The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in various fields such as manufacturing, smart city, transport, health and the Internet of Things, by providing pervasive real-time applications. In this paper, we analyze the existing research trends of Coverage, Deployment and Localization challenges in WSN with respect to Artificial Intelligence (AI) methods for WSN enhancement. We present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. This would guide the reader towards an understanding on up-to-date applications of AI methods with respect to different WSN challenges. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the Coverage, Deployment and Localization challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new directions for future research.

This study aims to develop a better Financial Statement Fraud (FSF) detection model by utilizing data from publicly available financial statements of firms in the MENA region. We develop an FSF model using a powerful ensemble technique, the XGBoost (eXtreme Gradient Boosting) algorithm, that helps to identify fraud in a set of sample companies drawn from the Middle East and North Africa (MENA) region. The issue of class imbalance in the dataset is addressed by applying the Synthetic Minority Oversampling Technique (SMOTE) algorithm. We use different Machine Learning techniques in Python to predict FSF, and our empirical findings show that the XGBoost algorithm outperformed the other algorithms in this study, namely, Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), AdaBoost, and Random Forest (RF). We then optimize the XGBoost algorithm to obtain the best result, with a final accuracy of 96.05% in the detection of FSF.

The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

This paper proposes a novel dynamic, distributive, and self-organizing entropy based clustering scheme (EBCS) that benefits from the local information of sensor nodes measured in terms of entropy and use that as criteria for cluster head election and cluster formation. It divides the WSN into two-levels of hierarchy and three-levels of energy heterogeneity of sensor nodes. The simulation results reveal that the proposed approach outperforms existing baseline algorithms in terms of energy consumption, stability period, and the network lifetime.