Search

Evolution of Wireless sensor networks (WSN) has found its identity in Wireless Body Area Networks (WBAN) to monitor the health status of adults undergone a surgery, etc. In addition, the mobility of the sensor nodes deployed on/in the human body plays a vital role in maintaining the accuracy of the physiological signal. In this paper, a Distributed Posture-based Traffic-Medium Access Control (DPT-MAC) protocol is proposed where the sensor nodes follow the Disc Communication model for the detection of neighbor nodes based on the proximity of the sensing range. Further, the modified IEEE 802.15.4 superframe structure determines the potential sleep posture of a subject to ensure a collision-free data transmission and prioritize the data traffic based on the criticality of the data like critical, periodic and nonregular data. The critical data detection occupies the channel on high priority and will have the probability to access the other channel space during the contention period of the noncritical data. The regular guaranteed time slot (GTS) is reduced from the tradition number of slots to five, which allows the nodes to handle the throughput. This uninterrupted data transmission minimizes the delay rate and collision ratio with any number of sensor nodes as the channel synchronization is strictly followed using the Carrier Sense Multiple Access/Collision Avoidance mechanism. Here, the optimization of the network quality parameters like energy consumption, throughput, delay and collision ratio assures a guaranteed increase in the Network Lifetime. The results show that the distributed topology outperforms the conventional star topology in sustaining the Network Lifetime. [ABSTRACT FROM AUTHOR]

This paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework "sense‐as‐you‐fly" based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes. [ABSTRACT FROM AUTHOR]

Silicon-based optical switches are integral to on-chip optical interconnects, and mode-division multiplexing (MDM) technology has enabled modes to function as carriers in routing, further boosting optical switches' link capacity. However, traditional multimode optical switches, which typically use Mach–Zehnder interferometer (MZI) structures and mode (de)multiplexers, are complex and occupy significant physical space. In this paper, we propose and experimentally demonstrate a novel demultiplexing-free dual-mode 3 × 3 thermal-optical switch based on micro-rings (MRs) and mode exchangers (MEs). All MRs are designed to handle TE1 mode, while the ME converts TE0 mode to TE1 mode, enabling separate routing of both modes. Bezier curves are employed to optimize not only the ME, but also the dual-mode 45° and 90° waveguide bends, which facilitate the flexible and compact layout design. Moreover, our structure can support multiple wavelength channels and spacings by adding pairs of MRs, exhibiting strong WDM compatibility. The switch has an ultra-compact footprint of 0.87 × 0.52 mm2. Under both "all-bar" and "all-cross" configurations, its insertion losses (ILs) remain below 8.7 dB at 1,551 nm, with optical signal-to-noise ratios (OSNRs) exceeding 13.0 dB. Also, 32 Gbps data transmission experiments validate the switch's high-speed transmission capability. [ABSTRACT FROM AUTHOR]

Edge computing enables prompt responses in IoT environments, such as the operation of autonomous vehicles and unmanned aerial vehicles. However, with the increase in sensor nodes, the computational burden on the computing node also increases. Specifically, data filtering and reduction at application nodes add to the energy burden for battery-operated devices. In this paper, we propose a preprocessing system at the application node that requires low power consumption for data transmission reduction. Based on our simulations, we identify the minimum data size needed to preserve the signal. We first design the preprocessing system using a hardware description language to evaluate its performance. Then, we implement the open-library-based MCU system, including the proposed preprocessing IP, to assess its operation and overhead. Our implementation of the preprocessing system reduces data transmission by 50% with acceptable information loss. Additionally, the area and power consumption after the logic synthesis of the preprocessing IP within the entire MCU system are evaluated at only 3.6% and 13.1%, respectively. By performing preprocessing using the MCU and proposed IP, nearly 74.4% power reduction is achieved compared to using the existing MCU core. [ABSTRACT FROM AUTHOR]

As a hot technology trend, the federated learning (FL) cleverly combines data utilization and privacy protection by processing data locally on the client and only sharing model parameters with the server, embodying an efficient and secure collaborative learning model between clients and aggregated Servers. During the process of uploading parameters in FL models, there is susceptibility to unauthorized access threats, which can result in training data leakage. To ensure data security during transmission, the Authentication and Key Agreement (AKA) protocols are proposed to authenticate legitimate users and safeguard training data. However, existing AKA protocols for client–server (C/S) architecture show security deficiencies, such as lack of user anonymity and susceptibility to password guessing attacks. In this paper, we propose a robust 2FAKA-C/S protocol based on ECC and Hash-chain technology. Our security analysis shows that the proposed protocol ensures the session keys are semantically secure and can effectively resist various attacks. The performance analysis indicates that the proposed protocol achieves a total running time of 62.644 ms and requires only 800 bits of communication overhead, showing superior computational efficiency and lower communication costs compared to existing protocols. In conclusion, the proposed protocol securely protects the training parameters in a federated learning environment and provides a reliable guarantee for data transmission. [ABSTRACT FROM AUTHOR]

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications, such as remote health monitoring, industrial monitoring, transportation, and smart agriculture. Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes. This paper presents a traffic-aware, cluster-based, and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks. The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies. The protocol considers multi-criteria attributes, i.e. energy, traffic load, and distance parameters to select the next hop for data delivery towards the base station. The performance of the proposed protocol is evaluated through the network simulator NS3.40. For different traffic rates, number of nodes, and different packet sizes, the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio, energy consumption, end-to-end delay, and network lifetime. For 100 nodes, the proposed protocol achieved a 13% improvement in packet delivery ratio, 10 ms improvement in delay, and 10 mJ improvement in average energy consumption over LoRaWAN. [ABSTRACT FROM AUTHOR]

Let = { T 1 , T 2 , ... , T k } be a set of k ≥ 2 spanning trees in a graph G. The k spanning trees are called completely independent spanning trees (CISTs for short) if the paths joining every pair of vertices x and y in any two trees have neither vertex nor edge in common except for x and y. Particularly, is called a dual-CIST provided k = 2. For data transmission applications in reliable networks, the existence of a dual-CIST can provide a configuration of fault-tolerant routing called protection routing. This paper investigates the problem of constructing a dual-CIST in the n -dimensional hierarchical folded cubic network H F Q n . The network is a two-level network using folded hypercube F Q n as clusters to reduce the diameter, hardware overhead and improve the fault tolerance ability. We propose a recursive algorithm to construct a dual-CIST of H F Q n in (2 2 n) time for n ≥ 2 , where the time required is the same scale as the number of vertices of H F Q n . Also, the diameter of each constructed CIST is 4 n + 1. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to optical communication systems by introducing a hybrid MGDM–FSO (Mode Group Diversity Multiplexing—Free Space Optical) system. The primary objective of this innovative system is to address the limitations of traditional communication systems, particularly in challenging environmental conditions. By combining the advantages of MGDM, which enhances transmission capacity in optical fibers, with the flexibility and high data rates offered by FSO technology, the hybrid MGDM–FSO system aims to overcome obstacles encountered during data transmission. In this study, we provide a detailed analysis of the proposed system's architecture and performance characteristics under various operating conditions. Specifically, we investigate both single-input single-output (SISO) and multiple-input multiple-output (MIMO) configurations of the MGDM–FSO system. Performance metrics such as received power levels, bit error rate (BER), and quality factor (Q-factor) are evaluated to assess the system's efficiency across different weather conditions. Numerical simulations are conducted to analyze the system's performance under a range of scenarios. Our findings indicate that under clear weather conditions, both SISO and MIMO configurations exhibit optimal performance, characterized by high Q-factor and low BER. However, as weather conditions deteriorate to heavy rain and heavy fog, the Q-factor of the received signal decreases, highlighting the impact of environmental factors on system performance. This study contributes valuable insights into the behavior of hybrid MGDM–FSO systems and emphasizes the importance of robust communication techniques to mitigate environmental challenges. The findings presented here have implications for the design and implementation of optical communication systems in real-world scenarios. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) play a critical role in ensuring safety and welfare applications for drivers and passengers amidst the escalating vehicular population in urban environments. The efficient functioning of VANETs hinges on addressing the challenge of load balancing among Road Side Units (RSUs). This paper introduces a groundbreaking approach aimed at enhancing real-time data transmission services within VANETs. The key contribution lies in the development of a multicast routing algorithm utilizing a geo-targeting protocol, facilitating simultaneous delivery of source data packets to multiple destinations. This innovative strategy aims to alleviate RSU congestion, thereby significantly enhancing the quality of real-time data transmission services. Moreover, this study presents advancements in the Statistical Match and Queuing algorithm, refining it over time to substantially mitigate network congestion and redundancy. Additionally, a Multi-Protocol Label Switching based algorithm is implemented to elevate service quality parameters, including end-to-end latency, packet loss, and overall network efficiency within in-vehicle networks. Importantly, this approach remains adaptable across various Layer two technologies, ensuring compatibility and scalability. Simulation results validate the efficacy of the proposed methodology, showcasing its superiority over existing methods. The findings underscore the innovative algorithms' prowess in addressing load balancing challenges across diverse scenarios, affirming their potential to significantly enhance VANET service quality. [ABSTRACT FROM AUTHOR]

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent's behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system's security in terms of authorized data access accuracy up to 55.49%, precision up to 43.15%, recall up to 55.49%, and F1-score up to 39.96%, respectively, as compared to state-of-the-art work. [ABSTRACT FROM AUTHOR]

One of the networking technologies is called a wireless sensor network, and what it does is link sensor nodes by using wireless communication as the channel of connection. A haphazard and random distribution of nodes is carried out throughout the deployment process within the network area. As a result, the distances between the nodes do not remain constant. The nodes have a high sensing capacity, but their range of perception and battery life are also severely limited. The wireless data may be sensed by all of the nodes inside their respective sensing regions. Consequently, there are a variety of new applications that make use of WSN applications, particularly in surveillance monitoring applications such as those used in hospitals, buildings, forests, and airspace monitoring. The transmission of the data to the appropriate server or data centre that is linked with the cloud or the internet is experiencing greater difficulties as a result of the growing amount of monitoring data. This indicates that a significant quantity of data is lost as a result of the fact that wireless transmission is affected by environmental factors such as precipitation, humidity, and wind storms. It is vital to create and execute an innovative routing protocol to deliver greater throughput and raise the overall Quality of Service of the WSN applications in order to solve the difficulties that have been outlined above. This study paper presents a metaheuristic algorithm based optimal routing protocol for WSN application, which boosts the overall quality of service of any surveillance monitoring applications. To achieve this goal, the protocol was designed for wireless sensor networks. It does so in an effective manner, resulting in decreased delay and energy consumption as well as enhanced throughput and PDR. Based on the findings of the experiments, it has been hypothesised that the proposed metaheuristic algorithm-based routing protocol is capable of performing better routing for any type of surveillance monitoring and remote monitoring, as well as successfully transmitting any type of data, including numerical, video, and image data. [ABSTRACT FROM AUTHOR]

The paper is devoted to the study of the steady-state transmission problem between two Herschel-Bulkley fluids in thin layers with different viscosities, yield limits and power law index. [ABSTRACT FROM AUTHOR]

The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response. [ABSTRACT FROM AUTHOR]

In the framework of the space-air-ground-ocean integrated network, the underwater acoustic sensor network (UASN) plays a pivotal role. The design of media access control (MAC) protocols is essential for the UASN to ensure efficient and reliable data transmission. From the perspective of differentiated services in the UASN, a service-aware and scheduling-based hybrid MAC protocol, named the SSH-MAC protocol, is proposed in this paper. In the SSH-MAC protocol, the centralized scheduling strategy is adopted by sensor nodes with environmental monitoring service, and the distributed scheduling strategy is adopted by sensor nodes with target detection service. Considering the time-varying data generation rate of sensor nodes, the sink node will switch the scheduling mode of sensor nodes based on the specific control packet and adjust the resource allocation ratio between centralized scheduling and distributed scheduling. Simulation results show that the performance of the SSH-MAC protocol, in terms of utilization, end-to-end delay, packet delivery ratio, energy consumption, and payload efficiency, is good. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to enhance spectral efficiency in communication systems by dynamically adjusting the mapping between cyclic permutation coding (CPC) and its injected form. By monitoring channel conditions such as interference levels and impulsive noise strength, the system optimises the coding scheme to maximise data transmission reliability and efficiency. The CPC method employed in this work maps information bits onto non-binary symbols in a cyclic manner, aiming to improve the Hamming distance between mapped symbols. To address challenges such as low data rates inherent in permutation coding, injection techniques are introduced by removing δ column(s) from the CPC codebook. Comparative analyses demonstrate that the proposed dynamic adaptation scheme outperforms conventional permutation coding and injection schemes. Additionally, we present a generalised mathematical expression to describe the relationship between the spectral efficiencies of both coding schemes. This dynamic approach ensures efficient and reliable communication in environments with varying levels of interference and impulsive noise, highlighting its potential applicability to systems like power line communications. [ABSTRACT FROM AUTHOR]

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively. [ABSTRACT FROM AUTHOR]

Fault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel cache replacement technique based on the notion of combining periodic popularity prediction with size caching. The popularity, size, and time updates characteristics are used to calculate the value of each cache item. When it comes to content replacement, the information with the least value is first eliminated. Simulation results show that the proposed method outperforms the current algorithms in terms of cache hit rate and delay. The hit rate of the proposed scheme is 15.3% higher than GDS, 17.3% higher than MPC, 20.1% higher than LRU, 22.3% higher than FIFO, and 24.8% higher than LFU when 350 different categories of information are present. In real-world industrial applications such as including supply chain management, smart manufacturing, automation energy optimization, intelligent logistics transportation, and e-healthcare applications, it offers a foundation for the selection of caching algorithms. [ABSTRACT FROM AUTHOR]

Digital twin technology, a new type of digital technology emerging in recent years, realizes real-time simulation, prediction and optimization by digitally modeling the physical world, providing a new idea and method for the design, operation and management of water conservancy projects, which is of great significance for the realization of the transformation of water conservancy informatization to intelligent water conservancy. In view of this, this paper systematically discusses the concept and development history of digital twin smart water conservancy, compares its differences with traditional water conservancy models, and further proposes the digital twin smart water conservancy five-dimensional model. Based on the five-dimensional model of digital twin water conservancy, the research progress of digital twin smart water conservancy is summarized by focusing on six aspects, namely digital twin water conservancy data perception, data transmission, data analysis and processing, digital twin water conservancy model construction, digital twin water conservancy interaction and collaboration and digital twin water conservancy service application, and the challenges and problems of digital twin technology in the application of smart water conservancy. Finally, the development trend of digital twin technology and the direction of technological breakthroughs are envisioned, aiming to provide reference and guidance for the research on digital twin technology in the field of smart water conservancy and to promote the further development of the field. [ABSTRACT FROM AUTHOR]

The advancement of the logistics industry has fostered the enhancement of operational efficacy within the socioeconomic domain. However, the current inevitable privacy leaks in the process of logistics transportation have seriously affected the development of the industry, which led to a crisis of consumer trust and even caused economic recession. This paper proposes a self-sovereign identity privacy protection scheme tailored specifically for logistics transportation contexts. First, the scheme entails furnishing users with one-time-use tokens while establishing decentralized identities capable of concealing identity information and ensuring the secure transmission of data. Furthermore, the scheme integrates fuzzy identity-based encryption to encrypt identity information, thereby guaranteeing the confidentiality and integrity of logistics user identities along with their associated data. Compared with other schemes, this scheme exhibits superior security in the realm of logistics transportation. Its targeted encryption technology and self-sovereignty strategies address the critical issue of privacy leakage, thereby safeguarding consumer privacy rights and interests while facilitating the sustainable development of the logistics industry. [ABSTRACT FROM AUTHOR]

The optimal design of Multiple-Input Multiple-Output (MIMO) antennas is significant for next-generation smartphone solutions with the appearance of further communication. This paper describes the design, simulation, and measurements of a ten-element MIMO antenna for n79 at 4.4–5.0 GHz, n102 at 5.925–6.425 GHz, and n104 at 6.425–7.125 GHz ranges that will be implemented in today's smartphones. The proposed antenna system has a maximum dimension of 158 mm × 60 mm and will use the low-cost FR-4 as the dielectric material. The design procedure is carried out with a lot of care to ensure that it does not compromise the spatial characteristics and at the same time minimizes the mutual coupling between the nearby antenna dimensions. High isolation and low ECC values are simulated using capturing techniques on the antenna geometry and its element position for proper MIMO performance improvement. The constructed antenna system portrays good performances in the intended bands as evidenced by the reflection coefficient values, radiation patterns, and individual gains. The performance of the above proposed ten-element MIMO antenna system in terms of bandwidth efficiency, diversity gain, and channel capacity loss is discussed. It also satisfies the demands of current standards of wireless communications and provides future possibilities for higher data transmission rates and message reliability in next-generation smartphone applications. Here, the application of the FR-4 substrate is remarkable as it provides comparatively better performance with significantly easier manufacturing and less cost, and thus, the design has good possibilities to be commercially viable. This work can be understood to form part of ongoing research in enhancing the advancement of the smartphone's antenna technology, which offers a scalable approach to the prospective spectrum altitude and the emergent surge in the data rates of mobile communication. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) continues to proliferate, the demand for efficient and secure data processing at the network edge has grown exponentially. Fog computing, a paradigm that extends cloud capabilities to the edge of the network, plays a pivotal role in meeting these requirements. In this context, the reliable and trustworthy forwarding of data is of paramount importance. This paper presents an innovative mechanism designed to ensure the trustworthiness of data forwarding in the context of MQTT (Message Queuing Telemetry Transport), a widely adopted IoT communication protocol. Our proposed mechanism leverages the inherent advantages of MQTT to establish a robust and secure data-forwarding scheme. It integrates fog computing resources seamlessly into the MQTT ecosystem, enhancing data reliability and security. The mechanism employs trust models to evaluate the credibility of IoT devices and fog nodes involved in data forwarding, enabling informed decisions at each stage of the transmission process. Key components of the mechanism include secure communication protocols, authentication mechanisms, and data integrity verification. The proposed secure communication protocols (TLS/SSL, MQTTS, and PKI) and data integrity verification methods (MAC, digital signatures, checksums, and CRC) provide a robust framework for ensuring secure and trustworthy data transmission in IoT systems. These elements collectively contribute to the establishment of a reliable data forwarding pipeline within MQTT. Additionally, the mechanism prioritizes low-latency communication and efficient resource utilization, aligning with the real-time requirements of IoT applications. Through empirical evaluations and simulations, the research demonstrates the effectiveness of our proposed mechanism in improving the trustworthiness of data forwarding, while minimizing overhead, as the experiment was conducted with 15 fog nodes, and the maximum Level of Trust (LoT) score was 0.968, which is very high, with an estimated accuracy of 97.63%. The results indicate that our approach significantly enhances data security and reliability in MQTT-based IoT environments, thereby facilitating the seamless integration of fog computing resources for edge processing. [ABSTRACT FROM AUTHOR]

Enhancing spectral efficiency in non-line-of-sight (NLoS) environments is essential as 5G networks evolve, surpassing 4G systems with high information rates and minimal interference. Instead of relying on traditional Orthogonal Multiple Access (OMA) systems to tackle issues caused by NLoS, advanced wireless networks adopt innovative models like Non-Orthogonal Multiple Access (NOMA), cooperative relaying, Multiple Input Multiple Output (MIMO), and intelligent reflective surfaces (IRSs). Therefore, this study comprehensively analyzes these techniques for their potential to improve communication reliability and spectral efficiency in NLoS scenarios. Specifically, it encompasses an analysis of cooperative relaying strategies for their potential to improve reliability and spectral efficiency in NLoS environments through user cooperation. It also examines various MIMO configurations to address NLoS challenges via spatial diversity. Additionally, it investigates IRS settings, which can alter signal paths to enhance coverage and reduce interference and analyze the role of Unmanned Aerial Vehicles (UAVs) in establishing flexible communication infrastructure in difficult environments. This paper also surveys effective energy harvesting (EH) strategies that can be integrated with NOMA for efficient and reliable energy-communication networks. Our findings show that incorporating these technologies with NOMA not only enhances connectivity and spectral efficiency but also promotes a stable and environmentally sustainable data communication system. [ABSTRACT FROM AUTHOR]

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]

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]