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The convergence of heterogeneous wireless sensor networks provides many benefits, including increased coverage, flexible load balancing capabilities, more efficient use of network resources, and the provision of additional data by different types of sensors, thus leading to improved customer service based on more complete information. However, despite these advances, the challenge of ensuring reliability and survivability remains due to low-cost sensor requirements and the inherent unreliability of the wireless environment. Integrating different sensor networks and unifying protocols naturally leads to the creation of a network with multiple sinks, necessitating the exploration of new approaches to rational reliability assurance. The failure of some sensors does not necessarily lead to a shutdown of the network, since other sensors can duplicate information and deliver data to sinks via an increased number of alternative routes. In this paper, the reliability indicator is defined as the probability that sinks can collect data from a given number of sensors. In this context, a dedicated reliability metric is introduced and examined for its effectiveness. This metric is computed using an algorithm rooted in the modified factoring method. Furthermore, we introduce a heuristic algorithm designed for optimal sink placement in wireless sensor networks to achieve the highest level of network reliability. [ABSTRACT FROM AUTHOR]

• Temporary continuous observation task using non-customized constellation is studied. • A hybrid method for multi-satellite scheduling and task planning is proposed. • A joint simulation software system is developed via STK and Python. • The proposed technique has higher efficiency and target adaptability. This paper put forward a method to address the problem, of how to select multiple satellites from a given heterogeneous constellation and plan their tasks to implement a temporary mission for ground or aerial targets successive observation, while hardly affecting the constellation's original mission as much as possible. First, considering the observation geometric constraints of the satellite on the target, an observability analysis model for effective observation of the satellite on the target is established to characterize the observable spatio-temporal scope. Considering the time-length requirement of sustained observation on the target, a multi-satellite-to-target sustained observation model is constructed for the scheduling of satellites. Second, to address the problem of selecting task satellites in a given heterogeneous constellation, a mathematical model for task planning is established to maximize observation benefits and minimize task burdens. Third, a hybrid method of multi-satellite scheduling and task planning is designed with fault tolerance, based on dynamic programming and an improved contract network protocol algorithm. Next, a joint simulation software system is established through the secondary development of STK and Python, which includes satellite-target observability analysis, task satellite selection, task allocation and planning, and full process visible demonstration functions. Finally, simulation cases verified the effectiveness and superiority of the proposed technique. [ABSTRACT FROM AUTHOR]

Efficient and reliable data routing is critical in Advanced Metering Infrastructure (AMI) within Smart Grids, dictating the overall network performance and resilience. This paper introduces Q-RPL, a novel Q-learning-based Routing Protocol designed to enhance routing decisions in AMI deployments based on wireless mesh technologies. Q-RPL leverages the principles of Reinforcement Learning (RL) to dynamically select optimal next-hop forwarding candidates, adapting to changing network conditions. The protocol operates on top of the standard IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), integrating it with intelligent decision-making capabilities. Through extensive simulations carried out in real map scenarios, Q-RPL demonstrates a significant improvement in key performance metrics such as packet delivery ratio, end-to-end delay, and compliant factor compared to the standard RPL implementation and other benchmark algorithms found in the literature. The adaptability and robustness of Q-RPL mark a significant advancement in the evolution of routing protocols for Smart Grid AMI, promising enhanced efficiency and reliability for future intelligent energy systems. The findings of this study also underscore the potential of Reinforcement Learning to improve networking protocols. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks are gaining popularity since supporting a broad range of applications, both military and civilian. Wireless acoustics is the most widespread technology adopted in underwater networks, the realization of which must face several challenges induced by channel propagation like signal attenuation, multipath and latency. In order to address such issues, the attention of researchers has recently focused on the concept of cooperative communication and networking, borrowed from terrestrial systems and to be conveniently recast in the underwater scenario. In this paper, we present a comprehensive literature review about cooperative underwater wireless sensor networks, investigating how nodes cooperation can be exploited at the different levels of the network protocol stack. Specifically, we review the diversity techniques employable at the physical layer, error and medium access control link layer protocols, and routing strategies defined at the network layer. We also provide numerical results and performance comparisons among the most widespread approaches. Finally, we present the current and future trends in cooperative underwater networks, considering the use of machine learning algorithms to efficiently manage the different aspects of nodes cooperation. [ABSTRACT FROM AUTHOR]

The rapid development of the Industrial Internet of Things (IIoT) and its application across various sectors has led to increased interconnectivity and data sharing between devices and sensors. While this has brought convenience to users, it has also raised concerns about information security, including data security and identity authentication. IIoT devices are particularly vulnerable to attacks due to their lack of robust key management systems, efficient authentication processes, high fault tolerance, and other issues. To address these challenges, technologies such as blockchain and the formal analysis of security protocols can be utilized. And blockchain-based Industrial Internet of Things (BIIoT) is the new direction. These technologies leverage the strengths of cryptography and logical reasoning to provide secure data communication and ensure reliable identity authentication and verification, thereby becoming a crucial support for maintaining the security of the Industrial Internet. In this paper, based on the theory of the strand space attack model, we improved the Fiber Channel Password Authentication Protocol (FACP) security protocol in the network environment based on symmetric cryptography and asymmetric cryptography. Specifically, in view of the problem that the challenge value cannot reach a consensus under the symmetric cryptography system, and the subject identity cannot reach a consensus under the asymmetric cryptography system, an improved protocol is designed and implemented to meet the authentication requirements, and the corresponding attack examples are shown. Finally, the effectiveness and security of the protocol were verified by simulating different networking environments. The improved protocol has shown an increase in efficiency compared with the original protocol across three different network configurations. There was a 6.43% increase in efficiency when centralized devices were connected to centralized devices, a 5.81% increase in efficiency when centralized devices were connected to distributed devices, and a 6.32% increase in efficiency when distributed devices were connected to distributed devices. Experimental results show that this protocol can enhance the security and efficiency of communication between devices and between devices and nodes (servers, disks) in commonly used Ethernet passive optical network (EPON) environments without affecting the identity authentication function. [ABSTRACT FROM AUTHOR]

The development of underwater acoustic networks is a significant expansion of Internet-of-Things technology to underwater environments. These networks are essential for a variety of marine applications. For many practical uses, it is more efficient to collect marine data from a remote location over multiple hops, rather than direct point-to-point communications. In this article, we will focus on the underwater acoustic string network (UA-SN) designed for this type of application. We propose a Robust and Adaptive Pipeline Medium Access Control (RAP-MAC) protocol to enhance the network's transmission efficiency, adaptability, and robustness. The protocol includes a scheduling-based concurrent algorithm, online real-time configuration adjustment function, a rate mode adaptive algorithm, and a fault recovery algorithm. We conducted simulations to compare the new protocol with another representative protocol, validating the RAP-MAC protocol's adaptability and fault recovery ability. Additionally, we carried out two large-scale sea trials. The results of these experiments indicate that the RAP-MAC protocol ensures effectiveness and reliability in large-scale multihop UA-SNs. In the South China Sea, we were able to achieve a communication distance of 87 km with a throughput of 601.6 bps, exceeding the recognized upper bound of underwater acoustic communication experiment performance by 40 km·kbps. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed optimization problem for a class of nonlinear uncertain multi-agent systems with unmeasured states, switched parameters, and directed communication topologies changing in the control process. To achieve the goal of optimizing the global objective function, a penalty function is constructed through making up of a sum of local objective functions and integrating consensus conditions of the multi-agent systems to utilize local and neighboring information. Radial basis function neural-networks and neural-networks state observer are applied to approximate the unknown nonlinear functions and obtain the unmeasured states, respectively. To avoid "explosion of complexity" and obtain derivatives for virtual control functions continuously, dynamic surface control technology is proposed to develop a distributed adaptive backstepping neural network control protocol to ensure that all the agents' outputs asymptotically reach consensus to the optimal solution of the global objective function. Simulations demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) applications from home applications to military applications have been expanding day by day. Energy efficient routing schemes are vital for resource constraint wireless sensor networks (WSNs) and IoT. Clustering based routing scheme is one of the basic methods to extend the lifetime of WSN-based IoT. On the other hand, clustering is NP-hard problem and metaheuristic algorithms are suitable for clustering. In this paper, a new energy-efficient routing scheme is presented for IoT using two metaheuristic algorithms Harris hawk's optimization (HHO) and whale optimization algorithm (WOA). The selection of the cluster header (CH) is based on the WOA using various parameters such as residual energy of nodes, distance to neighbors, distance to sink. After clustering phase, an energy efficient routing scheme was proposed using the HHO algorithm. The simulation results in MATLAB environment justifies that the proposed protocol improves network life by 10%, average energy consumption by 15%, packet delivery rate (PDR) by 12%, throughput by 11% and number of alive nodes by 15%. [ABSTRACT FROM AUTHOR]

In this study, we focus on real-time anomaly detection using the gated graph neural network (GGNN) and long short-term memory (LSTM) algorithms for the most commonly used network protocols of HTTP and TELNET. In a network, the flow of the various protocols is determined by their respective roles. Each protocol consists of options with individual tasks, and protocols are processed based on these tasks. Therefore, when analyzing flow, a unique repetitive pattern emerges according to the flow and mission. Accordingly, when an anomaly signal is involved, the pattern shows different characteristics from the existing pattern. This study identifies the flow of input and output values based on the correlation between the options defined at each port, whereby the correlation is analyzed, and the detection accuracy of the anomaly signal is determined using the GGNN and LSTM algorithms. The experimental results demonstrate an accuracy of 99.36% for GGNN and 89.46% for LSTM in detecting network anomalies. [ABSTRACT FROM AUTHOR]

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average. [ABSTRACT FROM AUTHOR]

The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics. [ABSTRACT FROM AUTHOR]

Background: Previous studies have found that psychological interventions have a positive effect on improving physical and psychological problems in colorectal cancer survivors. However, there is still a lack of high-quality evidence reviews that summarize and compare the impact of different psychological interventions. The aim of this study was to synthesize existing psychological interventions and use network meta-analysis to explore whether psychological interventions improve anxiety, depression, fatigue and quality of life in colorectal cancer (CRC) survivors. Methods: We will extract relevant randomized controlled trials of psychological interventions for CRC survivors from eight electronic databases, including PubMed, Embase, The Cochrane Library, Web of Science, CINAHL, PsycInFO, CNKI, and Wanfang database. Two reviewers will independently screen the literature and extract data. The risk of bias of the included studies will be assessed using the RoB2: Revised Cochrane Risk of Bias Tool. We will then conduct paired meta-analyses and network meta-analyses of the extracted data, using a frequency-based framework and random effects models. Discussion: To the best of our knowledge, this study is the first proposed qualitative and quantitative integration of existing evidence using systematic evaluation and network meta-analysis. This study will inform health policy makers, healthcare providers' clinical intervention choices and guideline revisions, and will help to reduce depression and anxiety in CRC survivors, reduce fatigue, improve quality of life. [ABSTRACT FROM AUTHOR]

A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluate their capabilities when applied to IoT-based botnets. Multiple simulation environments, using internally developed network traffic generation software, were created to test these techniques on traditional and IoT-based networks, with multiple scenarios differentiated by the total number of hosts, the total number of infected hosts, the botnet command and control (CnC) type, and the presence of aberrant activity. Externally acquired datasets were also used to further test and validate the capabilities of each botnet detection technique. The results indicated, contrary to expectations, that BotMiner and BotProbe were able to detect IoT-based botnets—though they exhibited certain limitations specific to their operation. The results show that traditional botnet detection techniques are capable of detecting IoT-based botnets and that the different techniques may offer capabilities that complement one another. [ABSTRACT FROM AUTHOR]

Time synchronization is vital for accurate data collection and processing in sensor networks. Sensors in these networks often operate under fluctuating conditions. However, an accurate timekeeping mechanism is critical even in varying network conditions. Consequently, a synchronization method is required in sensor networks to ensure reliable timekeeping for correlating data accurately across the network. In this research, we present a novel dynamic NTP (Network Time Protocol) algorithm that significantly enhances the precision and reliability of the generalized NTP protocol. It incorporates a dynamic mechanism to determine the Round-Trip Time (RTT), which allows accurate timekeeping even in varying network conditions. The proposed approach has been implemented on an FPGA and a comprehensive performance analysis has been made, comparing three distinct NTP methods: dynamic NTP (DNTP), static NTP (SNTP), and GPS-based NTP (GNTP). As a result, key performance metrics such as variance, standard deviation, mean, and median accuracy have been evaluated. Our findings demonstrate that DNTP is markedly superior in dynamic network scenarios, a common characteristic in sensor networks. This adaptability is important for sensors installed in time-critical networks, such as real-time industrial IoTs, where precise and reliable time synchronization is necessary. [ABSTRACT FROM AUTHOR]

The industrial control and automation sector has invested in the development and standardization of new wireless (WirelessHART, ISA 100.11a, and WIA-PA) and wired (Profibus/Profinet, Modbus, and LonWORK) solutions aimed at automating processes to support standard monitoring and control functions from the perspective of addressing critical applications, as well as those integrated within the Building Internet of Things (BIoT) concept. Distributed data acquisition and control systems allow modern installations to monitor and control devices remotely. Various network protocols have been proposed to specify communication formats between a client/gateway and server devices, with Modbus being an example that has been widely implemented in the latest industrial electrical installations. The main contribution made in this paper concerns the completion of the Modbus Extension (ModbusE) specifications for the server station in the classical Modbus communication architecture, as well as their implementation and testing in an STM32F4 kit. A general-purpose control architecture is proposed for BIoT sector, comprising both intelligent touch switches and communication protocols of which the Modbus protocol is used extensively for the monitoring and control part, especially between clients, smart switches, and devices. The specific contributions concern the presentation of a scientific and practical implementation of improved specifications and their integration as software modules on ModbusE protocol server stations. A client station with a VirtualComm USB PC connection is also implemented in the lab to test the operation of the proposed server with specific Modbus applications. [ABSTRACT FROM AUTHOR]

With the development of IoT technology and the improvement of people's living standards, smart locks have become prevalent in ordinary households. However, for enterprises and institutions requiring unified management and control capabilities for networked smart locks, the technology has been relatively lagging, and there is a lack of established brands. Existing products are mostly based on wireless low-power network protocols, leading to common issues of low stability, limited network scalability, and high maintenance efforts. This paper addresses the problems existing in current networked lock systems and leverages the centralized office characteristics of enterprises and institutions. By integrating IoT technology and Power over Ethernet- (PoE-) centralized power supply, we have developed a networked smart lock system with unified management and control capabilities. This system is primarily designed for applications in offices, collective dormitories, and hotels of enterprises, institutions, and schools. The test results demonstrate that even with a scale of over tens of thousands of networked locks under unified control, the system maintains fast response times and low packet loss rates. It also incorporates energy-saving and environmentally friendly features, as well as easy automatic updates, facilitating convenient postdeployment maintenance. Thus, it effectively meets the requirements for centralized management and control in different application scenarios with centralized use of rooms. In conclusion, this research has led to the development of a unified management and control networked smart lock system, which is well suited for enterprises, institutions, and schools. Its scalability, speed, energy efficiency, and ease of maintenance make it an excellent solution for various application scenarios requiring centralized management and control of access. [ABSTRACT FROM AUTHOR]

Background: VCI is a severe public health problem facing the world today. In addition to pharmacological treatment, non-invasive neuromodulation techniques have also been effective. At this stage, non-invasive neuromodulation techniques combined with pharmacological treatment are the mainstay of clinical treatment, and clinical trials are continuing to be conducted, which is becoming the direction of treatment for VCI. Therefore, we outline this systematic review and network meta-analysis protocol to evaluate and rank clinical data in future studies which can develop optimal protocols for the clinical treatment of VCI with non-invasive neuromodulation techniques in combination with drugs. Methods: The network meta-analysis will search eight databases, including PubMed, Embase, Cochrane Library, Web of Science, China Knowledge Infrastructure Library (CNKI), China Biology Medicine disc (CBM)), Wanfang Data Knowledge Service Platform and Vipshop Journal Service Platform (VIP), for a period of from the establishment of the library to January 30 2022. The quality of the studies will be evaluated using the Cochrane Review's Handbook 5.1 and the PEDro scale to assess the evidence and quality of the included randomised controlled trials. Risk of bias assessment and heterogeneity tests will be performed using the Review Manager 5.4 program, and Bayesian network meta-analysis will be performed using the Stata 16.0 and WinBUGS 1.4.3 program. Results: The results of the network meta-analysis will be published in a peer-reviewed journal. Conclusions: Our study is expected to provide high quality evidence-based medical evidence for the treatment of VCI by clinicians. Trial registration: PROSPERO:CRD42022308580. [ABSTRACT FROM AUTHOR]

Vehicular Internet of Things (VIoT) refers to integrating Internet of Things (IoT) technology into the transportation sector, specifically vehicles. It aims to enhance transportation safety, efficiency, and sustainability by using connected devices and real-time data exchange between vehicles, road infrastructure, and transportation management systems. RFID can provide valuable insights into the functioning and performance of vehicles, enabling the development of intelligent transportation systems and contributing to the growth of the VIoT. To secure essential driving data tag location, data privacy, and road safety are the primary concerns in the VIoT system. An attacker can perform a variety of attacks on the tag and compromise its privacy through the wireless channel between it and the reader. To ensure secure communication for the various application of the VIoT system, An RFID-based protocol is proposed, which is referred to as UPSRVNet (Ultralightweight, Privacy preserved, and Secure RFID-based authentication protocol for VIoT Networks). The proposed protocol guarantees secure authentication in VIoT networks using ultralightweight, privacy-preserving RFID tags by substituting bit formation and right-shift rotation for high-computing operations like hash functions and encryption/decryption algorithms. It also reduces RFID tags' storage and communication overhead and aims to secure the driver's location, personal information, and resistance from known attacks. As per the experiment result, the proposed UPSRVNet protocol showed a significant reduction in communication overhead between tag and reader, that is 50%, and the storage cost on the tag is also reduced by 33.33% compared to existing protocols. Due to this, the certification and authentication processes may be performed quickly. The protocol ensured the security requirements by informal analysis using the Scyther tool. A comprehensive analysis of the proposed protocol reveals its substantial advantages over the existing protocol concerning computation complexity, communication efficacy, and storage expenditure. [ABSTRACT FROM AUTHOR]

Ad hoc networks, due to their decentralized and dynamic nature, face significant challenges in terms of security and communication. These challenges are particularly pronounced due to the lack of a fixed infrastructure and the constant mobility of nodes. In this paper, we explore the implementation of secure communication protocols in ad hoc networks using public key cryptography (PKC). A detailed overview of how PKC can be used to address authentication, confidentiality and integrity issues in these networks is provided. The fundamentals of public key cryptography are discussed and various techniques for integrating it into ad hoc network protocols are presented. In addition, simulations and case studies are included to evaluate the effectiveness of these protocols, highlighting improvements in security compared to traditional methods. This article provides a comprehensive guide for the implementation of robust security solutions in ad hoc networks, adapted to their specific characteristics. [ABSTRACT FROM AUTHOR]

The accurate identification of internet traffic is crucial for network management. However, the use of encryption techniques and constant changes in network protocols make it difficult to extract useful features for traffic classification. Additionally, there may be limited data availability and a lack of diversity within the dataset, which poses further challenges. To address these issues, our research proposes a novel solution that uses an innovative data augmentation technique. This approach leverages the capabilities of LSTM networks to create synthetic data points that closely resemble real traffic data. By doing so, we can significantly enrich the dataset used for training and improve classification efficiency. We conducted thorough experiments to validate our approach and found that combining LSTM-generated data with actual traffic data leads to notable improvements in classification efficiency. We demonstrated the effectiveness of our methodology using academic and commercial datasets. Our classifier, trained on the generated data, showed a performance boost of 6%. Moreover, when classifying with only half of the time, thus utilizing half of the signal, our approach achieved a notable 4% improvement compared to the original classifier. The inclusion of augmented samples within the training set led to a noticeable improvement in both accuracy and F1-score. These findings compellingly demonstrate our data augmentation strategy's practical utility and efficiency in earlier prediction with improved performance for encrypted traffic classification systems. • Enhanced Traffic Analysis via LSTM Augmentation. • Efficiency Boost: Earlier Traffic Classification. • Improved Classifier Performance with Generated Data. • Diverse Data Synthesis for Improved Classification. [ABSTRACT FROM AUTHOR]

• Development of the Depth-Based Stable Election Routing Protocol, which is specifically designed for underwater environments, by combining the probabilistic CH election approach and stable election protocol to improve the stability and efficiency of the proposed routing protocol. • Consideration of energy efficiency as a priority, in addition to tackling the difficulty of energy imbalance in varied IoUT scenarios, by recognizing efficient energy utilization as a major issue in IoUT. • Use fitness functions adapted to underwater nodes with varying energy levels and depths. • The suggested depth-based SEP-IoUT protocol was compared to conventional SEP and SEP-IoUT protocols. Furthermore, higher performance in terms of network lifetime, stability period, and energy consumption reduction must be demonstrated. Internet of underwater things (IoUT) is a technology enabling underwater sensor nodes to communicate with the IoT networks to support various applications. Efficient energy utilization and network lifetime are the two main critical issues related to the IoUT. Both performance factors are related to how efficiently the routing protocol is tailored especially for heterogeneous IoUT. In this context, efficient routing protocols that consider underwater nodes (UNs) energy heterogeneity can adjust the energy imbalance, especially in heterogeneous IoUT scenarios. In IoUT, both the UN's energies and their depths can greatly affect the energy efficiency and clustering-based routing protocol. This paper provided an energy and depth-based stable election routing protocol (SEP) to suit the requirements of underwater communications. The objective of the depth-based SEP protocol is to enhance energy-efficient routing while considering the varying energy levels of UNs in IoUT. The proposed protocol depends on the depth-based cluster head (CH) election approach by defining distinct functions tailored for UNs with different energy levels and depths, in addition to the distance between the selected CH and the surface base station. Experimental results indicate that depth-based SEP-IoUT protocol outperforms network lifetime more than conventional SEP and SEP-IoUT. It gives an average stability period of 19.6 % in addition to reducing energy consumption by 39 % and 15.7 % when compared to SEP and SEP-IoUT respectively. [ABSTRACT FROM AUTHOR]

The significance of cognitive radios in satisfying the growing demand for bandwidth in wireless networks is anticipated to be significant. An innovative method for cognitive network beamforming and interference control based on machine learning and an intelligent network protocol is suggested in this study. A Reinforcement Nash Equilibrium Game Theory (RNEGT) model is used to control network interference, and the Spatiotemporal Non-Orthogonal Multiple Access (ST-NOMA) technique is used for beamforming inside the network. Throughput, spectrum efficiency, stability, Mean Square Error (MSE), and Signal-to-Interference and Noise Ratio (SINR) are some of the metrics used in experimental study. Examined approach can optimize received SINR at destinations while maintaining interference + noise power below a certain threshold by employing the suggested relay selection and a cooperative beamforming (CBF) mechanism in each cluster. [ABSTRACT FROM AUTHOR]

Exercise induced performance fatigue has been shown to impair many aspects of fine motor function in the distal upper limb. However, most fatiguing protocols do not reflect the conditions experienced with computer use. The purpose of this study was to determine how a prolonged, low-force mouse clicking fatigue protocol impacts performance fatigue of the distal upper limb for gamers and non-gamers. Participants completed a total of 1 h of mouse clicking at 5 clicks per second. Muscle fatigue and performance were intermittently assessed. RMS amplitude increased for the forearm flexors throughout the fatigue protocol. Accuracy decreased following the first bout of clicking and returned to baseline values after 40-min. EDC and ECU displayed the greatest muscle activity while aiming, producing 11.4% and 12.9% of MVC, respectively. These findings indicate that mouse clicking may not result in performance fatigue, however, high levels of extensor activity may explain common injuries among gamers. [ABSTRACT FROM AUTHOR]

Industrial Internet-of-Things systems suffer from a vast and vulnerable attack surface, raising widespread concerns about shielding IIoT devices from malicious attacks and reducing cyber risks. Device identification is the prerequisite to safeguard IIoT systems. We leverage the observation that IIoT network protocol implementations vary due to different hardware architectures/configurations and design tasks of IIoT devices, which cause the difference in their network traffic payloads. Specifically, we develop a novel neural network to learn the semantic/syntax features among multiple IIoT packets. The neural network has multiple branches, each of which consists of convolution layers, attention modules, and highway units for learning the classification model of IIoT devices. To validate the precision and recall of our neural network in IIoT devices fingerprinting, we have implemented a prototype of the proposed IIoT device identification system. Our results show that our approach achieves 95.8% precision and 95.4% recall, significantly outperforming other classification models. • We propose a novel IIoT fingerprinting approach based on neural network. • We conduct experiments to validate the effectiveness of it. • The results show that our approach can achieve 95.8% precision. • We demonstrate the usefulness of fingerprinting in application. • Our approach is useful in shielding industrial systems. [ABSTRACT FROM AUTHOR]