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This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation (CFA) problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses a large number of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks.

This article addresses the challenge of large error rate and low accuracy of the vibration signal collection of mechanical equipment failure, and proposes a mechanical equipment failure vibration signal collection and analysis based on computer simulation detection. Then, it uses the Kalman filter algorithm for data filtering, according to the mathematical model established by the system, thus choosing a suitable noise covariance calculation method. In the integration process after filtering, using a piecewise integration method between acceleration peaks, the integration calculation is optimized to obtain the vibration displacement. The simulation results of this article show the vibration data collected by the main controller, after Kalman filtering and piecewise trapezoidal integration method optimization. The error of the proposed method is 0.5% when the frequency is 80 Hz, relative to the displacement measurement method of the three-axis acceleration sensor at 8.3%, and the error of data calculation results is greatly reduced. The greater the amplitude of vibration, the smaller the error. This method significantly improves the accuracy of vibration signal collection of mechanical equipment.

The topology optimization of computer communication network is studied based on improved genetic algorithm (GA), a network optimization design model based on the establishment of network reliability maximization under given cost constraints, and the corresponding improved GA is proposed. In this method, the corresponding computer communication network cost model and computer communication network reliability model are established through a specific project, and the genetic intelligence algorithm is used to solve the cost model and computer communication network reliability model, respectively. It has been proved that GA can solve the complex problems of computer working environment better, which is 80% higher than the general algorithm, and can select the optimal scheme pertinently.

Wireless Body Area Networks (WBANs) are based on connected and dedicated sensor nodes for patient monitoring in the healthcare sector. The sensor nodes are implanted inside or outside the patient's body for sensing the vital signs and transmitting the sensed data to the end devices for decision-making. These sensor nodes use advanced communication technologies for data communication. However, they have limited capabilities in terms of computation power, battery life, storage, and memory, and these constraints make networks more vulnerable to security breaches and routing challenges. Important and sensitive information is exchanged over an unsecured channel in the network. Several devices are involved in handling the data in WBANs, including sink nodes, coordinator, or gateway nodes. Many cryptographic schemes have been introduced to ensure security in WBANs by using traditional confidentiality and key-sharing strategies. However, these techniques are not suitable for limited resource-based sensor nodes. In this paper, we propose a Lightweight Hybrid Cryptography Algorithm (LWHCA) that uses cryptographicbased techniques for WBAN networks to improve network security, minimize network overhead and delay issues, and improve the healthcare monitoring processes. The proposed solution is evaluated in a simulation scenario and compared with state-of-the-art schemes in terms of energy consumption, and ciphertext size. [ABSTRACT FROM AUTHOR]

This paper intended to investigate and analyze encryption algorithms that can be proposed to perform data security in 4G and 5G networks. The research explores different standards, services, and features presented via 4G and 5G networks. Also, the basic components of encryption algorithms (e.g., ZUC, SNOW 3G, and AES) are investigated. For instance, initialization keys have been identified and analyzed due to their vital roles in determining the security of the encryption algorithms. Moreover, the researchers analyzed the effective elements of these algorithms (i.e., LFSR registers, substitution boxes, NLF functions like finite state machines, Math transformations, secret encryption keys, and non-secret IV keys). Cryptanalysis methods play important roles in determining the security of these algorithms. Thus, some cryptanalysis methods have been explored and investigated. Various weak points have been identified in initialization process of these algorithms. Therefore, different recommendations are presented that enhance the security of these ciphers, and can be reflected in data security in 4G and 5G networks.

One of crucial activity related to road monitoring and maintenance is the occurrence of potholes. These potholes are also be major reason of road accidents, damaging of vehicles, discomfort of passenger journey and extensive in terms of time and cost. But, identification of potholes can significantly alleviate the aforementioned issues. Other side, the Internet of Things (IoT) plays a crucial role in different applications, and provides viable and state of art solutions for variety of problems. Hence, the aim of this work is to develop a real time automated pothole detection model to detect the potholes in asphalt roads based on IoT devices. The proposed model comprises of three main components such as collection of pothole data and labeling, image preprocessing and texture feature extraction, and extreme gradient boosting (XGBoost) algorithm. The potholes data on asphalt road is collected by three IoT sensors such as accelerometer, ultrasonic sensor, and GPS and further, the collected data is transmitted on cloud via Wi-Fi module. The texture features are extracted using Gaussian steerable and median filters. The extreme gradient boosting (XGBoost) classifier is adopted for prediction task. The simulation results showed that proposed XGBoost model obtains higher accuracy, recall, precision and F1-score rates as 94.56, 97.41, 96.40, and 96.90 respectively using 10-cross fold validation method. [ABSTRACT FROM AUTHOR]