Verification Cost Research Articles

Testing and Verification of Connected and Autonomous Vehicles: A Review

Connected and Autonomous Vehicles (CAVs) have the potential to revolutionize transportation by addressing critical challenges such as safety, energy efficiency, traffic congestion, and environmental impact. Realizing these benefits, however, requires the development of a rigorous testing and verification framework to enable the safe, efficient, and reliable deployment of CAVs across diverse operational scenarios. Despite the growing body of research, there remains a significant gap in review papers that comprehensively summarize recent studies related to the testing and verification of CAVs while identifying current challenges and highlighting future research directions. This paper seeks to address this gap by presenting a comprehensive review of the existing testing and verification frameworks for CAVs and identifying their associated challenges. Key topics covered include scenario generation, verification cost functions, assertion values, and security considerations. Furthermore, the paper highlights limitations within current frameworks, emphasizing the gaps that hinder systematic and comprehensive evaluations.

Haven++: Batched and Packed Dual-Threshold Asynchronous Complete Secret Sharing with Applications

Asynchronous complete secret sharing (ACSS) is a foundational primitive in the design of distributed algorithms and cryptosystems that require confidentiality. ACSS permits a dealer to distribute a secret to a collection of N servers so that everyone holds shares of a polynomial containing the dealer's secret. This work contributes a new ACSS protocol, called Haven++, that uses packing and batching to make asymptotic and concrete advances in the design and application of ACSS for large secrets. Haven++ allows the dealer to pack multiple secrets in a single sharing phase, and to reconstruct either one or all of them later. For even larger secrets, we contribute a batching technique to amortize the cost of proof generation and verification across multiple invocations of our protocol. The result is an asymptotic improvement in the worst-case amortized communication and computation complexity, both for ACSS itself and for its application to asynchronous distributed key generation. Our ADKG based on Haven++ achieves, for the first time, an optimal worst case amortized communication complexity of κN without a trusted setup. To show the practicality of Haven++, we implement it and find that it outperforms the work of Yurek et al. (NDSS 2022) by more than an order of magnitude when there are malicious, faulty parties.

A privacy protection scheme to resist ring selection attack in peer-to-peer double auction V2V electricity transaction for charging stations

With the development of Electric Vehicles (EVs), the number of public charging piles cannot meet the immediate charging needs of EV users sometimes. The charging stations provide flexible electricity transaction schemes for users by managing charging piles and Vehicle-to-Vehicle (V2V). Users can freely choose transaction partners and submit transaction plans to multiple charging stations. The mobility of EVs results in users randomly selecting rings multiple times, which may cause them to appear in different rings. Attackers can link multiple transaction plans and infer user privacy information. The leakage of transaction information may threaten the fairness of transactions. Aiming at the above situations, a privacy protection scheme to resist ring selection attack in Peer-to-Peer double auction V2V electricity transaction for charging stations is proposed. Firstly, the ring signcryption algorithm is improved to resist ring selection attack. Secondly, a real identity verification algorithm is designed to track malicious users. Theoretical analysis proves that the security of the proposed privacy protection scheme. The elliptic curve point multiplication is used in the scheme to improve computational efficiency. Experiments show that this scheme has low computational and communication costs. Verification cost increases slowly with the increase of the number of ring members.

Decision on the introduction of blockchain technology in green supply chains considering altruistic preferences

PurposeThis paper aims to examine the blockchain introduction and altruistic preference decisions of the supplier in agricultural food supply chains and discuss how the supplier decisions are influenced by blockchain technology and altruistic preference levels.Design/methodology/approachThe author considers a single period two-level supply chain model to describe the supplier’s decisions. The supplier, as the leader of the game, decides whether to introduce blockchain technology and his own level of altruistic preferences. Consumers have environmental awareness and heterogeneity in green trust. Supply chain members determine their own product pricing and green effort level under Stackelberg game.FindingsThe results reveal the negative impact of unit verification cost in the technology introduction process on the supply chain. In addition, the supplier can adjust their profits by adjusting their altruistic preferences after introducing blockchain to offset the impact of blockchain through the influence of altruistic preferences as discussed by the author.Originality/valueThis paper investigates how the profits and green efforts of supply chain members are influenced by blockchain technology and altruistic preferences.

Improving eSource Site Start-Up Practices.

eSource software that copies patient electronic health record data into a clinical trial electronic case report form holds promise for increasing data quality while reducing data collection, monitoring and source document verification costs. Integrating eSource into multicenter clinical trial start-up procedures could facilitate the use of eSource technologies in clinical trials. We conducted a qualitative integrative analysis to identify eSource site start-up key steps, challenges that might occur in executing those steps, and potential solutions to those challenges. We then conducted a value analysis to determine the challenges and solutions with the greatest impacts for eSource implementation teams. There were 16 workshop participants: 10 pharmaceutical sponsor, 3 academic site, and 1 eSource vendor representatives. Participants identified 36 Site Start-Up Key Steps, 11 Site Start-Up Challenges, and 14 Site Start-Up Solutions for eSource-enabled studies. Participants also identified 77 potential impacts of the Challenges upon the Site Start-Up Key Steps and 70 ways in which the Solutions might impact Site Start-Up Challenges. The most important Challenges were: (1) not being able to identify a site eSource champion and (2) not agreeing on an eSource approach. The most important Solutions were: (1) vendors accepting electronic data in the FHIR standard, (2) creating standard content for eSource-related legal documents, and (3) creating a common eSource site readiness checklist. Site start-up for eSource-enabled multi-center clinical trials is a complex socio-technical problem. This study's Start-Up Solutions provide a basic infrastructure for scalable eSource implementation.

A quantum-secure certificateless aggregate signature protocol for vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) have revolutionized communication between vehicles and infrastructure, notably enhancing traffic management and passenger safety. However, VANETs are vulnerable to security threats, especially regarding data authenticity. Aggregate signature is a powerful technique that reduces computational and communication burdens by aggregating multiple signatures from different signers into a single signature. Traditional aggregate signature schemes, based on large prime number decomposition and the discrete logarithm problem, cannot effectively resist quantum attacks. This paper introduces a novel quantum secure certificateless aggregate signature (QSCLAS) scheme designed to enhance data security and privacy in VANETs. Our proposed scheme employs the number theory research unit (NTRU) algorithm. As a lattice-based cryptographic algorithm, NTRU is renowned for its security against quantum computer attacks, making it an essential component of our quantum-secure solution. By eliminating the need for expensive bilinear pairing operations, our proposed scheme achieves high efficiency and practicality in resource-limited VANETs environments. The security analysis demonstrates our scheme's resilience against both Type-I and Type-II adversaries in the random oracle model under the small integer solution (SIS) problem on the NTRU lattice. Furthermore, compared with existing approaches, the results illustrate that our proposed scheme offers significant advantages in signature generation and verification cost, as well as lower transmission overhead than other lattice-based schemes, thereby making it highly suitable for VANETs environments.

A Cost Model for Ocean Iron Fertilization as a Means of Carbon Dioxide Removal That Compares Ship‐ and Aerial‐Based Delivery, and Estimates Verification Costs

AbstractWe present a cost model for implementing a deployment scale effort for conducting ocean iron fertilization (OIF) for marine‐based carbon dioxide removal (CDR). The model incorporates basic oceanographic parameters critical for estimating the effective export of newly fixed CO2 into biomass that is stimulated by Fe addition to an Fe‐limited region of the Southern Ocean. Estimated costs can vary by nearly 100‐fold between best‐case and worst‐case scenarios, with best‐case values of $7/net tonne C captured versus worst‐case $1,500/net tonne C captured, without accounting for verification costs. Primary oceanographic factors that influence cost are the net primary productivity increases achieved via OIF, the amount of C exported into the deep ocean, and the amount of CO2 ventilated back to the atmosphere. The model compares ship‐based versus aerial delivery of Fe to the ocean, and estimates aerial delivery can be 30%–40% more cost effective; however, the specific requirements for aerial delivery require additional research and development. The model also estimates costs associated with verification and environmental monitoring of OIF. These costs increase $/net tonne C captured by 3–4‐fold. Best, intermediate, and worst cases for aerial delivery and ship delivery are $21, $83, $2,033, and $24, $94, $4,691, respectively, inclusive of verification costs. The primary goal of this model is to demonstrate the variability in cost of OIF as a CDR method, and to better understand where additional research is needed to determine the major factors that may make OIF a tractable, nature‐based CDR method.

VConMC: Enabling Consistency Verification for Distributed Systems Using Implementation-Level Model Checkers and Consistency Oracles

Many cloud services are relying on distributed key-value stores such as ZooKeeper, Cassandra, HBase, etc. However, distributed key-value stores are notoriously difficult to design and implement without any mistakes. Because data consistency is the contract for clients that defines what the correct values to read are for a given history of operations under a specific consistency model, consistency violations can confuse client applications by showing invalid values. As a result, serious consequences such as data loss, data corruption, and unexpected behavior of client applications can occur. Software bugs are one of main reasons why consistency violations may occur. Formal verification techniques may be used to make designs correct and minimize the risks of having bugs in the implementation. However, formal verification is not a panacea due to limitations such as the cost of verification, inability to verify existing implementations, and human errors involved. Implementation-level model checking has been heavily explored by researchers for the past decades to formally verify whether the underlying implementation of distributed systems have bugs or not. Nevertheless, previous proposals are limited because their invariant checking is not versatile enough to check for the wide spectrum of consistency models, from eventual consistency to strong consistency. In this work, consistency oracles are employed for consistency invariant checking that can be used by implementation-level model checkers to formally verify data consistency model implementations of distributed key-value stores. To integrate consistency oracles with implementation-level distributed system model checkers, the partial-order information obtained via API is leveraged to avoid the exhaustive search during consistency invariant checking. Our evaluation results show that, by using the proposed method for consistency invariant checking, our prototype model checker, VConMC, can detect consistency violations caused by several real-world software bugs in a well-known distributed key-value store, ZooKeeper.

A Bitcoin-based Secure Outsourcing Scheme for Optimization Problem in Multimedia Internet of Things

With the development of the Internet of Things (IoT) and cloud computing, various multimedia data such as audio, video, and images have experienced explosive growth, ushering in the era of big data. Large-scale computing tasks in the Multimedia Internet of Things (M-IoT), such as mathematical optimization problems, have begun to be outsourced from IoT devices with limited computing power to cloud servers for execution. However, outsourcing computation brings security concerns, because the behaviors of clouds are invisible to users. The leakage of privacy data in outsourced optimization problems leads to immeasurable losses. The mutual distrust between clouds and users causes that the correctness of the optimal decisions and the fairness of the payment activities are not guaranteed. Blockchain technology has the characteristic of immutability and has become a new security paradigm for eliminating multi-party trust concerns. In this article, we propose a Bitcoin-based secure outsourcing scheme to address the aforementioned security concerns. To prevent confidential data leakage, the proposed scheme designs a computable privacy-preserving method for the outsourced optimization problems. To judge the correctness of the optimal decision and reduce verification costs, the proposed scheme designs a low-cost two-layer verification mechanism based on dual theory and blockchain technology. Blockchain nodes reach a consensus on the problem solutions and trigger an automatic fair payment protocol-based Bitcoin. Security analysis and experimental results demonstrate that our scheme guarantees privacy, fairness, and computational efficiency.

ДОСЛІДЖЕННЯ ПОДВІЙНИХ ЗАМКНЕНЬ НА ЗЕМЛЮ В МЕРЕЖАХ 10 КВ З ІЗОЛЬОВАНОЮ НЕЙТРАЛЛЮ

Reliability indicators of power supply systems as a whole are largely determined by the level of reliability of distribution electrical networks with a voltage of 6-35 kV. Emergency in networks with a voltage of 6-35 kV, operating in the mode with an isolated neutral, is often associated with internal overvoltages. Single-phase short-circuits occur in places of defects in the insulation of cable lines and substation equipment due to the aging of the insulation, non-compliance with the manufacturing technology of insulating structures at factories, non-compliance with the requirements of the instructions during the installation and operation of the equipment, as well as due to mechanical damage, which, sometimes at the time of their appearance, lead to only a partial decrease in electrical strength. Overloads lead to defects. Single-phase short-circuits in 10 kV networks that are not eliminated in time can lead to double earth faults. For protection in these networks, such protections as current cut-off, maximum current protection, current cut-off, zero-sequence current protection, current protections with blocking at minimum voltage are used. However, there are cases when there are cases of negative operation of such protections at a 10 kV substation during double earth faults. For example, a phase A short circuit occurred on the first line, which receives power from the buses of the investigated substation, and another short circuit (phase B) occurred on the second line. Instead of tripping the two damaged lines, the input circuit breaker tripped by mistake. This de-energized other (undamaged) lines that are fed from the same bus section as the damaged lines. Under such conditions, further studies of the currents in the lines during double circuits are required. For this, the use of the Matlab application program package is justified. This will significantly reduce the costs of physical modeling and verification of proposed proposals. The paper presents the results of studies of double earth faults in 10 kV networks with an isolated neutral in the "MATLAB" program package.

Privacy-Preserving and Lightweight Verification of Deep Packet Inspection in Clouds

In the trend of network middleboxes as a service, enterprise customers adopt in-the-cloud deep packet inspection (DPI) services to protect networks. As network misconfigurations and hardware failures notoriously exist, recent efforts envision to ensure the execution integrity of DPI services in untrusted clouds. However, they either require enterprise customers to know proprietary DPI rulesets of cloud providers or introduce forbidden overhead in the network context. In the paper, we propose a privacy-preserving and lightweight verification scheme that efficiently checks whether in-the-cloud DPI services run correctly without leaking private DPI rulesets. Particularly, our design introduces one trusted third party to perform privacy-preserving and trustworthy ruleset evaluation and DPI execution verification. Meanwhile, it devises a novel DPI ruleset authentication method that enables tamper-proof DPI operations and facilitates fast proof generation. The proofs can be verified without requiring the verifier to always maintain all rulesets. To further reduce the verification costs while resisting cloud cheating behaviors like bias treatments of packets, it employs a commitment-based delayed sampling mechanism which requires the DPI services to first demonstrate that all packets have been processed before receiving sampling decisions. Moreover, extensive experiments are conducted based on Click modules. The results show that the proposed scheme is practical and only incurs the real-time overhead of 10-20 microseconds.

Virtual Environment Model Generation for CPS Goal Verification using Imitation Learning

Cyber-Physical Systems (CPS) continuously interact with their physical environments through embedded software controllers that observe the environments and determine actions. Field Operational Tests (FOT) are essential to verify to what extent the CPS under analysis can achieve certain CPS goals, such as satisfying the safety and performance requirements, while interacting with the real operational environment. However, performing many FOTs to obtain statistically significant verification results is challenging due to its high cost and risk in practice. Simulation-based verification can be an alternative to address the challenge, but it still requires an accurate virtual environment model that can replace the real environment interacting with the CPS in a closed loop. In this article, we propose ENVI (ENVironment Imitation), a novel approach to automatically generate an accurate virtual environment model, enabling efficient and accurate simulation-based CPS goal verification in practice.To do this, we first formally define the problem of the virtual environment model generation and solve it by leveraging Imitation Learning (IL), which has been actively studied in machine learning to learn complex behaviors from expert demonstrations. The key idea behind the model generation is to leverage IL for training a model that imitates the interactions between the CPS controller and its real environment as recorded in (possibly very small) FOT logs. We then statistically verify the goal achievement of the CPS by simulating it with the generated model. We empirically evaluate ENVI by applying it to the verification of two popular autonomous driving assistant systems. The results show that ENVI can reduce the cost of CPS goal verification while maintaining its accuracy by generating accurate environment models from only a few FOT logs. The use of IL in virtual environment model generation opens new research directions, further discussed at the end of the article.

Commercial bank digital transformation, information costs, and corporate financial constraints

ABSTRACT The rapid development of digital finance has transformed traditional financial institutions. We investigate the effect and economic consequences of bank digital transformation on corporate financial constraints using data from China. The results show that bank digital transformation alleviates corporate financial constraints by decreasing information search, processing, and verification costs. Furthermore, the effect of bank digital transformation on corporate financial constraints is more pronounced for firms with higher contract intensity, more intangible assets, and poorer external information environment. We also find that bank digital transformation alleviates corporate financial constraints by increasing debt financing. In addition, we show that digital transformation promotes lending by big banks, resulting in the crowding-out effect. Finally, we find that bank digital transformation promotes the flow of credit resources to non-zombie firms, which effectively improves credit allocation efficiency. This paper extends research on digital finance and new structural finance from the perspective of bank digital transformation.

Build an Audit Framework for Data Privacy Protection in Cloud Environment

With the rapid development of cloud computing technology, the data outsourcing service model in cloud environment is becoming increasingly popular. However, while cloud storage technology provides users with efficient services, its complex architecture also brings significant challenges to data privacy security. Because cloud service providers are not completely trusted, there is a risk of tampering or leaking user data, and third-party audits or malicious attacks by unauthorized users may also lead to data damage or loss. Therefore, how to realize the privacy protection and integrity audit of outsourced data in the cloud environment has become an important research topic of information security management. This paper focuses on the information security management method in the cloud environment, and deeply discusses the privacy protection and integrity audit technology of outsourced data. Aiming at the possible risk of tampering or leakage in the process of managing outsourced data by cloud service providers, a variety of privacy protection schemes based on data staining, data segmentation confusion and secure access control of private data are proposed. We study and propose a multi-copy integrity audit method that supports dynamic update of data, which realizes efficient dynamic operation and security verification through signature algorithm and random mask technology. We construct a privacy-protected data dynamic update integrity audit method, which utilizes hierarchical multi-branch tree data structure and random mask technology to significantly improve audit efficiency and security. In this paper, hash message authentication code (HMAC) and indistinguishable obfuscation (IO) techniques are used to propose a low-cost and efficient third-party audit scheme, which further reduces the computational overhead and verification cost.

Advancing Federated Learning through Verifiable Computations and Homomorphic Encryption.

Federated learning, as one of the three main technical routes for privacy computing, has been widely studied and applied in both academia and industry. However, malicious nodes may tamper with the algorithm execution process or submit false learning results, which directly affects the performance of federated learning. In addition, learning nodes can easily obtain the global model. In practical applications, we would like to obtain the federated learning results only by the demand side. Unfortunately, no discussion on protecting the privacy of the global model is found in the existing research. As emerging cryptographic tools, the zero-knowledge virtual machine (ZKVM) and homomorphic encryption provide new ideas for the design of federated learning frameworks. We have introduced ZKVM for the first time, creating learning nodes as local computing provers. This provides execution integrity proofs for multi-class machine learning algorithms. Meanwhile, we discuss how to generate verifiable proofs for large-scale machine learning tasks under resource constraints. In addition, we implement the fully homomorphic encryption (FHE) scheme in ZKVM. We encrypt the model weights so that the federated learning nodes always collaborate in the ciphertext space. The real results can be obtained only after the demand side decrypts them using the private key. The innovativeness of this paper is demonstrated in the following aspects: 1. We introduce the ZKVM for the first time, which achieves zero-knowledge proofs (ZKP) for machine learning tasks with multiple classes and arbitrary scales. 2. We encrypt the global model, which protects the model privacy during local computation and transmission. 3. We propose and implement a new federated learning framework. We measure the verification costs under different federated learning rounds on the IRIS dataset. Despite the impact of homomorphic encryption on computational accuracy, the framework proposed in this paper achieves a satisfactory 90% model accuracy. Our framework is highly secure and is expected to further improve the overall efficiency as cryptographic tools continue to evolve.

Recurrent neural network implementation of digital integrated circuits to mitigate challenges in design verification

Design verification is the dominant stage that consumes the most resources in the digital integrated circuit (IC) design process. Design verification is important because human designers imperfectly convert high-level specifications to low-level circuit implementations using standard cell logic, which is nonlinear and complex to predict and characterize. The widening process variations in shrinking process technologies while digital designs grow in scale and complexity to the extent of being impossible to fully or intuitively identify all temporal interactions of a specific design. Deep neural networks (DNN) are being progressively integrated into the sophisticated software tool chain and design process flow of digital IC as artificial intelligence can learn relationships and correlations in complex, high-dimensional, and multi-factorial problems. In this work, we propose to apply DNN to implement digital IC to minimize the complexity of digital design verification. We posit that DNN can learn to implement circuit functions directly from high-level specifications without requiring detailed specifications from the designer. Trained neural networks can be implemented on neuromorphic hardware to achieve greater power and compute efficiencies than the conventional standard cell implementation. We demonstrate that over 150 randomly generated finite state machines (FSM) can be learned effectively with Recurrent Neural Network (RNN) comprising Gated Recurrent Units (GRU) with different complexity as indicated by the number of states and inputs to the FSM. Our proposed methodology of learning RNN GRU implementations of FSM demonstrates a way forward to reduce the cost and effort of design verification, ultimately leading towards faster digital IC design cycles.

Co-Movement Pattern Mining from Videos

Co-movement pattern mining from GPS trajectories has been an intriguing subject in spatial-temporal data mining. In this paper, we extend this research line by migrating the data source from GPS sensors to surveillance cameras, and presenting the first investigation into co-movement pattern mining from videos. We formulate the new problem, re-define the spatial-temporal proximity constraints from cameras deployed in a road network, and theoretically prove its hardness. Due to the lack of readily applicable solutions, we adapt existing techniques and propose two competitive baselines using Apriori-based enumerator and CMC algorithm, respectively. As the principal technical contributions, we introduce a novel index called temporal-cluster suffix tree (TCS-tree), which performs two-level temporal clustering within each camera and constructs a suffix tree from the resulting clusters. Moreover, we present a sequence-ahead pruning framework based on TCS-tree, which enables the concurrent utilization of all pattern constraints to filter candidate paths. Finally, to reduce verification cost on the candidate paths, we propose a sliding-window based co-movement pattern enumeration strategy and a hashing-based dominance eliminator, both of which are effective in avoiding redundant operations. We conduct extensive experiments for scalability and effectiveness analysis. Our results validate the efficiency of the proposed index and mining algorithm, which runs remarkably faster than the two baseline methods. Additionally, we construct a video database with 1169 cameras and perform an end-to-end pipeline analysis to study the performance gap between GPS-driven and video-driven methods. Our results demonstrate that the derived patterns from the video-driven approach are similar to those derived from groundtruth trajectories, providing evidence of its effectiveness.

SQCS: A sustainable quality control system for spatial crowdsourcing via three-party evolutionary game: Theory and practice

Quality control of sensing data poses a fundamental challenge in Spatial Crowdsourcing (SC) applications, and various incentive mechanisms have been proposed to effectively motivate workers. However, real-world SC systems involve multiple stakeholders, namely workers, platforms, and requesters, who exhibit bounded rationality. Moreover, prior research has often neglected the practical aspect of platforms needing to verify sensing data against ground truth information, which incurs a cost. Therefore, platforms face the constraint of verification cost and must judiciously select the verification rate. In this paper, a Sustainable Quality Control System (SQCS) is proposed, which employs a three-party evolutionary game to model the dynamic interaction among workers, platforms, and requesters in practical SC scenarios. Based on this dynamic model: (1) In theory, we derive the minimum verification rate required for effective worker monitoring and the maximum verification cost that platforms can sustainably bear via the analysis of evolutionary stable strategies. These findings serve as guidelines for achieving sustainable quality control within the SQCS framework; (2) In practice, we apply the SQCS framework to the SC scenario of urban air pollution monitoring. The optimal quality control strategy that maximizes overall social benefits is analyzed via numerical methods. Extensive experimental validation is conducted to demonstrate the effectiveness of the proposed theory and practical approach. These findings provide valuable insights for the sustainable development of SC systems.

Lagrangian Recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for Secure Image Transmission

Present-day evolution in communication and information technology dispenses straightforward and effortless access to data, but the most noteworthy condition is the formation of secure communication. Numerous approaches were designed for safety communication. One of the crucial approaches is image steganography. Moreover, provisioning of information security services is arrived at via cryptosystems where cryptosystems make certain the secure messages transmission between the users in an untrustworthy circumstance. The conventional method of providing encryption and signature is said to be first signing and then encryption, but both the computation and communication costs are found to be high. A certificateless signcryption mechanism is designed to transfer the medical data or images securely. This mechanism will minimize the storage and verification costs of public key certificates. The author of this article proposes a method named Lagrangian recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for transferring the medical data or images securely. In two sections the LRS-HECS method is split. They are medical image steganalysis and certificateless signcryption. First with the Chest X-Ray images obtained as input, a Codeword Correlated Lagrangian Recurrent Neural Network-based image steganography model is applied to generate steg images. Second, to transfer the medical images securely the steg images provided as input is designed a model named a Hyper Elliptic Curve-based Certificateless Signcryption. The issue of providing the integrity and validity of the transmitted medical images and receiver anonymity is addressed by the application of Hyper Elliptic Curve. Chest X-Ray pictures were used in experimental simulations, and the findings showed that the LRS-HECS approach had more advantages over existing state-of-the-art methods in terms of higher peak signal to noise ratio with data integrity and with reduced encryption time and transmission cost.

Continuous verification with acknowledged MAPE-K pattern and time logic-based slicing: A platooning system of systems case study

A system of Systems (SoS) has emerged to achieve goals beyond the capabilities of a single system. Platooning is a representative SoS where vehicles are driven in a group for energy efficiency. A leader of a platoon can control followers, but the followers can also leave the platoon independently. During follower leave, energy efficiency and the independent operation of followers may conflict. To resolve the conflicts of the platooning SoS and operate safe platooning maneuvers, continuous verification of platooning is required. Continuous verification is performed repeatedly in a control loop that allows system monitoring and verification. However, there are two problems in the existing approaches: there are no suitable control loop patterns to support the resilient reconfiguration, and the SoS verification cost is high. We propose an approach, called continuous verification of platooning (CVP), that solves these two problems. CVP includes an acknowledged MAPE-K pattern for resilience and a fast and accurate slicing for low verification costs. The acknowledged MAPE-K pattern and slicing algorithm proposed in the paper can be independently used for other systems and models. In the case of the acknowledged MAPE-K pattern, we applied it to a mass casualty incident response SoS, which is another acknowledged type of SoS in the paper, and showed its effectiveness. Our experiments on CVP showed that the pattern reduced the incidence rate of 10 types of failure by 97.3%, and ensured the leave of followers. We also proved the correctness of slicing and demonstrated experimentally that it reduces the verification costs by 68.62.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.