A roadside units positioning framework in the context of vehicle-to-infrastructure based on integrated AHP-entropy and group-VIKOR

The positioning of roadside units (RSUs) in a vehicle-to-infrastructure (V2I) communication system may have an impact on network performance. Optimal RSU positioning is required to reduce cost and maintain the quality of service. However, RSU positioning is considered a difficult task due to numerous criteria, such as the cost of RSUs, the intersection area and communication strength, which affect the positioning process and must be considered. Furthermore, the conflict and trade-off amongst these criteria and the significance of each criterion are reflected on the RSU positioning process. Towards this end, a four-stage methodology for a new RSU positioning framework using multi-criteria decision-making (MCDM) in V2I communication system context has been designed. Real time V2I hardware for data collection purpose was developed. This hardware device consisted of multi mobile-nodes (in the car) and RSUs and connected via an nRF24L01+ PA/LNA transceiver module with a microcontroller. In the second phase, different testing scenarios were identified to acquire the required data from the V2I devices. These scenarios were evaluated based on three evaluation attributes. A decision matrix consisted of the scenarios as alternatives and its assessment per criterion was constructed. In the third phase, the alternatives were ranked using hybrid of MCDM techniques, specifically the Analytic Hierarchy Process (AHP), Entropy and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The result of each decision ranking was aggregated using Borda voting approach towards a final group ranking. Finally, the validation process was made to ensure the ranking result undergoes a systematic and valid rank. The results indicate the following: (1) The rank of scenarios obtained from group VIKOR suggested the second scenario with, four RSUs, a maximum distance of 200 meters between RSUs and the antennas height of two-meter, is the best positioning scenarios; and (2) in the objective validation. The study also reported significant differences between the scores of the groups, indicating that the ranking results are valid. Finally, the integration of AHP, Entropy and VIKOR has effectively solved the RSUs positioning problems

Comprehensive review of data exchange in vehicle-to-pedestrian communications: State of the art

Pedestrian safety is a serious problem in transportation systems because pedestrian and vehicle crashes often result in fatalities amongst vulnerable road users. A vehicle-to-pedestrian (V2P) communication system allows data exchange between pedestrians and vehicles to prevent or minimise potential dangers of accidents from happening. This work aimed to analyse and review the previous work associated with information exchange in the V2P communication system and classify the existing technology utilized for this purpose. Motivation, accessible problems confronting researchers, and suggestions posed to researchers to develop this critical area of study have been among the reasons considered to enhance awareness of the field's numerous qualitative facets in reported investigations and properties. All of the papers have been divided into four categories: growth, analysis, and survey, FRAMEWORK, and data exchange in the V2P communication system. V2P communication is an area that necessitates automated solutions, instruments, and techniques that allow pedestrian detection and prediction. Pedestrian identification and data sharing on V2P have been the subject of several experiments in order to support pedestrian protection techniques. The reasons, open barriers that hinder the technology's usefulness, and authors' suggestions have been used to identify the essential characteristics of this evolving sector. This study is intended to provide researchers with new resources and enable them to focus on the holes that have been found

CLOUD-BASED ERP SYSTEM ADOPTION IN IRAQI SMES: A QUALITATIVE APPROACH TO IDENTIFY AND EVALUATE THE AFFECTING FACTORS

Cloud Enterprise Resource Planning (ERP) solutions offer broad advantages to small and medium-sized businesses (SMEs). Yet, the motivation of SMEs’ decision maker to adopt this system is limited. Our research sought to investigate the identified logistic determents to cloud ERP system adoption experienced by SMEs’ decision maker. To meet this objective, an exploratory research approach including semi-structured interviews with SMEs’ decision maker was adopted. Video call interviews were used to conduct semi-structured interviews. Interview methodology was utilised to obtain information, views, and issues experienced by SMEs’ decision maker on their approach to adopting cloud ERP systems. This study includes eighteen (18) SMEs' decision makers who are directly involved and responsible for technology acquisition and policy decisions for their SMEs in the Republic of Iraq. The current study uses a thematic analysis to identify themes from the exploratory method. Ten (10) themes found having positive perception to adopt cloud ERP among SMEs. Also, the interviews also highlighted new themes. This exploratory study addresses a gap in the body of knowledge on cloud ERP adoption and offers guidance for future research by academics and industry professional

Affecting Factors for the Adoption of Cloud-Based ERP System in Iraqi SMEs: An Empirical Study

Cloud-based enterprise resource planning (ERP) and cloud computing are critical requirements for all SMEs since they can be used to facilitate the SMEs’ growth by creating competitive and personalized innovations considering their required business scope. To date, the growth of cloud technologies has led to the development of new systems and applications in many fields and areas including businesses. Our previous study proposed an adoption model to investigate the main determinants and logistical factors that influence decision-makers of SMEs to adopt cloud-based ERP systems. The aim of this research is to enhance the previous work by evaluating and validating the new model in real life to determine whether it has achieved what it was developed for and determine the reliability of the research results. The methodology and results of the evaluation and validation process of the proposed model are presented in this research. Considering there is little documentation in the literature specifically relevant to how proposed models have been evaluated and validated, hence providing this insight will assist both the academic researchers and decision-makers. The evaluation and validation methodology and the model itself contribute toward a better understanding of adoption processes. Furthermore, the evaluation and validation procedure in future work can be used to measure, enhance and determine whether the proposed models can be used in real life

A comparative DFT study of electronic and optical properties of Pb/ Cd-doped LaVO4 and Pb/Cd-LuVO4 for electronic device applications

A First-principles study based on density functional theory was accomplished to examine the different properties of ABVO4 (A = Pb/Cd, B––La/Lu) materials such as structural, optical, and electronic properties. The band gap of Pb/Cd-doped LuVO4 is found to be remarkably and significantly decreased from 2.921 to 1.71eV as compared to a decrement of 3.455 to 2.650eV in Pb/Cd-doped LaVO4. Under the DFT study, Pb (Lead) and Cd (Cadmium) are appropriate materials for band gap decrement of LuVO4 and LaVO4. The nature of the band gap was found indirect moreover band gap indicated that materials are prominent semiconductors. Pb/Cd is doped at the vanadium (V) sites, which are more advantageous than the La/Lu sites. By capturing Pb/Cd at the V sites in LuVO4/ LaVO4, additional gamma points were incorporated into the electronic band gap energy (Eg). A significant decrement is found in the band gap as well as optical conductivity. After the substitution of different impurities of Pb/Cd the energy absorption peaks are increased. It is also examined that after doping of Pb/Cd optical conductivity shifted toward larger energy because of the band gap. Both Pb/Cd-doped LuVO4 and Pb/Cd-doped LaVO4 compounds have high optical conductivity, refractive index, and energy absorption moreover Pb/Cddoped LuVO4 is a more appropriate material as compared to Pb/Cd-doped LaVO4 for electronic device applications

Buergerraete in Gross-Berlin 1918-1919 eine Organisation der Bourgeoisie im Kampf gegen Arbeiter- und Soldatenraete

SIGLEFES Bonn(Bo133)-C85-297 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks

The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model’s strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%)

A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks

The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model’s strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%)