Fuzzy and Position Particle Swarm Optimized Routing in VANET

In Intelligent Transport Systems, traffic management and providing stable routing paths between vehicles using vehicular ad hoc networks (VANET's) is critical. Lots of research and several routing techniques providing a long path lifetime have been presented to resolve this issue. However, the routing algorithms suffer excessive overhead or collisions when solving complex optimization problems. In order to improve the routing efficiency and performance in the existing schemes, a Position Particle Swarm Optimization based on Fuzzy Logic (PPSO-FL) method is presented for VANET that provides a high-quality path for communication between nodes. The PPSO-FL has two main steps. The first step is selecting candidate nodes through collectively coordinated metrics using the fuzzy logic technique, improving packet delivery fraction, and minimizing end-to-end delay. The second step is the construction of an optimized routing model. The optimized routing model establishes an optimal route through the candidate nodes using position-based particle swarm optimization. The proposed work is simulated using an NS2 simulator. Simulation results demonstrate that the method outperforms the standard routing algorithms in packet delivery fraction, end-to-end delay and execution time for routing in VANET scenarios.

Rules-Based System for Writing Arabic Numerals in Indonesian Words

One of the algorithms stored in natural intelligence is the writing of Arabic numerals in Indonesian words. Algorithms in naturals intelligence are not easy to find. This problem gave us an idea to create artificial intelligence that tries to mimic natural intelligence algorithms. The proposed algorithm for building artificial intelligence is an R-Z rule-based system. This rule-based system contains a knowledge base of R-Z rules and a knowledge base of facts. In the knowledge base, the R-Z rule provides the R rule and the Z rule, while the facts knowledge base provides facts in the form of a definite standard number and an affix word. R-Z rule-based system for reasoning writing Arabic numerals in Indonesian words uses forward chaining. Artificial intelligence designs that mimic naturals intelligence in writing numbers in Indonesian words were made in C using Borland C++ 5.02 software. The experimental results show that by applying the R's rule of seven rules and Z's of twenty-five rules, the R-Z rule-based system can write Arabic numerals in Indonesian words from Arabic numerals "0" to Arabic numerals "9999999". For example, to write the Arabic number "10" in Indonesian words, the R-Z rule-based system starts with the R2 rule. Rule R2 takes action on Z3 to create new facts about Arabic numerals in the Indonesian word, namely "SEPULUH."

Data collection protocols for wireless sensor networks

Data collection in wireless sensor networks (WSNs) has a significant impact on the network’s performance and lifetime. Recently, several data collection techniques that use mobile elements (MEs) have been recommended, especially techniques that focus on maximising data delivery. However, energy consumption and the time required for data collection are significant for many WSN applications, particularly real-time systems. In this paper, a review of data collection techniques is presented, providing a comparison between the maximum amount shortest path (MASP) and zone-based energy-aware (ZEAL) data collection protocols implemented in the NS-3 simulator. Finally, the study provides a suitable data collection strategy that satisfies the requirements of WSN applications in terms of data delivery, energy consumption, and the time required for data collection.

Evaluation of Data Mining Techniques and Its Fusion with IoT Enabled Smart Technologies for Effective Prediction of Available Parking Space

After experiencing the hard times of pandemic situations we learned that if we could have a smart system that can help us in automatic parking of the vehicles then it could be a great help to society. This idea motivated us to carry out this current work. Though, nowadays, in almost every application domain, IoT techniques are the buzzword. IoT techniques can also be used to achieve efficacy in predicting free available parking space in advance. But the biggest challenge with IoT techniques is that they generate numerous data, which makes its analysis intangible. It was realized that if IoT techniques can be fused with outperforming data mining techniques, more efficient predictions can be performed. Thus, for this purpose, the main objective of our paper is to firstly, select the most appropriate data mining technique, based on performance evaluation, and then to perform prediction of available parking space in advance by fusing it with IoT techniques. Due to the busy schedule, the drivers need to get information about free parking spaces in advance by using smart phones. With the help of this information, it will be easy for the drivers to park their vehicle in the exact location without wasting their precious time and will maintain social distancing in crowded areas too. Data mining techniques can play an important role in the prediction of available parking space, by extracting only relevant and important information when applied to the given dataset. For this purpose, a comparative analysis of five data mining techniques such as the Support Vector Machine, K- Nearest approach, Decision Tree, Random Forest, and Ensemble learning approaches are applied on PK lot data set by using Python language. For calculation of result anaconda (spyder) is used as a supportive tool. The main outcome of the paper is to find the technique that will give better results for the prediction of the available space and if we fused data mining techniques with IoT technologies results are improvised. Evaluation parameters that are used for finding the best technique are precision, recall, accuracy, and F1-Score. For numerical calculation of the results, the k-fold cross-validation method is used. As the empirical results are calculated using the Pk lot dataset, the decision tree outperformed the best among all the techniques that are selected for analysis.

Implementation of MQTT Protocol in Health Care Based on IoT Systems: A Study

Internet of things IoT systems have become one of the most promising technologies in all fields. Data transmission is one of the important aspects, and the tendency to messaging protocols is an important aspect of IoT systems. One of these most important protocols is MQTT. This protocol depends on the Publish/Subscribe model, and it is a lightweight protocol. Reliability, simplicity, quality of service levels, and being Resource-constrained make MQTT common in the IoT industry. This paper designed an IoT device that consists of the sensor MLX 90614 non-contact IR Temperature connected to a development board (Node MCU ESP8266). A person's temperature is one of the important vital signs. This system measures human temperature values and transmits the measured values to the Mosquitto broker by using the MQTT protocol in real-time. The technology used is Wi-Fi. The person or the doctor can read the patient’s temperature remotely through a program (Flutter Android Client) representing the subscriber. Also, MQTT protocol control packets of the system were analyzed using Wireshark. The three levels of QoS were used in subscriber clients to compare the throughput. The results indicate that QoS2 is more reliable and offers more throughput but more delay. The results also show that the average round trip time (RTT) of the MQTT protocol is five milliseconds which means optimal performance for IoT applications.

Effect of temperature in electrical magnitudes of LED and HPS luminaires

Temperature effects on luminaires is usually referred to light output, that is luminaire efficiency. However, the effect on electrical magnitudes as power, current and third current harmonic is not widely studied. One major technology, Light-emitting- diode (LED) is fast replacing the other types of lighting all over the world, this opens the interrogate of how is temperature affecting LED luminaires development and how different is this effect compared to other technologies. This paper analyses these effects on LED luminaires of different wattage and one high pressure SODIUM luminaire. Luminaires were measure in two different environments, the first stage with a constant temperature-controlled system (±1°C) and the second one without a temperature-controlled system. The tests were performed on three samples of LED luminaires with different power ratings and one sample of SODIUM luminaire. It was found that the third current harmonic is directly related to temperature while power and current are inversely related.

Legacy Digital Transformation: TCO and ROI Analysis

Legacy Digital Transformation is modernizing or migrating systems from non-digital or older digital technology to newer digital technologies. Digitalization is essential for information reading, processing, transforming, and storing. Social media, Cloud, and analytics are the major technologies in today's digital world. Digitalization (business process) and Digital Transformation (the effect) are the core elements of newer global policies and processes. Recent COVID pandemic situation, Organizations are willing to digitalize their environment without losing business. Digital technologies help to improve their capabilities to transform processes that intern promote new business models. Applications cannot remain static and should modernize to meet the evolving business and technology needs. Business needs time to market, Agility, and reduce technical debt. Technology needs consist of APIs, better Security, Portability, Scalability, Cloud support, Deployment, Automation, and Integration. This paper elaborates different transformation/modernization approaches for Legacy systems written in very long or End of Life (EOL) systems to newer digital technologies to serve the business needs. EOL impacts application production, supportability, compliance, and security. Organizations spend money and resources on Digital Transformation for considering Investment versus Return on Investment, Agility of the System, and improved business processes. Migration and Modernization are critical for any Legacy Digital Transformation. Management takes decisions to proceed with Digital Transformation for considering Total Cost Ownership (TCO) and Return on Investment (ROI) of the program. The paper also includes a TCO-ROI calculator for Transformation from Legacy / Monolithic to new architectures like Microservices.

Appointment scheduling system in multi doctor/multi services environment

Appointment scheduling systems are used by health care providers to manage access to their services. In this paper an algorithm and a web application for automatic appointment scheduling is presented. Both are implemented using the concept of booking appointments for patients for a specific service offered by each doctor. The purpose of the application is to make signing up for a specific service easier for patients and to improve health tourism in Croatia by maximizing doctor’s efficiency and minimize patient waiting time. Medical providers are added to the system, they add the services which they provide, and each service offered has its own duration time. Users register, search for services matching their parameters, and schedule an appointment for the requested service. Available appointments are generated using the presented algorithm, which is the main part of this paper. The algorithm searches the database and returns possible appointments. If patient has more than one appointment, possible appointments time can be before the existing appointment, between two appointments, or at the end of the last appointment. Thus, web application enables the patient to reserve desirable appointment time.

Expiry Prediction and Reducing Food Wastage using IoT and ML

This paper details development of a low-cost, small-size, and portable electronic nose (E-nose) for the prediction of the expiry date of food products. The Sensor array is composed of commercially available metal oxide semiconductors sensors like MQ2 sensor, temperature sensor, and humidity sensor, which were interfaced with the help of ESP8266 and Arduino Uno for data acquisition, storage, and analysis of the dataset consisting of the odor from the fruit at different ripening stages. The developed system is used to analyze gas sensor values from various fruits like bananas and tomatoes. Responding signals of the e-nose were extracted and analyzed. Based on the obtained data we applied a few machine learning algorithms to predict if a banana is stale or not. Logistic regression, Decision Tree Classifier, Support Vector Classifier (SVC) & K-Nearest Neighbours (KNN) classifiers were the binary classification algorithms used to determine whether the fruit became stale or not. We achieved an accuracy of 97.05%. These results prove that e-nose has the potential of assessing fruits and vegetable freshness and predict their expiry date, thus reducing food wastage.

Performance evaluation and implementations of MFCC, SVM and MLP algorithms in the FPGA board

One of the most difficult speech recognition tasks is accurate recognition of human-to-human communication. Advances in deep learning over the last few years have produced major speech improvements in recognition on the representative Switch-board conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This raises two issues - what is human performance, and how far down can we still drive speech recognition error rates? The main objective of this article is the development of a comparative study of the performance of Automatic Speech Recognition (ASR) algorithms using a database made up of a set of signals created by female and male speakers of different ages. We will also develop techniques for the Software and Hardware implementation of these algorithms and test them in an embedded electronic card based on a reconfigurable circuit (Field Programmable Gate Array FPGA). We will present an analysis of the results of classifications for the best Support Vector Machine architectures (SVM) and Artificial Neural Networks of Multi-Layer Perceptron (MLP). Following our analysis, we created NIOSII processors and we tested their operations as well as their characteristics. The characteristics of each processor are specified in this article (cost, size, speed, power consumption and complexity). At the end of this work, we physically implemented the architecture of the Mel Frequency Cepstral Coefficients (MFCC) extraction algorithm as well as the classification algorithm that provided the best results.