Vol 14, No 1 (2024)

Background and Objective:Magnificent localization precision and low operating expenses are the main keys and essential issues to managing and operating outdoor wireless sensor networks. This work proposes a novel and rigorous efficiency localization algorithm utilizing a simplex optimization approach for node localization. This novel optimization method is a direct search approach, and is usually directed to solve nonlinear optimization problems that may not have wellknown derivatives, and it is called the Nelder-mead Method (NMM).

Methods:It is suggested that the objective function that will be optimized using NMM is the mean squared error of the range of all neighboring anchor nodes installed in the studied WSNs. This paper emphasizes employing a ranging technique called Received Signal Strength Indicator (shortly RSSI) to calculate the length of distances among all the nodes of WSNs.

Results:Simulation results perfectly showed that the suggested localization algorithm based on NMM can carry out a better performance than that of other localization algorithms utilizing other optimization approaches, including a particle swarm optimization, ant colony (ACO) and bat algorithm (BA). This obviously appeared in several metrics of performance evaluation, such as accuracy of localization, node localization rate, and implementation time.

Conclusions:The proposed algorithm that utilized NMM is more functional to enhance the precision of localization because of particular characteristics that are the flexible implementation of NMM and the free cost of using the RSSI technique.

Background:As the e-commerce product reviews and social media posts are increasing enormously, the size of the database for polarity/ sentiment detection is a challenging task, and again, predicting polarities associated with respect to aspect terms end to end in a sentence is a havoc in real-time applications. Human behavior is influenced by the various opinions generated in society. Public opinion influences our decisions most often. Businesses and establishments always need to collect the opinion of the society, which they try to obtain using customer feedback forms and questionnaires or surveys, which help them to be aware of the shortcomings if any, and to use suggestions to improve quality. It works in the same way for customers as well and the opinions of other customers about a particular product can come in handy when deciding to buy a product.

Objectives:In this work, an efficient Aspect-based Sentiment Classification technique has been introduced with a hybrid, multiple-word embedding methods and implemented using the CNN framework on large databases.

Methods:Most of the traditional models have a limitation on the dependency for one or more similar types of aspect words for sentiment classification problem. However, these conventional models such as TF-ID, Word 2Vec and Glove method consumes much more time for word embedding process and Aspect terms generation and further process of aspect level sentiment classification. Further, these models are facing problems of high true negative rate and misclassification rate on large aspect databases in sentiment classification. In this article, we have introduced an efficient Proposed ensemble word embedding model in the CNN network and defined Hybrid Word2 Vec method, Hybrid Glove word embedding method and Hybrid Random Forest model for sentiment classification.

Results:Experiments on a widely used benchmark prove that the proposed word embedding method- based classification technique results in to higher true positive rate with minimal misclassifications and also supports better runtime and accuracy than the traditional word embedding-based aspect level classification approaches.

Conclusion:In this article, a hybrid ensemble feature ranking-based classification model is proposed on the large aspect databases. In this work, advanced multiple-word embedding methods are implemented to improve the essential feature extraction problem in the aspect level sentiment process. These multiple-word embedding methods are applied to the sentiment databases in the CNN framework.

Aim:This paper will focus on promoting the ideas of green communication

Background:Pulse Width Modulation (PWM) generator is used to control the power transfer in any communication model. It is one of the most crucial parts of an electrical circuit. Our main focus in this research work is to develop an energy and power-efficient PWM generator by using Field Programmable Gate Array (FPGA) logic to elevate green communication

Objective:Our main focus in this research work is to develop an energy and power-efficient PWM generator by using Field Programmable Gate Array (FPGA) logic to elevate green communication.

Methods:In order to make the PWM suitable for GC, we have implemented the design on VIVADO ISE from Xilinx. The power analysis as well as resource utilization are targeted on three different FPGAs.

Results:Different IO standards of Stub Series Terminated Logic (SSTL) family are explored at different FPGA’s. Power analysis is deployed on the 7 series FPGAs of 3 different categories i.e., Spartan 7 (channel length = 28 nm), Kintex 7 (channel length = 20 nm) and ultra scale Zynq 7 (channel length = 16 nm).

Conclusion:It can be concluded from power analysis that SPARTAN-7 device is the most powerefficient and ZYNQ Ultra scale+utilizes the highest amount of power. However, KINTEX-7 Ultrascale device lies in the middle of both these devices as far as power consumption is concerned. There is a reduction of 43.07% TP consumption for SPARTAN-7 device with SSTL135 IO when equated with ZYNQ Ultra scale+ with SSTL18_I IO. Also, it can be observed from sections 5.1, 5.2 and 5.3 that there is more contribution of DP in TP consumption than SP. Hence the device utilizes additional power when it is in active state than static state. Since PWM generator is an integral part of data and wireless communication, it should require less power for proficient transmission and wellorganized green computing and communication.