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Ever since the pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China, it has been recognized as a global threat and several studies have been carried out nationally and globally to predict the outbreak with varying levels of dependability and accuracy. Also, the mobility restrictions have had a widespread impact on people’s behavior such as fear of using public transportation (traveling with unknown passengers in the closed area). Securing an appropriate level of safety during the pandemic situation is a highly problematic issue that resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The autoregressive integrated moving average (ARIMA) machine learning model is used to develop the best model for twenty-one worst-affected states of India and six worst-hit countries of the world including India. The best ARIMA models are used for predicting the daily-confirmed cases for 90 days future values of six worst-hit countries of the world and six high incidence states of India. The goodness-of-fit measures for the model achieved 85% MAPE for all the countries and all states of India. The above computational analysis will be able to throw some light on the planning and management of healthcare systems and infrastructure.

Cause-consequence Diagram (CCD) is widely used as a deductive safety analysis technique for decision-making at the critical-system design stage. This approach models the causes of subsystem failures in a highly-critical system and their potential consequences using Fault Tree (FT) and Event Tree (ET) methods, which are well-known dependability modeling techniques. Paper-and-pencil-based approaches and simulation tools, such as the Monte-Carlo approach, are commonly used to carry out CCD analysis, but lack the ability to rigorously verify essential system reliability properties. In this paper, we propose to use formal techniques based on theorem proving for the formal modeling and step-analysis of CCDs to overcome the inaccuracies of the simulation-based analysis and the error-proneness of informal reasoning by mathematical proofs. In particular, we use the HOL4 theorem prover, which is a computer-based mathematical reasoning tool. To this end, we developed a formalization of CCDs in Higher-Order Logic (HOL), based on the algebraic approach, using HOL4. We demonstrate the practical effectiveness of the proposed CCD formalization by performing the formal reliability analysis of the IEEE 39-bus electrical power network. Also, we formally determine the Forced Outage Rate ( $\mathcal {FOR}$ ) of the power generation units and the network reliability index, i.e., System Average Interruption Duration Index ( $\mathcal {SAIDI}$ ). To assess the accuracy of our proposed approach, we compare our results with those obtained with MATLAB Monte-Carlo Simulation (MCS) as well as other state-of-the-art approaches for subsystem-level reliability analysis.

Embedded systems operational environment poses tightened and usually conflicted design requirements. Software architects aim at introducing effective tradeoff methods to select the most appropriate design solutions to comply with the software specifications of an embedded system. When defining the software architecture for critical embedded systems it is mandatory to balance often conflicting goals to meet the different requirements in terms of resource consumption, schedulability, dependability, and security, among others. This is an engineering problem that can be addressed by employing Multiple-Criteria Decision-Making (MCDM) methods from the operational research domain. This paper combines two of these methods, Analytical Hierarchy Process (AHP) and Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS), to determine the most efficient fault detection design decisions according to relevant metrics. The paper employs two algorithm efficiency metrics: run-time complexity and memory-space complexity. Furthermore, the fault-detection strategy design decisions, Ping/Echo and Heartbeat, were the subjects of this study.

A wireless body area network (WBAN) is a wireless network of wearable computing devices and intelligent physiological sensors. The intelligent physiological sensors collect and process sensitive data from the patient body. The security, reliability, and trustworthiness of sensitive data collected and processed by intelligent physiological sensors are critical due to its unique application domain. Moreover, consistent and reliable data gathering along with its transmission also plays a vital role in WBANs. Trust management (in BANs) has been found as a useful tool to improve cooperation among sensor nodes, security as well as reliability. The paper recommends a novel and efficient, lightweight trust assessment scheme (ETAS) suitable for health application domains and does not rely purely on any encryption technique. The primary purpose is to develop an exciting comprehensive, novel trust estimation framework for BANs to enhance reliability, dependability, security by isolating compromised (hotspot) nodes with great resource efficiency. ETAS incorporates several exclusive (unique) features like efficient trust evaluator, secure and attack resistant, along with competent trust aggregator function to achieve comprehensive trust score. The trust evaluator function is a multi-trust (communication trust, data trust, and energy trust) strategy to deal with severe internal security threats such as badmouthing attack, ballot-stuffing attack, sybil attack, traitor attack, etc. with less resource consumption. Moreover, ETAS incorporates both the success rate and misbehavior component during trust evaluation. The success rate record the number of successful/unsuccessful interactions among sensor nodes in terms of packet send/receive. The misbehavior component keeps records of current and past misbehavior of sensor nodes for effective decision making. Moreover, ETAS focus on the frequency of interaction among biomedical sensor nodes within a specified period to analyze their behavior for efficient trust decisions. Furthermore, ETAS incorporates temperature, data trust as well as the trust score of biomedical sensors to identify hotspot nodes in BSN. ETAS offers full flexibility to adjust the trust threshold, trust domain, reward, and punishment term according to system and application requirements. ETAS’s efficiency is validated through several outcomes (MATLAB R2019a) along with theoretical analysis in terms of energy consumption, attack detection, mitigation, trust computation cost, and packet delivery ratio.

In several areas, including education, the use of machine learning, such as artificial neural networks, has resulted in significant improvements in predicting tasks. The opacity of these models is one of the problems with their use. Prediction models that may offer valuable insights while still being simple to comprehend are preferred by decision-makers in education. Hence, this study suggests an approach that improves the previous student performance prediction by enhancing performance and explaining why a student’s performance is attaining a certain score. A prediction model was proposed and tested using machine learning models. Our models outperform previous work models developed on the same dataset. Using a combined framework of data level and algorithm approaches, the proposed model achieves an accuracy of over 98%, inplying a 20.3% improvement compared with previous work models. As a balancing technique for upsampling data, we use the default strategy of synthetic minority oversampling technique (SMOTE) to oversample all classes to the number of examples in the majority class. We also use ensemble methods. For tuning the parameters, we use a simple grid search algorithm provided by scikit to estimate the optimal parameters of our model. This hyperparameter optimization along with a ten-fold cross-validation process demonstrates the dependability of the novel model. In addition, a novel visual and intuitive technique is used to help determine which factors most influence the score which helps to interpret and understand the entire model and visualizes feature attributions at the observation level for the machine learning model. Therefore, SHAP values are a powerful tool that should be incorporated within the student performance prediction framework by obtaining the prediction and explanation created through the experiment, educators can recognize students at risk early and provide suitable exhortation in an auspicious manner.

Islanding detection with the rising grid supporting inverter-based distributed generation is becoming more critical protection due to its high droop gains and overall decreased system inertia leading to rapid changes in the electrical parameters. Traditional methods for islanding detection in this regard are susceptible to significant problems such as non-detection zone, false-positive detection, and inefficient mode of validation. Therefore, to attenuate these problems, this paper proposes a hybrid islanding detection technique based on unsupervised anomaly detection using autoencoders. This technique uses the rate of change of frequency as primary and phase angles of the voltage and current as secondary detection parameters, demonstrating improved performance, reliability, and robustness due to its shared advantage of both active frequency drift and autoencoder. Furthermore, a dialectic model of offline and online validation schemes is also proposed to ensure the reliability of detection. Extensive simulations and validations have been carried out on multiple networks to generate data sets used to train, test, and validate the technique and compute its statistical significance, thereby confirming its effectiveness. The optimal islanding detection time for the base cases was recorded as 20 milliseconds with an F1-score of 0.991, dependability index of 0.998, security index of 0.995, with total harmonic distortion of 4.56% and zero non-detection zones, which complies with IEC 61000-3-2 and IEEE standard 1547’s requirement of detection within two seconds after islanding.

This paper describes a reconfigurable architecture for an on-board processor to be used in space exploration critical systems. It relies on, a dynamically reconfigurable multi-accelerator hardware architecture that provides transparent reconfiguration and scalable performance, dependability, and power consumption, at run-time. The architecture is integrated under an RTEMS operating system, which manages reconfiguration and fault mitigation in a fully-compatible way with space requirements. In this work, the proposed processor is used to implement a vision-based navigation system, providing fully autonomous adaptation to different phases of a space mission timeline or events that the spacecraft may encounter during its lifespan. Results show that reconfigurability enables the practical usage of Commercial Off-The-Shelf (COTS) Multiprocessor Systems-on-Chips (MPSoCs) in real scenarios.

5G is a critical infrastructure that will connect the whole society and bridge other critical infrastructure systems. Thus, cybersecurity emerges as crucial tenet within the 5G pathway. In this article, we discuss the concept of agile security within a 5G infrastructure taking into account two of its major technologies: Mobile Edge Computing (MEC) and Network Functions Virtualization (NFV). In this sense, we first discuss the 5G-driven MEC deployment from a NFV perspective. Secondly, we present the concept of agile security and how it can be embedded in the daily activities of mobile network operators (MNOs). Thirdly, we discuss risk management as a key element of the agile security framework. To illustrate its application, we propose the design of an agile security risk-aware edge server mechanism for 5G driven MEC deployment, which uses multiple thresholds and a load-balancing security control to mitigate the risks of resource exhaustion and violation of the service level agreement (SLA) faced by the edge servers while taking advantage of multiple cloud layers to increase the degree of availability and dependability of the system. Numerical results show that the proposed mechanism is able to keep the risk at lower levels.

Recently, the widespread use of Network Function Virtualization (NFV) has meant a significant shift in communication and network cloud-based services. NFV increases network function, allocation, and flexibility, for the implementation of new network services, The benefits from optimizing resource allocation in NFV data center include automation of the operational processes, reducing the operational efforts, and lowering capital costs. Despite being a hot topic, NFV data centers are still in their early stages. In this sense, there are some aspects involved in the current NFV implementations that required further investigation, such as performance evaluation and managemen. This paper presents an in-depth analysis of NFV applications in data center. Therefore, a Systematic Literature Review (SLR) was conducted aimed at studying and synthesizing the available works in the NFV state-of-the-art. Consequently, about 1,408 papers have been analyzed and filtered, evidencing a total of 65 relevant studies. This paper documents the relevance of the 65 papers, as well as provides an indepth insight into the primary open research questions concerning NFV architecture. In particular, highlighting the need for consolidating the integration of NFV in data centers by solving problems related to performance, dependability, resource allocation, cost, management, and resource interoperability. Based on the conducted SLR, several research gaps and open challenges have been identified, allowing the authors to provide new propositions and suggested future research avenues. Finally, the obtained results have shown that there is a strong need for further collaboration between academia and industry to conduct quality research and advance NFV practical implementations.

Highly accurate synchronization has become a major requirement because of the rise of distributed applications, regulatory requests and position, navigation and timing backup needs. This fact has led to the development of new technologies which fulfill the new requirements in terms of accuracy and dependability. Nevertheless, some of these novel proposals have lacked determinism, robustness, interoperability, deployability, scalability or management tools preventing them to be extensively used in real industrial scenarios. Different segments require accurate timing information over a large number of nodes. Due to the high availability and low price of global satellite-based time references, many critical distributed facilities depend on them. However, the vulnerability to jamming or spoofing represents a well-known threat and back-up systems need to be deployed to mitigate it. The recently approved draft standard IEEE 1588-2019 includes the High Accuracy Default Precision Time Protocol Profile which is intensively based on the White Rabbit protocol. White Rabbit is an extension of current IEEE 1588-2008 network synchronization protocol for sub-nanosecond synchronization. This approach has been validated and intensively used during the last years. This paper revises the pre-standard protocol to expose the challenges that the High Accuracy profile will find after its release and covers existing applications, promising deployments and the technological roadmap, providing hints and an overview of features to be studied. The authors review different issues that have prevented the industrial adoption of White Rabbit in the past and introduce the latest developments that will facilitate the next IEEE 1588 High Accuracy extensive adoption., This work was supported in part by the AMIGA6 under Grant AYA2015-65973-C3-2-R, in part by the AMIGA7 under Grant RTI2018-096228-B-C32, and in part by the Torres Quevedo under Grant PTQ2018-010198.

Cloud computing gained a lot of growth in the development of scientific and industrial domains due to its scalability, data analysis, portability, and storage abilities. The sensors and actuators facilitate wireless communication with real-time data gathering and local preprocessing. However, the huge amount of data collection and storage for such low powered nodes incurs the problem of reliability and efficient resource management. The technology of cloud computing provides an emerging paradigm for the sensors-based applications to improve processing, management, and storage of massive data. However, sensor nodes are organized and communicate in a distributed environment, which is harmful to network reliability and privacy. Recently, many solutions are presented for data transmissions in distributed systems through link-aware routing, but efficient utilization of network resources is still an open research issue. Also, most of the proposed solutions provide secure transmissions on the cost of computation and routing overhead. In this work, we propose a distributed and data reliability model for mobile edge-based sensor-cloud (DDR-ESC) to grow the data delivery performance and preserve the control of security. Moreover, edge-based communications with the cloud systems make the transmission intelligently and decrease the threshold of packets level overhead with high dependability. The extensive experiments validate the proposed model as compared to the state-of-the-art schemes.

The fifth generation (5G) technology offers a set of security services in order to provide secure communication to a large number of various devices. 5G Authentication and Key Agreement (5G-AKA) authentication service acts as the first step of securing the 5G network communication. This paper aims to quantitatively analyze the dependability of 5G-AKA authentication service. We develop the continuous-time Markov chain (CTMC) models to capture the operational details of the 5G-AKA authentication services when facing the service failure. We also derive formulas for calculating metrics of interest, including Defects Per Million (DPM), authentication service's first restoration time from failure and total cost of ownership (TCO). Extensive numerical analysis is applied to investigate the impact of parameters on the evaluation metrics.

The inclusion of multimodal inputs improves the accuracy and dependability of smart healthcare systems. A user satisfaction monitoring system that uses multimodal inputs composed of users' facial images and speech is proposed in this paper. This smart healthcare system then sends multimodal inputs to the cloud. The inputs are processed and classified as fully satisfied, partly satisfied, or unsatisfied, and the results are sent to various stakeholders in the smart healthcare environment. Multiple image and speech features are extracted during cloud processing. Moreover, directional derivatives and a weber local descriptor is used for speech and image features, respectively. The features are then combined to form a multimodal signal, which is supplied to a classifier by support vector machine. Our proposed system achieves 93% accuracy for satisfaction detection.

Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements. In the current scheme of things, this sought-after control is marred by limitations inherent in the underlying communication network(s) as well as by the uncertainty found in the physical world. These limitations hamper fine-grained control of elements that may be separated by large-scale distances. In this regard, soft computing is an emerging paradigm that can help to overcome the vulnerabilities, and unreliability of CPS by using techniques including fuzzy systems, neural network, evolutionary computation, probabilistic reasoning and rough sets. In this paper, we present a comprehensive contemporary review of soft computing techniques for CPS dependability modeling, analysis, and improvement. This paper provides an overview of CPS applications, explores the foundations of dependability engineering, and highlights the potential role of soft computing techniques for CPS dependability with various case studies, while identifying common pitfalls and future directions. In addition, this paper provides a comprehensive survey on the use of various soft computing techniques for making CPS dependable., IEEE Access

Failure mode and effects analysis (FMEA) is an effective reliability analysis technique and has been used for safety and dependability analysis in a wide range of fields. In the traditional FMEA, a method called risk priority number (RPN) has been widely used to determine the risk levels of failure modes. However, the method is deficient in dealing with imprecise data. To overcome that shortcoming, we propose a novel method based on fuzzy evidential reasoning rules to study the risk evaluation of failure modes in an uncertainty evaluation environment. The main contributions of this work are twofold: First, by analyzing the classical risk priority number method, we extract the reasoning knowledge from RPN method to construct fuzzy evidential reasoning rules for risk evaluation based on virtue of Dempster-Shafer evidence theory and fuzzy set theory; Second, the initial risk assessment is modeled with fuzzy form based on basic probability assignment (BPA) and fuzzy number, which can perfectly reflect the uncertainties in practice. The approach establishes a new reasoning model for fuzzy risk evaluation in FMEA. Finally, an example for risk evaluation of failure modes during general anesthesia process is given to illustrate the effectiveness of the proposed method.

With the increasing deployment of GPUs in many domains, it is important to enhance the dependability of GPU-based systems especially in mission critical environments. We believe that undefined behaviors present a major challenge for the CUDA platform, because its programming language and compiler are derived from traditional C/C++ languages. In this paper, we first present a suite of benchmarks for CUDA, which contains 77 categories of undefined behaviors corresponding to the core language in C11 standard. It is developed to evaluate how current CUDA compilers deal with undefined behaviors. Then, we present the design and implementation of a system that can detect and locate vulnerable code that may result in undefined behaviors in CUDA programs. Our system is composed of a program converter and a static analyzer; the former transforms CUDA programs into a portable and semantic-reserved C/C++ programs, and the latter performs analysis on the transformed programs to statically identify undefined behaviors. Using CUDA SDK and a set of real CUDA applications, we conduct extensive experiments to investigate the effectiveness of our system on finding undefined behaviors in CUDA C programs.

Heterogeneous wireless sensor networks (WSNs) are widely deployed, owing to their good capabilities in terms of network stability, dependability, and survivability. However, they are prone to the spread of malware because of the limited computational capabilities of sensor nodes. To suppress the spread of malware, a malware spread model is urgently required to discover the rules of malware spread. In this paper, a heterogeneous susceptible-iNsidious-infectious-recovered-dysfunctional (SNIRD) model was proposed, which not only considers the communication connectivity of heterogeneous sensor nodes but also reflects the characteristics of malware hiding and dysfunctional sensor nodes. Then, the fraction evolution equations of heterogeneous sensor nodes in different states in discrete time were obtained. Furthermore, the existence of equilibria for the heterogeneous SNIRD model was proved, and the malware spread threshold was obtained, which indicates whether malware will spread or fade out. Finally, the heterogeneous SNIRD model was simulated and it was contrasted with the conventional SIS and SIR models to validate its effectiveness. The results construct a theoretical guideline for administrators to suppress the spread of malware in heterogeneous WSNs.

With the wide applications of wireless sensor networks (WSNs) in various fields, such as environment monitoring, battlefield surveillance, healthcare, and intrusion detection, trust establishment among sensor nodes becomes a vital requirement to improve security, reliability, and successful cooperation. The existing trust management approaches for large-scale WSN are failed due to their low dependability (i.e., cooperation), higher communication, and memory overheads (i.e., resource inefficient). In this paper, we propose a novel and comprehensive trust estimation approach (LTS) for large-scale WSN that employs clustering to improve cooperation, trustworthiness, and security by detecting malicious (faulty or selfish) sensor nodes with reduced resource (memory and power) consumption. The proposed scheme (LTS) operates on two levels, namely, intra-cluster and inter-cluster along with distributed approach and centralized approach, respectively, to make accurate trust decision of sensor nodes with minimum overheads. LTS consists of unique features, such as robust trust estimation function, attack resistant, and efficient trust aggregation at the cluster, head to obtain the global feedback trust value. Data trust along with communication trust plays a significant role to cope with malicious nodes. In LTS, punishment and trust severity can be tuned according to the application requirement, which makes it an innovative LTS. Moreover, dishonest recommendations (outliers) are eliminated before aggregation at the base station by observing the statistical dispersion. The theoretical and mathematical validations along with simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes as well as communication overhead. Published version

One of the limitations of flash memory in real time, high dependability systems is its need for garbage collection and the resulting performance degradation due to non-deterministic response times. Recent work has presented RAID architectures for solid state storage systems. These RAID architectures increase the dependability from a data storage perspective but they do not provide application level dependability when real-time response times are required. In this study we present a garbage collection aware flash translation layer that offers guaranteed access time to a solid state RAID array by managing incoming requests and preventing them from being blocked by ongoing garbage collection. We present a novel serial technique with a dynamic page allocation mechanism that completely eliminates non deterministic behaviors of the garbage collectors in the array. The result is real-time access guarantees that maintain the data dependability enhancements using a run time parity migration technique. The mechanisms are evaluated using trace driven simulator and a number of synthetic and realistic traces. Simulation results indicate that the garbage collection aware techniques offer improved upper bound response times for I/O requests of up to 73% compared to an existing mechanism, without disturbing the data dependability at the storage level. Traces dominated by random writes exhibit similarly significant enhancements.

Effectiveness evaluation of LEO satellite communication system can not only guide the optimization of constellation program, but also play an important role in the stage of system design. In order to ensure the accuracy of the evaluation results, it is necessary to consider the impact of uncertain information on effectiveness evaluation, whether it is due to poor information or complex system. In order to solve this problem, this paper introduces birth and death process into satellite communication system. Firstly, according to the limit distribution of grey birth and death process with continuous time and limited state, we establish the availability model of the system. Then, the Kolmogorov forward equation is used to characterize the dependability of the system during operation. After that, a capability evaluation index system based on “four domains” is constructed. The attribute values of the index are unified into generalized standard interval grey numbers, and the weight allocation model of GSIGN-AHP-ME is established.The system capability is calculated according to the full probability formula. Subsequently, taking constellation X as an example, the system effectiveness is obtained by using the G-BDP-ADC model constructed in this paper. Finally, by comparing with the fuzzy evaluation method, it shows that the G-BDP-ADC model constructed in this paper is better than the fuzzy evaluation method, and can describe the LEO satellite communication system in the context of poor information more appropriately.

In this paper, we investigate the secure content delivery issue for device-to-device networks based on the trust evaluation mechanism and covert communication model. Specifically, in order to stimulate the lasting and rational cooperation among mobile devices, we propose a trust evaluation mechanism, where the trust degree between two mobile devices can be obtained through historical assessed values. Simultaneously, a covert communication model is introduced to guarantee the undetectability of content delivery, which helps to stop malicious wardens adopting some coming external attacks. In this way, the availability and dependability of content sharing, as well the confidentiality and integrity of transmitted contents cam be guaranteed. Then, combining security-aware metrics, in terms of trust degree and covert rate, with physical layer transmission performances, in terms of achievable rate and successful delivery probability, we propose the definition of secrecy-aware effective rate to serve as a guidance on the joint optimization issue of content delivery mode selection and resource management, which is formulated as a social welfare maximization problem. To solve the complex problem tactfully, it is decoupled into two subproblems, i.e., mode selection which is a many-to-one matching problem, and resource management which is a one-to-one matching problem. By analyzing the interaction between two issues, a novel hierarchical stable matching algorithm is proposed to obtain three-dimension stable matching results. Its properties, such as stability, convergence, optimality, and complexity, are theoretically analyzed and proved. Finally, extensive numerical results are provided to demonstrate the advantages of our proposed algorithms.

The performance of the process network in the smart substation is particularly important for relaying functions implementation, and the transmission quality of sampled value packets is becoming the key constraint on protections because they carry practical parameters of the power system without repeated transmission. By means of phasor form and simulation, the model is built to analyze the operating characteristics of differential protection during the normal condition, external faults, and internal faults of the transformer, while packet loss/delay occurring at a different time and using different corrective measures. Considering the change of power equipment capacity, external fault types, starting points on wave and sampling frequency, the security boundaries are constructed for consecutive loss of sampled value packets. In addition, two indices are put forward to weigh the tolerance thresholds. Finally, the degree of dependability of differential protection due to the packet loss is further analyzed under the transformer internal faults.

The evolution of business software technologies is constant and is becoming increasingly complex which leads to a great probability of software/hardware failures. Business processes are built based on web services as they allow the creation of complex business functionalities. To attack the problem of failures presented by the use of web services, organizations are extrapolating the autonomic computing paradigm to their business processes as it enables them to detect, diagnose, and repair problems improving dependability. Sophisticated solutions that increase system dependability exist, however, those approaches have drawbacks; for example, they affect system performance, have high implementation costs, and or they may jeopardize the scalability of the system. To facilitate evolution to self-management, systems must implement the monitoring, analyzing, planning, and execution (MAPE) control loop. An open challenge for MAPE loop is to carry out in an efficient manner the diagnosis and decision-making processes, recollecting data from which the system can detect, diagnose, and repair potential problems. Also, dealt by systems dependability, specifically as fault tolerant mechanisms. One useful tool for this purpose is the communication induced checkpointing (CiC). We use CiC in attacking the dependability problem of using web services in a distributed and efficient manner. First, we present an approach for web services compositions that supports fault tolerance based on the CiC mechanism. Second, we present an algorithm aimed at web services compositions based on an autonomic computing and checkpointing mechanism. Experimental results support the feasibility of this concept proposal.

Concurrent transmission (CT)-based wireless sensor networks, where nodes transmit at the same moment upon receiving successfully, begin to be applied to real-world scenarios. CT-based protocols have been proven experimentally that they can achieve good the end-to-end performance, namely high reliability, low latency, and high energy efficiency. For various communication patterns (one-to-many, many-to-one, and many-to-many), most current CT-based networks require a given and fixed host to realize global synchronization and scheduling. However, in real-world cases, there is a great deal of interference in the 2.4 GHz ISM band. Interference can partition the network unexpectedly due to the centralized scheduling in current CT-based networks. Even worse, current CT-based networks cannot complete the initialization phase if the unexpected partition occurs at an very beginning. To address this problem, we propose a dependable CT-based protocol (DeCoT) for wireless sensor network (WSN) to support information exchange under adverse conditions. In DeCoT, continuous transmission with a channel hopping mechanism maintains links under interference and an initiated mechanism decentralizes the network. Through our experiments in FlockLab, under interference, DeCoT achieves an average reliability of 87%, and outperforms the state-of-the-art flooding protocol, namely Robust Flooding that won the 1st place in the EWSN 2017 Dependability Competition. Especially when the source nodes are placed sparsely, DeCoT speeds up the information exchange. Above all, DeCoT can complete the initialization and work properly even when the network partitions unexpectedly. DeCoT has been evaluated as the most reliable protocol in the EWSN 2018 Dependability Competition with respect to resistance against interference. Thus, DeCoT can function dependably under interference.

Anomaly detection has been an important topic in cloud platforms to guarantee the dependability and robustness of services in the cloud. Most research works were proposed to tackle the detection performance problems of detection algorithms caused by the volume of data, the dynamic environment, various types of anomalies, and so on. However, almost all of them take only the optimization of algorithms into account, which leads to a situation that some key features of detector deployment and the scalability and dependability of the detection framework itself are omitted. Therefore, an anomaly detector deployment awareness detection framework based on multi-dimensional resources balance is proposed to address the problems. It balances the multi-dimensional resources by bringing four factors of resources into consideration to deploy detectors quietly, which are the current utilizations, the available capacity, the demands of detectors and the remaining resources. Three experiments and comparative analysis suggest that this framework achieves a higher scalability and detection accuracy than the existing framework.

The advent of cloud platform has promoted the complexity and scales of industries increasingly. Any deliberate or non-deliberate faults may cause enormous impact on system performance and server costs. Anomaly detection is a good way to identify anomalies and improve the dependability of the cloud platform. However, some of the anomaly detection methods are labeled data dependency, and some of them are sensitive to the dynamic runtime environment of the cloud platform. To address the problems, an adaptive and incremental clustering anomaly detection algorithm for virtual machines under the cloud platform runtime environment is proposed. Compared with the previous detection methods, the effect of the runtime environment factor is taken into account. Owning to the high level of dynamic cloud platform manages and the resources allocation of virtual machines, the environmental factors play an important role in the running performance of the virtual machines. In this paper, an improved adaptive and incremental clustering algorithm is introduced to perform the detection with the considerations of the cloud platform runtime environment. To demonstrate the effectiveness, two sets of experiments are performed. The experimental results indicate that the proposed anomaly detection method can greatly improve the detection accuracy rate even the cloud platform runtime environment changes.

Cyber-physical systems (CPSs) have dependability requirements that are associated with controlling a physical process. Cyber-attacks can result in those requirements not being met. Consequently, it is important to monitor a CPS in order to identify deviations from normal operation. A major challenge is inferring the cause of these deviations in a trustworthy manner. This is necessary to support the implementation of correct and timely control decisions, in order to mitigate cyber-attacks and other causes of reduced dependability. This paper presents evidential networks as a solution to this problem. Through the evaluation of a representative use case for cyber-physical control systems, this paper shows novel approaches to integrate low-level sensors of different types, in particular those for cyber-attack detection, and reliabilities into evidential networks. The results presented indicate that evidential networks can identify system states with an accuracy that is comparable to approaches that use classical Bayesian probabilities to describe causality. However, in addition, evidential networks provide information about the uncertainty of a derived system state, which is a significant benefit, as it can be used to build trust in the results of automatic reasoning systems.

The Internet of Things (IoT) is a ubiquitous system connecting many different devices—the things —which can be accessed from the distance. The cyber-physical systems (CPSs) monitor and control the things from the distance. As a result, the concepts of dependability and security get deeply intertwined. The increasing level of dynamicity, heterogeneity, and complexity adds to the system’s vulnerability, and challenges its ability to react to faults. This paper summarizes the state of the art of existing work on anomaly detection, fault-tolerance, and self-healing, and adds a number of other methods applicable to achieve resilience in an IoT. We particularly focus on non-intrusive methods ensuring data integrity in the network. Furthermore, this paper presents the main challenges in building a resilient IoT for the CPS, which is crucial in the era of smart CPS with enhanced connectivity (an excellent example of such a system is connected autonomous vehicles). It further summarizes our solutions, work-in-progress and future work to this topic to enable “Trustworthy IoT for CPS”. Finally, this framework is illustrated on a selected use case: a smart sensor infrastructure in the transport domain.