recognized as an efficient technique for reducing energy consumption. However, there is negative impact of DVFS on the reliability of system as it increases the transient faults during the application execution. Hence, it is essential to address the issue of reliability for mission critical applications. Recent studies on workflow scheduling in distributed environment have not considered reliability while minimizing the energy consumption. In this paper, we propose a new scheduling algorithm called the reliability and energy efficient workflow scheduling algorithm which jointly optimizes lifetime reliability of application and energy consumption and guarantees the user specified QoS constraint. The proposed algorithm works in four phases: priority calculation, clustering of tasks, distribution of target time and assigning the cluster to processing element with appropriate voltage/frequency levels. The simulation results obtained by using randomly generated task graphs and Gaussian Elimination task graphs shows that the proposed approach is effective in joint optimization of lifetime reliability of system and energy consumption compared to existing algorithms.

In this article, we study feasibility issues of mission-critical push-to-talk (MCPTT), which is an evolution of a group communication service for public safety use. Along with the overall standardization of the MCPTT in the Third Generation Partnership Project (3GPP), distinguishing features of MCPTT are investigated in comparison with Open Mobile Alliance (OMA) PTT over cellular (PoC). Based on the requirements of MCPTT, we elaborate on various technologies that need to be leveraged for minimizing end-to-end latency in terms of both communications and application processes. To validate MCPTT in practice, we show not only the realization of MCPTT with a practical testbed but also the verification with key performance indicators defined in the 3GPP specification as performance metrics. The results from our proof of concept reveal that MCPTT can be a reasonable candidate for group communication, guaranteeing a sufficient level of survivability and immediacy beyond its potential.

In this paper, a novel medium access control (MAC) layer protocol is introduced for unmanned aerial vehicle (UAV)-based mission critical wireless sensor networks (MC-WSN), which is an important application of mission critical sensor and sensor networks (MC-SSN). The UAVs hovering in the three-dimensional monitoring networks are distributed in subsets according to their distance from the center station. UAVs in the same subset are contending for access and transmission, while each subset is allotted a slot based on adaptive time division multiple access (TDMA) scheme. As a multi-channel system, a novel channel allocation algorithm is presented for subset relay transmission period to optimize the performance of networks. The UAV returning period for charging is utilized in the protocol to enhance the transmission throughput and mitigate delay. Detection slot and position prediction algorithm are designed in the UAV returning period to improve the throughput and reduce the delay. The simulation results verify the improvement on throughput and delays of the proposed protocol for UAV-based MC-WSN.

Mission-critical wireless sensor networks are attractive for information gathering in various complex environments and support many mission-critical applications, such as industrial automation and security surveillance. However, in order to fully exploit these networks for such applications, agile and scalable network management for data transfer and computing task implementation are essential. Thus, in this paper, we propose a software-defined mission-critical wireless sensor network (MC-SDWSN) which can solve the existing challenging issues in tradition WSNs, such as resource utilization, data processing, system compatibility, and strict latency requirement. The architecture is based on the idea of SDN architecture, combining the hierarchical cloud and edge computing technologies. Based on the MC-SDWSN architecture, a novel centralized computation offload strategy in sensor network application is proposed to show the feasibility. The simulation results in confirm the MC-SDWSN architecture, and the edge offloading strategy could support the critical missions effectively.

The machine-type communication (MTC) plays an essential role in the mobile network society and it has demonstrated its effectiveness to generate significant revenues for mobile network operators. The MTC has become the main communication paradigm for several emerging smart services, such as public safety, health care, industrial automation, drones/robotics, utilities, and transportation. The MTC requires a major model change to cope with the improvement in 5G and imposes various requirements on the enabling technology, such as ultra-reliability, low latency, and availability. The scheme that fulfills such requirements are called a mission-critical MTC (mcMTC). However, the mcMTC is still in the early-standardization phase of 5G new radio and it needs a lot of research efforts to be improved. This paper presents an extensive review and evaluations to highlight diverse challenges and future aspects of mcMTC on 5G-enabling technologies. A number of research opportunities from both academic communities and industrial partners are given as examples for encouragement purposes.

The emergence of mobile sensors dramatically improves the availability of mission critical sensors and sensor networks (MC-SSN), enabling it to be utilized in more challenging tasks. However, current researches aimed to target tracking in mobile MC-SSN are very limited. This paper first proposes an adaptive fuzzy tree system (AFS) for target tracking in MC-SSN. To avoid oversimplifying the target mobile pattern, which is unrealistic in real tracking tasks, we introduce the target circle into the model considerations to add a bit of uncertainty about the target position. At each time step of the tracking process, strategy-selection layer will activate the pursuit strategy or the diffusion strategy for the specific situation, which makes the system more intelligent and can be applicable to various scenarios. The pursuit strategy is built by a two-layer fuzzy tree to select and mobilize sensors that have not detected target, while the diffusion strategy uses a fuzzy inference system to balance the density of detected sensors. And all the hyper-parameters are tuned by particle swarm optimization (PSO). We performed a large number of simulations with two target trajectories: line and irregular. The simulation results show that the AFS significantly outperforms the state-of-the-art algorithm. Moreover, it is highly robust to various target motion patterns, making it competent for a variety of real target tracking scenarios.

A crucial problem in post-flood recovery actions is the ability to rapidly establish communication and collaboration among rescuers to conduct timely and effective search and rescue (SAR) mission given disrupted telecommunication infrastructure to support the service. Aimed at providing such proximity service (ProSe) for mission-critical data exchange in the post-flood environment, the majority of existing solutions rely heavily upon ad-hoc networking approaches, which suffer from restricted communication range and the limited scope of interaction. As an effort to broaden the ProSe coverage and expand integrated global-local information exchange in the post-flood SAR activities, this paper proposes a novel network architecture in the form of a cyber-enabled mission-critical system (CEMCS) for acquiring and communicating post-flood emergency data by exploiting TV white space spectrum as network backhaul links. The primary method of developing the proposed system builds upon a layered architecture of wireless local, regional and wide-area communications, and incorporates collaborative network components among these layers. The desirable functionalities of CEMCS are showcased through formulation and the development of an efficient global search strategy exploiting a wide range of collaboration among network agents. The simulation results demonstrate the capability of CEMCS to provide ProSe in the post-flood scenarios as reflected by reliable network performance (e.g., packet delivery ratio nearing 80%-90%) and the optimality of efficient search algorithm.

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.

Current communication networks are facing more and more threats from large-scale regional damages, such as natural disasters (e.g., earthquake or tornado) and physical attacks (e.g., electromagnetic pulse attack or dragging anchors). Recently, several region failure models have been proposed to evaluate the impact of such geographically correlated failures on communication networks. These works mainly adopt a kind of “deterministic” models, where network components within the affected area would fail simultaneously. Such failure models simplify the analysis but may fail to reflect some important behaviors of attacks and thus cause significant over- or under-estimation of region failures. To emulate the impact of realistic catastrophe events such as earthquake and tornado, this paper introduces two probabilistic failure models: 1) a concentric circle model and 2) a line segment model. In the probabilistic models, both the location and effect of the damage are treated as random events. The failure may randomly incur on the entire network plane, and the failure probability of a device depends on many factors, e.g., link length and distance to the damage center. We develop an efficient grid partition-based scheme to estimate the network vulnerability. Based on the grid partition scheme, we further develop a sampling scheme to significantly reduce the computation cost. The probability model helps us more deeply understand the network behaviors under region failure and facilitates the design and maintenance of future highly survivable mission critical networks.