scholarly journals IoT-Based Small Scale Anomaly Detection Using Dixon’s Q Test for e-Health Data

2021 ◽  
Vol 4 (4) ◽  
pp. 100
Author(s):  
Partha Pratim Ray ◽  
Dinesh Dash
Keyword(s):  
Anomaly Detection ◽  
Data Packet ◽  
Sensor Data ◽  
Small Scale ◽  
Quality Of Data ◽  
Specific Data ◽  
Data Packets ◽  
Kolmogorov Smirnov ◽  
Scale Point

Anomaly detection in the smart application domain can significantly improve the quality of data processing, especially when the size of a dataset is too small. Internet of Things (IoT) enables the development of numerous applications where sensor-data-aware anomalies can affect the decision making of the underlying system. In this paper, we propose a scheme: IoTDixon, which works on the Dixon’s Q test to identify point anomalies from a simulated normally distributed dataset. The proposed technique involves Q statistics, Kolmogorov–Smirnov test, and partitioning of a given dataset into a specific data packet. The proposed techniques use Q-test to detect point anomalies. We find that value 76.37 is statistically significant where P=0.012<α=0.05, thus rejecting the null hypothesis for a test data packet. In other data packets, no such significance is observed; thus, no outlier is statistically detected. The proposed approach of IoTDixon can help to improve small-scale point anomaly detection for a small-size dataset as shown in the conducted experiments.

Measuring patient engagement among cancer patients in eHealth and mHealth interventions on symptom management: an integrated review (Preprint)

2020 ◽  
Author(s):  
Juqing Zhao ◽  
Pei Chen ◽  
Guangming Wan
Keyword(s):  
Cancer Patients ◽  
Patient Engagement ◽  
Symptom Management ◽  
Subjective Experience ◽  
Participation Rate ◽  
Sensor Data ◽  
Quality Of Data ◽  
Mhealth Intervention

BACKGROUND There has been an increase number of eHealth and mHealth interventions aimed to support symptoms among cancer survivors. However, patient engagement has not been guaranteed and standardized in these interventions. OBJECTIVE The objective of this review was to address how patient engagement has been defined and measured in eHealth and mHealth interventions designed to improve symptoms and quality of life for cancer patients. METHODS Searches were performed in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify eHealth and mHealth interventions designed specifically to improve symptom management for cancer patients. Definition and measurement of engagement and engagement related outcomes of each intervention were synthesized. This integrated review was conducted using Critical Interpretive Synthesis to ensure the quality of data synthesis. RESULTS A total of 792 intervention studies were identified through the searches; 10 research papers met the inclusion criteria. Most of them (6/10) were randomized trial, 2 were one group trail, 1 was qualitative design, and 1 paper used mixed method. Majority of identified papers defined patient engagement as the usage of an eHealth and mHealth intervention by using different variables (e.g., usage time, log in times, participation rate). Engagement has also been described as subjective experience about the interaction with the intervention. The measurement of engagement is in accordance with the definition of engagement and can be categorized as objective and subjective measures. Among identified papers, 5 used system usage data, 2 used self-reported questionnaire, 1 used sensor data and 3 used qualitative method. Almost all studies reported engagement at a moment to moment level, but there is a lack of measurement of engagement for the long term. CONCLUSIONS There have been calls to develop standard definition and measurement of patient engagement in eHealth and mHealth interventions. Besides, it is important to provide cancer patients with more tailored and engaging eHealth and mHealth interventions for long term engagement.

scholarly journals ANALYSIS OF USER MOBILITY PERFORMANCE ON SOFTWARE DEFINED WIRELESS NETWORK USING DIJKSTRA ALGORITHM

2021 ◽  
Vol 2 (2) ◽  
pp. 127-133
Author(s):  
Icha Nurlaela Khoerotunisa ◽  
Sofia Naning Hertiana ◽  
Ridha Muldina Negara
Keyword(s):  
Wireless Network ◽  
Packet Loss ◽  
Network Performance ◽  
User Mobility ◽  
Dijkstra Algorithm ◽  
High Complexity ◽  
Quality Of Data ◽  
Data Packets ◽  
Mobility Performance

  Over the last decade, wireless devices have developed rapidly until predictions will develop with high complexity and dynamic. So that new capabilities are needed for wireless problems in this problem. Software Defined Network (SDN) is generally a wire-based network, but to meet the needs of users in terms of its implementation, it has begun to introduce a Wireless-based SDN called Software Defined Wireless Network (SDWN) which provides good service quality and reach and higher tools, so as to be able to provide new capabilities to wireless in a high complexity and very dynamic. When SDN is implemented in a wireless network it will require a routing solution that chooses paths due to network complexity. In this paper, SDWN is tested by being applied to mesh topologies of 4,6 and 8 access points (AP) because this topology is very often used in wireless-based networks. To improve network performance, Dijkstra's algorithm is added with the user mobility scheme used is RandomDirection. The Dijkstra algorithm was chosen because it is very effective compared to other algorithms. The performance measured in this study is Quality of Service (QoS), which is a parameter that indicates the quality of data packets in a network. The measurement results obtained show that the QoS value in this study meets the parameters considered by the ITU-T G1010 with a delay value of 1.3 ms for data services and packet loss below 0.1%. When compared with the ITU-T standard, the delay and packet loss fall into the very good category.

scholarly journals Virtual Link Aggregation Network Performance Using MikroTik Bonding

2020 ◽  
Vol 2 (2) ◽  
pp. 131-139
Author(s):  
Firmansyah Firmansyah ◽  
Mochamad Wahyudi ◽  
Rachmat Adi Purnama
Keyword(s):  
Quality Of Service ◽  
Transfer Rate ◽  
Network Performance ◽  
Data Packet ◽  
Virtual Link ◽  
Interface Bonding ◽  
Data Packets ◽  
Bandwidth Optimization ◽  
Network Link

Quality of Service in a network is a big thing that must be resolved and dealt with as best as possible. The limitation of the maximum transfer rate in network devices creates an obstacle in the process of transferring data packets. To maximize the transfer rate in network devices, you can use Virtual Link Aggregation which can offer bandwidth optimization and failover in the network. Link aggregation is a solution in combining several physical links into one logical link. The method used in this research is to consider the allocation of bandwidth, load balancing and failover in the link aggregation. From the results of the link aggregation test using two (2) interface bonding, the results of the bandwidth averages when there is a UPD data packet transfer to 0 bps / 184.9 Mbps, which was previously around 0 bps / 91.6 Mbps. While the result of the bandwidth averages when the TCP data packet transfer occurs is 0 bps / 105.5 Mbps, which was previously around 0 bps / 93.8 Mbps. Link Aggregation using a Mikrotik Router is a solution to produce a larger Throughput Bandwidth by combining two (2) Ethernet Physical Links into one logical link.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

Time series forecasting with model selection applied to anomaly detection in network traffic

2020 ◽  
Vol 28 (4) ◽  
pp. 531-545
Author(s):  
Łukasz Saganowski ◽  
Tomasz Andrysiak
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Model Parameters ◽  
Problem Solution ◽  
Quality Of Data ◽  
Initial Stage ◽  
Statistic Model ◽  
Short Memory ◽  
Optimal Values

Abstract In herein article an attempt of problem solution connected with anomaly detection in network traffic with the use of statistic models with long or short memory dependence was presented. In order to select the proper type of a model, the parameter describing memory on the basis of the Geweke and Porter-Hudak test was estimated. Bearing in mind that the value of statistic model depends directly on quality of data used for its creation, at the initial stage of the suggested method, outliers were identified and then removed. For the implementation of this task, the criterion using the value of interquartile range was used. The data prepared in this manner were useful for automatic creation of statistic models classes, such as ARFIMA and Holt-Winters. The procedure of calculation of model parameters’ optimal values was carried out as a compromise between the models coherence and the size of error estimation. Then, relations between the estimated network model and its actual parameters were used in order to detect anomalies in the network traffic. Considering the possibility of appearance of significant real traffic network fluctuations, procedure of updating statistic models was suggested. The results obtained in the course of performed experiments proved efficacy and efficiency of the presented solution.

scholarly journals Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5200
Author(s):  
Donghyun Kim ◽  
Gian Antariksa ◽  
Melia Putri Handayani ◽  
Sangbong Lee ◽  
Jihwan Lee
Keyword(s):  
Anomaly Detection ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Sensor Data ◽  
Maritime Industry ◽  
Specific Data ◽  
Explainable Ai ◽  
Data Driven Approach ◽  
The Common ◽  
Main Engine

In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not explain why the model classifies specific data instances as an anomaly. This study combines explainable AI techniques with anomaly detection algorithm to overcome the limitation above. As an explainable AI method, this study adopts Shapley Additive exPlanations (SHAP), which is theoretically solid and compatible with any kind of machine learning algorithm. SHAP enables us to measure the marginal contribution of each sensor variable to an anomaly. Thus, one can easily specify which sensor is responsible for the specific anomaly. To illustrate our framework, the actual sensor stream obtained from the cargo vessel collected over 10 months was analyzed. In this analysis, we performed hierarchical clustering analysis with transformed SHAP values to interpret and group common anomaly patterns. We showed that anomaly interpretation and segmentation using SHAP value provides more useful interpretation compared to the case without using SHAP value.

scholarly journals Pattern Matching Based Sensor Identification Layer for an Android Platform

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hong Min ◽  
Taesik Kim ◽  
Junyoung Heo ◽  
Tomas Cerny ◽  
Sriram Sankaran ◽  
...  
Keyword(s):  
Data Packet ◽  
Sensor Data ◽  
Strong Impact ◽  
Internet Of Things Environment ◽  
Android Platform ◽  
Quality Aspect ◽  
Important Quality ◽  
Ieee 11073 ◽  
Different Sources

As sensor-related technologies have been developed, smartphones obtain more information from internal and external sensors. This interaction accelerates the development of applications in the Internet of Things environment. Due to many attributes that may vary the quality of the IoT system, sensor manufacturers provide their own data format and application even if there is a well-defined standard, such as ISO/IEEE 11073 for personal health devices. In this paper, we propose a client-server-based sensor adaptation layer for an Android platform to improve interoperability among nonstandard sensors. Interoperability is an important quality aspect for the IoT that may have a strong impact on the system especially when the sensors are coming from different sources. Here, the server compares profiles that have clues to identify the sensor device with a data packet stream based on a modified Boyer-Moore-Horspool algorithm. Our matching model considers features of the sensor data packet. To verify the operability, we have implemented a prototype of this proposed system. The evaluation results show that the start and end pattern of the data packet are more efficient when the length of the data packet is longer.

Learnability of Interestingness with Semantic Similarity and Reasoning in the Knowledge Base of Decision Support Systems

2020 ◽  
Vol 12 (1) ◽  
pp. 26-44
Author(s):  
Sukumar Rajendran ◽  
Prabhu J.
Keyword(s):  
Artificial Intelligence ◽  
Semantic Similarity ◽  
Geographic Location ◽  
Mobile Technologies ◽  
Sensor Data ◽  
Massive Data ◽  
Quality Of Data ◽  
Prime Concern ◽  
Rapid Pace

The evolution of deep learning blended with GPU/TPU has elicited faster computation and assimilation of Big Data at a rapid pace with the exponential learning rate of models. Mobile technologies and cloud-based services are yielding massive data irrespective of geographic location at a rapid pace. Integrating the available plethora of data to find a semantic similarity while providing a rapid response without compromising on the quantity and quality of data is a prime concern. Learning from semantic similarity, utility algorithms turn this data into machine perceivable information, through learnability and utilization of Senticnet. The retainability of knowledge still has its own set of specific needs in terms of different machine learning and artificial intelligence algorithms. Utilization of the semantic similarity for ontology-based learning with interoperability helps preserve privacy for decoding the control attributes. The aspect of learning may further extend for rapidly generated sensor data through things and mobile devices.

scholarly journals Semantic Description of Quality of Data in Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6462
Author(s):  
Anupam Prasad Vedurmudi ◽  
Julia Neumann ◽  
Maximilian Gruber ◽  
Sascha Eichstädt
Keyword(s):  
Sensor Networks ◽  
Sensor Data ◽  
Sensor Calibration ◽  
Calibration Data ◽  
Semantic Description ◽  
Quality Of Data ◽  
General Applicability ◽  
Heterogeneous Sensor Networks ◽  
Calibration Models

The annotation of sensor data with semantic metadata is essential to the goals of automation and interoperability in the context of Industry 4.0. In this contribution, we outline a semantic description of quality of data in sensor networks in terms of indicators, metrics and interpretations. The concepts thus defined are consolidated into an ontology that describes quality of data metainformation in heterogeneous sensor networks and methods for the determination of corresponding quality of data dimensions are outlined. By incorporating support for sensor calibration models and measurement uncertainty via a previously derived ontology, a conformity with metrological requirements for sensor data is ensured. A quality description for a calibrated sensor generated using the resulting ontology is presented in the JSON-LD format using the battery level and calibration data as quality indicators. Finally, the general applicability of the model is demonstrated using a series of competency questions.

scholarly journals Let the Data Serve the Patient: Using Big-Scale Data Collection to Improve Small-Scale Patient Interactions

2018 ◽  
Vol 4 (Supplement 2) ◽  
pp. 156s-156s
Author(s):  
S. Rayne ◽  
J. Meyerowitz ◽  
G. Even-Tov ◽  
H. Rae ◽  
N. Tapela ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Collection ◽  
Cancer Care ◽  
Quality Data ◽  
Small Scale ◽  
Information Sheet ◽  
Quality Of Data ◽  
Data Points ◽  
Database Service

Background and context: Breast cancer is one of the most common cancers in most resource-constrained environments worldwide. Although breast awareness has improved, lack of understanding of the diagnosis and management can cause patient anxiety, noncompliance and ultimately may affect survival through compromised or delayed care. South African women attending government hospitals are diverse, with differing levels of income, education and support available. Often there is a lack of access for them to appropriate information for their cancer care. Aim: A novel bioinformatics data management system was conceived through an innovative close collaboration between Wits Biomedical Informatics and Translational Science (Wits-BITS) and academic breast cancer surgeons. The aim was to develop a platform to allow acquisition of epidemiologic data but synchronously convert this into a personalised cancer plan and “take-home” information sheet for the patient. Strategy/Tactics: The concept of a clinician “customer” was used, in which the “currency” in which they rewarded the database service was accurate data. For this payment they received the “product” of an immediate personalised information sheet for their patient. Program/Policy process: A custom software module was developed to generate individualized patient letters containing a mixture of template text and information from the patient's medical record. The letter is populated with the patient's name and where they were seen, and an personalised explanation of the patient's specific cancer stage according to the TNM system. Outcomes: Through a process of continuous use with patient and clinician feedback, the quality of data in the system was improved. Patients enjoyed the personalised information sheet, allowing patient and family to comprehend and be reassured by the management plan. Clinicians found that the quality of the information sheet was instant feedback as to the comprehensiveness of their data input, and thus assured compliance and quality of data points. What was learned: Using a consumer model, through a process of cross-discipline collaboration, where there is normally poor access to appropriate patient information and poor data entry by overburdened clinicians, a low-cost model of high-quality data collection was achieved, in real-time, by clinicians best qualified to input correct data points. Patients also benefitted from participation in a database immediately, through personalised information sheets improving their understanding of their cancer care.

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