Modeling in the OMNET ++ environment of connection processes in Wi-Fi networks

Предложен подход к разработке в среде OMNeT++ INET простейшей имитационной модели инфраструктурного режима функционирования Wi-Fi сети, который позволяет проводить подробный анализ функционирования таких сетей, а также строить и анализировать временные диаграммы взаимодействия всех элементов сети. Разработанную модель можно использовать как базовую для формирования более сложных моделей с произвольным числом мобильных клиентов, позволяя определять необходимое количество точек доступа и мест их размещения для обеспечения полноценного покрытия зоны мониторинга лесной территории. An approach to the development in the OMNeT ++ INET environment of the simplest simulation model of the infrastructure mode of Wi-Fi network operation is proposed, which allows a detailed analysis of the functioning of such networks, as well as to build and analyze the time diagram of the interaction of all network elements. The developed model can be used as a base for the formation of more complex models with an arbitrary number of mobile clients, allowing you to determine the required number of access points and their locations to ensure full coverage of the monitoring area of the forest area.

Real Time Tracing and Alerting System for Vehicles and Children to Ensure Safety and Security Using Labview

The Absolute time monitoring, detecting and Alerting System for vehicles and children is required to trace and transmit the collected information at regular intervals to ensure safety and security of children. The illustration of the Realtime detecting and warning System consists of two units: Tracing Unit that traces the location information, transfers to the monitoring area, records the data in the database and takes the help of these data to locate the exact point of area of the vehicle with Google/other maps. The second unit is Alerting Unit that tracks the students using active Radio Frequency Identification Devices (RFID)which will be placed on student ID card. radio-wave trans-receiver transmits a common radio wave which is received by the RFID in the ID card. This radio-wave is modified by the RFID’s coil and resent to the receive RFID tags are also used for attendance which is updated directly to the database and displays the other student information.

State of pollution of Ogosta River waters

The present research is related to one of the basic component of the environment – waters with study area the Ogosta river catchment. It is based on the investigation of water samples collected during field research in the river monitoring area and laboratory analysis. The research is focused on the state of the pollution and quality of the water. The results can be used in the processes of environmental optimization and realization of the strategies for sustainable development in the region.

Analisis Daya Dukung DAS Berdasarkan Kriteria Tata Air dan Daya Tampung Beban Pencemaran Sungai Gedek

The increase in population every year and rapid economic development cause environmental problems related to the use of air resources. A need as what condition the water system to monitoring area stream the river in which to a variety problem environment. The purpose of this study was to determine the value of the load carrying capacity and assess the carrying capacity of the water management criteria. Based on the calculation of the pollution load capacity in class I, the parameters DO, COD, BOD, TSS and Ammonia exceed the load capacity of each of 3,325.07 mg/l, -9,872.32 mg/l, - 799,0063 mg/l, -271,705.21 mg/l, -664.06 mg/l. In class II that exceeds the load carrying capacity, the parameters DO, TSS, and Ammonia are 1,425.08 mg/l, -262.704.016 mg/l, -569.0619 mg/l, respectively. In class III, which exceeds the carrying capacity of the pollution load, the parameters DO, TSS, and Ammonia are 475.01 mg/l, -214.704.016 mg/l, -284.0558 mg/l, respectively. In class IV all parameters meet the pollution load capacity. The carrying capacity value based on the criteria of quality, quantity, and water continuity (water management) is 102.5 % included in the category.

Reader Scheduling for Tag Population Estimation in Multicategory and Multireader RFID Systems

Radio Frequency Identification (RFID) technology has been used in numerous applications, e.g., supply chain management and inventory control. This paper focuses on the practically important problem of the rapid estimation of the number of tags in large-scale RFID systems with multiple readers and multicategory RFID tags. RFID readers are often static and have to be deployed strategically after careful planning to cover the entire monitoring area, but reader-to-reader collision (R2Rc) remains a problem. R2Rc decreases the reliability of the estimation of the tag population size, because it results in the failure of communication between the reader and tags. In this paper, we propose a coloring graph-based estimation scheme (CGE), which is the first estimation framework designed for multireader and multicategory RFID systems to determine the distribution of tags in different categories. CGE allows for the use of any estimation protocol to determine the number of tags, prevents R2Rc, and results in higher time efficiency and less power-consumption than the classic scheduling method DCS.

Concept of Short-Range Radar and Counter-Projectile for Detecting and Countering Armour-Piercing Projectiles

During warfare and acts of terrorism an extreme threat to vehicles and other high-value assets comes from armour-piercing projectiles. Under these conditions, defence systems should include devices capable of rapid detection of these threats. Defence assets should also be provided with counter-projectile systems capable of destroying incoming armour-piercing projectiles at a safe distance from the asset to be protected. This paper describes the concept of a system comprising of a lightweight short-range radar and a counter-projectile for countering armour-piercing projectiles. The purpose of the radar is to monitor the environment and search for incoming armour-piercing projectiles. When an armour-piercing projectile is detected in a designated monitoring area, an automatic command is given for the counter-projectile launcher to be fired. The counter-projectile deployed can be equipped with a single or multi-sensor detection head unit and an explosive payload module, both being the primary components of the warhead. When the signal analysis blocks interfaced with the detection head determine that the armour-piercing projectile to be struck down is in the target position in relation to the counter-projectile deployed, they automatically command the explosive payload module to detonate. The components of the system concept were tested in proving ground conditions. The successful results of these tests confirmed the validity of the solutions initially adopted and the execution of the individual systems.

A Frequency Domain Direct Localization Method Based on Distributed Antenna Sensing

Traditional two-step passive localization methods need to extract the parameters like the direction of arrival (DOA), time of arrival (TOA), and time difference of arrival (TDOA) from the original data to determine the source position, which causes the poor positioning accuracy due to error accumulation. In this paper, a direct position determination (DPD) method is proposed to improve the positioning accuracy and robustness, which is based on a correlation algorithm. Firstly, the cost function directly related to the location of the source can be established by synthesizing the data received by multiantenna in the frequency domain. Then, the position of the source is estimated by the correlation DPD method to search the monitoring area. Compared to the improved TDOA algorithm and Least Squares DPD algorithm, the proposed method shows better localization accuracy of different SNRs. Finally, based on real measured data, it can be seen that the results of the proposed algorithm are better than the improved TDOA algorithm.

Layout Method of Multiseismic Detectors for Shallow Underground Excavation Applications

In this study, we propose effective monitoring equipment intended for monitoring the underground tunnel of illegal excavation (such as theft, jailbreak, and smuggling). It mainly detects the microseismic information produced by underground excavation in a short distance to detect the status of underground excavation. Based on the arrival time difference principle, the positioning mathematical models of the 5-1-1 layout method, 4-3 layout method, and 7-0-0 layout method are established, respectively. In the research process, the measurement and the placement error caused by the installation of a seismic detector are joined into the detectors. Simulation results show that the relative error and its average value are obtained when mining outside the monitoring area. The experiment results demonstrate that, first, the depth positioning error is positively affected by the number of seismic detectors. Then, the relative error of plane positioning can be reduced when the installation distance among detectors is increased. Finally, the main causes of location error include time measurement error, propagation velocity difference caused by terrain, and the performance of detector hardware. The array of a ground motion detector has a weak influence on it. These emerging trends will have profound impacts on application of an underground excavation system.

Machine learning model and strategy for fast and accurate detection of leaks in water supply network

The water supply network (WSN) is subjected to leaks that compromise its service to the communities, which, however, is challenging to identify with conventional approaches before the consequences surface. This study developed Machine Learning (ML) models to detect leaks in the WDN. Water pressure data under leaking versus non-leaking conditions were generated with holistic WSN simulation code EPANET considering factors such as the fluctuating user demands, data noise, and the extent of leaks, etc. The results indicate that Artificial Neural Network (ANN), a supervised ML model, can accurately classify leaking versus non-leaking conditions; it, however, requires balanced dataset under both leaking and non-leaking conditions, which is difficult for a real WSN that mostly operate under normal service condition. Autoencoder neural network (AE), an unsupervised ML model, is further developed to detect leak with unbalanced data. The results show AE ML model achieved high accuracy when leaks occur in pipes inside the sensor monitoring area, while the accuracy is compromised otherwise. This observation will provide guidelines to deploy monitoring sensors to cover the desired monitoring area. A novel strategy is proposed based on multiple independent detection attempts to further increase the reliability of leak detection by the AE and is found to significantly reduce the probability of false alarm. The trained AE model and leak detection strategy is further tested on a testbed WSN and achieved promising results. The ML model and leak detection strategy can be readily deployed for in-service WSNs using data obtained with internet-of-things (IoTs) technologies such as smart meters.

Machine Learning Methods Performance Evaluation*

In this paper, we describe an approach for air pollution modeling in the data incompleteness scenarios, when the sensors cover the monitoring area only partially. The fundamental calculus and metrics of using machine learning modeling algorithms are presented. Moreover, the assessing indicators and metrics for machine learning methods performance evaluation are described. Based on the conducted analysis, conclusions on the most appropriate evaluation approaches are made.