Proposed Model for Real-Time Anomaly Detection in Big IoT Sensor Data for Smart City

A smart city represents an advanced urban environment that utilizes digital technologies to improve the well-being of residents, efficiently manage urban operations, and prioritize long-term sustainability. These technologically advanced cities collect significant data through various Internet of Things (IoT) sensors, highlighting the crucial importance of detecting anomalies to ensure both efficient operation and security. However, real-time identification of anomalies presents challenges due to the sheer volume, rapidity, and diversity of the data streams. This manuscript introduces an innovative framework designed for the immediate detection of anomalies within extensive IoT sensor data in the context of a smart city. Our proposed approach integrates a combination of unsupervised machine learning techniques, statistical analysis, and expert feature engineering to achieve real-time anomaly detection. Through an empirical assessment of a practical dataset obtained from a smart city environment, we demonstrate that our model outperforms established techniques for anomaly detection

Performance measurement with high performance computer of HW-GA anomaly detection algorithms for streaming data

Anomaly detection is very important in every sector as health, education, business, etc. Knowing what is going wrong with data/digital system help peoples from every sector to take decision. Detection anomalies in real time Big Data is nowadays very crucial. Dealing with real time data requires speed, for this reason the aim of this paper is to measure the performance of our previously proposed HW-GA algorithm compared with other anomaly detection algorithms. Many factors will be analyzed which may affect the performance of HW-GA as visualization of result, amount of data and performance of computers. Algorithm execution time and CPU usage are the parameters which will be measured to evaluate the performance of HW-GA algorithm. Also, another aim of this paper is to test the HW-GA algorithm with large amount of data to verify if it will find the possible anomalies and the result to compare with other algorithms. The experiments will be done in R with different datasets as real data Covid-19 and e-dnevnik data and three benchmarks from Numenta datasets. The real data have not known anomalies but in the benchmark data the anomalies are known this is in order to evaluate how the algorithms work in both situations. The novelty of this paper is that the performance will be tested in three different computers which one of them is high performance computer

Memoization method for storing of minimum-weight triangulation of a convex polygon

This study presents a practical view of dynamic programming, specifically in the context of the application of finding the optimal solutions for the polygon triangulation problem. The problem of the optimal triangulation of polygon is considered to be as a recursive substructure. The basic idea of the constructed method lies in finding to an adequate way for a rapid generation of optimal triangulations and storing - them in as small as possible memory space. The upgraded method is based on a memoization technique, and its emphasis is in storing the results of the calculated values and returning the cached result when the same values again occur. The significance of the method is in the generation of the optimal triangulation for a large number of n. All the calculated weights in the triangulation process are stored and performed in the same table. Results processing and implementation of the method was carried out in the Java environment and the experimental results were compared with the square matrix and Hurtado-Noy method

Encrypted Data Service for Security Electronic Communications: Symmetric Crypto - Classic Definition

The law on electronic communications has so far enumerated a considerable number of natural persons, legal entities as well as public institutions that use code systems and crypto devices during communication. Of particular interest is addressing the key role of operators and providers of encrypted data services in combating abuses committed through or against computer systems as the responsible performance of their duties to protect the security of networks and computer systems affects significantly in controlling illegal risks and attacks. In this perspective, the specific legal obligations for the protection of privacy regarding personal data that are processed for the purpose of providing information services are also analyzed. The purpose of the paper is, Utilizing communication between two parties sharing a common key, implementing a shared key to protect data communicated with different security attributes, Role of cryptography in data protection during communication, and Focus on privacy. Of the data communicated, against their authenticity. Key words: Communication, Security, Electronic Privacy

Comparison of Predictive Algorithms for IOT Smart Agriculture Sensor Data

This paper compares predictive algorithms for smart agriculture sensor data in Internet of Things (IoT) applications. The main objective of IoT in agriculture is to improve productivity and reduce production costs using advanced technology and artificial intelligence. In this study, we compared various predictive algorithms for analyzing IoT smart agriculture sensor data. Specifically, we evaluated the performance of NeuralProphet, Random Forest Regression, SARIMA, and Artificial Neural Networks (ANN) by KERAS algorithms on a dataset containing temperature, humidity, and soil moisture data. The dataset was collected using IoT sensors in a smart agriculture system. The results showed that Random Forest Regression, Seasonal ARIMA, and Artificial Neural Networks by KERAS algorithms outperformed NeuralProphet algorithm in terms of accuracy and computational efficiency