Power Aware Routing in Mobile Ad Hoc Networks

A survey on fault current limiters: Development and technical aspects

Background: Given the dynamics in resource allocation schemes offered by cloud computing, effective scheduling algorithms are important to utilize these benefits. Aim: In this paper, we propose a scheduling algorithm integrated with task grouping, priority-aware and SJF (shortest-job-first) to reduce the waiting time and make span, as well as to maximize resource utilization. Method: Scheduling is responsible for allocating the tasks to the best suitable resources with consideration of some dynamic parameters, restrictions and demands, such as network restriction and resource processing capability as well as waiting time. The proposed scheduling algorithm is integrated with task grouping, prioritization of bandwidth awareness and SJF algorithm, which aims at reducing processing time, waiting time and overhead. In the experiment, tasks are generated using Gaussian distribution and resources are created using Random distribution as well as CloudSim framework is used to simulate the proposed algorithm under various conditions. Results are then compared with existing algorithms for evaluation. Results: In comparison with existing task grouping algorithms, results show that the proposed algorithm waiting time and processing time decreased significantly (over 30%). Conclusion: The proposed method effectively minimizes waiting time and processing time and reduces processing cost to achieve optimum resources utilization and minimum overhead, as well as to reduce influence of bandwidth bottleneck in communication.

An Empirical Investigation on the Simulation of Priority and Shortest-Job-First Scheduling for Cloud-Based Software Systems

Wireless Sensors Networks (WSN) is the self-configured Wireless Ad hoc Networks (WANET) for Internet of Things (IoT) which consists of a huge measure of resource-restrained Sensor Nodes (SN). In WSN, the key parameters are effectual energy utilization and security. The adversary could send false information because of the Malicious Nodes’ (MNs’) presence. Thus, to shun security threats, it is vital to find and isolate those MNs. Consequently, this work proffered a solution for detecting MNs in WSN utilizing every SN's parameters. This work not only regards the security but also rendered energy-efficient data transmission (DT) by means of choosing the Cluster Head (CH) centred on the sensor's residual energy. The Improved Deep Convolutional Neural Network (IDCNN) identifies the MN and then isolates them into the malicious list box in the Malicious Nodes Detection (MND) phase. In the energy-efficient DT phase, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Extended K-Means (</i> EKM) algorithm clusters the Trusted Nodes (TN), and the t-Distribution based Satin Bowerbird Optimization (t-DSBO) algorithm selects an individual CH for each cluster centred on those nodes’ residual energy. The data of that cluster is transmitted to the Base Station (BS) through the CH. The t-DSBO selects an alternate CH if the current CH loses its energy. The proposed techniques effectively detect the MN and render energy-efficient DT, which is experimentally proved by comparing it with existing techniques. 

Improved Deep Convolutional Neural Network Based Malicious Node Detection and Energy-Efficient Data Transmission in Wireless Sensor Networks

Artificial Intelligence-based Solution for Sorting COVID Related Medical Waste Streams and Supporting Data-driven Decisions for Smart Circular Economy Practice

Wireless sensor networks are randomly deployed and responsible for monitoring geographical area wide. In WSN, the aggregation of data is very complex because of its limited power and computing capabilities. Issue in data aggregation is that the data may be passed on malicious node. All the existing data aggregation techniques undergo security issues because of the transfer of large amount of data. In this paper we propose a protocol named Secure Data Aggregation Protocol (SDAP) which identifies the malicious node by providing a logical group in the form of tree topology. In the tree topology the aggregation is formed by aggregating the nodes, which are non-leaf node and high level of trust is required to provide a better approximation and accuracy against the security threats. Thus the data is securely aggregated and the efficiency is achieved in data aggregation.

Malicious Node Detection in Wireless Sensor Networks Using an Efficient Secure Data Aggregation Protocol

Segmentation and classification of breast cancer using novel deep learning architecture

In this paper, we propose a design for segregation and monitoring of waste using Message Queuing Telemetry Transport (MQTT) in order to manage the waste collection. Smart waste management is essential for maintaining clean environment. Because, now-a-days unorganized and non-systematic waste collection is the major issue in the society. Therefore, in need of solution to collect waste in effective manner. The solution for this problem is given by embedded IoT system that will monitor the amount of waste deposited, for which an integrated platform where segregation and monitoring of waste have been presented. In segregation part, wastes are separated as dry waste and wet waste and in monitoring part, the bins with dry and wet wastes are embedded with sensors and the level of bin is transmitted via IoT. IoT is the system of interrelated computing devices which transfer data without requiring human to human interaction, it also introduce economical solution for massive data collection. The proprietary communication protocol that is used for transmission is MQTT. This protocol is the very light weight protocol used for messaging, designed with embedded systems, sensors and mobile applications in mind.

IOT Assisted MQTT for Segregation and Monitoring of Waste for Smart Cities

In this paper, we develop a detection module with strong training testing to develop a dense convolutional neural network model. The model is designed in such a way that it is trained with necessary features for optimal modelling of the cancer detection. The method involves preprocessing of computerized tomography (CT) images for optimal classification at the testing stages. A 10-fold cross-validation is conducted to test the reliability of the model for cancer detection. The experimental validation is conducted in python to validate the effectiveness of the model. The result shows that the model offers robust detection of cancer instances that novel approaches on large image datasets. The simulation result shows that the proposed method provides analyzes with 94% accuracy than other methods. Also, it helps to reduce the detection errors while classifying the cancer instances than other methods the several existing methods.

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Dense Convolutional Neural Network for Detection of Cancer from CT Images

The two major problems in grid computing applications are, resource management and job scheduling. These problems do occur due to distributed and heterogeneous nature of the resources. This paper introduces a model in job scheduling in grid computing environments. A dynamic scheduling algorithm is proposed to maximize the resource utilization and minimize processing time of the jobs. The proposed algorithm is based on job grouping. The results show that the proposed scheduling algorithm efficiently reduces the processing time of jobs.

S. Gomathi

Papers

Malicious Node Detection in Wireless Sensor Networks Using an Efficient Secure Data Aggregation Protocol

Segmentation and classification of breast cancer using novel deep learning architecture

IOT Assisted MQTT for Segregation and Monitoring of Waste for Smart Cities

Dense Convolutional Neural Network for Detection of Cancer from CT Images

An adaptive grouping based job scheduling in grid computing