Mobile network nodes are trustworthy nodes; the data access in transmitting and receiving the data packet is not efficient in different network procedures. The malicious nodes are available in routing path making the packet loss, in packet transmission for time instance. Since the regular mobile network must not verify every mobile node approved else illegal, subsequently the reserved level of nodes also was not considered, to injure the packet transmission procedure by link failure called connection loss. It minimizes the transmission rate and network lifetime and improves packet latency and energy usage. The proposed novel link establishment communication (NLEC) technique is used to find the dependable routing path against intruder node available in the network. This scheme selects genuine node for routing path production, by using the node reward with dependence level estimating algorithm to compute every node trust level and resource range, to disconnect higher trust level node and lower trust level node; higher trust level node is a genuine node which performs secure communication. Lower trust level node is selfish node and they are detected and ignored from path. It increases the lifespan of network and throughput and minimizes the end to end delay and energy consumption.

This article focuses on implementing wireless sensors for monitoring exact distance between two individuals and to check whether everybody have sanitized their hands for stopping the spread of Corona Virus Disease (COVID). The idea behind this method is executed by implementing an objective function which focuses on maximizing distance, energy of nodes and minimizing the cost of implementation. Also, the proposed model is integrated with a variance detector which is denoted as Controlled Incongruity Algorithm (CIA). This variance detector is will sense the value and it will report to an online monitoring system named Things speak and for visualizing the sensed values it will be simulated using MATLAB. Even loss which is produced by sensors is found to be low when CIA is implemented. To validate the efficiency of proposed method it has been compared with prevailing methods and results prove that the better performance is obtained and the proposed method is improved by 76.8% than other outcomes observed from existing literatures.

The process of incorporating robotic technology and autonomous vehicles are increasing in all applications where for all real-time application developments time and energy can be saved for every single movement transfer as compared to human classifications. Thus, considering the advantage of autonomous process without any presence of an individual, the supply chain management can be designed using robotic technology. The robotic technology provides an informal route where all goods can be transported to different places within a short span of time, and any false identification in transfer of goods can also be easily identified. To drive the autonomous vehicle towards correct location, a precise protocol is chosen, which is termed common industrial protocol (CIP) where proper solutions can be achieved for all control applications using time synchronization model. Further, the data monitoring process is trailed using an online contrivance which is termed as internet router (IR) where short distance can be identified using corresponding addressing scheme.

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Background: In many medically developed applications, the process of early diagnosis in cases of pulmonary disease does not exist. Many people experience immediate suffering due to the lack of early diagnosis, even after becoming aware of breathing difficulties in daily life. Because of this, identifying such hazardous diseases is crucial, and the suggested solution combines computer vision and communication processing techniques. As computing technology advances, a more sophisticated mechanism is required for decision-making. Objective: The major objective of the proposed method is to use image processing to demonstrate computer vision-based experimentation for identifying lung illness. In order to characterize all the uncertainties that are present in nodule segments, an improved support vector machine is also integrated into the decision-making process. Method: As a result, the suggested method incorporates an Improved Support Vector Machine (ISVM) with a clear correlation between various margins. Additionally, an image processing technique is introduced where all impacted sites are marked at high intensity to detect the presence of pulmonary syndrome. Contrary to other methods, the suggested method divides the image processing methodology into groups, making the loop generation process much simpler. Results: Five situations are taken into account to demonstrate the effectiveness of the suggested technique, and test results are compared with those from existing models. Conclusion: The proposed technique with ISVM produces 83 percent of successful results. other: This is real-time work done with the designed operative device for pulmonary disease identification.