Secure Sensor Research Articles

Trustworthy High-Performance Multiplayer Games with Trust-but-Verify Protocol Sensor Validation.

Multiplayer online video games are a multibillion-dollar industry, to which widespread cheating presents a significant threat. Game designers compromise on game security to meet demanding performance targets, but reduced security increases the risk of potential malicious exploitation. To mitigate this risk, game developers implement alternative security sensors. The alternative sensors themselves become a liability due to their intrusive and taxing nature. Online multiplayer games with real-time gameplay are known to be difficult to secure due to the cascading exponential nature of many-many relationships among the components involved. Behavior-based security sensor schemes, or referees (a trusted third party), could be a potential solution but require frameworks to obtain the game state information they need. We describe our Trust-Verify Game Protocol (TVGP), which is a sensor protocol intended for low-trust environments and designed to provide game state information to help support behavior-based cheat-sensing detection schemes. We argue TVGP is an effective solution for applying an independent trusted referee capability to trust-lacking subdomains and demands high-performance requirements. Our experimental results validate high efficiency and performance standards for TVGP. We identify and discuss the operational domain assumptions of the TVGP validation testing presented here.

Toward highly sensitive, selective, and stable palladium‐based MEMS gas sensors for hydrogen energy security

Toward highly sensitive, selective, and stable palladium‐based MEMS gas sensors for hydrogen energy security

Inovasi dalam Layanan Perbankan Berbasis Internet of Things (IOT) Peluang dan Tantangan di Era Digital

The development of the Internet of Things (IoT) has changed the paradigm of banking services by offering innovations in operational efficiency, security and customer experience. Despite this, IoT implementation in the banking sector faces significant challenges related to data security and infrastructure costs. This research aims to explore the impact of implementing IoT in banking services and to identify the opportunities and challenges faced in the current digital era. This research method involves case studies, in-depth interviews, and surveys to analyze the use of smart ATMs, wearable devices, security sensors, and smart queuing systems in various banking contexts. The analysis results show significant improvements in operational efficiency and customer satisfaction, while also highlighting challenges related to data security and implementation costs. The implications of this research include recommendations for further development in IoT technology in banking services, with a focus on enhanced data security and effective implementation strategies.

An energy efficient authentication scheme for cluster-based wireless IoT sensor networks

Wireless Internet of Things (IoT) sensor networks (WITSN) play a pivotal role in modern society, facilitating a myriad of applications ranging from smart homes to industrial automation. Therefore, safeguarding these networks against security threats is paramount to ensure the integrity, confidentiality, and availability of data transmitted within them. However, WITSNs face escalating security threats due to their diverse structures and platforms. Existing literature has identified these vulnerabilities but lacks comprehensive solutions to address them effectively. To bridge this gap, this paper proposes a novel security approach termed Randomized Bi-Phase Authentication Scheme (RBAS), which integrates digital watermarking techniques to fortify both external and internal network security. RBAS not only tackles data availability, confidentiality, and authenticity challenges prevalent in WITSNs but also strives to maintain a delicate equilibrium between robust security measures and energy efficiency. Key contributions of this work include the meticulous examination and validation of the integration of cyclic redundancy check (CRC) codes in IoT sensor network authentication, demonstrating their efficacy in error detection through simulations. Furthermore, RBAS employs advanced hashing, cryptography, and dynamic verification codes to ensure strong error detection and data authenticity, leveraging robust CRC codes and randomization to thwart potential attacks. The scheme's complexity acts as a deterrent against manipulation, while its cluster awareness enhances adaptability, and cryptographic principles bolster overall security. Extensive performance evaluation using the Network Simulator-2 (NS2) reveals significant benefits of RBAS, including an 8 % reduction in power consumption and a 7 % increase in network longevity. Notably, RBAS surpasses existing solutions with a 14 % reduction in dropping ratio and an 8 % decrease in latency. The success of RBAS stems from its innovative utilization of lightweight watermarking techniques and cluster-based routing, enabling proactive identification of data tampering and thereby enhancing the network's overall security posture. This pioneering work not only advances the state of the art in WITSN security but also holds profound implications for the practical deployment of secure and efficient IoT sensor networks in real-world scenarios.

X-ray Induced Electric Currents in Anodized Ta2O5: Towards a Large-Area Thin-Film Sensor.

We investigated the characteristics of radiation-induced current in nano-porous pellet and thin-film anodized tantalum exposed to kVp X-ray beams. We aim at developing a large area (≫cm2) thin-film radiation sensor for medical, national security and space applications. Large area (few cm2) micro-thin Ta foils were anodized and coated with a counter electrode made of conductive polymer. In addition, several types of commercial electrolytic porous tantalum capacitors were assembled and prepared for irradiation with kVp X-rays. We measured dark current (leakage) as well as transient radiation-induced currents as a function of external voltage bias. Large transient currents (up to 50 nA) under X-ray irradiation (dose rate of about 3 cGy/s) were measured in Ta2O5 capacitors. Small nano-porous Ta and large-area flat Ta foil capacitors show similar current-voltage characteristic curve after accounting for different X-ray attenuation in capacitor geometry. The signal is larger for thicker capacitor oxide. A non-negligible signal for null external voltage bias is observed, which is explained by fast electron production in Ta foils. Anodized tantalum is a promising material for use in large-area, self-powered radiation sensors for X-ray detection and for energy harvesting.

A Data Sharing Model for Blockchain Trusted Sensor Leveraging Mimic Hash Mechanism

Blockchain, as a distributed trust database, has been widely applied in the field of trustworthy sharing of Internet of Things (IoT) sensor data. A single hash mechanism has achieved, to some extent, the trustworthy on-chain storage of blockchain sensor data, that is, the consistency of data on and off the chain. However, it still faces potential security risks such as collision attacks, short password attacks, and rainbow table attacks. To address this issue, this paper proposes a resiliently secure blockchain sensor data trustworthy sharing model based on a mimic hash mechanism. Specifically, in response to the security risks that may arise from the single hash mechanism, this study innovatively introduces a mimic hash mechanism and proposes two methods for constructing mimic hashes based on Verifiable Random Function (VRF) and Cyber Mimic Defense (CMD) in dedicated Wireless Sensor Networks (WSNs) and open public networks, respectively. Theoretical analysis and experimental results demonstrate that this model effectively solves the problem of trustworthy on-chain storage of sensor data in edge computing environments, enhancing the trustworthiness and security of the data on the chain.

Secure and Efficient Wireless Sensor Network and Machine Learning -based Monitoring System for Student Physical and Mental Health

In this research we have reviewed the performance of machine learning algorithms and WSNs for the monitoring student physical and mental health. We have found that logistic regression is a stronger alternative after an exhaustive evaluation of machine learning algorithms that consistently produce superior performance metrics than Gaussian mixture models (GMM). This has demonstrated average accuracy of 87.1% across 10 experiments, besting the average accuracy of 83.8% of GMM. It also illustrates greater efficacy for assessing system security, producing average accuracy of 86.6%, with 91.9% for GMM. The findings demonstrate the significance of select a machine learning algorithms that meets the specific needs of monitoring student health systems. They are also an illustration of logistic regressions dependability, and adeptness at identifying subtle changes in student health indicators that allow for proactive initiation of interventions and support mechanisms tailored to individual students’ needs. Going forward, monitoring systems should next be further refined and optimised so that they can more effectively support both student well-being and academic success in the complex and diverse educational settings of Indian universities.

3D Printing Technology: Role in Safeguarding Food Security.

The rising threats to food security include several factors, such as population growth, low agricultural investment, and poor distribution systems. Consequently, food insecurity results from a confluence of issues, including diseases, processing limitations, and distribution deficiencies. Food insecurity usually occurs in vulnerable areas where certain technologies and traditional food safety testing are not a viable solution for foodborne disease detection. In this regard, 3D printing technologies and 3D printed sensors open the platform to produce portable, accurate, and low-cost sensors that address the gaps and challenges in food security. In this paper, we discuss the perspective role of 3D printed sensors in food security in terms of food safety and food quality monitoring to provide reliable access to nutritious, affordable food. In each section, we highlight the advantages of 3D printing technology in terms of cost-effectiveness, accuracy, accessibility, and reproducibility compared to traditional manufacturing methodologies. Recent developments in robotic technologies for mechanization, such as food handling with soft grippers, are also discussed. Lastly, we delve into the applications of advanced 3D printing technologies in agricultural monitoring, particularly the future of plant wearables, environmental sensing, and overall plant health monitoring.

Research on Oracle Technology Based on Multi-Threshold Aggregate Signature Algorithm and Enhanced Trustworthy Oracle Reputation Mechanism.

In the realm of IoT sensor data security, particularly in areas like agricultural product traceability, the challenges of ensuring product origin and quality are paramount. This research presents a novel blockchain oracle solution integrating an enhanced MTAS signature algorithm derived from the Schnorr signature algorithm. The key improvement lies in the automatic adaptation of flexible threshold values based on the current scenario, catering to diverse security and efficiency requirements. Utilizing the continuously increasing block height of the blockchain as a pivotal blinding parameter, our approach strengthens signature verifiability and security. By combining the block height with signature parameters, we devise a distinctive signing scheme reliant on a globally immutable timestamp. Additionally, this study introduces a reliable oracle reputation mechanism for monitoring and assessing oracle node performance, maintaining both local and global reputations. This mechanism leverages smart contracts to evaluate each oracle's historical service, penalizing or removing nodes engaged in inappropriate behaviors. Experimental results highlight the innovative contributions of our approach to enhancing on-chain efficiency and fortifying security during the on-chain process, offering promising advancements for secure and efficient IoT sensor data transmission.

An efficient and secure wearable sensor based remote healthcare monitoring system using adaptive dilated transformer Bi‐LSTM with gated recurrent unit

AbstractInnovative research works in the healthcare sector keep on advancing every day. As the “Internet of Things (IoT)” keeps on evolving, the application of IoT in the medical field is prominent these days. Utilizing IoT devices, alert messages can be sent directly to medical professionals in case of an emergency. So, monitoring the health condition of an individual using IoT technology has become a popular and beneficial method in today's contemporary medical field. With the help of mobile IoT medical equipment, the technology of smart Healthcare Monitoring System (HMS) is proliferating. By utilizing deep learning and IoT technology, the medical diagnosis system has evolved from direct face‐to‐face visits to the hospital to remote telemedicine method. Most of the data generated by the IoT wearable sensors are highly correlated and may consist of outliers. The extraction of the essential attributes from these data is a complicated task. So, “deep learning and machine learning” techniques are adapted to determine the most relevant and appropriate feature required for efficient diagnosis from the unstructured data produced by the IoT devices and thus help in minimizing the redundancy of unnecessary data. Fusing deep learning methods with healthcare IoT made only the essential details to be available for diagnosis. Therefore, a deep learning‐oriented IoT‐based HMS is executed in this work. With the support of several wearable healthcare devices, the required data are acquired. The encryption of the data acquired from standard sources using Optimal Key‐based Advanced Encryption Standard (OK‐AES) is carried out next to assure the security of the sensitive medical data. The keys for AES encryption are optimally chosen with the aid of the Enhanced Heap‐Based Optimizer Algorithm (EHBOA). The encrypted data is transferred to the “cloud platform” for data storage. Once there is a need for the data, then the encrypted data is initially downloaded from the cloud platform. Then using the same AES scheme, the decryption of the data to attain the original data is carried out. From the retrieved data, the extraction of the crucial attributes is carried out. The extracted features are chosen in an optimized manner and are concatenated with the tuned weights to form the weighted feature matrix. This formulated weighted feature matrix is provided as input to the “Adaptive Dilated Transformer Bidirectional Long Short‐Term Memory (Bi‐LSTM) with Gated Recurrent Unit (GRU) (ADTBi‐LSTM‐GRU) model.” The variables in the ADTBG are optimized using the EHBOA for providing an accurate classification outcome. The classified disease outcome is obtained from the deployed ADTBi‐LSTM‐GRU model. Simulations are done to verify the efficiency and reliability of the implemented deep learning and IoT‐based HMS.

Breaking the Built‐In Electric Field Barrier in p–n Heterojunction for Self‐Powered, Wavelength Distinguishable Photoelectrochemical Photodetectors: Toward Low Power Consumption and Secure Underwater Wireless Sensor Network

AbstractSelf‐powered, light wavelength distinguishable photodetectors (PDs) are appealing components to build a robust, secure, and low energy consumption underwater wireless sensor network (UWSN). However, achieving such devices is extremely difficult even today. In this context, the first self‐powered, light wavelength distinguishable PDs with photoelectrochemical (PEC) principles and using tunnel junction (TJ) to overcome the technical hurdles for self‐powered, light wavelength distinguishable PEC‐PDs with p–n junction working electrode is reported. For such devices, a single photoelectrode is used, that is, one photoelectrode is able to distinguish different light wavelengths without using any external electrical power, and they are able to distinguish light wavelengths in both the ultraviolet (UV) and blue wavelength ranges. High responsivities reaching mA/W range and ultrafast response time with less than 10 ms are achieved in self‐powered operation mode. Moreover, such devices are able to operate not only in acidic but also in NaCl electrolyte, making them potentially attractive for applications in ocean environment. In the end, it is demonstrated that leveraging such PEC‐PDs, excellent data security can be achieved in the data communication mimicking that in an UWSN in ocean environment. This study not only represents a breakthrough in PDs, but also significantly advances the development of UWSNs, especially for ocean environment.

An Enhanced and Dynamic Key AES Algorithm for Internet of Things Data Security

Internet of Things (IoT) applications have become ubiquitous in various aspects of daily life, including smart homes, healthcare, and other areas where human assistance is crucial. However, the valuable real-world data collected by IoT devices and transmitted over the Internet have become a prime target for numerous malicious actors and hackers. Therefore, ensuring secure communication to prevent unauthorized access to this transmitted data is of paramount importance. This research is dedicated to the development of a robust security system for IoT to protect sensor data. Traditionally, IoT systems have relied on fixed encryption keys shared between transmitters and receivers, which presented vulnerabilities as these keys could be easily compromised. In this study, we introduce an enhanced version of the AES algorithm with dynamic keys. In this modified algorithm, each cycle involves the XOR operation of four keywords with the sequence number. Each round of the AES algorithm incorporates various transformations, including DivideSwap, SubBytes, ShiftRows, MixColumns, and AddRoundKey, with the exception of the final round, which excludes the MixColumns operation. To implement and test this upgraded security approach, we utilized hardware components such as the Arduino Uno, ESP8266, and DHT11 sensors. The enhanced AES algorithm was integrated into the Arduino Uno to secure sensor data before transmission across the network. The results of our analysis demonstrate that the proposed IoT security method presents a significantly higher level of cryptographic resilience compared to the traditional AES algorithm, making it a robust solution for safeguarding IoT data.

Scaling Digital Biomarker Discovery with the Cloud and Wearable Devices

AbstractBackgroundIn clinical trials and other disease research, biomarkers provide a valuable indication of drug performance and disease progression. Traditionally, these biomarkers may include genomic factors, lab test results, and more, but we now can easily expand into longitudinally collected digital biomarkers from consumer smart devices.MethodThe Amissa platform, built on the Microsoft Azure cloud, enables the collection of high frequency data from consumer smartwatches (such as the Apple Watch and Samsung Galaxy watch devices). The API‐driven cloud architecture enables secure sensor data collection including location, gyrometry/accelerometry, activity monitoring/pedometry, and biosensors such as oxygen saturation and heart rate. This solution allows for sub‐minute capture frequency while balancing device battery drain. The ISO 27001:2013‐compliant platform is globally scalable through an innovative cloud‐native design that ensures security and compliance.ResultCaregivers, researchers, and healthcare providers can now monitor loved ones/patients through consumer‐grade wearables, providing a cost‐effective, accurate, and flexible solution for biomarker discovery, data collection, and remote monitoring.ConclusionThe Amissa platform successfully enables digital biomarker development and helps to address disparities in clinical trials and research studies of underrepresented groups through decentralized data collection. Plus, the platform address security and privacy considerations while enables collaborative research participation and flexible study designs. Note: In this presentation, we will discuss the NIH‐funded work of Amissa and the cloud‐first approach taken to build the world’s first global‐scale platform for disease research using wearable devices. We will cover technical design patterns, considerations for privacy and security, the effect of decentralized data collection on addressing health disparities and inclusion in clinical trials, and the future of digital biomarker discovery in Alzheimer’s disease and related dementias.

Secure Sensor Data Collection and Blockchain Storage System for IoT Networks

Our research presents a Secure Sensor Data Collection and Blockchain Storage System (SSDCBSS) for IoT networks. This system ensures the security and integrity of IoT-generated data by employing advanced encryption during data collection and transmission. Blockchain technology is utilized to create a decentralized, tamper- resistant ledger for data storage, eliminating the need for a central authority. Consensus mechanisms validate data entries, reducing the risk of fraudulent information. The SSDCBSS offers a robust and scalable solution to the security challenges faced by IoT networks, fostering trust and reliability in the IoT ecosystem for diverse applications. mood. This process is repeated for the other five emotions. Key Words: blockchain, IoT, storage, Encryption

CAPTAIN: Community-based Advanced Persistent Threat Analysis in IT Networks

Organizations that possess valuable information assets and critical infrastructure are prone to Advanced Persistent Threats (APTs). The life cycle of this type of modern attack consists of multiple stages called Intrusion Kill Chain (IKC). As one of the most common approaches to deal with these attacks, organizations’ security staff use various heterogeneous security and non-security sensors in different lines of defense (Network, Host, and Application) as the primary detection levels in the monitored IT network to log the attacker’s intrusive activities. They then model their behaviors by using logged events to detect the IKC of APT attacks. However, numerous methods proposed in the literature have three primary drawbacks: 1) the inability to use both security and non-security sensors of the three mentioned detection levels in event correlation analysis, 2) high dependence on expert knowledge in setting up and maintaining common attack patterns, and 3) incapability to provide a visual representation of the attack path for security administrators to better track on-the-fly attacks in a monitored network. This paper presents a system for Community-based Advanced Persistent Threat Analysis in IT Networks (CAPTAIN) to address the aforementioned issues and challenges. The CAPTAIN framework comprises two distinct phases (including 12 different activities) that receive raw events logged by heterogeneous sensors as input and detect possible IKCs of the APT attacks as output. This system implements a novel graph-based attackers’ behavior modeling technique for detecting the IKC of APT attacks by correlating analysis of logged events and leveraging knowledge discovery on the graph. Our evaluation of the two publicly available standard datasets, Bryant and DARPA Transparent Computing, indicates that the CAPTAIN is robust, reliable against high volume events, and can detect the IKC of APT attacks with high accuracy and low false positive rates.

Upconversion luminescence and temperature sensing performance of Er3+ ions doped self-activated KYb(MoO4)2 phosphors

In this work, a series of self-activated KYb(MoO4)2 phosphors with various x at% Er3+ doping concentrations (x = 0.5, 1, 3, 5, 8, 10, 15) was synthesized by the solid-state reaction method. The phase structure of the as-prepared samples was analyzed by X-ray diffraction (XRD), XRD Rietveld refinement and Fourier transform infrared (FT-IR) spectroscopy. The as-prepared samples retain the orthorhombic structure with space group of Pbcn even Er3+ doping concentration up to 15 at%. High-purity upconversion (UC) green emission with green to red intensity ratio of 55 is observed from the as-prepared samples upon the excitation of 980 nm semiconductor laser and the optimum doping concentration of Er3+ ions in the self-activated KYb(MoO4)2 host is revealed as 3 at%. The strong green UC emission is confirmed as a two-photon process based on the power-dependent UC spectra. In addition, the fluorescence intensity ratios (FIRs) of the two thermally-coupled energy levels, namely 2H11/2 and 4S3/2, of Er3+ ions were investigated in the temperature region 300–570 K to evaluate the optical temperature sensor behavior of the sample. The maximum relative sensitivity (SR) is determined to be 0.0069 K−1 at 300 K and the absolute sensitivity (SA) is determined to be 0.0126 K−1 at 300 K. The SA of self-activated KYb(MoO4)2:Er3+ is almost twice that of traditional KY(MoO4)2:Er3+/Yb3+ codoping phosphor. The results demonstrate that Er3+ ions doped self-activated KYb(MoO4)2 phosphor has promising application in visible display, trademark security and optical temperature sensors.

Secure Sensor Prototype Using Hardware Security Modules and Trusted Execution Environments in a Blockchain Application: Wine Logistic Use Case

The security of Industrial Internet of Things (IIoT) systems is a challenge that needs to be addressed immediately, as the increasing use of new communication paradigms and the abundant use of sensors opens up new opportunities to compromise these types of systems. In this sense, technologies such as Trusted Execution Environments (TEEs) and Hardware Security Modules (HSMs) become crucial for adding new layers of security to IIoT systems, especially to edge nodes that incorporate sensors and perform continuous measurements. These technologies, coupled with new communication paradigms such as Blockchain, offer a high reliability, robustness and good interoperability between them. This paper proposes the design of a secure sensor incorporating the above mentioned technologies—HSMs and a TEE—in a hardware device based on a dual-core architecture. Through this combination of technologies, one of the cores collects the data extracted by the sensors and implements the security mechanisms to guarantee the integrity of these data, while the remaining core is responsible for sending these data through the appropriate communication protocol. This proposed approach fits into the Blockchain networks, which act as an Oracle. Finally, to illustrate the application of this concept, a use case applied to wine logistics is described, where this secure sensor is integrated into a Blockchain that collects data from the storage and transport of barrels, and a performance evaluation of the implemented prototype is provided.

A novel scheme based on coarse‐grained localization and fine‐grained isolation for defending against Sybil attack in low power and lossy networks

AbstractDue to the lack of security mechanisms and sensor resources, the IPv6 Routing Protocol for Low power and Lossy networks (RPL) is vulnerable to various attacks, among which Sybil attack has received a lot of attention. This paper proposes a novel scheme based on coarse‐grained localization and fine‐grained isolation (CLFI) for defending Sybil attack. In CLFI, normal neighbor nodes are identified based on received signal strength indicator (RSSI). To further identify Sybil nodes, a physical address identifier called is defined, which is calculated as the difference of RSSI at two different receivers when they are receiving the same packet. In order to facilitate the calculation of , a set of anchor nodes are deployed to broadcast the received RPL control packets. Then Sybil behaviors can be detected by comparing the physical address identifier with the logical address embedded in the received packets. The proposed CLFI scheme has been completely implemented in the Contiki operating system. The extensive evaluation in comparison with the state‐of‐the‐art scheme indicates that CLFI can quickly localize and isolate Sybil nodes.

Evaluation of IoT-Based Smart Home Assistance for Elderly People Using Robot

In the development of Internet-of-things (IoT)-based technology, there is a pre-programmed robot called Cyborg which is used for assisting elderly people. It moves around the home and observes the surrounding conditions. The Cyborg is developed and used in the smart home system. The features of a smart home system with IoT technology include temperature control, lighting control, surveillance, security, smart electricity, and water sensors. Nowadays, elderly people may not be able to maintain their homes appropriately and may feel uncomfortable performing day-to-day activities. Therefore, Cyborg can be used to carry out the activities of elderly people. Such activities include switching off unnecessary lights, watering plants, gas control, monitoring intruders or unknown persons, alerting elderly people in emergency situations, etc. These activities are controlled by web-based platforms as well as smartphone applications. The issues with the existing algorithms include that they are inefficient, require a long time for implementation, and have high storage space requirements. This paper proposes the k-nearest neighbors (KNN) with an artificial bee colony (ABC) algorithm (KNN-ABC). In this proposed work, KNN-ABC is used with wireless sensor devices for perceiving the surroundings of the smart home. This work implements the automatic control of electronic appliances, alert signal processors, intruder detection, and performs day-to-day activities automatically in an efficient way. GNB for intruder detection in the smart home environment system using the Cyborg human intervention robot achieved an accuracy rate of 88.12%, the Artificial Bee Colony algorithm (ABC) achieved 90.12% accuracy on the task of power saving in smart home electronic appliances, the KNN technique achieved 91.45% accuracy on the task of providing a safer pace to the elderly in the smart home environment system, and our proposed KNN-ABC system achieved 93.72%.

Radicalization of Airspace Security: Prospects and Botheration of Drone Defense System Technology

The development of a comprehensive and decisive drone defense integrated control system that can provide maximum security is crucial for maintaining territorial integrity and accelerating smart aerial mobility to sustain the emerging drone transportation system (DTS) for priority-based logistics and mobile communication. This study explores recent developments in the design of robust drone defense control systems that can observe and respond not only to drone attacks inside and outside a facility but also to equipment data such as CCTV security control on the ground and security sensors in the facility at a glance. Also, it considered DDS strategies, schema, and innovative security setups in different regions. Finally, open research issues in DDs designs are discussed, and useful recommendations are provided. Effective means for drone source authentication, delivery package verification, operator authorization, and dynamic scenario-specific engagement are solicited for comprehensive DDS design for maximum security Received: 2023-03-07 Revised: 2023-04-27