Event Prediction in an IoT Environment Using Naïve Bayesian Models

AbstractIn many Internet of Things (IoT) scenarios, there is a need to predict events generated by objects. However, because of the dynamicity of IoT environments, it is difficult to predict with certainty if/when such events will occur. Probabilistic reasoning allows us to infer dependent probabilities of events, from other events that are either easier to detect or to predict. In this paper we propose an architecture that employs a Bayesian event prediction model that uses historical event data generated by the IoT cloud to calculate the probability of future events. We demonstrate the architecture by implementing a prototype system to predict outbound flight delay events, based on inbound flight delays, based on historical data collected from aviation statistics databases

Indeterminacy-aware prediction model for authentication in IoT.

The Internet of Things (IoT) has opened a new chapter in data access. It has brought obvious opportunities as well as major security and privacy challenges. Access control is one of the challenges in IoT. This holds true as the existing, conventional access control paradigms do not fit into IoT, thus access control requires more investigation and remains an open issue. IoT has a number of inherent characteristics, including scalability, heterogeneity and dynamism, which hinder access control. While most of the impact of these characteristics have been well studied in the literature, we highlighted “indeterminacy” in authentication as a neglected research issue. This work stresses that an indeterminacy-resilient model for IoT authentication is missing from the literature. According to our findings, indeterminacy consists of at least two facets: “uncertainty” and “ambiguity”. As a result, various relevant theories were studied in this work. Our proposed framework is based on well-known machine learning models and Attribute-Based Access Control (ABAC). To implement and evaluate our framework, we first generate datasets, in which the location of the users is a main dataset attribute, with the aim to analyse the role of user mobility in the performance of the prediction models. Next, multiple classification algorithms were used with our datasets in order to build our best-fit prediction models. Our results suggest that our prediction models are able to determine the class of the authentication requests while considering both the uncertainty and ambiguity in the IoT system

Mobile-Bayesian Diagnostic System for Childhood Infectious Diseases

About 5.9 million children under the age of 5 died in 2015, Preterm birth, delivery complications and infections source a great number of neonatal deaths. the Sustainable Development goals (SDGs) 3.2 is to end preventable deaths of newborns and children under 5 years of age, with a target to reduce neonatal mortality to at least 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births in all countries. However quality and accessible healthcare service is essential to achieve this goal whereas most undeveloped and developing countries still have poor access to quality healthcare. with the emergences on mobile computing and telemedicine, this work provide diagnostics alternative for childhood infectious diseases using Naïve Bayesian classier which has been proven to be efficient in handling uncertainty as regards learning of incomplete data. In this research, sample data was collected from hospitals to model a pediatric system using Naïve Bayes classifier, which produce a 70% accuracy level suitable for a decision support system. The model was also integrated into a SMS platform to enable ease of usage

Predictive Models for ABS and TPMS based on Gaussian Naïve Bays

The car industry is currently preoccupied with the issue of safety. The increasing number of accidents occurring around the world as a result of automobile problems is a major contributing factor to these incidents. The amount of complicated electronics that is used in vehicles is becoming more prevalent every day. A great effort has been made in evaluating vehicle features in relation to vehicle components. Through such systems, a smart architecture and complex function designs are involved. During all of this vehicle transformation and evolution, the automotive industry recognises a high demand for vehicle safety. While designing and manufacturing this system, automotive experts understand a need for a strict monitoring and feedback system for complex vehicle architecture, which can notify the end user if there is any indication of a failure ahead of time. In order to effectively participate in vehicle design activities, it is critical to grasp the significance of safety features. Tire system failures and braking system failures have played a large role in several recent traffic accidents. The failures of the tyre system and the braking system in the vehicle are addressed in this study. While investigating this system, it is discovered that it is supported by complex electrical systems, which include an ECU (electronic controller unit), sensors, and a wire system. Through the use of these technologies, censored data can be processed in a timely manner and made available for diagnostic purposes. Nevertheless, car diagnostics is needed after any vehicle failure but that does not serve the aim of maintaining vehicle safety. As a result, predictive analysis or predictive diagnostics may be a viable option for informing the driver about the health of a particular vehicle component in advance. In this study, the author discusses the concepts of vehicle prognostics for the tyre pressure monitor system and the antilock braking system, which are accomplished using a statistical method of machine learning. In today's world, machine learning is expanding in breadth, and the world is becoming more aware of its enormous potential in the field of data analytics. It is the purpose of this study to introduce methodologies by which machine learning can assist vehicle predictive analytics to attain the intended goal of vehicle safety.The author of this article discusses how Bayesian statistics may be used to produce predictions in the form of probability estimation. The prediction's outcome is thoroughly analysed

Understanding and Personalising Smart City Services Using Machine Learning, the Internet-of-Things and Big Data

This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather data (especially rainfall and temperature) on short journeys made by cyclists in London. The performance of the algorithms was assessed in terms of accuracy, trustworthy and speed. The data sets were provided by Transport for London (TfL) and the UK MetOffice. We employed a random sample of some 1,800,000 instances, comprising six individual datasets, which we analysed on the WEKA platform. The results revealed that there were a high degree of correlations between weather-based attributes and the Big Data being analysed. Notable observations were that, on average, the decision tree J48 algorithm performed best in terms of accuracy while the kNN IBK algorithm was the fastest to build models. Finally we suggest IoT Smart City applications that may benefit from our work

An implentation of IoT for environmental monitoring and its analysis using k-NN algorithm

Environmental monitoring is a process for observing around with various conditions. Recently, internet of things (IoT) and wireless sensor network (WSN) technologies support to solve these problems. In this paper, we implemented a system to monitor environmental conditions using IoT and WSN technology. The data measure is temperature, humidity, carbon monoxide (CO) and carbon dioxide (CO2) sensors. All sensor data will be sent and stored to the cloud through the internet in real-time. We provide applications for monitoring website and mobile phone-based environmental conditions, so users can access wherever and whenever. Furthermore, we also confirm the evaluation of analyst data that usedk-NN method is better than other methods with an accuracy rate of 99.0657%

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Layered performance modelling and evaluation for cloud topic detection and tracking based big data applications

“Big Data” best characterized by its three features namely “Variety”, “Volume” and “Velocity” is revolutionizing nearly every aspect of our lives ranging from enterprises to consumers, from science to government. A fourth characteristic namely “value” is delivered via the use of smart data analytics over Big Data. One such Big Data Analytics application considered in this thesis is Topic Detection and Tracking (TDT). The characteristics of Big Data brings with it unprecedented challenges such as too large for traditional devices to process and store (volume), too fast for traditional methods to scale (velocity), and heterogeneous data (variety). In recent times, cloud computing has emerged as a practical and technical solution for processing big data. However, while deploying Big data analytics applications such as TDT in cloud (called cloud-based TDT), the challenge is to cost-effectively orchestrate and provision Cloud resources to meet performance Service Level Agreements (SLAs). Although there exist limited work on performance modeling of cloud-based TDT applications none of these methods can be directly applied to guarantee the performance SLA of cloud-based TDT applications. For instance, current literature lacks a systematic, reliable and accurate methodology to measure, predict and finally guarantee performances of TDT applications. Furthermore, existing performance models fail to consider the end-to-end complexity of TDT applications and focus only on the individual processing components (e.g. map reduce). To tackle this challenge, in this thesis, we develop a layered performance model of cloud-based TDT applications that take into account big data characteristics, the data and event flow across myriad cloud software and hardware resources and diverse SLA considerations. In particular, we propose and develop models to capture in detail with great accuracy, the factors having a pivotal role in performances of cloud-based TDT applications and identify ways in which these factors affect the performance and determine the dependencies between the factors. Further, we have developed models to predict the performance of cloud-based TDT applications under uncertainty conditions imposed by Big Data characteristics. The model developed in this thesis is aimed to be generic allowing its application to other cloud-based data analytics applications. We have demonstrated the feasibility, efficiency, validity and prediction accuracy of the proposed models via experimental evaluations using a real-world Flu detection use-case on Apache Hadoop Map Reduce, HDFS and Mahout Frameworks

Anomaly detection for IoT networks using machine learning

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning. This thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets. This thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58%. This thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature