scholarly journals A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 328 ◽  
Author(s):  
Mahmood Safaei ◽  
Shahla Asadi ◽  
Maha Driss ◽  
Wadii Boulila ◽  
Abdullah Alsaeedi ◽  
...  
Keyword(s):  
Literature Review ◽  
Outlier Detection ◽  
Systematic Literature Review ◽  
Machine Learning Algorithms ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distributed Data ◽  
Detection Techniques ◽  
Distributed Data Processing ◽  
Spatially Distributed

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

Wireless Sensor Network Using ARM Processors

2013 ◽  
Vol 4 (4) ◽  
pp. 48-59 ◽  
Author(s):  
Manivannan Doraipandian ◽  
Periasamy Neelamegam
Keyword(s):  
Critical Role ◽  
Sensor Nodes ◽  
Base Stations ◽  
Wireless Sensor ◽  
Processing Unit ◽  
Distributed Data ◽  
Embedded Devices ◽  
Arm Processor ◽  
Distributed Data Processing ◽  
Efficient Data

The hardware design of Wireless Sensor Networks (WSN) is the crux of its effective deployment. Nowadays these networks are used in microscopic, secure and high-end embedded products. WSN's potentiality in terms of efficient data sensing and distributed data processing has led to its usage in applications for measurement and tracking. WSN comprises of small number of embedded devices known as sensor nodes, gateways and base stations. Sensor nodes consist of sensors, processors and transceivers. The property of embedded sensor devices, also called motes, is to determine the strength of WSN. Thus processor selection for the motes plays a critical role in determining a WSN's competency. In this article, the absolute and obvious hardware characteristics of available and proposed sensor nodes are discussed. The objective of this work was to increase the efficiency and provision of sensor nodes by evaluating their processing and transceiver units. During this work, a sensor node was developed with ARM processor and XBee series 2 Unit. LPC 2148, LPC 2378 ARM processors were posed as processing unit and XBee series 2 acted as communication unit. Results of this experimental setup were recorded. Also a comparative study of the various available sensor nodes and proposed sensor nodes was done extensively.

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

scholarly journals Spam Review Detection Techniques: A Systematic Literature Review

2019 ◽  
Vol 9 (5) ◽  
pp. 987 ◽  
Author(s):  
Naveed Hussain ◽  
Hamid Turab Mirza ◽  
Ghulam Rasool ◽  
Ibrar Hussain ◽  
Mohammad Kaleem
Keyword(s):  
Feature Extraction ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Online Reviews ◽  
Detection Methods ◽  
Spam Detection ◽  
Successful Implementation ◽  
Comprehensive Review ◽  
Detection Techniques ◽  
Review Spam

Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.

scholarly journals Use of AI and Machine Learning for Asthma Patients: A Systematic Literature Review

2020 ◽  
Vol 7 (2) ◽  
pp. 129-134
Author(s):  
Takudzwa Fadziso
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Modern Times ◽  
Predictive Algorithms ◽  
Asthma Patients

In modern times, the collection of data is not a big deal but using it in a meaningful is a challenging task. Different organizations are using artificial intelligence and machine learning for collecting and utilizing the data. These should also be used in the medical because different disease requires the prediction. One of these diseases is asthma that is continuously increasing and affecting more and more people. The major issue is that it is difficult to diagnose in children. Machine learning algorithms can help in diagnosing it early so that the doctors can start the treatment early. Machine learning algorithms can perform this prediction so this study will be helpful for both the doctors and patients. There are different machine learning predictive algorithms are available that have been used for this purpose.  

scholarly journals Proposing a novel method for clock synchronization in WSN

2015 ◽  
Vol 4 (2) ◽  
pp. 95-106
Author(s):  
Seyed Kazem Kazeminezhad ◽  
Shahram Babaie ◽  
Amir Shiri
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Time Synchronization ◽  
Clock Synchronization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Crystal Oscillator ◽  
Error Factor ◽  
Spatially Distributed ◽  
Two Factors

Wireless sensor networks (WSNs) of spatially distributed autonomous sensors are used to monitor physical or environmental conditions such as temperature, sound, pressure, etc. They are also used to cooperatively pass the collected data through the network to a main location. Due to the application of wireless sensor networks as a monitoring device in the real world, the physical time of the occurrence of events is important. Since WSNs have particular constraints and limitations, synchronizing the physical times for these networks is considered to be a complex task. Although many algorithms have been proposed for synchronizing time in the network, there are two main error factors in all the proposed algorithms. The first factor is the clock drift which might be caused by the influence of different environmental factors such as temperature, ambient temperature, humidity, it might be generated on crystal oscillator which is inevitable The second error factor is indeterminacy which is attributed to the existence of non-deterministic delays in sending and receiving messages between sensor nodes. These two factors together reduce the precision of synchronization algorithms. In this paper, the researchers proposed a new approach for dealing with the above-mentioned two problems and achieving better synchronization. The proposed approach is a combination of flooding time synchronization protocol (FTSP) and reference broadcast synchronization (RBS).This approach is intended to increase synchronization accuracy and network lifetime by reducing the number of synchronization messages sent between nodes and eliminating the most of non-deterministic errors in sending messages. The results of simulations conducted in the study indicated that the proposed approach is significantly more efficient than the FTSP and RBS methods in terms of parameters such as accurate synchronization, amount of sent packets and power consumption.

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