Formation Control of a Multi-Autonomous Underwater Vehicle Event-Triggered Mechanism Based on the Hungarian Algorithm

Among the key technologies of Autonomous Underwater Vehicle (AUV) leader–follower formations control, formation reconfiguration technology is one of the main technologies to ensure that multiple AUVs successfully complete their tasks in a complex operating environment. The biggest drawback of the leader–follower formations technology is the failure of the leader and the excessive communication pressure of the leader. Aiming at the problem of leader failure in multi- AUV leader–follower formations, the Hungarian algorithm is used to reconstruct the failed formation with a minimum cost, and the improvement of the Hungarian algorithm can solve the problem of a non-standard assignment. In order to solve the problem of an increased leader communication task after formation reconfiguration, the application of an event-triggered mechanism (ETM) can reduce unnecessary and useless communication, while the efficiency of the ETM can be improved through increasing the event-triggered conditions of the sampling error threshold. The simulation results of multi-AUV formation control show that the Hungarian algorithm proposed in this paper can deal with the leader failure in the multi-AUV leader–follower formation, and the ETM designed in this paper can reduce about 90% of the communication traffic of the formation which also proves the highly efficient performance of the improved ETM in the paper.

TDCA: Improved Optimization Algorithm with Degree Distribution and Communication Traffic for the Deployment of Software Components Base on AUTOSAR Architecture

AUTOSAR (Automotive Open System Architecture), as an open, standardized framework for automotive electronic software development, has gradually become the standard followed by major automotive manufacturers and automotive electronic device suppliers. The electronic software system problem improves the development efficiency and portability of the system by reducing the development cost of automotive electronic software while ensuring the quality of products and services, which is beneficial for subsequent upgrades and updates of the system. In order to improve the reliability of the software component deployment algorithm based on AUTOSAR architecture, we proposed the TDCA algorithm. During the execution of the algorithm, communication volume and communication degree are introduced to improve the accuracy of the deployment plan by optimizing the bus load and ECU balancing. Algorithm comparison experiments show that comparing heuristic and linear optimization algorithms, the TDCA algorithm proposed in this paper has significant advantages in reducing bus load and balancing ECU utilization. The algorithm can reduce the communication between cores and balance ECU load according to the constraints of AUTOSAR architecture.

Identifying IoT Devices Based on Spatial and Temporal Features from Network Traffic

With the rapid growth of the Internet of Things (IoT) devices, security risks have also arisen. The preidentification of IoT devices connected to the network can help administrators to set corresponding security policies according to the functionality and heterogeneity of the devices. However, the existing methods are based on manually extracted features and prior knowledge to identify the IoT devices, which increases the difficulty of the device identification task and reduces the timeliness. In this paper, we present CBBI, a novel IoT device identification approach. On the one hand, CBBI uses a hybrid neural network model Conv-BiLSTM to automatically learn the representative spatial and temporal features from the network traffic, such as the position relationship of the internal organization structure in network communication traffic, the time sequence of the data packets, and the duration of the network flow. On the other hand, CBBI contains the data augmentation module FGAN that solves the problem of data imbalance in deep learning and improves the accuracy of the model. Finally, we used the public dataset and laboratory dataset to evaluate CBBI from multiple dimensions. The evaluation results for different datasets show that our approach achieves the accurate identification of IoT devices.

Terahertz wireless communications in space

The New Space Era has increased communication traffic in space by new space missions led by public space agencies and private companies. Mars colonization is also targeted by crewed missions in the near future. Due to increasing space traffic near Earth and Mars, the bandwidth is getting congested. Moreover, the downlink performance of the current missions is not satisfactory in terms of delay and data rate. Therefore, to meet the increasing demand in space links, Terahertz band (0.1-10 THz) wireless communications are proposed in this study. In line with this, we discuss the major challenges that the realization of THz band space links pose and possible solutions. Moreover, we simulate Mars-space THz links for the case of a clear Mars atmosphere, and a heavy dust storm to show that even in the worst conditions, a large bandwidth is available for Mars communication traffic.

Analysis the impact of sun outage and satellite orbit at performance of the telkom 3S satellite communication system

Satellites of communication are located at altitude of thousands kilometers above the earth's surface, so the signal is transmitted by satellite to earth station is very susceptible to interference. Every March and September equinox or when the sun crosses the Earth's equator for several days, earth station occurs a naturally interference called by sun outage. At this time, satellite and the sun reach the closest distance because satellite's position is same direction with the sun. This interference makes the signal received by earth station weaken and even disappears due to temperature noise which increases drastically. Loss of signal on the downlink side caused by noise greatly affects to the performance of satellite communication system. This study aims to analyze the effect of sun outage and satellite orbit to determine sun outage period on the performance of Telkom 3S satellite communication system. The results obtained that indicate the signal quality is represented by degradation in the Carrier to Noise Ratio (C/N) from 14,777 dB to 6,0 dB, Energy bits per Noise Ratio (Eb/No) from 11,515 dB to 2,738 dB, and increase the Bit Error Rate (BER) from 8,29×10-7 to 11,08×10-3. In addition, sun outage makes lost of satellite communication traffic and affecting link availability to 99,855324%. Meanwhile, the result from satellite orbit calculation for sun outage period based on ITU-R S.1525 standard and based by satellite’s handbook. Keywords – Telkom 3S satellite, sun outage, C/N, Eb/No, BER, link availability, sun outage period.

Optimization of AUTOSAR Communication Stack in the Context of Advanced Driver Assistance Systems

New trends in the automotive industry such as autonomous driving and Car2X require a large amount of data to be exchanged between different devices. Radar sensors are key components in developing vehicles of the future, therefore these devices are used in a large spectrum of applications, where data traffic is of paramount importance. As a result, communication traffic volumes have become more complex, leading to the research of optimization approaches to be applied at the AUTOSAR level. Our paper offers such an optimization solution at the AUTOSAR communication level. The radar sensor is accessed in a remote manner, and the experiments aimed at performance measurements revealed that our solution is superior to the Full AUTOSAR implementation in terms of memory usage and runtime measurements.

Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages

Increased connectivity is required to implement novel coordination and control schemes. IEC 61850-based communication solutions have become popular due to many reasons—object-oriented modeling capability, interoperable connectivity and strong communication protocols, to name a few. However, communication infrastructure is not well-equipped with cybersecurity mechanisms for secure operation. Unlike online banking systems that have been running such security systems for decades, smart grid cybersecurity is an emerging field. To achieve security at all levels, operational technology-based security is also needed. To address this need, this paper develops an intrusion detection system for smart grids utilizing IEC 61850’s Generic Object-Oriented Substation Event (GOOSE) messages. The system is developed with machine learning and is able to monitor the communication traffic of a given power system and distinguish normal events from abnormal ones, i.e., attacks. The designed system is implemented and tested with a realistic IEC 61850 GOOSE message dataset under symmetric and asymmetric fault conditions in the power system. The results show that the proposed system can successfully distinguish normal power system events from cyberattacks with high accuracy. This ensures that smart grids have intrusion detection in addition to cybersecurity features attached to exchanged messages.