A survey on fault current limiters: Development and technical aspects

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 ( 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

Experts confirm that 85% of the world’s population is expected to live in cities by 2050. Therefore, cities should be prepared to satisfy the needs of their citizens and provide the best services. The idea of a city of the future is commonly represented by the smart city, which is a more efficient system that optimizes its resources and services, through the use of monitoring and communication technology. Thus, one of the steps towards sustainability for cities around the world is to make a transition into smart cities. Here, sensors play an important role in the system, as they gather relevant information from the city, citizens, and the corresponding communication networks that transfer the information in real-time. Although the use of these sensors is diverse, their application can be categorized in six different groups: energy, health, mobility, security, water, and waste management. Based on these groups, this review presents an analysis of different sensors that are typically used in efforts toward creating smart cities. Insights about different applications and communication systems are provided, as well as the main opportunities and challenges faced when making a transition to a smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly about how these new sensing capabilities and digitization developments improve quality of life. Smarter communities are those that socialize, adapt, and invest through transparent and inclusive community engagement in these technologies based on local and regional societal needs and values. Cyber security disruptions and privacy remain chief vulnerabilities.

Sensors for Sustainable Smart Cities: A Review

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

With urbanization, rising income and consumption, the production of waste increases. One of the most important directions in the field of sustainable development is the design and implementation of monitoring and management systems for waste collection and removal. Smart waste management (SWM) involves for example collection and analytics of data from sensors on smart garbage bins (SGBs), management of waste trucks and urban infrastructure; planning and optimization of waste truck routes; etc. The purpose of this paper is to provide a comprehensive overview of the existing research in the field of systems, applications, and approaches vis-à-vis the collection and processing of solid waste in SWM systems. To achieve this objective, we performed a systematic literature review. This study consists of 173 primary studies selected for analysis and data extraction from the 3,732 initially retrieved studies from 5 databases. We 1) identified the main approaches and services that are applied in the city and SGB-level SWM systems, 2) listed sensors and actuators and analyzed their application in various types of SWM systems, 3) listed the direct and indirect stakeholders of the SWM systems, 4) identified the types of data shared between the SWM systems and stakeholders, and 5) identified the main promising directions and research gaps in the field of SWM systems. Based on an analysis of the existing approaches, technologies, and services, we developed recommendations for the implementation of city-level and SGB-level SWM systems.

IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review

Segmentation and classification of breast cancer using novel deep learning architecture

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.

An adaptive grouping based job scheduling in grid computing

The maximum hard side of Mobile Ad Hoc Network (MANET) is that they are power resource-limited wherein energy cannot be replenished. Our evaluation intention is to give a high power saving management system for MANET. Cluster approaches in MANET make other nodes into small disjoint groups, wherever every cluster node encompasses an organizer known as Cluster Head (CH). The primary difficult task within the methods is retaining created clusters. To decide any node as Cluster Head, it is essential to summarize its constraints and eligibility, supported by the information of the node’s properties like its remaining power or energy. There may be a possibility of the Cluster Heads might be unsuccessful and act wrongly due to power instability. During failure, the Cluster Heads are unable to collect the data and transfer information well. This influences the effectiveness of the MANET. We can decrease the information failure by detecting CH’s failure earlier, and it needs the lowest improvement efforts. A self-configurable cluster mechanism is proposed. We planned to identify the unstable Cluster Heads, and soon it will be replaced by some other nodes. k-means protocol method has been developed to select Cluster Heads effectively. This protocol (k-means) operates on an interval or periodic irregular rotations of the Cluster Heads, and some of the clusters vary between 0 and 1. If the arbitrary number is smaller than the pre-decided preset (threshold) value, the node becomes a Cluster Heads for the existing round. We have achieved and realized a decrease in power and energy loss in comparison to other protocols like transmission and direct communication protocols. The experimental result suggests that the proposed scheme works appropriately to reduce energy dissipation compared to other protocols like transmission and direct communication protocols.

High energy efficient lifetime management system and trust management framework for manet using self-configurable cluster mechanism

This research study applies the predictive control method to implement a distributed maximum power point tracking algorithm, which enhances the energy-extracting capabilities of a cascaded power optimizer-based system in variable environmental conditions. Two control loops are used in the developed method, one for each converter to implement a model predictive control loop based on tracking the maximum power point and the other for power optimization across all cascaded PV modules. Closed loop response of FOPID and ANN controllers simulation results are compared and verified with the prototype. The experimental findings support high energy output, rapid dynamic response, and almost distinct oscillations near the MPP.

MPPT Control using Modified Sepic Converter in PV Modules Connected DC Micro Grid Systems

It intended to acquire SEPIC converters, which have been utilized in a variety of applications It has two major drawbacks, the first of which is that it places power semiconductor devices are subjected to high voltages, and secondly, the power switches must work very hard, resulting in low efficiency. Power semiconductor devices with a higher voltage rating should be used, especially for high voltage applications. When the voltage rating goes up, power MOSFETs tend to have a higher Rds(on) value. As a consequence of this, it results in a greater amount of conduction loss when the current level remains the same. Consequently, if the current is held constant, decreasing the voltage stress can improve efficiency. The dynamic performance is enhanced.

Performance Improvement of Partially-Shaded PV Modules Connected DC Micro grid Systems

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