The establishment of the latest IoT systems available today such as smart cities, smart buildings, and smart homes and wireless sensor networks (WSNs) are let the main design restriction on the inadequate supply of battery power. Hence proposing a solar-based photovoltaic (PV) system which is designed DC-DC buck-boost converter with an improved modular maximum power point tracking (MPPT) algorithm. The output voltage depends on the inductor, capacitor values, metal oxide semiconductor field effect transistor (MOSFET) switching frequency, and duty cycle. This paper focuses on the design and simulation of min ripple current/voltage and improved efficiency at PV array output, to store DC power. The stored DC power will be used for smart IoT systems. From the simulation results, the current ripples are observed to be minimized from 0.062 A to 0.02 A maintaining the duty cycle at 61.09 for switching frequencies ranges from 300 kHz to 10 MHz at the input voltage 48 V and the output voltage in buck mode 24 V, boost mode 100 V by maintaining constant 99.7 efficiencies. The improvised approach is compared to various existed techniques. It is noticed that the results are more useful for the self-powered Embedded & Internet of Things systems.

This research article proposes a novel Smart Communication Platform (SCP) to improve the Quality of Service (QoS) parameters in real time by using MSP430F2618. A static network has been implemented with narrow band Internet of Things (IoT) architecture which contains 10 nodes. SCP performs tracking of environmental parameters like Temperature, Humidity, Pressure, Proximity and light. A prototype has been developed by using Open source Red hat Linux 14.4 version and programmed in Embedded C.MSP430F2618 has been configured as master and slave nodes, the output is observed in a serial monitor and Gateway as well. The QoS parameters of MSP430F2618 and ESP8266 are compared in terms of power. The power consumption improvements of QoS (Quality of Service) analysis results are around 1.01mW has been seen with the experimental setup. These empirical results are much useful for wireless sensor network and IoT applications.

This paper specifies about the monitoring environmental parameters regularly to forecast the weather prediction. Nowadays, Weather prediction plays a vital role for citizens living in the coastal areas. Mostly the parameters of the weather prediction vary to different fields and areas which hep the agricultures and travelers. This application makes the system more reliable, accurate, and flexible and dynamically varies the parameters of environment. We mainly consider the two factors; one is to acquire the parameter value accurately and second is to monitor continuously acting with reliability. The Environmental parameters considered in this paper are temperature and humidity, atmospheric pressure and light intensity. The MSP430 Micro controller observes the parameters by which the threats can be easily identified by the users and get alerted from the situations.

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In recent times, text summarization has gained enormous attention from the research community. Among the many uses of natural language processing, text summarization has emerged as a critical component in information retrieval. In particular, within the past two decades, many attempts have been undertaken by researchers to provide robust, useful summaries of their findings. Text summarizing may be described as automatically constructing a summary version of a given document while keeping the most important information included within the content itself. This method also aids users in quickly grasping the fundamental notions of information sources. The current trend in text summarizing, on the other hand, is increasingly focused on the area of news summaries. The first work in summarizing was done using a single-document summary as a starting point. The summarizing of a single document generates a summary of a single paper. As research advanced, mainly due to the vast quantity of information available on the internet, the concept of multidocument summarization evolved. Multidocument summarization generates summaries from a large number of source papers that are all about the same subject or are about the same event. Because of the content duplication, the news summarization system, on the other hand, is unable to cope with multidocument news summarizations well. Using the Naive Bayes classifier for classification, news websites were distinguished from nonnews web pages by extracting content, structure, and URL characteristics. The classifier was then used to differentiate between the two groups. A comparison is also made between the Naive Bayes classifier and the SMO and J48 classifiers for the same dataset. The findings demonstrate that it performs much better than the other two. After those important contents have been extracted from the correctly classified newscast web pages. Then, extracted relevant content is used for the keyphrase extraction from the news articles. Keyphrases can be a single word or a combination of more than one word representing the news article’s significant concept. Our proposed approach of crucial phrase extraction is based on identifying candidate phrases from the news articles and choosing the highest weight candidate phrase using the weight formula. Weight formula includes features such as TFIDF, phrase position, and construction of lexical chain to represent the semantic relations between words using WordNet. The proposed approach shows promising results compared to the other existing techniques.