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To send and receive data simultaneously, reduce network interference, and effectively improve energy efficiency, this paper analyzes the network characteristics of Multi-Radio Multi-Channel Wireless Sensor Network and optimizes the power and channel of each radio frequency interface. Based on the novel energy efficiency model and energy consumption efficiency model, this paper establishes a distributed optimization game model using Game Theory to maximize network energy efficiency, minimize energy consumption, and ensure network connectivity. Based on the game model, an efficient distributed resource allocation game algorithm is designed, in which a joint power control and channel allocation can quickly converge to Nash Equilibrium with low complexity. At the same time, the simulation results show that the distributed resource allocation game algorithm can effectively improve the energy efficiency, reduce network interference and bit error rate to ensure the successful data transmission. [ABSTRACT FROM AUTHOR]

Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability. Additionally, it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops. These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios. Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes. The evaluation spans varying sensor densities, wireless channel conditions, and packet transmission rates, showcasing the protocol's superiority in ensuring reliable and efficient data transfer. Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs. By prioritizing fairness, reliability, and efficiency, the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services, thereby offering a valuable contribution to the field of medical event-drivenWSNs. [ABSTRACT FROM AUTHOR]

A large number of interconnected smart devices or objects interact with the physical environment known as the Internet of Things (IoT). These networks efficiently perform complex tasks with a high level of intelligence without human intervention and require run-time processing and computation. Edge computing is introduced where devices are placed at the edge between the data source and the cloud. These devices can provide more powerful computational and storage capabilities to IoT users. With various benefits, one of the significant requirements is secure and trustworthy data communication. Various trust management schemes are introduced to guarantee the trustworthiness and isolation of the malicious nodes and give preference to the most trustworthy ones. The existing schemes selected trustworthy nodes again and again from the path without considering the load factor which leads them to be more vulnerable for the Denial of Services (DoS) attacks. In this paper, we propose a Link-based Penalized Trust Management scheme to provide trust management and consider load factors for the selection of most trustworthy nodes and provide a preemptive measure to secure the network from DoS attacks based on its link's association. Simulation results indicate the better performance of the proposed scheme as compared with existing schemes. [ABSTRACT FROM AUTHOR]

In 5G, users can easily enjoy services by accessing the edge devices (EDs) deployed around the base stations. Generally, due to the uneven distribution and mobility of users, one ED is required to serve multiple users simultaneously, that results in the ED overload, seriously affecting the quality of experience (QoE) of the users. By responding to users' requests, service migration realizes the cross edge device migrations, that dynamically provides services for mobile users, which effectively improves the resource utilization rate of edge servers and the QoE of users. However, during the service migrations, malicious users may deploy pseudo base stations, gateways and other devices to illegally eavesdrop or tamper with user service data, that causes user information loss or disclosure. To prevent these security issues, a fountain-coding based cooperative jamming strategy is proposed in this paper. Specifically, the fountain coding technology is introduced to construct a three-node transmission model, including an original node, a target node and a malicious node, to realize the secure data transmission process in service migration. Besides, a group of relay nodes are employed to carry out cooperative jamming, that deteriorate the illegal eavesdropping quality of malicious nodes on service data. Then secrecy rate and outage probability are utilized to evaluate the security and reliability of the whole service migration. Finally, the theoretical analysis is shown by simulation results. [ABSTRACT FROM AUTHOR]

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to monitor the status of electrical equipment using Internet of Things (IoT) and relaying-aided transmission technologies, where the data rate is used as the metric for evaluating the system monitoring performance. In this framework, relaying plays a pivotal role, enhancing the efficiency of data transmission in the monitoring process. We employ the optimal relay selection criterion to choose the most effective relay to assist the data transmission, thereby optimizing the communication link between the electrical equipment and the monitoring system. To provide a comprehensive understanding of the system performance, we delve into the analytical aspects by deriving an analytical expression for the data rate. This expression offers some insights into the theoretical performance limits and the factors influencing the efficiency of the system. The theoretical framework is further complemented by a series of simulations. These simulations validate the analytical expression developed in the study, and provide practical scenarios to demonstrate the real-world applicability and effectiveness of the proposed IoT and relaying-aided transmission technologies in monitoring electrical equipment. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an Internet of Things (IoT) network in severe fading environments, where the source sends some information to the destination. Due to the fading obstacle, the source cannot directly send the information to the destination. To tackle this issue, we consider the communication of IoT networks assisted by multi-hop relaying, where multiple relays can help assist the data transmission from the source to the destination. For the considered IoT networks assisted by multi-hop relaying, we evaluate the system performance by studying the transmission outage probability, where an analytical expression is derived to evaluate the IoT outage. We then evaluate the system performance by studying the transmission achievable data rate, where an analytical expression is provided to evaluate the IoT rate. Finally, we present some experimental results as well as the analytical results to verify the derived expressions for the IoT networks assisted by multi-hop relaying. In particular, the usage of multi-hop relaying can help extend the coverage area of IoT networks effectively. [ABSTRACT FROM AUTHOR]

Free space optical communication is a potential technology that exploits the visible light frequency band for communication. In this paper, a free-space optical link (FSOL) optimization in visible light communication (VLC) is used to improve the channel performance for signal strength and analyze the effect of signal failures in a communication system. The data transmission and reception phenomenon are established using a light-emitting diode (LED) as a carrier at transmitter and photodiode as a detector at the receiver, respectively. The OptiSystem tool uses to optimize the component parameters of this proposed communication system. The bit error rate (BER) analyzer displays the signals as an eye diagram with the challenges of components uncertainty for better signal performance. The optimized components solve the problem encountered transmitting improve and enhance the signals strength. The acceptable eye diagram displayed from the BER analyzer has been resolved as a result of component optimizations by adding and removing noise to ascertain the behavior of the channel. A better signal retrieve by the display of the acceptable eye diagram from the OptiSystem software used. The FSOL components optimization improve the loss of data, weak signal, loss of power, signal failure, and signal errors which guarantee multi-mobile users with the shared signal. OptiSystem software package used to fully characterize the FSOL system. [ABSTRACT FROM AUTHOR]

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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]

Safety helmet wearing detection based on video surveillance is an important means of safety monitoring in many industrial scenes. The training of safety helmet wearing detection models requires large and well-labeled dataset. However, the incidence of security violations is relatively low, which results in insufficient samples for training deep detection models. Safety helmet wearing detection is a common requirement in many scenarios such as construction sites, substations, and factory workshops. Aggregating data from multiple companies for model training would improve the performance of the detection model. Traditional centralized training methods are not feasible because aggregating data in centralized locations (such as the cloud) can raise concerns about data privacy and the high cost of data communication and storage. This paper proposes FedSH, a novel cloud-edge-based federated learning framework, which learns a shared global safety helmet wearing detection model in the cloud from multiple companies at the network edges and achieves data privacy protection by keeping company data locally. In addition, this paper designs reweighting mechanisms and applies transfer learning to address class imbalance and non-IID problems in the training data, so as to obtain an accurate and personalized detection model. Extensive experiments have been conducted on real surveillance video datasets. The experimental results demonstrate that FedSH outperforms the existing widely used federated learning methods with an accuracy improvement of at least 3.4%; the reduction in accuracy is within the range of 5% compared with centralized learning methods. FedSH effectively achieves a good balance between model performance, privacy protection, and communication efficiency. [ABSTRACT FROM AUTHOR]

A staggering number of applications rely on the network architecture to carry out their tasks, which has led to a fast growth in wireless sensor networks (WSN). The possibility of harmful activity and data theft is growing as a result of the growth in devices and data. Thus, the network's regular users have an impact on legitimate data delivery, which lowers customer happiness and worsens network standards. The data have been saved using a variety of security procedures that have been developed in past research studies. However, harmful activity continues to engage in its illegal operations despite their efforts to safeguard data transmission in the network. As a result, a number of recent research projects have concentrated on predicting innovative techniques and processes to offer security in WSN. In comparison to existing methods, this work attempted to offer an effective tighter security for WSN and suggested an ML‐Based Secured Routing Protocol (MLSRP) for WSN with improved energy efficiency and overall performance. Energy efficiency is the main requirement of WSNs, hence a clustered network is proposed where the data are routed through the cluster head nodes. In this paper, a multicriteria based decision‐making (MCDM) model is used by the MLSRP to perform data routing, clustering, and cluster head election while also analyzing a number of network characteristics that might affect the quality of a node, route, and data. In NS2 software, the suggested framework is put into practice and simulated. The results are then validated to gauge performance. The observed quantitative results reveal that the proposed MLSRP method attains an improved network lifetime by 5% and network throughput of 6%. It reduces energy consumption by 40%, curtails overhead to 37%, and minimizes end‐to‐end delay by 30% than the other conventional methods. The suggested framework performs better than others when its total performance is compared to that of older methods. [ABSTRACT FROM AUTHOR]

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment, but has also posed significant challenges to network security, especially physical layer security, which is crucial for the reliable transmission of messages among interconnected devices and sensors. Focusing on enhancing the physical layer security in IIoT communication, this paper proposes a mechanism for the Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate devices is known, while the eavesdropper devices have a random distribution of locations, forming the SSOP model related to the device locations from the perspective of insecure regions (ISRs) and secure regions (SRs), and the closed-form expression for its upper bound is derived. Subsequently, under the constraint of the SSOP conditions, we establish an optimization model with the objective of system throughput maximization based on the Spatial Secrecy Outage Probability (SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of the system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in MATLAB, we verified the accuracy of the definition and derived the upper bound of the SSOP. The experimental results also show that the SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that, when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. This performance is crucial for understanding and optimizing the security performance of IIoT communication systems. [ABSTRACT FROM AUTHOR]

In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks. [ABSTRACT FROM AUTHOR]

The Dongbao River Xin'an Bridge is the first hybrid continuous girder bridge with corrugated steel webs in China. To assess its service conditions and guarantee its safe operation, a continuous structural health monitoring (SHM) system is designed in this paper. The SHM system consists of a sensor system, a data acquisition and transmission system, and a data processing and control system. The measurement parameters, including vehicle loads, temperature, deformation, strain, and acceleration, are determined, and a detailed sensor layout is given based on the mechanical characteristics of the bridge. The SHM system adopts a bus gateway and an IP modem to collect and transmit sensor data. Monitoring data from October 9, 2022, to March 16, 2023, were analyzed. The probability distributions of the daily maximum temperature differences and vehicle loads are reconstructed. The correlation of girder deflection and strain with ambient temperature has been demonstrated. The identification and tracking of modal parameters are implemented. The results show that the maximum deflection is within the threshold range and that the modal parameters are basically stable. All the results demonstrate the healthy structural condition of the bridge and the excellent working performance of the SHM system. The collected data provide scientific guidance for the performance evaluation of the Dongbao River Xin'an Bridge and the optimized design of similar bridges. [ABSTRACT FROM AUTHOR]

Internet of vehicles (IoV) technology connects various types of vehicles through the Internet of Things to achieve mutual communication and data sharing, thereby promoting the development of intelligent transportation systems. Present data sharing in IoV mainly relies on complicated centralized certificate management and modern public-key cryptosystems, which are insecure and inefficient because the rapid development of quantum computing has put the security of modern public-key cryptosystems under serious threat. Considering the potential quantum adversaries, this paper proposes a quantum blockchain-based data sharing scheme called quantum blockchain for IoV (QBIoV) to realize secure and efficient data sharing in IoV. Specifically, QBIoV discards vulnerable modern cryptographic components, such as classical hash functions, public key signatures, and voting protocols, and innovatively introduces quantum replacement items, such as a quantum hash function (QHF), a quantum public key signature, and a quantum binary voting (QBV) protocol, to eliminate potential quantum cyberspace attack hazards. Furthermore, QBIoV improves the conventional proof of authority (PoA) consensus algorithm and introduces a more secure and fair consensus mechanism called quantum proof of authority (QPoA). QPoA enhances the efficiency of the blockchain system, enabling the secure and efficient sharing of data in IoV. Additionally, a security analysis of QBIoV is conducted, demonstrating that QBIoV is secure against both classic and quantum adversaries, with performance comparisons proving that QBIoV outperforms peer work regarding resource efficiency. In conclusion, this paper provides an effective solution for enhancing data security protection in IoV, assisting in designing blockchain systems in the post-quantum era. [ABSTRACT FROM AUTHOR]

Intrusion detection has become a research focus in internet information security, with deep learning algorithms playing a crucial role in its development. Typically, intrusion detection data are transformed into a two-dimensional matrix by segmenting, stacking and padding them with zeros for input into deep learning models. However, this method consumes computational resources and fails to consider the correlation between features. In this paper, we transform the data into images through visualization operations and propose an information entropy weighted scheme to optimize the collision element problem during the transformation process. This method enhances the correlation between pixel frame features, leading to approximately 2% improvement in accuracy of the classification model when using the generated image samples for detection in experiments. To address the issues of insensitivity to target feature locations and incomplete feature extraction in traditional neural networks, this paper introduces a new network model called CBAM-CapsNet, which combines the advantages of the lightweight Convolutional Block Attention Module and capsule networks. Experimental results on the UNSW-NB15 and IDS-2017 datasets demonstrate that the proposed model achieves accuracies of 92.94% and 99.72%, respectively. The F1 scores obtained are 91.83% and 99.56%, indicating a high level of detection. [ABSTRACT FROM AUTHOR]

This paper examines a nonlinear Networked Control System (NCS) incorporating Takagi–Sugeno (T–S) fuzzy models to address the output tracking control problem in the presence of input state delay. The paper introduces a strategy based on an event-triggered scheme to reduce bandwidth utilization in the NCS by leveraging inherent constraints, including communication time delays and data transmission limitations. The presence of communication time delay leads to an asynchronous interaction between the designed control algorithm for T–S fuzzy systems, which utilizes novel LMI–based conditions, and the proposed T–S fuzzy controller. Additionally, this paper proposes a model of operation based on synchronous behaviour, enabling the design of a linear controller that is more easily implementable. In comparison to recent literature results, these results are obtained by using an associate Lyapunov Krasovskii Function (LKF) with fuzzy properties, in addition to different forms of integral inequalities as alternatives. A fuzzy model for NCSs is illustrated through an example of tracking control for output response. [ABSTRACT FROM AUTHOR]

This paper aims to solve the problems in the data download and analysis process of CIR equipment (Cab Integrated Radio) in the railway industry. In response to the current challenges of long data download time and difficult manual analysis, etc., a design scheme for wireless data transmission and intelligent analysis system for CIR equipment is proposed. The current CIR equipment still relies on manual operations for downloading and analyzing data from recording units, which poses potential risks to railway transportation safety due to time lag and low efficiency. The system architecture designed in this article is divided into two parts: onboard acquisition equipment and trackside intelligent analysis system, which are connected through the existing LMD system for communication. The onboard data acquisition equipment is connected to the monitoring interface of the recording unit of locomotive CIR equipment through a dedicated cable, and the data is transmitted to the trackside server through wireless network. The trackside intelligent analysis system realizes functions such as data storage, intelligent analysis, and report statistics. Based on big data technology and distributed architecture, it improves the efficiency of data processing and analysis. The intelligent analysis module in the system can achieve functions such as remote monitoring of device status, intelligent analysis, intelligent evaluation of device usage, and standardized detection task evaluation. After on-site application verification, the system can shorten data download and analysis time, reduce labor costs, prevent CIR equipment failures in advance, and ensure railway transportation safety and efficiency. [ABSTRACT FROM AUTHOR]

A small set of pilots in underwater acoustics OFDM (UWA-OFDM) systems tends to be insufficient for recovering the channel impulse response (CIR). This may result from the fast changes in the propagation environment and the requirement for high transmission data rates. A previous work, namely PE, considered the received subcarriers whose distances to their closest constellation points below the predetermined threshold T as potential pilots. However, extracting these pilots at the same time gives no chance to use some of them to facilitate searching for others. Fixing threshold T is also another limitation for searching pilot candidates. This paper proposes a reliable pilot search (RPS) method that consists of a multi-iterations pilot searching process with an adaptive threshold T a to improve the estimator in UWA-OFDM. The pilot search process gradually extracts reliable pilots at each iteration based on the threshold T a and evaluates them by performing channel estimation. Our method is compared to the MMSE and PE estimators on a wide range of settings, including the number of channel taps, pilot spacing, and various modulation schemes (i.e., MPSK and MQAM). The experimental results show that the RPS method often outperforms the MMSE and PE methods in terms of bit error rate (BER). [ABSTRACT FROM AUTHOR]

The RPL protocol is essential for efficient communication within the Internet of Things (IoT) ecosystem, yet it remains vulnerable to various attacks, particularly in dense and mobile environments where it shows certain limitations and susceptibilities. This paper presents a comprehensive simulation-based analysis of the RPL protocol's vulnerability to the Hello Flood attack in mobile environments. Using four different group mobility models—the Column Mobility Model (CMM), Reference Point Group Mobility Model (RPGM), Nomadic Community Mobility Model (NCM), and Pursue Mobility Model (PMM)—within the Cooja simulator, this study uniquely investigates the Hello Flood attack in mobile settings, an area previously overlooked. Our systematic evaluation focuses on critical performance metrics, including the Packet Delivery Ratio (PDR), End-to-End Delay (E2ED), throughput, Expected Transmission Count (ETX), and Average Power Consumption (APC). The findings reveal several key insights: PDR decreases significantly, indicating increased packet loss or delivery failures; ETX values rise, necessitating more packet retransmissions and routing hops; E2ED increases, introducing delays in routing decisions and data transmission times; throughput declines as the attack disrupts data flow; and APC escalates due to higher energy usage on packet transmissions, especially over extended paths. These results underscore the urgent need for robust security measures to protect RPL-based IoT networks in mobile environments. Furthermore, our work emphasizes the exacerbated impact of the attack in mobile scenarios, highlighting the evolving security requirements of IoT networks. [ABSTRACT FROM AUTHOR]

In different modern and future wireless communication networks, a large number of low-power user equipment (UE) devices like Internet of Things, sensor terminals, and smart modules have to be supported over constrained power and bandwidth resources. Therefore, wireless-powered communication (WPC) is considered a promising technology for varied applications in which the energy harvesting (EH) from radio frequency radiations is exploited for data transmission. This requires efficient resource allocation schemes to optimize the performance of WPC and prolong the network lifetime. In this paper, harvest-then-transmit-based WP non-orthogonal multiple access (WP-NOMA) system is designed with time-split (TS) and power control (PC) allocation strategies. To evaluate the network performance, the sum rate and UEs' rates expressions are derived considering power-domain NOMA with successive interference cancellation detection. For comparison purposes, the rate performance of the conventional WP orthogonal multiple access (WP-OMA) is derived also considering orthogonal frequency-division multiple access and time-division multiple access schemes. Intensive investigations are conducted to obtain the best TS and PC resource parameters that enable maximum EH for higher data transmission rates compared with the reference WP-OMA techniques. The achieved outcomes demonstrate the effectiveness of designed resource allocation approaches in terms of the realized sum rate, UE's rate, rate region, and fairness without distressing the restricted power of far UEs. [ABSTRACT FROM AUTHOR]

Healthcare (HC) is among the most promising sectors for assessing Internet of things (IoT) based technological advances, as patients can use wearable or injected medical sensors to monitor medical parameters any place and at any time. The data gathered by IoT devices can be sent to medical professionals, and doctors have real-time access to their patient's data. Nevertheless, the IoT devices within the network have limited resources and minimal computing capability, causing energy conservation issues. Even though clustering saves energy in network nodes, existing clustering methods could be more effective because of the higher Energy Consumption (EC), poorly balanced network load, and enhanced end-to-end delays. Furthermore, the integrity and security of medical information have become significant issues for HC applications. This paper proposes a secure and energy-efficient data transmission (DT) framework for IoT-based HC (IoT-HC) systems using enhanced mayfly clustering-based Q learner routing (EMCQLR) and exponential key-based elliptical curve cryptography (EKECC) techniques. The proposed work consists of the following steps. Double hash biometric-based authentication (DHABA) is initially used for IoT user authorization that prevents the HC network from unauthorized data access. The cluster head (CH) is then selected using the enhanced mayfly optimization algorithm (EMOA) to build clusters of IoT medical sensors and collect data from the nodes. Data is routed to the sink node by checking for data duplication. The path-weighted Q reinforcement learning (PWQRL) model can perform data routing. Finally, the EKECC algorithm encrypts the data packets received from the body sensors, providing security to the patient's data when DT is performed over an unsecured wireless channel. The performance of the suggested system is evaluated using the energy, loss ratio, throughput, success rate, and delay. The simulation outcomes show that the proposed method outperforms existing methods. Furthermore, false data elimination check and reliability metrics show that our proposed scheme ensures information privacy, message authenticity, and integrity while requiring minimal communication overhead and computation. [ABSTRACT FROM AUTHOR]

Three LED drivers which can be used for illumination, but whose main task is the transmission of information (data) via the light of the LEDs, are explored in this paper. The converter circuits need no capacitors for the energy transformation and avoid an inrush current. The lack of necessity of electrolytic capacitors reduces cost and space. Dimming the illumination is also easy to achieve. The control concept of the converters and the generation of pulsing of the LEDs for transmitting the information (data) are explained. The converters can also be expanded to more stages to drive more LEDs with different types of information. All three converters are explained in detail; all presented circuits are built up and simulated with LTSpice. Several data transmission concepts are applied and demonstrated through simulations. [ABSTRACT FROM AUTHOR]

Spatial Modulation(SM) and Orthogonal Frequency Division Multiplexing(OFDM) Multi-Mode Index Modulation (MM-OFDM-IM) techniques have significant advantages in Energy Efficiency(EE) and Spectral Efficiency(SE), respectively. The combination of multi-dimensional index modulation technology can apply multiple physical resources to meet the high demand for data transmission and system capacity in wireless communication systems. Therefore, SM and MM-OFDM-IM systems can be flexibly combined to construct an OFDM Multiple Mode Space Frequency Composite Index Modulation (MM-OFDM-SFCIM) based on space frequency combination. This system can retain the silent subcarriers in traditional subcarrier index modulation technique, expanding new composite coefficient dimensions and improving its SE and EE. Aiming at the complex characteristics of this system, this paper proposes a joint detection algorithm EL-REML (REML based on symbolic Energy and LLR) based on symbolic energy and log-likelihood. The simulation results show that the MM-OFDM-SFCIM system achieves about 30% improvement in SE compared to traditional space-frequency combination systems, and the EL-REML algorithm proposed in this system achieves a 3-4 dB improvement in BER (Bit Error Rate) compared to the LLR algorithm. Compared with traditional ML algorithm, this algorithm has lower computational complexity. [ABSTRACT FROM AUTHOR]

The use of sensor networks in monitoring applications has increased; they are useful in security, environmental, and health applications, among others. These networks usually transmit data through short-range stations, which makes them attractive for incorporation into applications and devices for use in places without access to satellite or mobile signals, for example, forests, seas, and jungles. To this end, unmanned aerial vehicles (UAVs) have attractive characteristics for data collection and transmission in remote areas without infrastructure. Integrating systems based on wireless sensors and UAVs seems to be an economical and easy-to-use solution. However, the main difficulty is the amount of data sent, which affects the communication time and even the flight status of the UAV. Additionally, factors such as the UAV model and the hardware used for these tasks must be considered. Based on those difficulties mentioned, this paper proposes a system based on long-range (LoRa) technology. We present a low-cost wireless sensor network that is flexible, easy to deploy, and capable of collecting/sending data via LoRa transceivers. The readings obtained are packaged and sent to a UAV. The UAV performs predefined flights at a constant height of 30 m and with a direct line-of-sight (LoS) to the stations, during which it collects information from two data stations, concluding that it is possible to carry out a correct data transmission with a flight speed of 10 m/s and a transmission radius of 690 m for a group of three packages confirmed by 20 messages each. Thus, it is possible to collect data from routes of up to 8 km for each battery charge, considering the return of the UAV. [ABSTRACT FROM AUTHOR]

The security issue of CPS (cyber-physical systems) is of great importance for their stable operation. Within the framework of system identification, this paper proposed a maximum likelihood estimation algorithm for FIR (finite impulse response) systems with binary observations and data tampering attacks. In the case of data transmission in the communication network being subjected to data tampering attacks after the FIR system sends out data, the objective of this study was to design an algorithm for estimating the system parameters and infer the attack strategies using the proposed algorithm. To begin, the maximum likelihood function of the available data was established. Then, parameter estimation algorithms were proposed for both known and unknown attack strategies. Meanwhile, the convergence condition and convergence proof of these algorithms were provided. Finally, the effectiveness of the designed algorithm was verified by numerical simulations. [ABSTRACT FROM AUTHOR]

Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis. [ABSTRACT FROM AUTHOR]

Energy efficiency and data reliability are important indicators to measure network performance in wireless sensor networks. In existing research schemes of routing protocols, the impact of node coverage on the network is often ignored, and the possibility that multiple sensor nodes may sense the same spatial point is not taken into account, which results in a waste of network resources, especially in large-scale networks. Apart from that, the blindness of geographic routing in data transmission has been troubling researchers, which means that the nodes are unable to determine the validity of data transmission. In order to solve the above problems, this paper innovatively combines the routing protocol with the coverage control technique and proposes the node collaborative scheduling algorithm, which fully considers the correlation characteristics between sensor nodes to reduce the number of active working nodes and the number of packets generated, to further reduce energy consumption and network delay and improve packet delivery rate. In order to solve the problem of unreliability of geographic routing, a highly reliable link detection and repair scheme is proposed to check the communication link status and repair the invalid link, which can greatly improve the packet delivery rate and throughput of the network, and has good robustness. A large number of experiments demonstrate the effectiveness and superiority of our proposed scheme and algorithm. [ABSTRACT FROM AUTHOR]

The internet of everything (IoEs) has become a well-known tool for transforming the concept of connected environments into reality in near future. The cognitive radio network is a potential candidate that addresses the issues of efficient spectrum utilization for next generation connected environments. Further, the spectrum mobility plays a significant role in cognitive connected environments during communication, for switching the channel on the appearance of primary user (PU) throughout the cognitive users' (CUs') data transmission. The performance of spectrum mobility relies on the ability of the system: 1) to detect the appearance of PU as soon as possible and 2) to stop the data transmission as-well-as switch to another channel. The potential approaches to detect the appearance of PU during CUs' data transmission are the "spectrum prediction (SP)" and "spectrum monitoring" (SM). The SP relies on the pre-available information about the channel and PUs' activities which is well explored technique, however, the SM is a real-time approach and is in its infancy. In this paper, several SM techniques with their effects on the spectrum mobility are illustrated. Moreover, the concept of imperfect SM is introduced and its effects on various performance metrics are investigated. Further, a potential approach of cooperative SM is proposed to diminish the effects of imperfections. In addition to this, the potential issues as well as research challenges regarding these techniques are presented. [ABSTRACT FROM AUTHOR]

The increasing use of cloud services in several areas has led to the growth of data-intensive applications. It is necessary to find ways to enhance the efficiency of communications within the networks of data centers to improve the performance of cloud environments. Explicit Congestion Notification (ECN) is used by Data Center TCP (DCTCP) to enhance congestion control in data center networks. The DCTCP uses ECN to assess the amount of congestion, whereas normal TCP congestion management can simply detect its presence. This paper examines DCTCP using the Random Early Detection (RED) queue management strategy. The evaluation reveals that employing Random Early Detection incurs certain costs. The RED is criticized on the one hand for both short- and medium-term connections due to longer completion time delays compared to typical DCTCP techniques. Because of ECN, DCTCP may maintain small queue sizes. However, because RED uses the average queue size, it penalizes short-lived traffic because it does not reach the bottleneck quickly. An intelligent queue management mechanism with ECN is believed to enhance DCTCP's performance in a cloud-computing environment by predicting sending rates and providing fast feedback on queue length. [ABSTRACT FROM AUTHOR]

The rapid development of wireless technology has led to the availability of a wide range of networked devices that support numerous applications. Small wireless devices that are powered by batteries create a Wireless Sensor Network (WSN), which collaborates to communicate data through wireless channels to a Base Station (BS). However, a WSN system faces a number of difficulties, with energy efficiency being the most critical one. In order to provide energy efficiency and increase network lifespan, it is crucial to lessen the energy required for data transmission. This research suggests an energy-efficient optimal cluster-based routing strategy to extend the lifespan of a network. Energy conservation is of paramount importance in WSNs featuring mobile nodes. Numerous routing techniques have been proposed to reduce packet loss and boost energy efficiency in such networks. These protocols are not particularly energy-efficient though, because they cannot build the right clusters. In this paper, the tree-based Hybrid Fuzzy C-Means Genetic Algorithm (HFCM-GA) is presented in an attempt to reduce energy loss and increase the packet delivery ratio. Using node mobility and the node energy attribute, this protocol proposes a centralized cluster creation mechanism that produces optimal clusters. Node mobility, node energy, and node distance are additional criteria that a detached node considers while choosing its ideal cluster head. Simulation outcomes demonstrate that the recommended HFCM-GA is superior to the conventional routing protocols regarding the residual energy and coverage ratio. [ABSTRACT FROM AUTHOR]

Processing in Memory based on memristors is considered the most effective solution to overcome the Von Neumann bottleneck issue and has become a hot research topic. The execution efficiency of logical computation and in-memory data transmission is crucial for Processing in Memory. This paper presents a design scheme for data transmission and multi-bit multipliers within MAT (a data storage set in MPU) based on the memristive alternating crossbar array structure. Firstly, to improve the data transfer efficiency, we reserve the edge row and column of the array as assistant cells for OR AND (OA) and AND data transmission logic operations to reduce the data transfer steps. Furthermore, we convert the multipliers into multi-bit addition operations via Multiple Input Multiple Output (MIMO) logical operations, which effectively improves the execution efficiency of multipliers. PSpice simulation shows that the proposed data transmission and multi-bit multiplier solution has lower latency and power consumption and higher efficiency and flexibility. [ABSTRACT FROM AUTHOR]

With the rapid development of new power systems, diverse new power services have imposed stricter requirements on network resources and performance. However, the traditional method of transmitting request data to the IoT management platform for unified processing suffers from large delays due to long transmission distances, making it difficult to meet the delay requirements of new power services. Therefore, to reduce the transmission delay, data transmission, storage and computation need to be performed locally. However, due to the limited resources of individual nodes in the local power wireless communication network, issues such as tight coupling between devices and resources and a lack of flexible allocation need to be addressed. The collaborative allocation of resources among multiple nodes in the local network is necessary to satisfy the multi-dimensional resource requirements of new power services. In response to the problems of limited node resources, inflexible resource allocation, and the high complexity of multi-dimensional resource allocation in local power wireless communication networks, this paper proposes a multi-objective joint optimization model for the collaborative allocation of communication, storage, and computing resources. This model utilizes the computational characteristics of communication resources to reduce the dimensionality of the objective function. Furthermore, a mouse swarm optimization algorithm based on multi-strategy improvements is proposed. The simulation results demonstrate that this method can effectively reduce the total system delay and improve the utilization of network resources. [ABSTRACT FROM AUTHOR]

The present industrial Internet platform (IIP) is subjected to numerous complex security issues, and thus technologies that focus on achieving data security of the IIP has become top priority. The data security crisis of IIP gets mainly reflected in the malicious deletion and theft of data, the random access of terminal devices, and the low security of traditional security authentication methods. Blockchain technology adopts distributed network architecture, wherein through asymmetric encryption mechanism, it helps to solve problems associated with single point of downtime and privacy data leakage of central management of industrial Internet. In addition, considering the accept of random access and deletion of industrial interconnection terminal equipment, this paper uses the role access control mechanism based on Hyperledger, thereby regulating user access according to the user's role in the platform and management of terminal equipment through chain code. The paper includes the following contributions: (1) firstly, it expounds the research background and significance of the IIP and introduces the research status of the industrial Internet and blockchain at home and abroad. (2) Secondly, the IIP architecture is designed based on blockchain technology and implements a sensor node management and monitoring system. (3) Thirdly, the paper uses the gateway to preprocess the collected data, thereby reducing the data transmission delay and improving the real-time and effectiveness of the data. The paper finally implements neural network to evaluate the construction quality of industrial Internet sensor nodes and obtains promising test results. [ABSTRACT FROM AUTHOR]

The mixed reality conference system proposed in this paper is a robust, real-time video conference application software that makes up for the simple interaction and lack of immersion and realism of traditional video conference, which realizes the entire process of holographic video conference from client to cloud to the client. This paper mainly focuses on designing and implementing a video conference system based on AI segmentation technology and mixed reality. Several mixed reality conference system components are discussed, including data collection, data transmission, processing, and mixed reality presentation. The data layer is mainly used for data collection, integration, and video and audio codecs. The network layer uses Web-RTC to realize peer-to-peer data communication. The data processing layer is the core part of the system, mainly for human video matting and human-computer interaction, which is the key to realizing mixed reality conferences and improving the interactive experience. The presentation layer explicitly includes the login interface of the mixed reality conference system, the presentation of real-time matting of human subjects, and the presentation objects. With the mixed reality conference system, conference participants in different places can see each other in real-time in their mixed reality scene and share presentation content and 3D models based on mixed reality technology to have a more interactive and immersive experience. [ABSTRACT FROM AUTHOR]

The occurrence of 'sensing holes' not only hinders seamless data collection but also leads to misinterpretation of information in certain areas under extensive data analysis. In order to overcome this, various sensor relocation strategies have been proposed, but the existing relocation strategies revealed problems such as the ping-pong, shaded area, network disconnection, etc. This paper conducted research on relocation protocols in a distributed environment that is very suitable for real-world situations and efficiently recovering the problem of sensing holes. First, a simulation was performed on the distribution of the shaded area for data collection, which is a problem with the existing representative relocation protocol. After that, a data collection capability was newly added to the relay node, which had been in charge of only communication between cluster zones so far, and with this additional functionality, the performance of the revised sensor relocation algorithm was dramatically improved to overcome the existing problems. In addition, the performance and validity of the proposed algorithm were verified through various simulations. [ABSTRACT FROM AUTHOR]

This research paper examines the Information Visualization in Library and Information Science: Exploring Principles, Types, and Tools for Effective Data Communication. The paper provides a historical overview of information visualization, including its evolution, milestones, and key figures. It also explores different types of information visualization, including data visualization, scientific visualization, information design, visual analytics, infographics, and geospatial visualization. The paper then discusses the principles of information visualization, including visual perception, cognitive load, design principles, interaction principles, and usability principles. It also reviews different data visualization tools, such as web-based visualization tools, open-source visualization tools, and commercial visualization tools, and emerging technologies in information visualization. Additionally, the paper highlights challenges and opportunities in information visualization research, such as data quality and reliability, user-cantered design, ethical considerations, interdisciplinary collaboration, and emerging trends. Finally, the paper provides a summary of findings, implications for LIS practice, recommendations for future research, and a conclusion. [ABSTRACT FROM AUTHOR]

M2M is a concept and a general term for all technologies that enhance the communication and network capabilities of machines and equipment. It is a combination of different types of communication technologies. M2M enables all devices and users to share information, saving operation time and improving work efficiency. M2M is not a simple data transmission, but it is an intelligent and interactive communication between machines. Even if no real-time signal is sent, the machine will actively communicate according to the established program and select intelligently according to the obtained data. The communication system refers to the general term of the technology that completes the information transmission function. The modern communication system is mainly realized by the propagation of electromagnetic waves in free space or the transmission mechanism in the guiding medium. When the wavelength of the electromagnetic wave reaches the light wave range, such a telecommunication system is called an optical communication system, and the communication system in the other electromagnetic wave range is called an electromagnetic communication system, abbreviated as a telecommunication system. The purpose of this paper is to explore the study of wireless data sharing method for M2M mobile telecommunication systems in the 5G era. It is expected that the new algorithm will make the wireless resource allocation more reasonable and improve the resource utilization rate. The performance of the deterministic measurement matrix proposed in this paper is close to or even better than that of the random measurement matrix. The M-SP algorithm greatly improves the performance of the SP algorithm. The experimental results of this paper show that when the number of microbase stations is 10, the energy consumption of the direct algorithm is 175000 W, the energy consumption of the indirect algorithm is 350000 W, and the energy consumption of the direct algorithm is much smaller than that of the indirect algorithm. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT) system, data leakage can easily occur due to the differing security of edge devices and the different processing methods of data in the transmission process. Blockchain technology has the advantages of good non-tamperability, decentralization, de-trust, openness, and transparency, and it can protect data security on the Internet of Things. This research integrates the means by which data flow can be combined with blockchain technology to prevent privacy leakage throughout the entire transportation process from sender to receiver. Through a keyword search of the last five years, 94 related papers in Web of Science and IEEE Xplore were extracted and the complex papers and frameworks explained using a reconstruction graph. The data processing process is divided into five modules: data encryption, data access control, data expansion, data storage, and data visualization. A total of 11 methods combining blockchain technology to process IoT data were summarized. The blockchain application technology in the IoT field was summarized objectively and comprehensively, and a new perspective for studying IoT data flow was given. [ABSTRACT FROM AUTHOR]

In the development of technology in various fields like big data analysis, data mining, big data, cloud computing, and blockchain technology, security become more constrained. Blockchain is used in providing security by encrypting the sharing of information. Blockchain is applied in the peer-to-peer (P2P) network and it has a decentralized ledger. Providing security against unauthorized breaches in the distributed network is required. To detect unauthorized breaches, there are numerous techniques were developed and those techniques are inefficient and have poor data integrity. Hence, a novel technique needs to be implemented to tackle the new breaches in the distributed network. This paper, proposed a hybrid technique of two fish with a ripple consensus algorithm (TF-RC). To improve the detection time and security, this paper uses efficient transmission of data in the distributed network. The experimental analysis of TF-RC by using the metric measures of performance in terms of latency, throughput, energy efficiency and it produced better performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose and study an energy-efficient trajectory optimization scheme for unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks. In such networks, a single UAV is powered by both solar energy and charging stations (CSs), resulting in sustainable communication services, while avoiding energy outage. In particular, we optimize the trajectory design of UAV by jointly considering the average data rate, the total energy consumption, and the fairness of coverage for the IoT terminals. A dynamic spatial-temporal configuration scheme is operated for terminals working in the discontinuous reception (DRX) mode. The module-free, action-confined on-policy and off-policy reinforcement learning (RL) approaches are proposed and jointly applied to solve the formulated optimization problem in this paper. We evaluate the effectiveness of the proposed strategy by comparing it with other dynamic benchmark algorithms. The extensive simulation results provided in this paper reveal that the proposed scheme outperforms the benchmarks in terms of data transmission, energy efficiency and adaptivity of avoiding battery depletion. By deploying the proposed trajectory scheme, the UAV is able to adapt itself according to the temporal and dynamic conditions of communication networks. [ABSTRACT FROM AUTHOR]

The number of sensor nodes in the sensor network is massive, and the network running time is relatively long, with a large number of data sampled, forming the big data of the sensor network. As the energy and communication capacities of the sensor nodes are highly limited, the design of an effective optimization algorithm for the wireless transmission of distributed cable tunnel data and the application of the mobile edge computing method to accomplish the task of big data transmission efficiently has become a major challenge faced by the wireless sensor network design. In this paper, the mobile edge computing method is used to estimate the packet reception ratio (PRR) of the wireless link, and the average number of data transmission times in the wireless link is calculated based on the reception rate. On this basis, a mobile edge computing method is proposed, which can determine the optimal path from all sensor nodes to sink nodes, so as to ensure the minimum sum of the average link transmission times in the optimal path. According to the distributed environment of the sensor network, the mobile edge computing method is improved. The distributed implementation protocol of the mobile edge computing method is proposed to identify the optimal path with the smallest average number of transmission times. The smallest number of transmission times means the minimum energy consumption. This study is of important practical significance for the wireless sensor network with limited energy. Finally, the experiment has verified that the optimization model for the wireless transmission of the distributed cable tunnel data in this paper has relatively high performance and feasibility. [ABSTRACT FROM AUTHOR]

Based on mobile edge computing and user perception technology, this paper analyzes and discusses the respective advantages and disadvantages of the important optimization models and mobile models in the animation art design, as well as the wireless block data transmission mechanism and protocol. In order to solve the problem that user mobility cannot be sensed, a content-centric mobile edge animation art design mechanism based on user mobility perception is proposed. This mechanism comprehensively calculates the centrality of users' perception of nodes, the idle rate of animation design, and the staying time of users in a small area. The mobile edge network controller integrates the information of each edge user's perception node, calculates the importance of each edge user's perception node and prioritizes it, and selects the appropriate content animation to design the user perception node according to the ranking result. Finally, various simulation or platform test experiments were carried out for all the design schemes in this paper, and the experimental results were analyzed. The simulation experiment results show that compared with the traditional animation design mechanism, the animation art design system effectively reduces the average number of hops for users to obtain content by up to 15.9%, improves the hit rate of edge user perception node animation design by at least 13.7%, and reduces the traffic entering the core network by up to 32.1%. According to the comparison results, the various designs in this work can successfully use sensor data to preclassify migration tasks in the mobile edge network environment. Compared with the latest block data transmission protocol, it has a significant performance improvement, reducing the data distribution delay by 34.8%, thereby helping to improve the overall efficiency of mobile edge computing. [ABSTRACT FROM AUTHOR]

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With the rise of satellite-as-a-service subscriptions as technology improves, our paper examines how satellite operators (SOs) can choose between traditional channels for selling satellites, satellite-as-a-service (SataaS), and dual-channel strategies. In the space supply chain, commercial satellites display several distinct characteristics that differentiate them from other physical commodities in traditional supply chains, most notably their exposure to significant launch risks. In addition, SataaS suffers from the risk of secure data transmission. However, the emergence of novel technologies such as blockchain technology (BCT) could help to mitigate such risks. Therefore, our study analyzes the optimal strategy among the sell, SataaS, and dual-channel approaches for SOs by considering the above distinctive characteristics in the case without blockchain and the case with blockchain. Our paper reveals the existence of a dual-channel strategy that generates the highest profit for the SO and yields the highest consumer utility when launch success is high and when SataaS fixed costs are low. When the launch success rate is very high, the dual-channel strategy transforms into a pure sell strategy. After the adoption of BCT, when the cost of BCT is low, the SO's profit is enhanced in all three sales channels compared to the case without blockchain. Notably, in SataaS as well as in the dual-channel approach, the adoption of BCT always improves consumer utility compared to the case without blockchain. However, in the sell channel, the consumer benefits depend on the relatively low cost of blockchain customization. [ABSTRACT FROM AUTHOR]

Energy efficiency, real-time response, and data transmission reliability are important objectives during networked systems design. This paper aims to develop an efficient task mapping scheme to balance these important but conflicting objectives. To achieve this goal, tasks are triplicated to enhance reliability and mapped on the wireless nodes of the networked systems with Dynamic Voltage and Frequency Scaling (DVFS) capabilities to reduce energy consumption while still meeting real-time constraints. Our contributions include the mathematical formulation of this task mapping problem as mixed-integer programming that balances node energy consumption, enhancing data reliability, under real-time and energy constraints. Compared with the State-of-the-Art (SoA), a joint-design problem is considered in this paper, where DVFS, task triplication, task allocation, and task scheduling are optimized concurrently. To find the optimal solution, the original problem is linearized, and a decomposition-based method is proposed. The optimality of the proposed method is proved rigorously. Furthermore, a heuristic based on the greedy algorithm is designed to reduce the computation time. The proposed methods are evaluated and compared through a series of simulations. The results show that the proposed triplication-based task mapping method on average achieves 24.84% runtime reduction and 28.62% energy saving compared to the SoA methods. [ABSTRACT FROM AUTHOR]

This paper proposes the development of a wheelchair control system by combining a Mindwave electroencephalogram (EEG) sensor, an MPU6050 inertial sensor, an ultrasonic sensor, and internet of things (IoT) technology. Stroke survivors often face challenges in controlling their wheelchairs due to cognitive and physical impairments, requiring specific solutions to ensure their safety and comfort. The integration of the Mindwave EEG sensor captures the user's cognitive state, such as level of attention and focus, while the MPU6050's inertial sensor monitors physical movement and changes in head orientation. Additionally, the ultrasonic sensor is incorporated to detect obstacles and provide proximity information for safe navigation. This wheelchair control system is also linked with IoT technology to enable monitoring of the user's cognitive, physical and environmental condition, and remote assistance. In this paper, to implement this wheelchair control system, a T-BEAM with an ESP32 controller is used, which is equipped with GPS to provide wheelchair location data and LORA radio communication for remote data transmission, and a TTGO LORA32 to forward the data to Arduino IoT cloud service over Wi-Fi. The results of the development show that the wheelchair control system is safer and more monitored, and users are easier and more flexible in operating wheelchairs. [ABSTRACT FROM AUTHOR]