In many applications of Internet of Things (IoT), the huge amount of data are generated by sensor nodes and processing them are complex. Offloading data classification and anomaly event detection tasks to sink nodes in sensor networks can reduce the computing complexity, lower remote communication loads, and improve the response time for the delay-sensitive IoT applications. Many existing classification and anomaly detection methods cannot be directly applied to these IoT applications, because the computing and energy resources of sensors are limited. In this paper, a new feature-based learning system for IoT applications is proposed to effectively classify data and detect anomaly event. Especially, based on the theory of distributed compression, the sparsity and relativity of the data are exploited to obtain the classification features, which can reduce the computation overhead and energy consumption. Further, an RBF-BP hybrid neural network is employed to detect the anomaly event based on the classification results, by which the training time of neural network can be significantly reduced and the accuracy can be improved for users’ decisions.

A Feature-Based Learning System for Internet of Things Applications

Outlier detection approaches for wireless sensor networks

Sensor data quality plays a vital role in Internet of Things (IoT) applications as they are rendered useless if the data quality is bad. This systematic review aims to provide an introduction and guide for researchers who are interested in quality-related issues of physical sensor data. The process and results of the systematic review are presented which aims to answer the following research questions: what are the different types of physical sensor data errors, how to quantify or detect those errors, how to correct them and what domains are the solutions in. Out of 6970 literatures obtained from three databases (ACM Digital Library, IEEE Xplore and ScienceDirect) using the search string refined via topic modelling, 57 publications were selected and examined. Results show that the different types of sensor data errors addressed by those papers are mostly missing data and faults e.g. outliers, bias and drift. The most common solutions for error detection are based on principal component analysis (PCA) and artificial neural network (ANN) which accounts for about 40% of all error detection papers found in the study. Similarly, for fault correction, PCA and ANN are among the most common, along with Bayesian Networks. Missing values on the other hand, are mostly imputed using Association Rule Mining. Other techniques include hybrid solutions that combine several data science methods to detect and correct the errors. Through this systematic review, it is found that the methods proposed to solve physical sensor data errors cannot be directly compared due to the non-uniform evaluation process and the high use of non-publicly available datasets. Bayesian data analysis done on the 57 selected publications also suggests that publications using publicly available datasets for method evaluation have higher citation rates.

https://link.springer.com/content/pdf/10.1186/s40537-020-0285-1.pdf

Sensor data quality: a systematic review

The prevention of intrusion in networks is decisive and an intrusion detection system is extremely desirable with potent intrusion detection mechanism. Excessive work is done on intrusion detection systems but still these are not powerful due to high number of false alarms. One of the leading causes of false alarms is due to the usage of a raw dataset that contains redundancy. To resolve this issue, feature selection is necessary which can improve intrusion detection performance. Latterly, principal component analysis (PCA) has been used for feature reduction and subset selection in which features are primarily projected into a principal space and then features are elected based on their eigenvalues, but the features with the highest eigenvalues may not have the guaranty to provide optimal sensitivity for the classifier. To avoid this problem, an optimization method is required. Evolutionary optimization approach like genetic algorithm (GA) has been used to search the most discriminative subset of transformed features. The particle swarm optimization (PSO) is another optimization approach based on the behavioral study of animals/birds. Therefore, in this paper a feature subset selection based on PSO is proposed which provides better performance as compared to GA.

Feature selection using particle swarm optimization in intrusion detection

Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning. Outlier detection is important in many applications, including fraud detection in credit card transactions and network intrusion detection. There are two general types of outlier detection: global and local. Global outliers fall outside the normal range for an entire dataset, whereas local outliers may fall within the normal range for the entire dataset, but outside the normal range for the surrounding data points. This paper addresses local outlier detection. The best-known technique for local outlier detection is the Local Outlier Factor (LOF), a density-based technique. There are many LOF algorithms for a static data environment; however, these algorithms cannot be applied directly to data streams, which are an important type of big data. In general, local outlier detection algorithms for data streams are still deficient and better algorithms need to be developed that can effectively analyze the high velocity of data streams to detect local outliers. This paper presents a literature review of local outlier detection algorithms in static and stream environments, with an emphasis on LOF algorithms. It collects and categorizes existing local outlier detection algorithms and analyzes their characteristics. Furthermore, the paper discusses the advantages and limitations of those algorithms and proposes several promising directions for developing improved local outlier detection methods for data streams.

A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams

Outliers detection and classification in wireless sensor networks

Electrical power networks are expanded regularly to meet growing energy requirements. Reactive power dispatch (RPD) optimization is a powerful tool to enhance a system’s efficiency, reliability, and security. RPD optimization is classified as a non-linear and non-convex problem. In this paper, the RPD optimization problem is solved based on novel hybrid genetic algorithms—equilibrium optimizer (GAEO) optimization algorithms. The control variables are determined in such a way that optimizes RPD and minimizes power losses. The efficiency of the proposed optimization algorithms is compared to other techniques that have been used recently to solve the RPD problem. The proposed algorithm has been tested for optimization RPD for three test systems, IEEE14-bus, IEEE-30bus, and IEEE57-bus. The obtained results show the superiority of GAEO over other techniques for small test systems, IEEE14-bus and IEEE-30bus. GAEO shows good results for large system, IEEE 57-bus.

/pdf/reactive-power-management-based-hybrid-gaeo-njmiwtge.pdf

Reactive Power Management Based Hybrid GAEO

Single widespread employment of renewable energy sources (RESs) contributes to the shortage in the inertia of the microgrid (MG). After this, frequency stability may regress as a result of power imbalance or minor load fluctuations. This paper proposes an explicit adaptive modified virtual inertia control (VIC) based on an online Archimedes optimization algorithm (AOA) identifier for MG containing thermal, wind, and solar photovoltaic power plants. The Rung Kutta approach is used to construct the proposed online identifier, which acts as a model of the MG. AOA predicts the coefficients of the online identifier based on the input and output of MG to mimic the frequency deviation of the MG online. AOA estimates online the inertia and damping coefficients of the VIC system with an energy storage device based on online AOA identifier coefficients. The frequency deviation of the MG based on the proposed explicit adaptive modified VIC is compared with the conventional VIC based on fixed parameters and the VIC system based on optimal parameters using AOA offline under mutation in loads, weather-dependent input, and MG parameters using MATLAB/Simulink software. Furthermore, the proposed explicit adaptive modified VIC based on an online AOA identifier is evaluated with the adaptive VIC system based on fuzzy logic control, which adjusts only the inertial gain online. The simulation results demonstrate the capabilities of the proposed explicit adaptive modified VIC to improve the frequency stability and enhance low-inertia islanded MGs with RESs.

/pdf/an-online-archimedes-optimization-algorithm-identifier-2o7rzavm.pdf

Asmaa Fawzy

Papers

Outliers detection and classification in wireless sensor networks

Reactive Power Management Based Hybrid GAEO

An Online Archimedes Optimization Algorithm Identifier-Controlled Adaptive Modified Virtual Inertia Control for Microgrids

Optimized PID controller using Archimedes optimization algorithm for transient stability enhancement