DEVELOPING OF BLUETOOTH MESH FLOODING BETWEEN SOURCE-DESTINATION LINKING OF NODES IN WIRELESS SENSOR NETWORKS (p. 6—14)

Bluetooth uses 2.4 GHz in ISM (industrial, scientific, and medical) band, which it shares with other wireless operating system technologies like ZigBee and WLAN. The Bluetooth core design comprises a low-energy version of a low-rate wireless personal area network and supports point-to-point or point-to-multipoint connections. The aim of the study is to develop a Bluetooth mesh flooding and to estimate packet delivery ratio in wireless sensor networks to model asynchronous transmissions including a visual representation of a mesh network, node-related statistics, and a packet delivery ratio (PDR). This work provides a platform for Bluetooth networking by analyzing the flooding of the network layers and configuring the architecture of a multi-node Bluetooth mesh. Five simulation scenarios have been presented to evaluate the network flooding performance. These scenarios have been performed over an area of 200×200 meters including 81 randomly distributed nodes including different Relay/End node configurations and source-destination linking between nodes. The results indicate that the proposed approach can create a pathway between the source node and destination node within a mesh network of randomly distributed End and Relay nodes using MATLAB environment. The results include probability calculation of getting a linking between two nodes based on Monte Carlo method, which was 88.7428 %, while the Average-hop-count linking between these nodes was 8. Based on the conducted survey, this is the first study to examine and demonstrate Bluetooth mesh flooding and estimate packet delivery ratio in wireless sensor networks.

As an effectual simple wireless equivalent created in the telecommunications (telephone) industry, Wireless Asynchronous Transfer Mode (WATM) is utilized to stream unified traffics like video, data, and voice data. In the asynchronous data transfer mode, voice data transfer a packet with the same medium, and data share the networks and burst data. Effective WATM data transmission requires an extensive array of designs, techniques used for control, and simulation methodologies. The congestion of the network is among the key challenges that lower the entire WATM performance during this procedure, in addition to the delay in cell and the overload of traffic. The congestions cause cell loss, and it requires expensive switches compared to the LAN. Consequently, in this current study, the application of an effectual switching model together with a control mechanism that possesses multiple accesses is employed. The multiple access process and switching model are utilized to establish an effective data sharing process with minimum complexity. The switching model uses the synchronous inputs and output ports DOI: 10 The work is devoted to the problem of interpretation of fuzzy semantics of cognitive descriptions of spatial relations in natural language and their visualization in a geographic information system (GIS). The solution to the problem of determining the fuzzy spatial location of an object based on vague descriptions of the observer in natural language is considered. The task is relevant in critical situations when there is no way to report the exact coordinates of the observed object, except by describing its location relative to the observer itself. Such a situation may be the result of a crime, terrorist act or natural disaster. An observer who finds itself at the scene transmits a text message, which is a description of the location of the object or place (for example, the crime scene, the location of dangerous objects, the crash site). The semantics of the spatial location of the object can be further extracted from the text message.
The proposed fuzzy approach is based on the formalization of the observer's phrases, with which it can describe spatial relations, in the form of a set of linguistic variables that determine the direction and distance to the object. Examples of membership functions for linguistic variables are given.
The spatial knowledge base is built on the basis of the phrases of observers and their corresponding fuzzy regions. Algorithms for constructing cognitive regions in GIS have been developed. Methods of their superposition to obtain the final fuzzy location of the object are proposed. An example of the implementation of a fuzzy model for identifying cognitive regions based on vague descriptions of several observers, performed using developed Python scripts integrated into ArcGIS 10.5, is considered.
Keywords: cognitive description of spatial relationships, spatial modeling, fuzzy logic, geographic information system.   The method and structural scheme of an information-measuring system for determining the parameters of objects' movements (technological equipment in the quarry for extracting block natural stone) have been proposed. A distinctive feature of time video sequences containing images of measured objects is their adaptation and adjustment in accordance with the intensity of movement and accuracy requirements for measurement results. Structural and software-algorithmic methods were also applied for improving the accuracy of measurements of motion parameters, namely: complexation of two measuring channels and exponential smoothing of digital references. One of the measuring channels is based on a digital video camera, the second is based on an accelerometer mounted on an object and two integrators. Exponential smoothing makes it possible to take into consideration the previous countdowns of movement parameters with weight coefficients. That ensures accounting for the existing patterns of movement of the object and reducing the errors when measuring the parameters of movement by (1.4...1.6) times.
The resulting solutions have been implemented in the form of an information and measurement system. The technological process of extracting blocks of natural stone in the quarry was experimentally investigated using a diamond-rope installation. Based on the contactless measurement of motion parameters, it is possible to ensure control over this process and improve the quality of blocks made of natural stone.
Based on the experimental study of measurement errors, recommendations were given for the selection of adaptive parameters of a video sequence, namely the size of images and the value of the interframe interval. In addition, methods for the software-algorithmic processing of measuring information were selected, specifically exponential smoothing and averaging the coordinates of the contour of an object, measured in 30 adjacent lines of the image.
Keywords: motion parameters, software-algorithmic processing of measuring video information, exponential smoothing, complexation. plitude increasing exponentially. As a model of observation noise, an additive discrete Gaussian process with zero mean and variable value of the mean square deviation was considered. It was established that for small values of the mean square deviation of observation noise, a self-adjusting model under the conditions of dynamics uncertainty produces a smaller error in the process forecast. For the test jumplike dynamics of the process, the variance of the forecast error was less than 1 %. At the same time, for the adaptive model, with an adaptation parameter from the classical and beyond-the-limit sets, the variance of the error was about 20 % and 5 %, respectively. With significant observation noises, the variance of the error in the forecast of the test process dynamics for the self-adjusting and adaptive models with a parameter from the classical set was in the range from 1 % to 20 %. However, for the adaptive model, with a parameter from the beyond-the-limit set, the variance of the prediction error was close to 100 % for all test models. It was established that with an increase in the mean square deviation of observation noise, there is greater masking of the predicted test process dynamics, leading to an increase in the variance of the forecast error when using a self-adjusting model. This is the price for predicting processes with uncertain dynamics and observation noises. Accurate and objective object analysis requires multi-parameter estimation with significant computational costs. A methodological approach to improve the accuracy of assessing the state of the monitored object is proposed. This methodological approach is based on a combination of fuzzy cognitive models, advanced genetic algorithm and evolving artificial neural networks. The methodological approach has the following sequence of actions: building a fuzzy cognitive model; correcting the fuzzy cognitive model and training knowledge bases. The distinctive features of the methodological approach are that the type of data uncertainty and noise is taken into account while constructing the state of the monitored object using fuzzy cognitive models. The novelties while correcting fuzzy cognitive models using a genetic algorithm are taking into account the type of data uncertainty, taking into account the adaptability of individuals to iteration, duration of the existence of individuals and topology of the fuzzy cognitive model. The advanced genetic algorithm increases the efficiency of correcting factors and the relationships between them in the fuzzy cognitive model. This is achieved by finding solutions in different directions by several individuals in the population. The training procedure consists in learning the synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The use of the method allows increasing the efficiency of data processing at the level of 16-24 % using additional advanced procedures. The proposed methodological approach should be used to solve the problems of assessing complex and dynamic processes characterized by a high degree of complexity.