Mission-critical Scenarios Research Articles

Assuring Autonomy of UAVs in Mission-critical Scenarios by Performability Modeling and Analysis

Uncrewed Aerial Vehicles (UAVs) have been used in mission-critical scenarios such as Search and Rescue (SAR) missions. In such a mission-critical scenario, flight autonomy is a key performance metric that quantifies how long the UAV can continue the flight with a given battery charge. In a UAV running multiple software applications, flight autonomy can also be impacted by faulty application processes that excessively consume energy. In this article, we propose Flight Autonomy Assurance as a framework to assure the autonomy of a UAV considering faulty application processes through performability modeling and analysis. The framework employs hierarchically configured stochastic Petri nets, evaluates the performability-related metrics, and guides the design of mitigation strategies to improve autonomy. We consider a SAR mission as a case study and evaluate the feasibility of the framework through extensive numerical experiments. The numerical results quantitatively show how autonomy is enhanced by offloading and restarting faulty application processes.

A reliable spacecraft power supply subsystem based on discrete water cycle multi-objective optimization

The successful execution of spacecraft missions relies heavily on the performance and reliability of their power supply subsystems. Achieving optimal component size and configuration is crucial for efficient power generation, distribution, and storage. Designing an efficient power supply subsystem for spacecraft missions is a complex and multi-objective optimization problem. Traditional optimization techniques, such as genetic algorithm and particle swarm optimization, have been employed, but their limitations in handling discrete variables and achieving global optima necessitate the exploration of alternative methods. In this context, this research paper explores the application of discrete water cycle algorithm (DWCA) optimization as a novel approach for optimizing the component size and configuration of spacecraft power supply subsystems. By emulating the dynamic flow and distribution of water, DWCA seeks to efficiently explore the design space and identify optimal solutions.Through comprehensive experimentation and comparative analysis, this research demonstrates that the proposed DWCA outperforms/surpasses other traditional optimization techniques in optimizing component size and configuration for spacecraft power supply subsystems. It consistently determines designs that are more efficient in power generation and storage. Moreover, DWCA enhances the efficiency and reliability of spacecraft power supply systems, especially in mission-critical scenarios. The successful optimization of spacecraft power supply subsystems is paramount to the overall success of space missions. Therefore, this advancement contributes significantly to the ongoing efforts to ensure the success of future space missions.

Local machine learning model-based multi-objective optimization for managing system interdependencies in production: A case study from the ironmaking industry

Modeling interdependencies in a production process is a vital aspect of process engineering. Being built on the fundamental understanding of the process and capturing underlying physical and chemical mechanisms, this task is traditionally carried out through first-principle modeling. However, due to various assumptions and parameter approximation, such approach sometimes struggles to accurately model process dynamics. This is partially due to the requirements for process stability in mission-critical scenarios, where it is not viable to conduct extensive experiments that would enable the development of comprehensive models, and hence, the process control is still partially dependent on the experience of the process operator. In this paper, a data-driven approach based on production data is introduced to address the challenge of discovering, understanding, and modeling interdependencies in a production process by constructing a constrained multi-objective optimization problem. The applicability of the proposed approach is demonstrated through a case study in the ironmaking industry, whereby a set of Machine Learning and causality-based methods is provided while highlighting the significance of transparency and use of domain knowledge in the development of data-driven models. Based on the real data from a Sintering plant, interdependencies are quantified between five key process parameters, which, when supplemented with other control parameters, result in a higher overall process output leading to reductions in operational costs and environmental impact. Such approach shows potential for applications in a broader context across other fields of science and engineering, where it can be used to improve the discovery of interdependencies in complex real-world industrial settings.

Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this paper, we propose the Multimodal Fusion Framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio-vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio-vision tasks reveal the model’s uncertainty. Using Expected Calibration Errors, we measure calibration errors and Monte-Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for Advanced Driver Assistance Systems.

Intercell Interference Coordination for UAV Enabled URLLC With Perfect/Imperfect CSI Using Cognitive Radio

Ultra-reliable and low latency communications (URLLC) will be the backbone of the upcoming sixth-generation (6G) systems and will facilitate mission-critical scenarios. A design accounting for stringent reliability and latency requirements for URLLC systems poses a challenge for both industry and academia. Recently, unmanned aerial vehicles (UAV) have emerged as a potential candidate to support communications in futuristic wireless systems due to providing favourable channel gains thanks to Line-of-Sight (LoS) communications. However, usage of UAV in cellular infrastructure increases interference in aerial and terrestrial user equipment (UE) limiting the performance gain of UAV-assisted cellular systems. To resolve these issues, we propose low-complexity algorithms for intercell interference coordination (ICIC) using cognitive radio when single and multi-UAVs are deployed in a cellular environment to facilitate URLLC services. Moreover, we model BS-to-UAV (B2U) interference in downlink communication, whereas in uplink we model UAV-to-BS (U2B), UAV-to-UAV (U2U), and UE-to-UAV (UE2U) interference under perfect/imperfect channel state information (CSI). Results demonstrate that the proposed perfect ICIC accounts for fairness among UAV especially in downlink communications compared to conventional ICIC algorithms. Furthermore, in general, the proposed UAV-sensing assisted ICIC and perfect ICIC algorithms yield better performance when compared to conventional ICIC for both uplink and downlink for the single and multi-UAV frameworks.

A Bayesian Approach To Distributed Anomaly Detection In Edge AI Networks

The challenge of anomaly detection is to obtain an accurate understanding of expected behaviour which is intensified when the data are distributed heterogeneously. Transmitting raw data to a central site incurs high communication overhead and raises privacy issues. The concept of Edge AI allows computation to be performed at the edge site allowing for quick decision making in mission critical scenarios such as self-driving cars. A model is learnt locally and its parameters are transmitted and aggregated. However, existing methods of aggregation do not account for variance and heterogeneous distribution of data. They also do not consider edge constraints such as limited computational, memory and communication capabilities of edge devices. In this work, a fully Bayesian approach is employed by means of a Bayesian Random Vector Functional Link AutoEncoder being incorporated with Expectation Propagation for distributed training. Our anomaly detection system operates without any transmission of raw data, is robust under inhomogeneous network densities and under uneven and biased data distributions. It allows for asynchronous updates to converge in a few iterations and is a relatively simple neural network addressing edge constraints without compromising on performance as compared to existing more complex models.

Channel-Based Trust Model for Security in Underwater Acoustic Networks

Underwater acoustic networks are often used in mission-critical scenarios, such as military underwater networks and assets deployed for tsunami prevention; hence, an attack performed against these types of networks can easily lead to disastrous consequences. Nevertheless, countermeasures to possible network attacks have not been widely investigated so far. A reputation system, where a node gains trust each time it exhibits a good behavior, and loses trust each time it behaves suspiciously, is an effective way to identify possible attackers in the network. The main challenge when applying a reputation system in an underwater network is to understand whether the network performance degrades because a node is acting maliciously intentionally, or because of the changed channel conditions, causing a large packet drop. For instance, when a ship travels close to an underwater network deployment, it causes an increased packet loss, and so does the change of environmental conditions, such as a drop of temperature, the presence of rain or the increase of the wind speed. This behavior of the acoustic channel can be characterized with a hidden Markov model, whose parameters are obtained observing the time evolution of the acoustic channel in a sea experiment. This article presents a trust model based on the knowledge of the channel state, inferred from the perceived noise and received signal strength, in which misbehavior and correct behavior are considered differently according to the actual channel state. We evaluate the model both analytically and through simulations, implementing the trust mechanism in the DESERT Underwater Network framework.

Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond.

Industry 4.0 is a new paradigm of digitalization and automation that demands high data rates and real-time ultra-reliable agile communication. Industrial communication at sub-6 GHz industrial, scientific, and medical (ISM) bands has some serious impediments, such as interference, spectral congestion, and limited bandwidth. These limitations hinder the high throughput and reliability requirements of modern industrial applications and mission-critical scenarios. In this paper, we critically assess the potential of the 60 GHz millimeter-wave (mmWave) ISM band as an enabler for ultra-reliable low-latency communication (URLLC) in smart manufacturing, smart factories, and mission-critical operations in Industry 4.0 and beyond. A holistic overview of 60 GHz wireless standards and key performance indicators are discussed. Then the review of 60 GHz smart antenna systems facilitating agile communication for Industry 4.0 and beyond is presented. We envisage that the use of 60 GHz communication and smart antenna systems are crucial for modern industrial communication so that URLLC in Industry 4.0 and beyond could soar to its full potential.

An Intelligent System to Detect Advanced Persistent Threats in Industrial Internet of Things (I-IoT)

The Industrial Internet of Things (I-IoT) is a manifestation of an extensive industrial network that interconnects various sensors and wireless devices to integrate cyber and physical systems. While I-IoT provides a considerable advantage to large-scale industrial enterprises, it is prone to significant security challenges in the form of sophisticated attacks such as Advanced Persistent Threat (APT). APT is a serious security challenge to all kinds of networks, including I-IoT. It is a stealthy threat actor, characteristically a nation-state or state-sponsored group that launches a cyber attack intending to gain unauthorized access to a computer network and remain undetected for a longer period. The latest intrusion detection systems face several challenges in detecting such complex cyber attacks in multifarious networks of I-IoT, where unpredictable and unexpected cyber attacks of such sophistication can lead to catastrophic effects. Therefore, these attacks need to be accurately and promptly detected in I-IoT. This paper presents an intelligent APT detection and classification system to secure I-IoT. After pre-processing, several machine learning algorithms are applied to detect and classify complex APT signatures accurately. The algorithms include Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, Bagging, Extreme Gradient Boosting and Adaboost, which are applied on a publicly available dataset KDDCup99. Moreover, a comparative analysis is conducted among ML algorithms to select the appropriate one for the targeted domain. The experimental results indicate that the Adaboost classifier outperforms the others with 99.9% accuracy with 0.012 s execution time for detecting APT attacks. Furthermore, results are compared with state-of-the-art techniques that depict the superiority of the proposed system. This system can be deployed in mission-critical scenarios in the I-IoT domain.

Machine learning based MAC protocol design for pipeline leakage detection in smart city project

The integration of WSN with IoT is growing exponentially for implementing real time applications in today’s world. Also an Intelligent WSN design is required for implementing these applications. This paper discusses a MWSN based system for detection and alert of the pipeline leakage. In this method all the nodes are not allowed to communicate with the central server directly. Whenever a node receives a data of some irregularities in the system, the node sends it in multihop fashion to the nearest cluster node, which is the only node that is allowed to communicate with the central server. In the remote areas with no infrastructure of internet connection, this system can used to transmit the data to the server, thus the system is cost effective and efficient. As the number of sensor nodes increases and time bound data is required at the IoT devices and cloud for implementing smart applications. In the first phase the paper discussed the deployment strategy of wireless sensor nodes on pipe. In Phase 2, the paper discussed the ML based MAC protocol design. Here ML algorithm is discussed in the paper for energy efficient information transmission node to node. The duty cycle will be tuned based on ML models as discussed. The decision tree classifier will be finally chosen to tune the duty cycle of wireless sensor node. In the final phase the IoT application and website designing is discussed to collect the data and send it to cloud for observing the status of leakage in each zone and sending the alerts to area manager. The work is novel in terms of intelligent model development for pipeline leakage detection and has can be modified for number of mission critical scenarios.

Cognitive RF-based localization for mission-critical applications in smart cities: An overview

The accessibility of accurate location information for operators in mission-critical scenarios would considerably increase their mission success. In order to obtain precise location information, numerous algorithms and technologies have been suggested. These methods and systems show varying performances under different conditions, and with the help of machine learning techniques, their reliability can be enhanced dramatically. In this paper, we overview the state-of-the-art in emerging algorithms and technologies employing cognitive solutions in mission critical localization applications. We compare these algorithms in terms of different localization parameters such as scalability, power consumption, availability, service quality and accuracy. Consequently, this survey will assist researchers who are working in the area of RF-based localization to achieve better performance in mission critical scenarios that can be experienced in smart city applications.

Extending Network Programmability to the Things Overlay Using Distributed Industrial IoT Protocols

Current industrial Internet of Things (IoT) demands are calling for more flexible and programmable networks that ensure high reliability in dynamic mission-critical scenarios. Centralized software-defined networking (SDN) offers high levels of flexibility and programmability that traditional distributed IoT protocols cannot offer. However, the use of SDN in IoT is currently not really lifting off due to wireless links unreliability, excessive control overhead, and devices’ limited resources. In order to reduce the impact of these issues, Whisper enables SDN-like capabilities in IoT by centrally controlling the distributed routing and scheduling planes in the IoT network (things overlay). To do so, the Whisper controller carefully sends computed messages compatible with the standardized distributed protocols already running in the network that change the default protocols’ behavior. However, as many other SDN-on-IoT approaches, Whisper is currently limited to the IoT network scope and remains as yet another independent network management silo. In this article, we argue that IoT network control should be jointly coordinated by the same SDN instance that also manages the wired segments. In order to do so, we present a new fully programmable solution that shifts the Whisper scope from the edge to the core, deploying and testing such architecture in real-world large-scale testbeds. We use 6TiSCH as industrial IoT enabler and the open network operating system platform to orchestrate all network segments. Finally, we report the technical challenges, discussing the lessons learned, and demonstrating the feasibility and suitability of this Whisper-based solution to provide an efficient and programmable end-to-end control over a heterogeneous network domain.

CAOS Smart Camera-based Robust Low Contrast Image Recovery over 90 dB Scene Linear Dynamic Range

Experimentally demonstrated for the first time is Coded Access Optical Sensor (CAOS) camera empowered robust and true white light High Dynamic Range (HDR) scene low contrast target image recovery over the full linear dynamic range. The 90 dB linear HDR scene uses a 16 element custom designed test target with low contrast 6 dB step scaled irradiances. Such camera performance is highly sought after in catastrophic failure avoidance mission critical HDR scenarios with embedded low contrast targets.

From Pre-Quantum to Post-Quantum IoT Security: A Survey on Quantum-Resistant Cryptosystems for the Internet of Things

Although quantum computing is still in its nascent age, its evolution threatens the most popular public-key encryption systems. Such systems are essential for today’s Internet security due to their ability for solving the key distribution problem and for providing high security in insecure communications channels that allow for accessing websites or for exchanging e-mails, financial transactions, digitally signed documents, military communications or medical data. Cryptosystems like Rivest–Shamir–Adleman (RSA), elliptic curve cryptography (ECC) or Diffie–Hellman have spread worldwide and are part of diverse key Internet standards like Transport Layer Security (TLS), which are used both by traditional computers and Internet of Things (IoT) devices. It is especially difficult to provide high security to IoT devices, mainly because many of them rely on batteries and are resource constrained in terms of computational power and memory, which implies that specific energy-efficient and lightweight algorithms need to be designed and implemented for them. These restrictions become relevant challenges when implementing cryptosystems that involve intensive mathematical operations and demand substantial computational resources, which are often required in applications where data privacy has to be preserved for the long term, like IoT applications for defense, mission-critical scenarios or smart healthcare. Quantum computing threatens such a long-term IoT device security and researchers are currently developing solutions to mitigate such a threat. This article provides a survey on what can be called post-quantum IoT systems (IoT systems protected from the currently known quantum computing attacks): the main post-quantum cryptosystems and initiatives are reviewed, the most relevant IoT architectures and challenges are analyzed, and the expected future trends are indicated. Thus, this article is aimed at providing a wide view of post-quantum IoT security and give useful guidelines to the future post-quantum IoT developers.

Performance evaluation of ADMC-MAC mission critical protocol for wireless sensor 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 surveillance, Tsunami alert systems, and earthquake safety systems, fire fighting applications, industrial automation, pipeline leakage detection system, critical health monitoring, etc. The Wireless sensor networks are deployed for this purpose may be termed as Mission-critical wireless sensor networks, and MAC protocols designed are termed as Mission Critical MAC Protocols for WSN. ADMC-MAC protocol is one of these latest developed protocols. In previous work, the protocol is tested for 40% duty cycle. This paper does the performance evaluation of this protocol in NS-2.35 for different missioncritical scenarios at different duty cycles for mission-critical performance parameters where the actual novelty lies. The results show that the proposed protocol gives best performance when set at 40% duty cycle. It is adaptive to the mission-critical applications and maintains the mission-critical data transport performance along with the energy of the wireless sensor networks. Simulation results confirm that ADMC-MAC model can further be enhanced to reduce data processing delay and for improving data delivery performance. The model may be enhanced by including intelligent nodes only for analyzing and data processing to reduce processing delay .The enhanced model suggested may be used for Mission-critical Wireless Sensor networks in integration with IoT for quick actions in mission-critical application.

Automating orthogonal defect classification using machine learning algorithms

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.

ADMC-MAC: Energy efficient adaptive MAC protocol for mission critical applications in WSN

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.

Editorial: Security and Dependability of Cloud Systems and Services - Part II

This is the second part of the special issue on security and dependability of cloud systems and services, which was organized to solicit novel results in these important and closely related research areas. Indeed, security and dependability are increasingly important concerns for cloud systems and services, due to their spread in a large variety of application fields, including business- and mission-critical scenarios. This demands for innovative methodologies, algorithms and techniques for building services in which companies, organizations and citizens can trust and rely upon.

Editorial: Security and Dependability of Cloud Systems and Services

The papers in this special issue on security and dependability of cloud systems and services. Service-based cloud computing systems are used nowadays in many business- and mission-critical scenarios. As the service-oriented paradigm increasingly spreads in a wide range of application fields, including big data, cloud storage, mobile cloud computing, and sensor cloud, there is a growing need for sound methodologies, algorithms and techniques for building services in which companies, organizations and citizens can trust and rely upon. Security and dependability are therefore becoming more and more relevant concerns for such systems, whose complexity, heterogeneity, and fast-changing dynamics bring difficult challenges to the research and industry communities.

Frequency recovery techniques for TM/TC satellite modem in critical scenarios

SummaryReliability and effectiveness are essential features of satellite transceivers for telemetry and telecommand applications. Modem performance has a strong impact on the success of a satellite mission, in particular, during critical scenarios as the early operation phase, the disposal of a satellite at the end of its life, or the deep‐space missions. In these specific mission critical scenarios, fast and correct data reception is even more important than high channel capacity. An unknown and fast variable channel condition, which can be caused by uncertain spacecraft attitude and large Doppler shift with respect to the data rate, requires efficient and innovative receiver architecture. This paper introduces a complete digital implementation of a transceiver for TM/TC application in low Earth orbit mission that is perfectly compliant with aforementioned requirements. Particular attention is dedicated to the definition and selection of the most appropriate frequency recovery technique; 2 open‐loop techniques that are derived from ML optimal estimator are presented and compared. Additionally, the performance of the proposed receiver is extensively studied and compared with an incoherent technique that is based on the double differential PSK modulation and is known to be suitable for sat‐com in critical scenarios.