Search Results (5)

5G New Radio (NR) systems promise to expand offered services to enable industrial automation scenarios. To enable ultra-low latency at the air interface and to exploit spatial redundancy for applications such as synchronization and motion control, user equipment (UE) will naturally require device-to-device (D2D) and base station (BS) to UE communications and directional transmissions provided by millimeter wave (mmWave) frequencies. However, the performance of such systems is affected by the blockage phenomenon. In this paper, we propose a simple line-of-sight (LoS) blockage model for Industrial mmWave-based industrial Internet of Things (IIoT) deployments. The model is based on two sub-models, where each part can be changed/replaced to fit the scenario of interest. The first part is based on photogrammetry and provides the transparency probability for a single element on the factory floor. The second one utilizes these models of industrial elements to form the deployment and then applies stochastic geometry to derive the blockage probability. The proposed model can be utilized for any type of industrial machine, accounts for their inherent regular deployments on the factory floor, and provides the final results in an easy-to-compute form. Our results indicate that direct UE-UE communications are feasible in sparse deployments (less than 0.1 machine/m²) or at small communications distances (less than 5–10 m) or in deployments with highly transparent machines (machine transparency less than 0.5). Otherwise, BS-UE communications need to be utilized. In this case, BS height becomes a critical parameter affecting the LoS probability. Specifically, using a BS height of 10 m allows blockage probability to be improved by 20–30% as compared to a BS of height 4 m. Finally, as UE height produces more impact on the blockage probability as compared to a machine height, in real deployments, one needs to ensure that the height of communications modules at UEs are maximized. Full article

One of the foundational and key means of optimizing network service in the field of network security is traffic identification. Various data transmission encryption technologies have been widely employed in recent years. Wrongdoers usually bypass the defense of network security facilities through VPN to carry out network intrusion and malicious attacks. The existing encrypted traffic identification system faces a severe problem as a result of this phenomenon. Previous encrypted traffic identification methods suffer from feature redundancy, data class imbalance, and low identification rate. To address these three problems, this paper proposes a VPN-encrypted traffic identification method based on ensemble learning. Firstly, aiming at the problem of feature redundancy in VPN-encrypted traffic features, a method of selecting encrypted traffic features based on mRMR is proposed; secondly, aiming at the problem of data class imbalance, improving the Xgboost identification model by using the focal loss function for the data class imbalance problem; Finally, in order to improve the identification rate of VPN-encrypted traffic identification methods, an ensemble learning model parameter optimization method based on optimal Bayesian is proposed. Experiments revealed that our proposed VPN-encrypted traffic identification method produced more desirable VPN-encrypted traffic identification outcomes. Meanwhile, using two encrypted traffic datasets, eight common identification algorithms are compared, and the method appears to be more accurate in identifying encrypted traffic. Full article

The ecological degradation caused by unreasonable development and prolonged utilization threatens economic development. In response to the development crisis triggered by ecological degradation, the Chinese government launched the National Barrier Zone (NBZ) Construction Program in 2006. However, few in-depth studies on the Loess Plateau Ecological Screen (LPES) have been conducted since the implementation of that program. To address this omission, based on the remote sensing image as the primary data, combined with meteorological, soil, hydrological, social, and economic data, and using GIS spatial analysis technology, this paper analyzes the change characteristics of the ecosystem pattern, quality, and dominant services of the ecosystem in the LPES from 2005 to 2015. The results show that from 2005 to 2015, the ecosystem structure in the study area was relatively stable, and the area of each ecosystem fluctuated slightly. However, the evaluation results based on FVC, LAI, and NPP showed that the quality of the ecosystem improved. The vegetation coverage (FVC) increased significantly at a rate of 0.91% per year, and the net primary productivity (NPP) had increased significantly at a rate of 6.94 gC/(m²∙a) per year. The leaf area index (LAI) in more than 66% of the regions improved, but there were still about 8% of the local regions that were degraded. During these 10 years, the soil erosion situation in LPES improved overall, and the amount of soil conservation (ASC) of the ecosystem in the LPES increased by about 0.18 billion tons. Grassland and forest played important roles in soil conservation in this area. Pearson correlation analysis and redundancy analysis showed that the soil conservation services (SCS) in the LPES were mainly affected by climate change, economic development, and urban construction. The precipitation (P), total solar radiation (SOL), and temperature (T) can explain 52%, 30.1%, and 17% of the change trends of SCS, respectively. Construction land and primary industry were negatively correlated with SCS, accounting for 22% and 8% of the change trends, respectively. Overall, from 2005 to 2015, the ecological environment of LPES showed a gradual improvement trend, but the phenomenon of destroying grass and forests and reclaiming wasteland still existed. Full article

Wireless Sensor Networks (WSNs) have gained great significance from researchers and industry due to their wide applications. Energy and resource conservation challenges are facing the WSNs. Nevertheless, clustering techniques offer many solutions to address the WSN issues, such as energy efficiency, service redundancy, routing delay, scalability, and making WSNs more efficient. Unfortunately, the WSNs are still immature, and suffering in several aspects. This paper aims to solve some of the downsides in existing routing protocols for WSNs; a Lightweight and Efficient Dynamic Cluster Head Election routing protocol (LEDCHE-WSN) is proposed. The proposed routing algorithm comprises two integrated methods, electing the optimum cluster head, and organizing the re-clustering process dynamically. Furthermore, the proposed protocol improves on others present in the literature by combining the random and periodic electing method in the same round, and the random method starts first at the beginning of each round/cycle. Moreover, both random and periodic electing methods are preceded by checking the remaining power to skip the dead nodes and continue in the same way periodically with the rest of the nodes in the round. Additionally, the proposed protocol is distinguished by deleting dead nodes from the network topology list during the re-clustering process to address the black holes and routing delay problems. Finally, the proposed algorithm’s mathematical modeling and analysis are introduced. The experimental results reveal the proposed protocol outperforms the LEACH protocol by approximately 32% and the FBCFP protocol by 8%, in terms of power consumption and network lifetime. In terms of Mean Package Delay, LEDCHE-WSN improves the LEACH protocol by 42% and the FBCFP protocol by 15%, and regarding Loss Ratio, it improves the LEACH protocol by approximately 46% and FBCFP protocol by 25%. Full article

Indoor positioning systems (IPS) have been recently adopted by many researchers for their broad applications in various Internet of Things (IoT) fields such as logistics, health, construction industries, and security. Received Signal Strength (RSS)-based fingerprinting approaches have been widely used for positioning inside buildings because they have a distinct advantage of low cost over other indoor positioning techniques. The signal power RSS is a function of the distance between the Mobile System (MS) and Access Point (AP), which varies due to the multipath propagation phenomenon and human body blockage. Furthermore, fingerprinting approaches have several disadvantages such as labor cost, diversity (in signals and environment), and computational cost. Eliminating redundancy by ruling out non-informative APs not only reduces the computation time, but also improves the performance of IPS. In this article, we propose a dimensionality reduction technique in a multiple service set identifier-based indoor positioning system with Multiple Service Set Identifiers (MSSIDs), which means that each AP can be configured to transmit N signals instead of one signal, to serve different kinds of clients simultaneously. Therefore, we investigated various kinds of approaches for the selection of informative APs such as spatial variance, strongest APs, and random selection. These approaches were tested using two clustering techniques including K-means and Fuzzy C-means. Performance evaluation was focused on two elements, the number of informative APs versus the accuracy of the proposed system. To assess the proposed system, real data was acquired from within the College of Engineering and Applied Sciences (CEAS) at the Western Michigan University (WMU) building. The results exhibit the superiority of fused Multiple Service Set Identifiers (MSSID) performance over the single SSID. Moreover, the results report that the proposed system achieves a positioning accuracy <0.85 m over 3000 m², with an accumulative density function (CDF) of 88% with a distance error of 2 m. Full article