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Colourimetric quantification of analytes on paper analytical devices (PADs) using smartphones has been gaining popularity. However, the accuracy of these devices is prone to be affected by different environmental conditions, including the intensity and colour of the ambient light. This work explains in depth the design and fabrication of a handheld colourimeter whose accuracy is unaffected by ambient light conditions using the commercially available spectral sensor AS7262. The device works on the principle of light reflection from the sample and can quantify as well as display the concentration of the analyte on an organic light-emitting-diode (OLED) display. The data acquisition, processing, and transmission of the data to a smartphone using Bluetooth in less than 7 seconds is also a salient feature. The accuracy of the device was validated by testing different concentrations of alkaline phosphatase (ALP) and total protein on PADs explicitly developed for this study. The colourimeter exhibited linearity with R² values of 0.98 for ALP and 0.97 for total protein. The LOD and LOQ obtained for ALP and total protein are 1.1, 3.89 U/L and 0.037, 0.12 g/dL, respectively. The relative standard deviation (RSD) calculated was 0.006 for ALP and 0.004 for total protein. The validation of the colourimeter was carried out with the clinical samples to obtain a relative error of less than 10 % for both the analytes. [Display omitted] • A low-power, handheld, reflection-based colourimeter was developed. • The device employed 0°/45° optical configuration for analyte quantification. • The device exhibited excellent linearity for alkaline phosphatase and total protein on PADs. • The developed colourimeter was validated with real and laboratory samples. • By reconfiguring the calibration equation, the device can quantify multiple analytes. [ABSTRACT FROM AUTHOR]

The emergence of the Internet has brought revolutionary changes to modern information technology. The development of electronic communication information storage technology facilitates people to access information anytime and anywhere, effectively achieving resource sharing. However, traditional methods scatter communication information to all nodes, but neglect the extraction of electronic communication information association features, resulting in low information storage efficiency. This article analyzes the efficiency management of electronic communication information storage in multi-threaded networks and sets up a control experiment. Comparing the method proposed in this article with traditional methods, this study focuses on five aspects: information redundancy storage, information space utilization, productivity, data accuracy, and data transmission efficiency. It is concluded that the average information redundancy storage capacity of traditional electronic information storage methods is 8.35 x 107 T which is 6 x 107 T higher than using multi wire network electronic communication information storage methods. It indicates that traditional redundant information occupies a large amount of storage space, and its storage value exceeds the upper limit of electronic communication information storage. This means that the stored information may be overwritten by redundant information, resulting in the loss of stored information. The experimental results show that using the method proposed in this paper for electronic communication information storage results in a high distribution density of storage space and a significant improvement in information storage efficiency. [ABSTRACT FROM AUTHOR]

The transition from conventional standalone metrology to integrated metrology has been accelerating in advanced manufacturing over the past decade. This keynote paper defines the concept of integrated metrology, which extends beyond parts inspection and encompasses processes and manufacturing equipment to enhance efficiency and productivity. The paper presents the characteristics, benefits, constraints, and future possibilities of integrated metrology for parts, processes, and equipment. It also includes a classification of the physical quantities of measurands, the corresponding measuring instruments, data and communication methods, uncertainty, and traceability. The paper also discusses future challenges and emerging trends. [ABSTRACT FROM AUTHOR]

The security of patient data is a critical problem for health networks due to the rising popularity of telehealth services and the requirements for clinical data sharing between surgeons, consultants, and medical groups. In contrast to protecting medical data, which includes the cover (i.e., clinical picture), the requirements for protecting other pertinent data are more general. The paper advocates for data encryption before concealment as a robust strategy to uphold patient privacy during medical information exchange. The primary focus of this analysis revolves around a comprehensive exploration of different steganography methods. Through a meticulous examination of various steganographic techniques, including LSB, PVD, and transform domain approaches, this paper provides a detailed analysis of their strengths and limitations. Objective metrics like PSNR and SSIM are employed to dissect the trade-offs between data security and visual fidelity. The research leads to the conclusion that the diagonal queue-based steganog-raphy methodology, supported by chaotic methods, the Linear Feedback Shift Register (LFSR), and the durable Rabin encryption system, is the best course of action. This method enables autonomous data transmission among numerous contacts while ensuring the highest level of confidentiality and integrity of patient data inside e-health networks. In summary, this study provides a comprehensive solution that protects patient data and speeds data transmission inside the expanding framework of e-health networks in order to meet urgent data security concerns in telemedicine and healthcare data exchange. [ABSTRACT FROM AUTHOR]

In this paper, an innovative event-triggered optimal tracking control algorithm is proposed for input saturated strict-feedback nonlinear systems with unknown dynamics. In order to reduce the requirement of configuring a complete suit of sensors and enhance the reliability of the controlled system, a neural networks (NNs) based adaptive state observer is developed firstly to reconstruct the system states. Subsequently, based on the state estimation information, a hybrid-triggered feedforward controller is designed to transform the original tracking control problem into an equivalent regulation issue, which is then solved by developing an event-triggered optimal controller. Therefore, the final controller consists of a hybrid-triggered feedforward controller and an event-triggered optimal controller. In order to make the actual input signals of the two controllers be updated simultaneously, a synchronization-oriented triggering rule is established by using multiple triggering errors. By virtue of this unique framework, the proposed control scheme can not only minimize the predefined cost function, but also greatly reduce the data transmission. What is more, the convergence properties of the proposed control strategy are achieved by using Lyapunov theory. It is important to note that unlike the widely adopted observer-controller framework, where the separation principle holds for the design of the state observer, there is a considerable coupling relationship between the error dynamics of the state observer and the event-triggered optimal controller in this paper. The distinguishing feature of the proposed method is its ability to ensure a satisfactory level of precision in both state estimation and tracking control, even in the presence of control saturation issues. At last, the proposed control strategy is applied to the tracking control problem of a high-order robot system and marine surface vehicle to demonstrate its effectiveness. • A novel event-triggered optimal tracking control framework is proposed. • A phenomenon we defined as the "jump of error surfaces" is noticed and tackled via filter technique. • A multi-error based adaptive triggering rule is established to improve the control performance. • The proposed method not only achieves the optimality but also lowers data transmission. [ABSTRACT FROM AUTHOR]

This paper investigates the fault-tolerant attitude control problem for a spacecraft with actuator faults using an event-triggered-based concurrent-learning control (CLC) approach. A concurrent-learning controller, updating itself through previous control signal values to generate the current control signal, is studied in this paper. The concurrent-learning controller is designed under an event-triggered policy to compensate for the actuator's data transmission constraints caused by bandwidth limit. In addition, a robust fault observer is designed to facilitate the concurrent-learning controller in compensating for the actuator faults. The key feature of the given approach is in the simplicity of control structure to stabilize spacecraft attitude considering actuator faults, model uncertainty, actuators' dynamics effect, and data transmission; simulation results show the proposed approach's efficiency. [ABSTRACT FROM AUTHOR]

Due to the rapid development of confidential digital communication over international networks, security is the most significant factor. Steganography is the science of concealed communication and is used to conceal the presence of secret information among the various methods of hiding data in communications. Steganography techniques are usually implemented either in spatial or frequency domains. Some spatial domain methods fundamentally utilize the absolute difference value between neighbor pixels to encode the secret message, categorized as Pixel-Value Differencing Steganography methods (PVDS). In PVDS, the hiding capacity of the adjacent pixels depends on their difference value. Consequently, the secret data will be embedded with a high visual quality of the stego-image. Indicator-based PVDS (IPVDS) technique is derived from the PVDS method. It depends on an Indicator pixel (IP) to extract the secret data correctly. The Genetic Algorithm (GA) approach to optimization depends on the concept of survival of the fittest. This paper introduces two proposals, GA-based steganography schemes (GA-IPVD) and (GA-IPVDM). These new techniques encoded secret data using the IPVDS method. Before embedding data in the cover image, the order of the pixels corresponding to the secret data is modified and rearranged. GA controls the operation of rearranging and modifying the order of pixels and all parameters. The current paper addresses steganography as an optimization problem and seeks to determine the optimal order of the pixels that enhance the matching between the cover image and stego-image to reduce distortion despite high embedded capacity. In the first proposed technique GA-IPVD, GA concentrates on the imperceptibility of embedded data and maximizes the quality of the stego-image according to the given secret data. In contrast, the GA maximizes the capacity of embedded data while maintaining an acceptable amount of stego-image quality in the second proposed technique GA-IPVDM. The proposed systems have embedding key that increase the security of proposed schemes. The experimental results demonstrate that the proposed methods can embed a large quantity of secret data while maintaining an acceptable visual quality of the stego-images. The proposed technique has also demonstrated excellent resistance to a variety of stego-attacks, including the pixel difference histogram (PDH) as well as regular and singular (RS) analyses. Additionally, the out-of-boundary pixel issue (BI), which endures in the majority of modern data hiding algorithms, has been successfully resolved. [ABSTRACT FROM AUTHOR]

Network security technology is capable of mitigating attack signals launched by malicious attackers, thereby safeguarding and bolstering system security. Secure state estimation is a type of network defense technology that involves reconstructing the system state by measuring potential attacks. The relevant research has also been expanded to distributed systems to enable collaborative secure state estimation among different agents facing homogeneous attacks; however, such research significantly enhance the communication load of the system. On the other hand, if the homogeneous attack is non-existent or consistent, excessive network communication is definitely inefficient. An event-triggered approach is a potent technique that can alleviate communication overhead while having minimal impact on system performance. This paper proposes a mechanism for event-triggered transmission in the context of distributed secure state estimation. While communication latency and data transmission disorder may be present within the communication network, the estimation error of the proposed observer is constrained and bounded. The simulation results indicate that for stable malicious attack signals, system communication can be divided into high-frequency and low-frequency communication periods. The simulation results also shows that the maximum estimation error is proportional to the trigger threshold, allowing the observer's performance to be finely tuned to the required precision. • In this paper, the observer presented implements an event-triggered communication strategy, which effectively decreases the frequency of data transmission and reduces the communication load on the network. • Network latency and data disorder are also taken into consideration. • The maximum estimation error is proportional to the trigger threshold, allowing the observer's performance to be finely tuned to the required precision. [ABSTRACT FROM AUTHOR]

This paper proposes an improved dual-phased framework for energy-efficient, cluster-based routing in Wireless Sensor Networks (WSNs). It addresses the critical challenge of balancing energy consumption with reliable network performance. Cluster-based routing is a crucial parameter for WSN operational efficiency, especially in applications demanding minimal energy use and dependable data transmission. The proposed framework integrates two advanced meta-heuristic algorithms: Sailfish Optimization (SFO) and Spotted Hyena Optimization (SHO). This combined approach leverages SFO's rapid exploration for efficient clustering and optimal Cluster Head (CH) selection. Additionally, SHO's refined exploitation capabilities optimize efficient routing paths. This innovative methodology significantly improves network performance metrics like energy efficiency, network lifetime, and Packet Delivery Ratio (PDR). The originality of this work lies in the dual-phased optimization strategy. It distinctively outperforms traditional single-algorithm based approaches by employing a unique hybrid optimization approach, offering greater originality and value. Experimental simulations demonstrate that the proposed framework outperforms several popular algorithms in terms of key performance metrics. This makes it a valuable contribution to the field and an efficient solution for diverse applications. [ABSTRACT FROM AUTHOR]

This paper is concerned with the secure output consensus problem for the heterogeneous multi-agent systems under the event-triggered scheme in the presence of the denial-of-service attack. Without detecting the attack, the hold-input controller update strategy is adopted when some transmission data may be lost due to the effect of the attack. Based on the tolerable duration of the attack, a novel edge-based event-triggered scheme is developed. The scheme can avoid continuous communication and exclude Zeno behavior. With the aid of the switched system theory, output consensus is preserved. An example shows the effectiveness. • Secure problem for the heterogeneous multi-agent systems is solved in the presence of the denial-of-service attack. • A novel edge-based event-triggered scheme is developed based on the tolerable duration of the attack. • Without detecting the attack, the hold-input controller update strategy is adopted. • Output consensus is preserved with the aid of the switched system theory. [ABSTRACT FROM AUTHOR]

The rise of the national fitness trend has led more and more people to join the ranks of physical fitness, and the current physical education resources and human resources have not been able to keep pace with the development of sports. Due to the lack of guidance, many beginners of sports are not standardized, and their movements are not effective and even cause injuries. Sports action recognition technology can make up for this lack and realize the action recognition and guidance for sports players. In this paper, we build a sports movement and posture recognition model based on acceleration sensors, introduce Selectable Mode Vocoder (SMV) algorithm to optimize the performance, and ensure the effectiveness of sensor data transmission through a low energy multi-layer clustered wireless sensor network. The experiment mainly compares the generation results of the first energy depleted node in the entire network with the number of rounds with energy depleted nodes reaching 10 %. The experimental results show that the low-energy multi-layer clustered wireless sensor network effectively shortens the data transmission path, reduces the energy consumption of nodes, extends the network life time, ensures the data transmission effectiveness, and provides network guarantees for sports action recognition. In addition, in the comparison experiments, the action recognition model in this paper shows better recognition effect and efficiency with obvious performance advantages. [ABSTRACT FROM AUTHOR]

In this paper, we consider stability and latency analysis of a two-user cognitive radio network while considering Age of Information requirements imposed on the data transmissions of the users. We provide two different formulations. In the first formulation, our objective is to maximize the stable throughput of the secondary user subject to Age of Information constraints imposed on the data transmission of the users. While, in the second formulation, we introduce a multi-objective programming approach that investigates the trade-off between the secondary user stable throughput and its Age of Information. Our numerical results characterize the effect of imposing different Age of Information restrictions on the stable throughput region of the network; we show that the stability region, in certain cases, is reduced by only 11 % with strict Age of Information restrictions. Our results also investigate the trade-off between the secondary user throughput and the Age of information. Finally, our results show the potential accuracy of the algorithm adopted in this paper to solve the formulated non-linear optimization problems. [ABSTRACT FROM AUTHOR]

This paper presents radiation hardened SRAM (namely LPDNUR and HSDNUR), both of which can self-recover from single-node and double-node upsets. LPDNUR uses a two-input C-element structure, which reduces the average power consumption because the stacked effect. Moreover, in order to reduce read access time (RAT) and write access time (WAT), the paper proposes HSDNUR, which uses a combination of NMOS and PMOS as the transistor for one-node data transmission, an approach that increases the current drive capability and reduces transmission delays. Compared to the state-of-the-art radiation-hardened SRAM design, such as QUCCE10T, QUCCE12T, We-Quatro, etc. The model results show that the average power consumption of LPDNUR is reduced by 24.59% on average, and the RAT and WAT of HSDNUR are reduced by an average of 35.71% and 24.14% respectively. • LPDNUR and HSDNUR fully achieve SNU and DNU recovery. • Average power of LPDNUR is 24.59% lower on average than other SRAM hardened since stack structure. • WAT and RAT of HSDNUR are 24.14% and 35.71% lower than other SRAM hardened on average by using TG. [ABSTRACT FROM AUTHOR]

Infrastructure construction companies encounter numerous challenges in managing large and dispersed teams, along with fragmented or non-existent operational data. These challenges often result in delays, inefficiencies, and over-budget projects. Road pavement works are an example of this, as they heavily rely on expensive heavy construction equipment that requires detailed planning and real-time adjustments. Also, pavement quality is closely linked to the quality of the asphalt mixture in terms of viscosity and compactability, which is significantly influenced by temperature. This paper describes the features, challenges and results of a road paving real-time management system that was conceived in a co-creation environment with a construction company. Such a partnership has allowed to specify the requirements of such an application aligned with the identified needs of a real-world development. According with the state-of-the-art, this innovative system is unique in the way it is manufacturer-agnostic and designed to be compatible with most situations. It is also data production-oriented to allow future developments that may provide business analytics or scientific research in the road paving area. This work also presents the development of sensors such as a high precision geolocalized infrared matricial temperature sensor for the application of the bituminous mixture, the data and communication structure, and a web-based interface that manages the construction projects for different stakeholders. [ABSTRACT FROM AUTHOR]

This paper examined uplink and downlink di-state (primary and secondary) situations to build the theoretical and practical realization of a bidirectional cooperative wireless cellular network. In the primary scenario, full-duplex (FD) base stations support two sets of half-duplex (HD) users, where the transmitter (uplink) and receiver (downlink) respectively. Each uplink transmitted signal is rotated in a variant phase through a set of scalars known to the base stations before transmission. The signal from the transmitter ensures the reconfigurable receiver antennas at the base stations can configure the received signal as decoded signals. The proposed work exposed strong self and inter-user interference from the transmitter during demonstrating FD wireless communication mode that destroyed network functionality at the base stations. The occurred self-interference signals from the transmitter are neutralized using precoding and beam-forming technique. During the secondary scenario, decoded base station signals are independently encoded and transmitted to the downlink users. Base stations aid the inter-user interference management by precoding the uplink transmission data and forwarding it to the downlink users. Consequently, the inter-user interference signals are aligned using the span interference alignment technique. A proposed cooperative additional receiver (listener U1) can decode the desired signals and helps in recovering and reconstructing the interference signals. This proposed work completely characterized and evaluated the degree of freedom (DoF) for unknown channel state information at the transmitter (CSIT); whereas achievable lower and upper bound DoF are demonstrated for the partial CSIT. At last, the analytical and simulation results reveal the sum of DoF improvement for unknown and partial CSIT. [ABSTRACT FROM AUTHOR]

Performance evaluation plays a vital role in the field of cryptography. It is essential to assess the security, efficiency, and suitability of different algorithms in various applications. By evaluating the performance of cryptography algorithms, we can identify vulnerabilities, weaknesses, and strengths, which are crucial for ensuring the integrity and confidentiality of sensitive data. Moreover, performance evaluation allows for benchmarking, algorithm selection, and continuous improvement in the field, driving advancements in cryptography techniques and enhancing the overall security of systems that rely on cryptography. This paper implements a test algorithm that can evaluate the performance of different parameters for a range of cryptography algorithms. The performance of input is evaluated using parameters such as the Avalanche effect, Correlation coefficient, Bit independence test, Frequency test, Encryption and decryption execution time, Throughput, Brute Force attack, and Information entropy. By comparing results obtained from the performance tests, we can gain valuable insights into the relative strengths and weaknesses of the algorithms. This aids in making informed decisions regarding algorithm selection and improvement, ultimately contributing to the advancement of secure communication and data protection. [ABSTRACT FROM AUTHOR]

The demand for high data throughput and security in Unmanned Aerial Vehicle (UAV) communication is rising. Efficient data transmission remains challenging due to limited wireless resources. This paper introduces a Scale Perception Compression Method that leverages compression and blockchain technology to optimize UAV communication. The method involves normalizing data into matrices, generating compression and decompression matrix codebook pairs, and selecting suitable pairs for small or large-scale matrices. Simulation results show that the error rate is associated with the distributions of the data and compression matrix. It remains relatively low when the data and compression matrix subjected to Uniform distribution compared to Gaussian distribution, regardless of the values of the number of rows m and columns r in compression matrix. Bubble method is more suitable to find out the proper matrix pair to compress and decompress transmitted data for small-scale codebook because it is stable, fast and results in lower difference between original and recovered data matrices. The same applies to Merge method for the large-scale codebook. The ER is always lower based on a small-scale codebook searching by Bubble method than that of on a large-scale codebook searching by Merge method, which indicates the former is sufficient for compression. [ABSTRACT FROM AUTHOR]

In modern computing environments, speed of execution of any operations are gaining more attentions. The faster processing of the unit operations, opens the room for the applications such as cryptography, data communication etc. Hence, in this work a new software-oriented approach is proposed to use in the environments that requires faster executions. This proposed method speeds up the computations of the logical operations – bitwise XOR. This paper first elaborates on the procedure of how to construct the bitwise XOR vector space efficiently using recursive approach based on Pauli's matrix. The corresponding time complexity for the construction of the bitwise XOR vector space is computed by master theorem and is O (log 2 (n / 2 n)). Later, the paper presents on the implementation of this bitwise XOR vector space constructed to facilitate the XOR – Free operations. For the evidential proof, the proposed bitwise XOR-free operations are implemented on standard key stream generation scheme, SNOW 3G. The average speed up gained by the bitwise XOR-Free operations on the SNOW 3G is 1.36 x and the corresponding throughput attained is 21.85 Mbps. [ABSTRACT FROM AUTHOR]

In the present paper, several literature studies on various combinational photonic devices (PDs) using silicon based photonic crystals (PhC) have been studied. On considering various PhC structures, waveguides (WGs) and ring resonators (RRs) have been preferred because of their quality factor (QF), high transmission efficiency (TE), as well as stability in tuning the structures resonant wavelengths, as opposed to other designs. The articles have been classified based on the type of the material used, during the design manufacture and have been further divided according to the operation of the structure. The designed and performance parameters obtained in the collected articles like Contrast Ratio (CR), Bit Rates (BR), Response Times (RT), transmission time etc., have been summarized in this paper. The combinational photonic devices have been preferred because of their immense applications in calculators and computers that enables quick and accurate computations, data processors to execute logical operations, communication systems for smooth data transmission and handle error detection consumer electronics like TVs, smartphones and digital cameras, industrial control systems in automation and managing data, error detection and correction for data transmission and storage, digital signal processing to process and manipulate signals, instruction decoding to decode instructions and perform specific operations. These are seemed to be more advantageous because of their use in high-speed optoelectronic integrated devices and communication systems. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) is a key technology for sixth-generation (6G) wireless communication to obtain high energy and spectral efficiency with a minimum cost. On the other hand, non orthogonal multiple access (NOMA) enhances the spectrum efficiency of the network. The combining schemes are implemented to obtain error-free data at the receiver. This paper proposes an IRS-aided NOMA-based packet combining (PC) and aggressive packet combining (APC) schemes-assisted communication architecture to enhance service quality and achieve sustainable and resilient connectivity among users for 6G wireless networks. In this paper, the mathematical model has been derived for the instantaneous achievable data rate, bit error rate, outage probability, and packet correction capability. The derived analytical expressions are validated through detailed Monte Carlo simulations. The obtained results show that the proposed schemes outperform the conventional NOMA with PC and NOMA with APC schemes and facilitate enhanced error control and reliable data transmission capabilities. Moreover, the proposed schemes have been compared with the IRS-aided orthogonal multiple access (OMA) schemes, where IRS-aided OMA schemes are considered benchmarks of the proposed schemes. It is presented that the proposed schemes are superior to the conventional OMA with PC and APC schemes and IRS-aided OMA with PC and APC schemes. [ABSTRACT FROM AUTHOR]

Today's Internet of Vehicles (IoV) faces many security risks in the data transmission process, and image data is more vulnerable to security threats in the transmission process due to its special characteristics such as large amounts of information and high visibility. Therefore, to guarantee the dependability of data transmission in the IoV environment, this paper designs a blind signature scheme for IoV based on two-dimensional sine cosine cross-chaotic mapping (2D-SCML) image encryption and lattice cipher (BSS-IoV). The innovation of this scheme is that it aims at blind signature of image information, blinds it before sending the information, and combines the lattice public key encryption algorithm to better ensure the safe and reliable transmission of information and reduce the risk of information disclosure. To further ensure the security of the scheme, an image encryption algorithm based on 2D-SCML and pixel splitting (2PS-IEA) is proposed, which is used to blind the information and thus reduce the risk of information leakage on the one hand, on the other hand, it is used in the signature process to ensure the security of the signed information. The 2D-SCML is derived from the cross-model structure proposed in this paper. Through simulation results and experimental analysis, the values of NPCR and UACI, respectively, 99.6094% and 33.4635%, are close to ideal values. And 50% of the cut image can also recover the rough information, which indicates that the signature scheme has the security against differential attacks, cut attacks and noise attacks. Moreover, the security analysis shows that the scheme has the anti-tamper, anti-repudiation and traceability. • Designed blind signature scheme for IoV using image encryption and lattice cipher. • Proposed a crossover model structure is proposed. • Designed a 2D-SCML mapping with superior performance based on this model. • Devised an image encryption algorithm based on 2D-SCML and pixel splitting. • The signature scheme and encryption scheme are analyzed experimentally. [ABSTRACT FROM AUTHOR]

This paper is concerned with the adaptive event-triggered recursive state estimation (RSE) issue for a class of nonlinear complex dynamical networks (CDNs) with random coupling parameter, missing measurements (MMs) and coding-decoding-based communication mechanism (CDBCM). First of all, a random variable uniformly distributed in a fixed interval is adopted to model the varying topologies. Then, the Bernoulli random sequence with uncertain statistical properties is considered to characterize the phenomenon of MMs subject to the uncertain occurrence probability situation. Furthermore, in order to ensure the security and reliability of the shared network channel, the adaptive event-triggered scheduling strategy (AETSS) and CDBCM are both employed to govern the data transmission thereby enhancing the communication quality. The aim of this paper is to present an RSE scheme for a class of stochastic CDNs such that for all MMs, AETSS and CDBCM, the state estimation error covariance (SEEC) is given the SEEC upper bound (SEECUB) is derived. Then, the state estimator gain matrix (SEGM) is parameterized by means of optimizing the trace of SEECUB. Moreover, the monotonicity of the trace of SEECUB with respect to the available missing probability is clarified detailed. Finally, an illustrative simulation is executed for the purpose of verifying the validity of the proposed RSE scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the dual-channel event-triggered deployment control for the space tethered system with intermittent output. Two different dynamic event-triggered mechanisms are designed to implement the event-based state sampling and the event-based input sampling, in which the data transmission frequencies are reduced at the dual channel, namely sensor-to-controller and controller-to-actuator. Then, the neural network (NN) state observer based on the intermittent output is designed to estimate the unmeasurable state under external disturbance, and the observer-based sliding mode controller is designed. Furthermore, because of the non-periodic sampling of the state signal and the output signal, the closed-loop system is proved via the analysis of the hybrid system, and the Zeno behavior is avoidance under these two event-triggered conditions. Finally, the simulation tests are implemented to verify the effectiveness of the proposed scheme. • A control scheme incorporating a dual-channel event-triggered mechanism is investigated. • A neural network-based state observer on intermittent output is designed. • Two dynamic event-triggered mechanisms are designed to reduce the event-triggered numbers at two channels. [ABSTRACT FROM AUTHOR]

Spacecraft modularity is a powerful design methodology for shortening satellite development cycles and reducing costs. The exploitation of standardized and reusable modules, universal interfaces and protocols, generic design, and a set of defined conventions, allows for rapidly integrating a flexible and cost-effective platform with ease of component modifications. Anyhow, the implementation of modularity poses several issues at any level of mission and spacecraft design, including subsystem design. This paper deals with the Attitude Determination and Control Subsystem (ADCS), with a particular accent on making the estimation software independent of any specific requirements dictated by the application in question. In this respect, a generic and reusable Murrell's Multiplicative Extended Kalman Filter (MMEKF) that can operate on different satellites, with diverse estimation requirements, working with changeable integrated ADCS boxes and sensor suites, but without changes either in the equations or in the interface with the rest of the system, has been proposed. The algorithm has been designed to maximize flexibility and ease of applicability to different missions without a priori-knowledge of the actual configuration. The execution of the algorithm is based on a plug-and-play logic in which middleware software enables the automatic discovery of the available sensors and the transmission of required data to the estimation algorithms. An adaptable and modular 3D simulator has been built to test and validate the performance of the generic MMEKF over multiple test cases with assigned configurations. The results of the simulations confirm the capability of the algorithm to meet estimation requirements for each simulated case, without ad-hoc , case-by-case, optimization, or customization. • A generic and reusable estimation algorithm is proposed to estimate the attitude in a modular plug-and-play satellite. • The algorithm is designed by adapting Murrell's Kalman Filter to be fully parametric and mission abstracted. • The method involves a plug-and-play execution based on centralized middleware software. • Relevant requirements and constraints to reuse the same estimation algorithm in heterogenous missions are pointed out. • Performance in the estimation accuracy is verified over multiple cases with assigned configurations. [ABSTRACT FROM AUTHOR]

Among the different challenges related to the improvement of wireless sensor network (WSN) performance, the optimization of energy consumption takes an important place. Basically, sensor nodes in WSN are powered by limited battery. These batteries can allow sensor nodes to be functional for a limited period of time then sensor nodes turn out to be dead. Enhancing energy consumption and finding new manners to save sensor nodes' energy is becoming essential and more challenging. Improving energy consumption for each sensor nodes leads to enhance the lifetime of WSN. Clustering is among the outstanding strategies to reach this goal. In this paper, a new scheme of routing protocol in WSN will be presented in order to minimize the energy consumption and improve WSN lifetime. The new scheme is proposed in order to find the best selection of cluster head (CH) by taking in consideration the energy at each sensor node. It has been proposed based on Firefly Algorithm. We have compared the proposed scheme with LEACH, EAMMH, SEP, E-SEP, BRE, NEAHC and WEB protocol. Experimental results show that the proposed scheme yields better performance than the compared routing protocols in terms of better energy consumption and packet delivery between sensor nodes and base station. Results obviously prove that the proposed scheme could improve the WSN lifetime. • A new scheme of routing protocol has been proposed in order to optimize the energy consumption by sensor nodes in WSN. • The proposed scheme has been proposed based on Fireflies optimization algorithm. • Results of simulation showed the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Automation systems are soaring due to the innovation of artificial intelligence (AI) technologies. In this paper, we mainly concentrate on the security and efficiency of data transmission in AI-based automation systems, especially, for data sharing in a group manner in distributed networks. To this end, an authenticated group key agreement protocol is proposed for secure data transmission in AI-based automation systems. In order to release the computational overhead of distributed nodes, a semi-trusted authority (STA) is introduced to enable precomputation operations. Moreover, to overcome the predominantly distributed denial of service (DDoS) attack, a dynamic batch verification mechanism is devised. The presented dynamic batch verification mechanism guarantees that the proposed protocol can be run properly among legitimate nodes regardless of the existence of nodes who suffered from the DDoS attack. Finally, the session key security of the proposed protocol is proved and the performance is evaluated. [ABSTRACT FROM AUTHOR]

The paper presents a methodology for multi-track transmission processing of signals from mechanical-navigation sensors describing the state of the ship's motion vector by using a neural network computer system. The integration of mechanical systems with navigation systems on a sea vessel is aimed at creating a uniform decision-making module for the crew steering the ship and supervising propulsion systems in order to minimize the level of risk in controlling a sea vessel in hazardous situations. The concept of integrating radio devices with navigation and engineering systems will enable effective ship management based on sources coming from external and internal sensors of the ship. The processing of navigational and mechanical signals proposed in the article will allow to build an effective prediction of the developing situation in case of danger according to the proposed algorithm for the officer of the watch in charge of the ship. The paper presents a model of mathematical processing of signals from navigational-mechanical systems and illustrates its operation in an exemplary situation of a threat of collision of a sea vessel [ABSTRACT FROM AUTHOR]

This paper studies the adaptive event-based H ∞ control problem for a class of T-S fuzzy distributed parameter system with parameter uncertain and actuator faults. To overcome the drawback of the period control scheme, an event-triggered control scheme with adaptive threshold is proposed to minimize the number of unnecessary sampled data transmission and to reduce the update frequency of the controller. Furthermore, a T-S fuzzy controller based on the adaptive event-triggered sampled data is designed to ensure the stochastic exponential stability of the distributed parameter system with H ∞ disturbance attenuation performance. Based on a new Lyapunov functional and inequality technique, the stochastic exponential stability criterion of the closed-loop system is obtained, and the controller parameters are designed. The new developed inequality can reduce the conservatism of the stability criterion. Finally, the effectiveness of the theoretical calculation results is verified by numerical simulation, and the results are compared with the relevant literature in the simulation, showing that the methods and results in this paper are less conservative. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) includes more low-cost and less-power sensor nodes. All the sensor nodes are positioned in a particular area and form a wireless network by way of self-organizing. They has the ability to work normally at any of the special or wicked environ that people cannot close. However, the data transmission among nodes in an effective way is almost not possible due to various complex factors. Clustering is a renowned technique to make the transmission of data more effective. The clustering model divides the sensor nodes into various clusters. Every cluster in network has unique cluster head node, which send the information to other sensor nodes in cluster. In such circumstances, it is the key role of any clustering algorithm to choose the optimal cluster head under various constraints like less energy consumption, delay and so on. This paper develops a new cluster head selection model to maximize the lifetime of network as well as energy efficiency. Further, this paper proposes a new Fitness based Glowworm swarm with Fruitfly Algorithm (FGF), which is the hybridization of Glowworm Swarm Optimization (GSO) and Fruitfly Optimization algorithm (FFOA) to choose the best CH in WSN. The performance of developed FGF is compared to other existing methods like Particle swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), GSO, Ant Lion Optimization (ALO) and Cuckoo Search (CS), Group Search Ant Lion with Levy Flight (GAL-LF), Fruitfly Optimization algorithm (FFOA) and grasshopper Optimization algorithm (GOA) in terms of alive node analysis, energy analysis and cost function and the betterments of proposed work is also proven. [ABSTRACT FROM AUTHOR]

This paper focuses on the topic of fault-tolerant control for discrete-time systems with nonlinear uncertainties and actuator faults. It considers both passive and active faults as part of the analysis and design. The proposed adaptive controller, based on a nonlinear electronic circuit, handles offset-biasing, sensitivity variation, and dead-zone effects. An event-triggered mechanism, utilizing a sliding surface, enhances robustness and reduces data transmission. Adaptive networks called MiFRENs are employed, trained using reinforcement learning. Theoretical analysis guarantees boundedness of internal signals and tracking error. Experimental results validate the scheme, demonstrating required conditions, reduced data transmission, and robust performance. Comparative evaluations confirm its superiority [ABSTRACT FROM AUTHOR]

Content poisoning attacks compromise the integrity of web platforms by injecting malicious data. This paper presents a comprehensive study of these attacks, examining their methodologies and potential impacts. Through rigorous analysis, we highlight key vulnerabilities and propose effective countermeasures to ensure robust digital content security. Under the security hardness of the HCDLP, the suggested approach is technically secured. We are changing medical data depending on priority. The fuzzy-based priority aware scheme is proposed in order to have priority-based data transmission for the essential and high priority data from the essential patients. [ABSTRACT FROM AUTHOR]

This paper addresses the memory-based event-triggered consensus control issue for multi-UAV systems subject to deception attacks. In order to alleviate network bandwidth burden and reduce unnecessary data transmission, a memory-based event-triggered scheme (METS) is proposed by applying historic data information (HDI). Meanwhile, the average mechanism (AM) is introduced to replace the input of conventional event triggering scheme, which eliminates adverse event-triggering caused by instantaneous random jitter and deception attacks. Through this method, data mutation, peak/trough information loss, and energy consumption issues of UAV can be effectively addressed. With the aid of attack observer, a consensus control strategy is devised for each UAV to achieve control objective and compensate for the impact of attacks on multi-UAV system. Then, sufficient conditions are constructed to co-design the parameters and guarantee the attacked multi-UAV system can achieve consensus. The simulation results are provided to demonstrate the validity and practicality of the proposed strategy. • A novel METS is developted by utilizing the average value of HDI. • An attack observer is developed to compensate the influence of attacks on the system. • A distributed consensus control is designed for the system with deception attacks. • Sufficient conditions to maintain the desired consensus performance are developed. [ABSTRACT FROM AUTHOR]

Emotional aware intelligent healthcare mechanism is seen as one of the emergent techniques that can be used to overcome the traditional communication issues like storage overhead and delay in transmission process in healthcare systems. It is a smart technique primarily used to ensure faster connectivity and transmission as well as healthcare data processing through various heterogeneous networks by understanding human emotions. Researchers have focused on various processing and transmitting techniques of emotional aware intelligent systems. However, very few of them have focused on the need for security while transmitting or reading the patients' record. The aim of this paper is to propose a secure and transparent communication mechanism in intelligent emotional aware systems using Analytical Hierarchical Process (AHP), blockchain system and a mathematical model. AHP and the mathematical model are used to ensure accuracy during the data transmission process by analysing the legitimacy of each device. In addition, the identity based trust and blockchain mechanism is used to ensure the continuous analysis and transparency among network entities while transferring the information. The proposed framework is further analysed against existing models over various security matrices such as data alteration, report generation, accuracy etc. The out performance of proposed mechanism is approximate 89% as compare to existing mechanism. [ABSTRACT FROM AUTHOR]

Usually a complete computer network system has the following components: source of signals, signal host, cryptographic regime, attack, and interceptor. It is more vulnerable to attacks during the operation of the system, so the security of the system becomes more important. In this paper, by constructing a dual BP neural network combinatorial model and applying it to the computer data communication large integer decomposition encryption process, the constructed model is used to analyze the security against digital certificates, digital signatures, and the algorithms employed in the communication process of connected computer network. [ABSTRACT FROM AUTHOR]

The security of data transmission is one of the focus issues in vehicular ad hoc networks (VANETs). The timely and effective dissemination of emergency messages can help drivers make intelligent decisions and improve road safety and traffic efficiency. However, malicious nodes in the network seriously interfere with the proper judgement of vehicles via data tampering or spreading false messages, which brings significant risks to traffic safety. In order to guarantee the security and reliability of data transmission, this paper proposes a hybrid-trust-based emergency message dissemination (HTEMD) model that effectively integrates entity trust into the data consistency evaluation. In the proposed model, for emergency messages from different sources, the receiver first calculates the probability of the event based on the sender's entity trust and event status. Then, it uses the principle of information entropy to conduct consistent analysis of the results to help the vehicle make correct decisions. Afterwards, a series of simulations are conducted in four typical scenarios. The results show that the HTEMD model has strong environmental adaptability and robustness. Moreover, compared with other classical message dissemination models, the correct decision rate of the HTEMD model always remains above 80%, while other models drop to 75.5%, 63.2%, and 40.2%. The model proposed in this paper significantly outperforms the baseline model with a higher correct decision rate and resistance to attack. [ABSTRACT FROM AUTHOR]

The W-MON project goal is to establish an automatic control mechanism of the presence of radioactive material in conventional waste containers at CERN using a distributed network of interconnected low-power radiation sensors. This network facilitates continuous data recording, transfer and storage in a database while allowing online and offline data analysis, in addition to alarm triggering. Data transmission, processing and evaluation is achieved by a centralized IoT end-to-end data architecture that has been developed for real-time monitoring and visualization of the radiation levels in waste containers. In this paper the results of field tests of the W-MON system described in two previous papers are presented for three different types of sensors. Estimation of failure detection probability, long-term stability tests and sensitivity studies carried out using radioactive samples of various activities placed in standard waste containers are described. A comparison between the manual monitoring procedure currently used at CERN and the W-MON system is discussed in detail. • W-MON system is a network of low-power radiation sensors. • Automatic control of radioactive material in waste containers at CERN. • Centralized IoT architecture enables continuous data transmission. • Real-time monitoring and visualization of radiation levels. • Long-term stability tests using radioactive samples in standard containers. • W-MON system can efficiently replace existing manual monitoring procedures. [ABSTRACT FROM AUTHOR]

• This paper proposes a concept that utilizes historical time-series data from transmission system operators (TSOs) to estimate the optimal day-ahead energy-mix proportion for a power system dominated by converters. • This paper employs a long short-term memory (LSTM) network as a data-driven model to forecast the day-ahead values of key parameters. • To ensure secure system functioning, an energy-mix operation and reserve scheduling model is developed. • This paper emphasizes the importance of power system security in incorporating inverter based resources (IBRs). Advancements in various scientific fields have encouraged the development of novel tools, techniques, components, methodologies, and innovations aimed at addressing the challenges encountered in modern power systems dominated by inverter-based resources (IBRs). This paper focuses on a concept that leverages historical time-series data obtained from transmission system operators (TSOs) to enhance the secure management and operation of power systems. By employing a data-driven model, the day-ahead values of power generation and load consumption are estimated and integrated with a dynamic model of the power system for further analysis. To optimize energy generation and ensure grid stability, an energy-mix operation and reserve scheduling model is utilized. This model optimally combines different power-generating technologies, including synchronous generators (SGs), grid-following converters (GFLs), and grid-forming converters (GFMs), to meet the energy demands of the day while enhancing the overall system strength. The findings are supported by quantitative analysis utilizing variables such as frequency, power production, terminal voltages, and system non-synchronous penetration (SNSP). Simulation results demonstrate that implementing the proposed concept enables the power system under consideration to operate securely, even in the face of a 38% increase in immediate load, with a maximum SNSP ratio of 59%. These findings highlight the effectiveness of the proposed approach in addressing the reliability, system dynamics, stability, control efficiency, and security challenges posed by IBR-dominated power systems. Furthermore, it is believed that this research contributes to the ongoing efforts in decarbonization, renewable energy integration, and combating global warming by facilitating the secure and optimized operation of renewable energy-dominated power systems. [ABSTRACT FROM AUTHOR]

Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified. • Microgrid (MG) control as a cyber-physical system (CPS) against cyber-attacks (CAs) is one of the concerns of researchers. • CAs include deception attacks, denial of service (DoS) attacks and replay attacks. • Defense mechanisms against CAs consist of prevention, detection and isolation of CAs and resilience control of MG. • Future trends in the context include new methods of CAs prevention, detection and isolation, new resilient control methods. • Retrofitting existing equipment and/or accommodating advanced technologies into networks enhance resilience of MG. [ABSTRACT FROM AUTHOR]

The widespread development of monitoring devices in power system has indeed led to the generation of large amounts of power consumption data. Storing and transmitting this enormous volume of data has become a significant challenge for power system operators and researchers. Although high data rates are available for transmission, data compression is still necessary for power system applications to lessen the load of data transmission and storage. The wavelet transform plays an important role in data compression for power system applications. This paper presents an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. In general, some wavelet coefficients are neglected to produce DWT-based data compression by using hard or soft thresholds. But the volume of compressed data differs for various threshold values. In this context, selecting an optimal threshold is a challenging task in power system data compression. This paper proposes a multi-objective particle swarm optimisation (MO-PSO) for optimal threshold selection. The proposed approach is tested using IEEE power quality wave data sets and Dutch Residential Energy Datasets (DRED). The proposed MO-PSO algorithm finds the global optimum threshold, and it outperforms the other existing algorithms. The proposed algorithm efficiently compresses the test dataset with a maximum compression ratio (CR) of 2.07. • The development of an adaptive algorithm for data compression is applied in power system data. • MO-PSO algorithm is proposed to find the optimal threshold in WT based data compression. • Results are compared with recent research works. • The efficiency of data compression algorithm is evaluated by using compression ratio, mean square error and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

This paper discusses a leader-following consensus problem for nonlinear multi-agent systems (MASs) subjected to generalized Lipschitz-type nonlinearity using output feedback. An event-triggered (ET) leader-following control scheme, based upon estimated states using observers, is proposed for efficient bandwidth utilization by application of invariant sets. Distributed observers are designed to estimate the states of the followers because actual states are not always readily available. Besides, in order to reduce unnecessary data communication among the followers, an ET strategy has been formulated which excludes Zeno behavior as well. Under this proposed scheme, sufficient conditions are formulated using Lyapunov theory. These conditions not only guarantee the asymptotic stability of estimation error, but also ensure the tracking consensus of nonlinear MASs. Further, a simple and less conservative design approach using a decoupling scheme for assuring necessity and sufficiency for the main design approach has also been explored. The decoupling scheme is similar to separation principle for linear systems. Contrary to the existing works, the nonlinear systems considered in this study cover a wide family of Lipschitz nonlinearities, including both globally and locally Lipschitz systems. Moreover, the proposed approach is more efficient in handling ET consensus. Finally, the obtained results are verified with single link robots and modified Chua's circuits. • Invariant sets for consensus and observation errors are focused. • Leader-following consensus for generalized Lipschitz agents is proposed. • A less conservative event-triggered consensus method is developed. • A condition for elimination of Zeno behavior is revealed. [ABSTRACT FROM AUTHOR]

Riskful and aggressive driving are significant problems in road transportation. Numerous studies prove drivers' behavior is critical in most road accidents and contributes significantly to fuel consumption and emissions. In an attempt to improve road safety, it is important to be able to detect such unskilful and irresponsible drivers on a mass scale. Thus an automated onboard driving safety assessment system is required. Transmitting amounts of telemetric data required to assess driving style is unpractical and economically unjustified, thus some data processing must take place in an Onboard Unit (OBU). As a first step in driving safety assessment is detecting maneuvers, this is the task, that can be done in the OBU. In this paper, we propose a Finite State Machine based algorithm, which could run on OBU and allows marking of basic maneuvers in the telemetry datastream. This allows the calculation of additional features, aggregation, and transmission of data useful for classification the of driving safety, and thus a vast reduction in data volume sent over the air. Also, an example of detecting unsafe maneuver using such aggregated data is given. [ABSTRACT FROM AUTHOR]

• Integrated scheduling data transmission and observation tasks to achieve better schedule results. • In order to make more flexible scheduling, a time-continuous model for the data transmission process is established to achieve accurate time decisions. • A hybrid Actor-Critic reinforcement learning method is designed for solving single-satellite observation and transmission task scheduling problems, which performs well under intensive observation scenarios. • Different training methods are applied to meet various scheduling requirements and random scenarios. Earth observation satellites(EOS) generate a large amount of observation data in intensive observation scenarios, while the ability of data storage on the EOS is limited. It makes integrating satellite observation and data transmission tasks for EOS imperative. This paper establishes a time-continuous model for the single EOS integrated scheduling problem, which considers data transmission and observation simultaneously. A hybrid Actor-Critic reinforcement learning method is adopted to solve the EOS integrated scheduling problem for a more efficient solution in intensive observation scenarios. Furthermore, the algorithm can flexibly determine the start and end time of the data transmission task. Experimental results show that the hybrid Actor-Critic reinforcement learning method deals with a large scale of problems with high efficiency and good results. [ABSTRACT FROM AUTHOR]

This paper is concerned with stability for networked control systems with a transmission delay and data packet dropouts. A hybrid model is formulated for the networked control systems to separate the constant delay and data packet dropouts, and a stability theorem is established for the hybrid model. Based on the stability theorem, a Lyapunov–Krasovskii functional plus (LKFP) approach is proposed to revolutionize the normal Lyapunov–Krasovskii functional approach. By fully employing the system information to construct an LKFP and by exploiting integral equations of the hybrid model to deal with the derivative of the LKFP, new stability results are obtained. Finally, numerical examples illustrate that the stability results are of less conservatism than some existing ones. • A new stability theorem is established for networked control systems. • Based on the stability theory, a Lyapunov–Krasovskii functional plus method is proposed. • Less conservative stability results are obtained. [ABSTRACT FROM AUTHOR]