Information Transmission Path Research Articles

Energy Optimization Approach in Wireless Sensor Network

In recent years, the importance of routing protocols in wireless sensor networks and the Internet of Things has been demonstrated by the numerous studies that have been presented in this area. because it is crucial to the network's functionality and effectiveness. Among routing's main goals are lowering latency, enhancing reliability, and averting network collisions. Additionally, it can be claimed that the type of network routing affects service quality, network longevity, and network traffic load control. Thus, in order to lower routing error and boost network performance and efficiency, the firefly is employed in this study to identify the best information transmission path within the network. It is also employed to reduce energy consumption. Taking into account the ability of evolutionary algorithms to optimize specified objective functions, we attempted to lower the network's energy consumption. The network can enhance the number of sending packets with the beginning energy of 3 joules from 1480 rounds to 1798 rounds, based on the proposed algorithm's results. Additionally, by contrasting the approaches described in the same article, it was discovered that the suggested approach can send roughly 40.11% more packets than the RM-LB algorithm and 1.9573% more than the OMS-LB technique. Additionally, it was discovered that by raising the starting energy to 150 joules, the suggested technique outperformed the other two algorithms by 51.23% and 45.17%, respectively, and was able to lengthen the longevity. This was in comparison to the RM-LB method and the OMS-LB method.

About restoration of information transmission paths in MPLS network

About restoration of information transmission paths in MPLS network

Data transmission path planning method for wireless sensor network in grounding grid area based on MM‐DPS hybrid algorithm

AbstractAt present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption.

Reliability analysis for vertical integration of protection, measurement, merge unit, and intelligent terminal device

The reliability analysis of vertically integrated protection devices is crucial for designing International Electrotechnical Commission (IEC) 61850-based substations. This paper presents the hardware architecture of a four-in- one vertically integrated device and the information transmission path of each function based on the functional information transmission chain of protection devices, measurement and control devices, merging units, and intelligent terminals. Additionally, a reliability analysis model of the protection device and its protection system is constructed using the fault tree analysis method while considering the characteristics of each module of the vertically integrated device. The stability probability of the protection system in each state is analyzed by combining the state-transfer equations of line and busbar protection with a Markov chain. Finally, the failure rate and availability of the protection device and its protection system are calculated under different ambient temperatures using a 110 kV intelligent substation as an example. The sensitivity of each device module is analyzed.

Analysis of the information transmission path of national sports culture based on gradient descent algorithm in the era of informationization

Abstract The digitization inheritance of national sports culture is an inevitable requirement of the new era, so studying its communication characteristics in the digital age is necessary. In this paper, the cognitive semantics of its cultural imagery is obtained based on the analysis of the connotation of national sports culture, and the informational imagery is extracted based on the cultural semantic analysis. The fast gradient descent algorithm optimizes the sampling and dissemination process of information during establishing the SIRI communication network for cultural imagery. Changes in the state and popularity of the dissemination nodes were explored by changing the external social environment and the input of the information carrier. When the probability of social environment increases from 0.15 to 0.95, the node with state S transforms into an information dissemination node faster and finally tends to a stable value of 0. In terms of information carriers, the degree of the flow of video dissemination will be graded higher than other carriers, and the popularity of video dissemination is 2.45%, 3.14%, and 5.21% higher than audio, image, and text, respectively, when the dissemination reaches the 4th generation. The information dissemination model optimized using a gradient descent algorithm can accurately capture the dissemination characteristics of national sports culture, thus proposing a better information transmission path.

A Distributed Cooperative Continuous Coverage Strategy of UAVs for MEC Services of Smart Grids Considering Energy Consumption

<p>Unmanned aerial vehicles (UAVs) play an essential role in scenarios such as smart grid data collection and power line inspection for their maneuverability and low cost. However, the most obvious defect of UAVs is still the energy problem. The battery life of UAVs is too short of completing long-term tasks. Therefore, this paper proposes a distributed cooperative and continuous coverage strategy for UAVs. When the UAV is short of power, we select the path through the relay algorithm and send backhaul information such as the remaining power to the macro base station (mBS). mBS will send auxiliary UAVs to replace UAVs with weak power to complete the task of UAV replacement. We jointly optimize the relay information transmission path and UAV trajectory to achieve the goal of minimizing the total energy consumption of the system. The simulation results show that the optimization algorithm can significantly reduce the energy consumption of UAVs.</p> <p> </p>

BIoTS-Path: Certification Transmission of Supply Chains Based on Blockchain–Internet of Things Architectures by Validating the Information Path

A food traceability system (FTS) can record information about processes along a production chain to determine their safety and quality. Under the Internet of Things (IoT) concept, the communication technologies that support FTSs act as platforms for mass access to information with limited security. However, the integrity of the collected data is not immune to security attacks. This paper proposes a point-to-point information transmission path with no edges or access boundaries (no intermediaries) to transmit data with integrity. This route is possible thanks to the architectural articulation of a hardware device (sensor BIoTS) at the perception layer, with the Blockchain architecture at the application layer. This pairing makes an ecosystem with the ability to trace and certify in parallel the products, the supply chain processes, and the data recorded in it possible. The design of the security testing ecosystem is based on the theoretical and technical principles of cybersecurity. It is executed through mathematical models that define the probability of attacks’ success against the transmitted data’s integrity. The security tests performed allow for establishing that this BIoTS information transmission route is unlikely to suffer from transmission vulnerabilities and that it is not prone to security attacks against integrity. This work paves the way toward fully integrating Blockchain technology in dedicated IoT architectures.

Risk evolution model of marine traffic via STPA method and MC simulation: A case of MASS along coastal setting

Marine traffic safety for Maritime Autonomous Surface Ship (MASS)is affected by the maritime environment and complex human-machine technical systems, and it is necessary to reveal its risk evolution characteristics involving new technology. The novel risk evolution model based on the system-theoretic process analysis (STPA)method for specific scenarios is established to determine the risk mechanism in random processes. First, by combining Markov chain (MC) with cloud model based on the information transmission path, the Markov process hypothesis is proposed to devise a coupling effect model of system components and external interference environment. Second, operation and control modes for MASS under different scenarios based on STPA are constructed and analyzed, which are based on clarifications regarding two models for the parallel control of MASS. Third, a systemic risk model using safety control methodology is constructed considering the operation scenario of MASS. Last and not least, the risk performance in the MASS navigation process is simulated to reveal the emergence characteristics of ship navigation process risk after applying specific scenarios under three modes. Application examples show that the process risk emergence of MASS navigation is characterized by the dependence on safety information transmission path. Different transmission paths lead to inconsistent risk evolution results. The randomness and complexity of external environmental disturbances are the main factors involved in the formation of navigation risks in MASS.

MSIA-Net: A Lightweight Infrared Target Detection Network with Efficient Information Fusion.

In order to solve the problems of infrared target detection (i.e., the large models and numerous parameters), a lightweight detection network, MSIA-Net, is proposed. Firstly, a feature extraction module named MSIA, which is based on asymmetric convolution, is proposed, and it can greatly reduce the number of parameters and improve the detection performance by reusing information. In addition, we propose a down-sampling module named DPP to reduce the information loss caused by pooling down-sampling. Finally, we propose a feature fusion structure named LIR-FPN that can shorten the information transmission path and effectively reduce the noise in the process of feature fusion. In order to improve the ability of the network to focus on the target, we introduce coordinate attention (CA) into the LIR-FPN; this integrates the location information of the target into the channel so as to obtain more expressive feature information. Finally, a comparative experiment with other SOTA methods was completed on the FLIR on-board infrared image dataset, which proved the powerful detection performance of MSIA-Net.

River Chief Information-Sharing System as a River Information Governance Approach in China

Controling river water pollution is one of the complex ecoenvironmental challenges facing China’s development today. The river chief information-sharing system (RCISS) in China is an institutional innovation carried out by the government to promote collaborative water governance in the era of big data. In order to explore the mechanism of the RCISS in China, this paper analyzed this system by establishing a theoretical analysis framework from the perspective of government data governance. Using this framework, this paper discussed the mechanism, institutional context and driving forces of the current river chief information-sharing system. Provincial-level practices of the RCISS were then analyzed in terms of information content, information transmission paths, intelligent platform and practice achievements, and finally the advantages and problems of the RCISS were summarized. The conclusions were drawn as follows: the river chief information-sharing system has huge advantages regarding the coordinated management of rivers, but there are problems regarding the imbalanced sharing of power among subjects and also disputes in terms of information security, fairness, authenticity and legality. This study provides insights into the operation of the RCISS and serves as a reference for other countries seeking a suitable solution to manage water environments.

Occurrence of super-diffusion in two-layer networks.

Super-diffusion is a phenomenon that can be observed in multilayer networks, which describes that the diffusion in a multilayer network is faster than that in the fastest individual layer. In most studies of super-diffusion on two-layer networks, many researchers have focused on the overlap of edges in the two layers and the mode of interlayer connectivity. We discover that the occurrence of super-diffusion in two-layer networks is not necessarily related to the overlap degree. In particular, in a two-layer network, sparse topological structures of individual layers are more beneficial to the occurrence of super-diffusion than dense topological structures. Additionally, similar diffusion abilities of both layers favor super-diffusion. The density of interlayer edges and interlayer connection patterns also influence the occurrence of super-diffusion. This paper offers suggestions to improve the diffusion ability in two-layer networks, which can facilitate the selection of practical information transmission paths between different systems and optimize the design of the internal framework of a company composed of multiple departments.

Key Node Identification Method Integrating Information Transmission Probability and Path Diversity in Complex Network

Abstract Previous key node identification approaches assume that the transmission of information on a path always ends positively, which is not necessarily true. In this paper, we propose a new centrality index called Information Rank (IR for short) that associates each path with a score specifying the probability that such path successfully conveys a message. The IR method generates all the shortest paths of any arbitrary length coming out from a node $u$ and defines the centrality of u as the sum of the scores of all the shortest paths exiting $u$. The IR algorithm is more robust than other centrality indexes based on shortest paths because it uses alternative paths in its computation, and it is computationally efficient because it relies on a Beadth First Search-BFS to generate all shortest paths. We validated the IR algorithm on nine real networks and compared its ability to identify super-spreaders (i.e. nodes capable of spreading an infection in a real network better than others) with five popular centrality indices such as Degree, Betweenness, K-Shell, DynamicRank and PageRank. Experimental results highlight the clear superiority of IR over all considered competitors.

Design of Ideological and Political Communication Path of Curriculum under the Background of Intelligent Information of New Media

In order to improve the effectiveness of the safe, high-quality, and efficient communication mechanism of ideological and political courses in the new media perspective, the ideological and political communication path of courses in the new media perspective based on blockchain technology is designed. Mainly through the organic combination of online communication, offline-online dynamic feedback, and offline communication, based on the blockchain structure, an online information transmission path of online ideological and political courses leading to offline and offline feedback online is formed. In this path, the new media network platform uses the method based on bad information screening to screen out bad information, obtain a relatively safe ideological and political course information and cultural source, and then spread the information. Through the optimization strategy of PBFT consensus protocol based on a C4.5 decision tree, the trust environment for the dissemination of ideological and political courses is constructed. The network load balancing channel allocation algorithm based on the maximum flow of curriculum ideological and political communication path is adopted to ensure the network fluency and realize the efficient dissemination of curriculum ideological and political information. The experimental results show that the designed path can realize information security, high quality, and efficient sharing of ideological and political courses and has good performance.

Traffic Sign Detection Based on SSD Combined with Receptive Field Module and Path Aggregation Network.

The traditional traffic sign detection algorithm can not deal with the application scenarios such as intelligent transportation system or advanced assisted driving environment, and it is difficult to meet the application requirements in detection accuracy and efficiency. Focusing on the above problems, this paper proposes a traffic sign detection algorithm based on Single Shot Multibox Detector (SSD) combined with Receptive Field Module (RFM) and Path Aggregation Network (PAN). The proposed algorithm is abbreviated to SSD-RP. The SSD-RP uses the RFM to improve the receptive field and semantics of the predicted feature maps, thus improving the detection performance of small traffic signs. At the same time, the path aggregation network is introduced to integrate multiscale features, which makes the abstract semantic information and rich detailed information shared among multiscale feature maps, enhances the discrimination ability of feature system, and improves the location and classification accuracy of traffic signs. Following that, the spatial pyramid pooling module is used to pool the shallow features and integrate them into the bottom-up information transmission path of the path aggregation network, thus continuing to supplement the fine-grained features for the feature system and further improve the detection performance. The experimental results on GTSDB and CCTSDB data sets show that SSD-RP has higher mean average precision (mAP) than traditional SSD algorithm and can better detect small traffic signs, which means that SSD-RP has higher detection precision. In addition, the experimental results also show that, compared with the common object detection algorithms such as Faster R-CNN, RetinaNet, and YOLOv3, the SSD-RP achieves a better balance between detection time and detection precision.

Hematoma Expansion Context Guided Intracranial Hemorrhage Segmentation and Uncertainty Estimation.

Accurate segmentation of the Intracranial Hemorrhage (ICH) in non-contrast CT images is significant for computer-aided diagnosis. Although existing methods have achieved remarkable 1 1 The code will be available from https://github.com/JohnleeHIT/SLEX-Net. results, none of them incorporated ICH's prior information in their methods. In this work, for the first time, we proposed a novel SLice EXpansion Network (SLEX-Net), which incorporated hematoma expansion in the segmentation architecture by directly modeling the hematoma variation among adjacent slices. Firstly, a new module named Slice Expansion Module (SEM) was built, which can effectively transfer contextual information between two adjacent slices by mapping predictions from one slice to another. Secondly, to perceive contextual information from both upper and lower slices, we designed two information transmission paths: forward and backward slice expansion, and aggregated results from those paths with a novel weighing strategy. By further exploiting intra-slice and inter-slice context with the information paths, the network significantly improved the accuracy and continuity of segmentation results. Moreover, the proposed SLEX-Net enables us to conduct an uncertainty estimation with one-time inference, which is much more efficient than existing methods. We evaluated the proposed SLEX-Net and compared it with some state-of-the-art methods. Experimental results demonstrate that our method makes significant improvements in all metrics on segmentation performance and outperforms other existing uncertainty estimation methods in terms of several metrics.

Few-Shot Object Detection of Remote Sensing Images via Two-Stage Fine-Tuning

In recent years, there have been many developments in object detection on remote sensing images. However, those deep convolutional neural network (CNN) models always demand a great number of labeled samples, which leads to a significant decrease in performance on rare categories. Recently, the fine-tuning-based method of few-shot learning has drawn attention in the field of computer vision. In this letter, we proposed a multiscale few-shot object detection approach for remote sensing images, which is built upon faster region-CNN (R-CNN) architecture. First, we build the whole backbone of the detector with the involution operator, which enhances the classification ability of the features extractor. Then, our proposed detector learns multiscale features with the assistance of a path-aggregation module, which shortens the information transmission path by a bottom-up flow and uses semantic information of low-level features for localization. Eventually, we increase the shape bias in the detector in the training phase, which further improves the robustness and performance of the model. Experiments on two optical remote sensing datasets demonstrate that the proposed method outperforms the current few-shot detection models in the remote sensing field.

Концепция программно-аппаратного комплекса защиты IIoT-устройств

The Internet of Things is a new trend in the development of information technologies, actively spreading as interconnected functional nodes that by default have the means to ensure interaction directly with each other, or with the external environment. The development of such networks is a phenomenon that can radically change the world economy, as well as social processes. As a result of the application of this layer of information technologies, the need for direct human control over actions and operations will be eliminated. One of the most common household examples is a “smart home” – a computer network of interconnected devices using various data transfer protocols with the ability to access this network from an external network. So, thanks to IoT, it is possible to configure devices in the house by remote access. The paper deals with the main security methods against attacks and threats of IIoT devices. The information infrastructure was analyzed, an idea of the architecture of IIoT systems was obtained, and the information transmission path was identified. An analysis of the IIoT regulatory framework governing security, architecture and data exchange in IIoT systems has been carried out, documents governing the Industrial Internet of Things have been identified. The best communication protocol has been identified. Subsequently, the threats of the protocol were selected, and possible means and methods of protection were identified. For IIoT gateways, the concept of a hardware-software complex was presented as a prototype, and a comparison was made with existing solutions.

Research on the influence of mental fatigue on information resources allocation of working memory

Mental fatigue is the subjective state of people after excessive consumption of information resources. Its impact on cognitive activities is mainly manifested as decreased alertness, poor memory and inattention, which is highly related to the performance after impaired working memory. In this paper, the partial directional coherence method was used to calculate the coherence coefficient of scalp electroencephalogram (EEG) of each electrode. The analysis of brain network and its attribute parameters was used to explore the changes of information resource allocation of working memory under mental fatigue. Mental fatigue was quickly induced by the experimental paradigm of adaptive N-back working memory. Twenty-five healthy college students were randomly recruited as subjects, including 14 males and 11 females, aged from 20 to 27 years old, all right-handed. The behavioral data and resting scalp EEG data were collected simultaneously. The results showed that the main information transmission pathway of the brain changed under mental fatigue, mainly in the frontal lobe and parietal lobe. The significant changes in brain network parameters indicated that the information transmission path of the brain decreased and the efficiency of information transmission decreased significantly. In the causal flow of each electrode and the information flow of each brain region, the inflow of information resources in the frontal lobe decreased under mental fatigue. Although the parietal lobe region and occipital lobe region became the main functional connection areas in the fatigue state, the inflow of information resources in these two regions was still reduced as a whole. These results indicated that mental fatigue affected the information resources allocation of working memory, especially in the frontal and parietal regions which were closely related to working memory.

Modeling and Performance Optimization of Unmanned Aerial Vehicle Channels in Urban Emergency Management

With the development of smart cities, the use of unmanned aerial vehicles (UAVs) for interactive information exchange between air and ground can provide effective support for the deployment of emergency work. However, the existing UAV air-to-ground channels often use a single channel model. Considering that the density and distribution of obstructions on information transmission paths at different heights are different, only using a single channel model greatly affects the reliability of communications. Aiming at addressing the different channel characteristics of air-to-ground channels at different heights, a height-based adaptive SUUL-SULA channel model is proposed in this paper. Firstly, in the ultra-low altitude environment, the influence of large-scale fading and small-scale fading on the envelope of the received signal is discussed based on the classic LOO model, and the probability density function and bit error rate model of the received signal are derived. Secondly, a SULA channel model based on Jakes’ model is proposed in the low-altitude environment. The uniform circular array beamforming technology is adopted to realize the design of the Doppler frequency shift compensation algorithm. Finally, the simulation results show that the SUUL-SULA model effectively reduces the bit error rate of the system and improves the reliability of communication. Therefore, this model can provide effective physical support for the application of UAV in smart city emergency management.

Multi-target detection method based on YOLOv4 convolutional neural network

In order to solve the problems of low detection accuracy, false detection and high miss detection rate of small targets in target detection tasks, this paper is a multi-target detection method based on YOLOv4 convolutional neural network. The proposed method is based on YOLOv4. The semantic information of high-level features is first propagated to the low-level network through FPN sampling, and then it is fused with the high-resolution information of the underlying features to improve the detection effect of small target detection objects. The information transmission path from the bottom to the top is enhanced by downsampling the feature pyramid, and finally the feature maps of different layers are fused to achieve relevant predictions. Experiments prove that the method proposed in this paper has good results.