International Journal of Reconfigurable and Embedded Systems (IJRES)
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Intrusion detection systems for internet of thing based big data: a review
Network security is one of the foremost anxieties of the modern time. Over the previous years, numerous studies have been accompanied on the intrusion detection system. However, network security is one of the foremost apprehensions of the modern era this is due to the speedy development and substantial usage of altered technologies over the past period. The vulnerabilities of these technologies security have become a main dispute intrusion detection system is used to classify unapproved access and unusual attacks over the secured networks. For the implementation of intrusion detection system different approaches are used machine learning technique is one of them. In order to comprehend the present station of application of machine learning techniques for solving the intrusion discovery anomalies in internet of thing (IoT) based big data this review paper conducted. Total 55 papers are summarized from 2010 and 2021 which were centering on the manner of the single, hybrid and collaborative classifier design. This review paper also includes some of the basic information like IoT, big data, and machine learning approaches are discussed
Ultra-low leakage static random access memory design
An ultra-low leakage static random-access memory (SRAM) cell structure with 8 transistors is proposed in this paper. Compared to the 6T SRAM and other existing 8T SRAM cells, leakage power of the proposed cell in hold mode reduced significantly. The stability parameters of the proposed cell are calculated using butterfly method and also N-curve method. Proposed SRAM achieves better write margin with slightly less read margin than 6T SRAM. Proposed technique consumes 790 PW of power in hold mode, which is very less compared to other existing techniques. Therefore, the proposed cell is appropriate for hold mode applications. The simulations are carried out by using Cadence (Virtuoso Schematic and layout editor) tools with GPDK45-nm technology
Design of ant lion optimization-based PEGASIS routing protocol for energy efficiency in networks
In sensor networks the main problem facing by many researchers is regarding the energy efficiency. Different protocols are evaluated for communicating between the sensor nodes. The routing protocol when combined with evolutionary algorithms gives best optimal solution for the problem incurred in wireless sensor networks. In this paper, ant lion energy efficient-power efficient gathering in sensor information systems (ALEEPEGASIS) is used to develop the chain. This technique can achieve a global optimization solution by finding the best cluster head or the leader node for data communication. The techniques help in distributing the paths equally while the transmission of data process is performed. By performing this process, the power consumption near the sensor nodes can be reduced. The proposed technique is compared with other techniques like energy efficient PEGASIS and swarm energy efficient PEGASIS. The parameters used to compare are number of alive nodes, number of dead nodes and residual energy. The performance is observed using MATLAB simulation results
Application of artificial intelligence techniques in the intensive care unit
Intensive care unit deals with data that are dynamic in nature like real time measurement of health condition to laboratory test data that are continuously changes accordingly with time. Artificial intelligence (AI’s) potential ability to perform complex pattern analyses using large volumes of data. Generated pattern discovers the new symptoms of the disease in the Intensive care units (ICUs), helps the doctors to prescribe the new drug discovery which is helpful to intelligent use. Currently research work has been focused in the ICU making more efficient clinical workflow by generation of high-risk patterns from improved high volumes of data. Emerging area of AI in the ICU includes mortality prediction, uses of powerful sensors, new drug discovery, prediction of length of stay and legal role in uses of drugs for severity of disease. This review focuses latest application of AI drugs and other relevant issues for the ICU
IoT-enabled system for monitoring and controlling vertical farming operations
In this paper, we present an internet of things (IoT) powered solution that facilitates effortless monitoring and management of vertical farming operations. Our proposed approach employs cost-effective embedded microcontrollers and sensors to keep a tab on crucial parameters like soil moisture, air humidity, and temperature. The data acquired from these sensors can be accessed through a web page that is compatible with all web browsers and smart gadgets such as mobile phones and tablets. Furthermore, the IoT platform offers users the ability to regulate soil moisture and administer ultraviolet light to plants. The system can bring many benefits such as enabling real-time monitoring and control of environmental conditions, reducing energy consumption, improving scalability and flexibility, and contributing to the sustainable and efficient production of food
Balancing of four wire loads using linearized H-bridge static synchronous compensators
In this paper, a load balancing system is designed to balance the secondary phase currents of 11 kV/380 V, 50 Hz, 100 kVA power transformer in a three phase 4-wire, distribution network. The load balancing system is built of six identical modified static synchronous compensators (M-STATCOMs). Each M-STATCOM is constructed of a voltage source converter-based H-bridge controlled in capacitive and inductive modes as a linear compensating susceptance. The M-STATCOM current is controlled by varying its angle such that it exchanges pure reactive current with the utility grid. Three identical M-STATCOMs are connected in delta-form to balance the active phase currents of the power transformer, whereas the other three identical M-STATCOMs are connected in star-form to compensate for reactive currents. The M-STATCOMs in the delta-connected compensator are driven by 380 V line-to-line voltages, whilst, those connected in star-form are driven by 220 V phase voltages. The results of the 220 V and 380 V M-STATCOMs have exhibited linear and continuous control in capacitive and inductive regions of operation without steady-state harmonics. The proposed load balancing system has offered high flexibility during treating moderate and severe load unbalance conditions. It can involve any load unbalance within the power transformer current rating and even unbalance cases beyond the power transformer current rating
Design and memory optimization of hybrid gate diffusion input numerical controlled oscillator
The numerically controlled oscillator (NCO) is one of the digital oscillator signal generators. It can generate the clocked, synchronous, discrete waveform, and generally sinusoidal. Often NCOs care utilized in the combinations of digital to analog converter (DAC) at the outputs for creating direct digital synthesizer (DDS). The network on chips (NOCs) are utilized in various communication systems that are fully digital or mixed signals such as synthesis of arbitrary wave, precise control for sonar systems or phased array radar, digital down/up converters, all the digital phase locked loops (PLLs) for cellular and personal communication system (PCS) base stations and drivers for acoustic or optical transmissions and multilevel phase shift keying/frequency shift keying (PSK/FSK) modulators or demodulators (modem). The basic architecture of NCO will be enhanced and improved with less hardware for facilitating complete system level support to various sorts of modulation with minimum FPGA resources. In this paper design and memory optimization of hybrid gate diffusion input (GDI) numerically controlled oscillator based on field programmable gate array (FPGA) is implemented. compared with NCO based 8-bit microchip, memory optimization of hybrid GDI numerically controlled oscillator based on FPGA gives effective outcome in terms of delay, metal-oxide-semiconductor field-effect transistors (MOSFET’s) and nodes
A hybrid wrapper spider monkey optimization-simulated annealing model for optimal feature selection
In this research, a hybrid wrapper model is proposed to identify the featured gene subset from the gene expression data. To balance the gap between exploration and exploitation, a hybrid model with a popular meta-heuristic algorithm named spider monkey optimizer (SMO) and simulated annealing (SA) is applied. In the proposed model, ReliefF is used as a filter to obtain the relevant gene subset from dataset by removing the noise and outliers prior to feeding the data to the wrapper SMO. To enhance the quality of the solution, simulated annealing is deployed as local search with the SMO in the second phase, which will guide to the detection of the most optimal feature subset. To evaluate the performance of the proposed model, support vector machine (SVM) as a fitness function to recognize the most informative biomarker gene from the cancer datasets along with University of California, Irvine (UCI) datasets. To further evaluate the model, 4 different classifiers (SVM, na¨ıve Bayes (NB), decision tree (DT), and k-nearest neighbors (KNN)) are used. From the experimental results and analysis, it’s noteworthy to accept that the ReliefF-SO-SA-SVM performs relatively better than its state-of-the-art counterparts. For cancer datasets, our model performs better in terms of accuracy with a maximum of 99.45%
Cloud-based control systems: a systematic literature review
Control systems and computer science are two distinct and important fields of engineering. The development of cloud computing in computer science has become an enabler for the widely used controller in control systems to migrate to the cloud and has created a new field of research in cloud-based control systems (CCS). The paper used the systematic literature review approach to obtain insight into current CCS research. The objectives include a review in areas such as the demographics, topics of the research, evaluation method, and application domain. To that end, systematic literature review (SLR) has been conducted. The study obtained 64 primary studies from 581 articles. The CCS has a distinct characteristic; despite the fact that the cloud and network dynamics system, when coupled with the controlled plant, is inherently nonlinear, research efforts have used linear models with optimal control to approach it successfully in a limited case of control objectives. Furthermore, cloud-centric and cloud-fog network architecture approaches are considered in the studies—whereas, the quantitative method mainly uses simulation and discussion. Finally, the SLR summarizes open challenges for CCS in the future
Optimized load balancing mechanism in parallel computing for workflow in cloud computing environment
Cloud computing gives on-demand access to computing resources in metered and powerfully adapted way; it empowers the client to get access to fast and flexible resources through virtualization and widely adaptable for various applications. Further, to provide assurance of productive computation, scheduling of task is very much important in cloud infrastructure environment. Moreover, the main aim of task execution phenomena is to reduce the execution time and reserve infrastructure; further, considering huge application, workflow scheduling has drawn fine attention in business as well as scientific area. Hence, in this research work, we design and develop an optimized load balancing in parallel computation aka optimal load balancing in parallel computing (OLBP) mechanism to distribute the load; at first different parameter in workload is computed and then loads are distributed. Further OLBP mechanism considers makespan time and energy as constraint and further task offloading is done considering the server speed. This phenomenon provides the balancing of workflow; further OLBP mechanism is evaluated using cyber shake workflow dataset and outperforms the existing workflow mechanism