Data Transmission Mechanism Research Articles

A novel blockchain-based system for improving information integrity in building projects from the perspective of building energy performance

Global population growth has long been intertwined with environmental concerns and sustainable development. The building sector, a significant energy consumer worldwide, contributes substantially to total energy consumption and greenhouse gas emissions. With the projected global population projected to reach 9.7 billion by 2050, building energy consumption is expected to rise continuously, emphasizing the need to optimize energy performance for environmental sustainability. Despite efforts by many countries to formulate energy conservation objectives and strategies, empirical evidence reveals a substantial disparity between designed and actual building energy consumption, known as the building energy performance gap (BEPG). This gap poses a significant challenge to energy conservation efforts, and the lack of information integrity is a significant contributor to this gap. To address the challenge of inadequate information integrity in building energy projects, this research proposes an innovative solution based on blockchain technology. By utilizing blockchain's decentralized, transparent, and tamper-resistant nature, the proposed model aims to enhance information transmission mechanisms among stakeholders. Ten key energy-related stakeholders and various information flows within building projects are identified. Based on this, an exploratory architectural information management model incorporating blockchain technology is developed. The model features functionalities such as data recording, retrieval, transmission, and incentive mechanisms to ensure data immutability and reliable access security. Through a case study, the model's performance is evaluated in terms of storage cost, latency, and privacy, demonstrating significant time savings in uploading and transferring files. The proposed blockchain-based model offers a feasible solution to the challenges of inadequate information integrity in building energy projects, promoting collaboration and accurate information transmission. This model contributes to improving project outcomes and advancing sustainable development in the construction industry.

IoT-based framework for optimizing energy efficiency and reliability in acoustic sensor networks using mobile sinks

In today’s world, there is an increasing demand for environmental monitoring, surveillance, and oceanographic research, which poses challenges in improving energy efficiency and data transfer reliability in Acoustic Sensor Networks. Existing methods face hurdles due to limited energy resources and unreliable data transmission. We propose a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM) to address these issues. This framework optimizes energy consumption and enhances data reliability by incorporating advanced energy management strategies such as adaptive duty cycling and efficient data transmission mechanisms by minimizing forwarding nodes. Simulation results demonstrate that REEFSM reduces energy consumption by up to 43% and increases data reliability by 35% compared to protocols like EERBCR and DEADS. REEFSM ensures zero dead nodes, minimizes packet drops, and maintains high data accuracy throughout the simulation. This research outcome provides a sustainable and reliable solution for energy-efficient data collection in underwater environments. The future research directions, including integrating autonomous path planning, energy harvesting, and machine learning techniques, hold great potential for further advancements in the field.

Research on Network Security and Privacy Protection Technology of Autonomous Vehicle

With the rapid development of information technology and the increasing maturity of intelligent transportation systems, autonomous vehicles, as an important carrier of future travel, are gradually moving from concept to commercial application. However, the highly connected and intelligent nature of autonomous vehicles also makes them face unprecedented cybersecurity and privacy protection challenges. This paper deeply discusses the security threats and privacy disclosure risks of autonomous vehicles in network architecture, data transmission, data processing and other links, and systematically reviews the current technical means and research progress to address these challenges. This paper Outlines the data collection and transmission mechanism of autonomous vehicles, including the collection and processing of sensor data as well as the communication process between vehicles and the cloud, other vehicles and transportation infrastructure, and analyzes in detail the network security threats faced by autonomous vehicles, including remote control attacks, malware attacks, communication hijacking, etc. These threats can lead to serious consequences such as vehicle failure, data breach, or system crash. At the same time, the paper also points out the challenges faced by autonomous vehicles in terms of privacy protection, such as the risk of leakage of sensitive data such as personal location information and driving habits, as well as the risk of data abuse and sharing. In order to address the above challenges, this paper focuses on the technical research progress of cybersecurity and privacy protection of autonomous vehicles. In the aspect of network security, this paper discusses the key technical means such as data encryption, access control, intrusion detection and response system, and emphasizes the importance of security hardware and software in improving the overall security of the system. In terms of privacy protection, this paper proposes strategies such as anonymization and desensitization technology, privacy protection protocols and standards to protect users' privacy from invasion.

Efficient data transmission mechanisms in energy harvesting wireless body area networks: A survey

Efficient data transmission mechanisms in energy harvesting wireless body area networks: A survey

Two-level parallel load balancing strategy for accelerating DSMC simulations in near-continuum gases

The Direct Simulation Monte Carlo (DSMC) algorithm is widely employed for simulating rarefied gas flows and is increasingly applied in near-continuum regimes for research and engineering purposes. However, its computational demands, notably load imbalance and extended simulation time, hinder widespread adoption. Addressing these challenges, this paper introduces the Two-Level parallel load balancing strategy. This novel approach combines thread-level and multi-process parallelism to enhance load balancing and reduce simulation time. Key features include a thread-level load-decoupling strategy implemented via OpenMP and a multi-process load balancing mechanism employing distributed memory via MPI. Building upon our previous [Formula: see text] [L. Li, W. Ren and B. Zhang, J. Aeronaut. Astronaut. Aviat. Ser. A 46, 88 (2014)] approach, the load balancing mechanism utilizes Stop At Risk (SAR) criteria for repartitioning with METIS. Additionally, a specialized data transmission mechanism utilizing MPI nonblocking communication minimizes global communication between processes. Validation and evaluation are performed using four hypersonic flow cases around a cylinder and sphere, demonstrating significant improvements. Notably, the proposed strategy achieves [Formula: see text] enhancement over the [Formula: see text] strategy under 512 CPU cores compared to 16 CPU cores, and reduces between-process communication time with [Formula: see text]. These advancements contribute to enhancing the effectiveness of the DSMC algorithm in near-continuum aerodynamic simulations.

An Anonymous and Secure Data Transmission Mechanism With Trajectory Tracking for D2D Relay Communication in 3GPP 5G Networks

An Anonymous and Secure Data Transmission Mechanism With Trajectory Tracking for D2D Relay Communication in 3GPP 5G Networks

TRAFFIC ANOMALY DETECTION IN VEHICLE BUS BY RECURRENT LSTM NEURAL NETWORK

Modern high-end cars have many electronic control units for driving assistance that combine huge amounts of data about the functioning of car components. A significant part of these vehicles use a controller area network for communication between electronic units. Controller area network is a simple and reliable network protocol that due to its simplicity lacks any security mechanisms for data transmission. The problem of controller area network vulnerability is worsening as constantly growing amounts of data between cars, road infrastructure and the Internet. The traffic of attacks on controller area networks can be treated as abnormal that allows using anomaly detection methods for their recognition. In this work we propose the recurrent long short-term memory encoder-decoder neural network for controller area network attacks detection.

GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring

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.

Integrated Fault Detection System for Underground Lines Using ESP32 and LORA Communication Using IOT

Abstract: This paper introduces an advanced fault detection system tailored for underground line infrastructure, featuring the integration of ESP32 microcontroller technology and LORA communication protocols. The primary objective of this system is to enhance the reliability and efficiency of fault detection processes, crucial for maintaining the integrity of underground lines. At the core of this system lies the ESP32 microcontroller, serving as the central processing unit responsible for coordinating various functionalities. The detection mechanism incorporates six toggle switches strategically positioned along the underground lines, each assigned to monitor a specific location segment. In the event of a fault occurrence, the corresponding toggle switch is triggered, promptly initiating the fault detection process. Upon fault detection, the system seamlessly orchestrates the transmission of GPS location data to a LORA communication device. This data transmission mechanism ensures rapid dissemination of critical information regarding the fault's precise location. Additionally, an LED indicator is employed to provide immediate visual feedback, enabling quick identification of fault occurrences. Moreover, a LCD display unit offers realtime updates on the system's operational status, facilitating efficient monitoring and management. On the receiving end, the LORA communication device securely receives the transmitted data and forwards it to the cloud infrastructure using IOT technology. This cloud integration enables seamless data aggregation, storage, and analysis, empowering stakeholders with valuable insights into underground line health and performance.

From Conventional to Programmable Matter Systems: A Review of Design, Materials, and Technologies

Programmable matter represents a system of elements whose interactions can be programmed for a certain behavior to emerge (e.g., color, shape) upon suitable commands (e.g., instruction, stimuli) by altering its physical characteristics. Even though its appellation may refer to a morphable physical material, programmable matter has been represented through several approaches from different perspectives (e.g., robots, smart materials) that seek the same objective: controllable behavior such as smart shape alteration. Researchers, engineers, and artists have expressed interest in the development of smart modeling clay as a novel alternative to conventional matter and classical means of prototyping. Henceforth, users will be able to do/undo/redo forms based on computed data (CAD) or interactions (sensors), which will help them unlock more features and increase the usefulness of their products. However, with such a promising technology, many challenges need to be addressed, as programmable matter relies on energy consumption, data transmission, stimuli control, and shape formation mechanisms. Furthermore, numerous devices and technologies are created under the name of programmable matter, which may pose ambiguity to the control strategies. In this study, we determine the basic operations required to form a shape, then review different realizations using the shape shifting ability of programmable matter and their fitting classifications, and finally discuss potential challenges.

A Lightweight, Efficient, and Physically Secure Key Agreement Authentication Protocol for Vehicular Networks

In the contemporary era, Vehicular Ad Hoc Networks (VANETs) have emerged as a vital technology in intelligent transportation systems, substantially enhancing the overall travel experience by providing advanced services to vehicles while ensuring driver safety. Despite the notable improvements, the inherent complexity of VANETs presents persistent security challenges, encompassing issues such as privacy preservation for vehicles, message authentication, and constraints in computational power and network bandwidth. Various authentication protocols have been designed for VANETs. However, many of these protocols exhibit significant vulnerabilities, rendering them insecure and unreliable in the face of diverse security threats, such as denial of service, replay, forgery, and impersonation attacks. Moreover, some existing schemes encounter limitations, including high computational complexity and the introduction of additional communication overhead and computational costs. To tackle these concerns, we designed a lightweight and secure identity authentication protocol based on elliptic curve cryptography with the objective of furnishing an effective and secure data transmission mechanism across a public communication channel for the Internet of Vehicles. In addition, we introduce Physically Unclonable Functions (PUFs) to ensure physical layer security during the communication process. A detailed security analysis demonstrates that the proposed protocol is resilient against various attacks. Through a comparative analysis with existing relevant protocols, in scenarios with a high density of vehicles, the algorithm demonstrates significantly lower computational costs and communication overhead than the related protocols, indicating that the proposed protocol is lightweight and efficient. Consequently, the empirical findings indicate that our protocol surpasses others in terms of reliability, user convenience, and practicality for ensuring secure data transmission within VANETs.

Free space optical communication system: a review of practical constraints, applications, and challenges

Abstract FSO (free space optical) communication holds emanated position in the role of propitious innovation with exorbitant-speed using the data transmission mechanism without the wire, propounding an accomplishable explication to the surging need for bandwidth in communication networks. This research paper probes into the improvements and obstacles in FSO communication systems, pursuing to anticipate a compendious encapsulation of the state-of-the-art establishment in this discipline. The evaluation incorporates a meticulous inspection of vital constituents such as laser sources, modulators, and receivers, elucidating their contribution to accomplishing coherent data transfer. The scrutinizing also investigates innumerable atmospheric circumstances affecting FSO links, including weather conditions and turbulence, and puts forward ingenious approaches to alleviate their repercussion on communication performance. Furthermore, the paper explores the homogenization of ultra-modern signal processing techniques to amplify the credibility and robustness of FSO systems, corroborating seamless communication even in challenging environments. Moreover, the research inquiries into the neoteric experimental framework and real-world execution of FSO communication, shedding light on heuristic contemplation, and substantiating theoretical unearthing. The conclusion of this research shells out treasures perception of the ongoing attempt to optimize FSO communication for variegated applications, including, inter-satellite communication, high-speed internet access, and last-mile connectivity. As FSO perpetuates progress, this paper suffices as a recent and inclusive resource for researchers, engineers, and policymakers maneuvering the edges of optical communication technologies.

Remote Serial Port Virtualization Management System based on Brotli Compression Algorithm

With the rapid development of information technology, data communication and management systems are becoming more and more important in various industries. Especially in remote device management and data center operations, effective data processing and transmission mechanisms are crucial to ensuring system performance and reliability. In this context, serial port virtualization technology is particularly important because it allows remote devices to be controlled and monitored over the network, so that physical location constraints are no longer an obstacle to efficiency and effectiveness. However, as the amount of data increases, the efficiency of data transmission and storage becomes a key issue. Although traditional compression algorithms solve this problem to a certain extent, they often fail to provide ideal compression ratios and processing speeds when processing large amounts of data, especially in environments with limited bandwidth or limited computing resources. Therefore, finding an efficient compression algorithm has become the key to improving the performance of the remote serial port virtualization management system. This article adopts the method of integrating the Brotli algorithm into the system to solve the data compression and transmission efficiency problems faced in the traditional serial port virtualization system, thereby improving the overall performance and user experience.

An optimized deep learning-based fault-tolerant mechanism for energy efficient data transmission in IoT

<p class="Author">Artificial Intelligence (AI) based framework for the Internet of Things (IoTs) have gained worldwide attention in recent years, mainly with the explosion of Micro-Electro-Mechanical Systems (MEMS) technology. Basically, MEMS has facilitated the development of tiny and smart sensors for the IoT-based framework. An AI-based IoT model is an emerging technology that helps in both fault-tolerant as well as energy-efficient data transmission purposes. For efficient data transmission in an IoT-based model, the concept of Wireless Sensor Network (WSN) plays a vital role that comprises various sensor nodes that communicate together to monitor and gather information from the various Region of Interest (RoI). Generally, sensor nodes are tiny in size and having a small battery life, limited sensing, processing, and communication capabilities. So, the fault-tolerant mechanism for energy efficient data transmission in IoT is a good initiative with the combination of Deep Learning as an AI approach. In this research article, the concept of deep learning-based fault-tolerant mechanism in IoT frameworks for energy efficient data transmission is proposed in an optimized manner. Here, the concept of the Grouped-Bee Colony (GBC) algorithm is designed for the fault detection mechanism as an optimization approach along with the Deep Learning as an AI.<em></em></p>

An Enhanced Location-Aided Ant Colony Routing for Secure Communication in Vehicular Ad Hoc Networks

The dynamic characteristics of vehicular ad hoc networks (VANETs) demand reliable and secure communication over wireless media. However, there are significant contradictions in autonomous vehicular systems related to security and privacy. Furthermore, VANETs require seamless data transmission to exchange time-critical data about accidents and traffic. Therefore, VANETs require secure seamless data transmission mechanisms to provide secure reliable communication without communication interruption to exchange time-critical data between vehicles. This paper proposes an Enhanced Location-Aided Ant colony Routing (ELAACR) for VANETs to achieve seamless secure data transmission. The proposed ELAACR is a two-fold scheme in which Phase 1 implements Location-Aided Key Management (LAKM) and Phase 2 implements Ant Colony Routing (ACR). An ACR combined with an LAKM pattern for offering secure and reliable communication. The LAKM scheme works based on the group signature concept to construct a secure network, and an ACR is used to find an efficient secure shortest path for seamless data transmission. The proposed work is simulated in Network Simulator (NS 2.35). By using an efficient and secure shortest path with secure seamless data transmission, the proposed ELAACR achieves an average throughput (T) of 640 Kbps, packet loss rate (PLR) of 0.018%, packet delivery ratio (PDR) of 98.5%, overhead (OH) of 0.157%, and 0.16 s end-to-end delay (EED) compared to Enhanced Hybrid Ant Colony Optimization Routing Protocol (EHACORP) and Fuzzy Based Ant Colony Optimization (F-ANT). The simulation results show that the proposed ELAACR registers better performance in all aspects compared to the existing EHACORP and F-ANT.

Incentive-based demand response optimization method based on federated learning with a focus on user privacy protection

Considering the flexible capacity and privacy needs of numerous flexible energy users in the context of demand response (DR), this study establishes an influence model (IM) to describe the DR participation capabilities of users considering privacy budget. Using the designed Stackelberg game mechanism that can achieve optimal selection for DR responders from users with different characteristics described by IM, a federated learning (FL)-based optimization method that uses differential privacy (DP) as the data transmission mechanism for DR economic optimal dispatch is proposed. Ideas of controlling the number of participating users and financially compensating for the privacy leakage risk by the FL-based optimization method are the guarantees for users with private data to participate in DR. The performance of the proposed optimization method is also compared with that of the Moth-flame optimization algorithm in a case study, and the guiding value of the former in selecting among user groups with different characteristics is then discussed. Results show that the proposed method exhibits good economic benefits and universal applicability.

Tampering attack detection for remote interval observer

In cyber–physical systems (CPSs), secure estimation observes the states of a physical plant remotely with potentially attacked data. Most attacks in CPSs are carefully designed such that they can not only ruin the estimation but also bypass the detector. These attacked estimations would corrupt decision-making and control, leading to decreased production efficiency and even equipment damage. Therefore, detecting these attacks is of great importance. It is noted that one of the reasons that these attacks can be successfully launched is that attackers can inject arbitrary false data into the communication channel at any time step, which makes it possible that the transmission data is tampered with by any designed attack sequences. In this paper, a modified data transmission mechanism is deployed to limit the attacker’s ability, and attack detectors are designed for detecting tampering attacks. Specifically, first of all, a one-bit event-triggered mechanism is deployed to binarize the transmitted data. Secondly, a primary detector is deployed to detect non-binary data received by the observer, and hence the attacker’s ability is greatly reduced. Thirdly, a main detector is designed, which definitely detects tampering attacks that can bypass the primary detector.The necessary and sufficient conditions for definite attack detection are given, with which the gain of interval observable and the threshold of the event-trigger mechanism are designed. Finally, simulation results are presented to verify the analysis.

A simplified optimization for resource management in cognitive radio network-based internet-of-things over 5G networks

<span lang="EN-US">With increasing evolution of applications and services in internet-of-things (IoT), there is an increasing concern of offering superior quality of service to its ever-increasing user base. This demand can be fulfilled by harnessing the potential of cognitive radio network (CRN) where better accessibility of services and resources can be achieved. However, existing review of literature shows that there are still open-end issues in this regard and hence, the proposed system offers a solution to address this problem. This paper presents a model which is capable of performing an optimization of resources when CRN is integrated in IoT using five generation (5G) network. The implementation uses analytical modeling to frame up the process of topology construction for IoT and optimizing the resources by introducing a simplified data transmission mechanism in IoT environment. The study outcome shows proposed system to excel better performance with respect to throughput and response time in comparison to existing schemes.</span>

A Multi-Phase SMO-Based MAC Protocol for IoT-Enabled WBAN Systems

This paper proposes an optimised Medium Access Control (MAC) protocol to minimise the number of channel access collisions and to improve the bandwidth efficiency of IoT-based Wireless Body Area Networks (WBANs). The frame structure of the proposed MAC protocol consists of three different phases, namely Contention Phase (CP), Transmission Phase (TP), and Inactive Phase (IP). During CP, the proposed protocol prioritises the life-sensitive data. Based on allocated priority, the communicating sensor nodes (SNs) compete for channel access. During TP, the successful SNs transmit their data. We use TDMA mechanism for data transmission and Spider Monkey Optimisation (SMO) technique to improve the bandwidth efficiency of the system by selecting the best possible TDMA time slot according to the data packet size of the SNs. During IP, the SNs go into sleep mode to save energy. We simulate the scenario on MATLAB simulator. The simulation results show that the proposed SMO-MAC improves the throughput by 22.23%, 14.00%, and 17.62%, residual energy by 60.76%, 81.17%, and 124.34%, communication reliability by 70.32%, 52.60%, and 52.60%, and data packet transmission delay by 80.74%, 70.10%, and 77.40% as compared to DRT-MAC, PSO-MAC, and GA-MAC, respectively.

An Empirical Study of Destructive Nodes Characterization in Wireless Networks

Mobile Ad hoc Networks (MANETs) are deployed in various new public and domestic environments, going up to new needs in terms of concert and effectiveness. According to the wireless dynamic nature, some needed services like security for network maintenance, trust-based routing and resource management among network nodes are not carried out as good as expected. Also, the ad-hoc networks are vulnerable to secure communications and multiple attacks can participate in various layers of a network stack. Destructive nodes have chances to change or discard routing specifications, sometimes it can send false routes to capture source data packets to pass through themselves. Some protocols have been designed to address the complication from secure data communication. Even though, a secure protocol cannot handle all kinds of attack detection and elimination in all situations. New secure data communication wireless protocols need to focus these challenges, because security care is not natively built in MANET. Therefore, in this research paper, analysis of destructive nodes characterization and impacts on wireless networks investigated the multiple attacks behaviour, activities of the attacks all through neighbour selection, path establishment from source to destination, creating awareness of attack presence detection knowledge to the normal devices during path discovery and data transmission mechanisms. Legitimate nodes need to be building with secure transmission knowledge to make sure trusted communication, ensure validation, honesty, and privacy to classify the attacks in MANETs.