An Intelligent IoT Monitoring and Prediction System for Health Critical Conditions

Diabetes is considered among the major critical health conditions (chronic disease) around the world. This is due the fact that Glucose level could change drastically and lead to critical conditions reaching to death in some advance cases. To prevent this issues, diabetes patient are always advised to monitor their glucose level at least three times a day. Fingertip pricking - as the traditional method for glucose level tracking - leads patients to be distress and it might infect the skin. In some cases, tracking the glucose level might be a hard job especially if the patient is a child, senior, or even have several other health issues. In this paper, an optimum solution to this drawback by adopting the Wireless Sensor Network (WSN)-based non-invasive strategies has been proposed. Near-Infrared (NIR) -as an optical method of the non-invasive technique - has been adopted to help diabetic patients in continuously monitoring their blood without pain. The proposed solution will alert the patients’ parents or guardians of their situation when they about to reach critical conditions specially at night by sending alarms and notifications by Short Messages (SMS) along with the patients current location to up to three people. Moreover, a Machine Learning (ML) model is implemented to predict future events where the patient might have serious issues. This model prediction is best practice in this chronic health domain as it has never been implemented to predicted a future forecast of the patient chart. Multivariate Time-Series data set (i.e. AIM ’94) has been used to train the proposed ML model. The collected data shows a high level of accuracy when predicting serious critical conditions in Glucose levels

Cloud Digital Forensics Evaluation and Crimes Detection

© 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Cloud computing is one of the significant topics of today’s era; due to the enhancement it brings to the Information Technology world. This transformation lead to its rapid adoption by different sectors, ranging from enterprise to personal usage. Organizations are constantly looking for ways to increase productivity with optimum cost; which derived the need for Cloud environments and its underlying virtualized infrastructure. With the increase usage of Cloud based infrastructure, criminals utilized its anonymity factor to hide their criminal activities; escaping from legal actions. This paper highlights the obstacles experienced during Cloud virtual layer forensics acquisition and analysis, due to lack of specialized forensics tools. We have developed a framework to aid in assessing the virtual environment readiness for forensics investigation and examine the applicability of existing state-of-the-art forensics tools to Cloud environment. The paper reveals the need for having specialized forensics tools for Cloud infrastructure forensics

Forecasting Internal Temperature in a Home with a Sensor Network

© 2016 The Authors. We forecast internal temperature in a home with sensors, modeled as a linear function of recent sensor values. The Smart∗Project provides publicly available data from an inhabited home over a three month period, reporting on 38 sensors including environmental readings, circuit loads, motion detectors, and switches controlling lights and fans. We select 13 of these sensors that have some influence on the internal temperature, and create forecasts that are accurate to within about 1.6°F (0.9°C) over the next six hours. Temperature prediction is important for saving energy while maintaining comfortable conditions in the home

The design and implementation of a wireless healthcare application for WSN-enabled IMS environments

The IP Multimedia Subsystem (IMS) is one of the key components of third generation (3G) networks, while Wireless Sensor Networks (WSNs) are an emerging type of networks formed by a set of distributed sensor nodes that collaborate to monitor environmental and physical conditions. Combining the capabilities of WSNs and the IMS opens the door to a wide range of personalized and adaptive value added services for 3G users. We have previously proposed a solution for WSN/IMS integration. This solution enriches the IMS architecture with context acquisition and management components, and enables access to those capabilities via standard IMS interfaces. Wireless healthcare is one of the important application areas that can benefit from the combined IMS/WSNs capabilities. In this paper, we focus on this application area and present a case study on the design and implementation of a context-aware IMS wireless healthcare application, that leverages the capabilities of our WSN/IMS integration solution. The application\u27s detailed scenario and IMS deployment architecture are presented and a prototype is built and tested using Ericsson\u27s IMS simulated environment. © 2013 IEEE

A Spam Email Detection Mechanism for English Language Text Emails Using Deep Learning Approach

© 2020 IEEE. Phishing emails are emails that pretend to be from a trusted company that target users to provide personal or financial information. Sometimes, they include links that may download malicious software on user\u27s computers, when clicked. Such emails are easily detected by spam filters that classify any email with a link as a phishing email. However, emails that have no links, link-less emails, requires more effort from the spam filters. Although many researches have been done on this topic, spam filters are still classifying some benign emails as phishing and vice-versa. This paper is focused on classifying link-less emails using machine learning approach, deep neural networks. Deep neural networks differs from simple neural network by having multiple hidden layers where data must be processed before reaching the output layer. The data used in this research is publicly available online. Hyper parameter optimization, was performed, using different settings on the data. In order to demonstrate the effectiveness of the approach, precision, recall and accuracy were computed. The results show that the deep neural network performed well in many of its settings

An IoT-Based Non-invasive Diabetics Monitoring System for Crucial Conditions

Diabetes is among the major chronic disease around the world since the Glucose level could change drastically and lead to critical conditions reaching to death sometimes. To avoid this, diabetes patient are always advised to track their glucose level at least three times a day. Fingertip pricking - as the traditional method for glucose level tracking - leads patients to be distress and it might infect the skin. In some cases, tracking the glucose level might be a hard job especially if the patient is a child. In this manuscript, we present an optimum solution to this drawback by adopting the Wireless Sensor Network (WSN)-based non-invasive strategies. Near-Infrared (NIR) -as an optical method of the non-invasive technique - has been adopted to help diabetic patients in continuously monitoring their blood without pain. The proposed solution will alert the patients’ parents or guardians of their situation when they about to reach critical conditions specially at night by sending alarms and notifications by Short Messages (SMS) along with the patients current location to up to three people

Assessment and hardening of IOT development boards

© IFIP International Federation for Information Processing 2019. Internet of Things (IoT) products became recently an essential part of any home in conjunction with the great advancements in internet speeds and services. The invention of IoT based devices became an easy task that could be performed through the widely available IoT development boards. Raspberry Pi is considered one of the advanced development boards that have high hardware capabilities with a reasonable price. Unfortunately, the security aspect of such products is overlooked by the developers, revealing a huge amount of threats that result in invading the privacy and the security of the users. In this research, we directed our study to SSH due to its extensive adoption by the developers. It was found that due to the nature of the Raspberry Pi and development boards, the Raspberry Pi generates predictable and weak keys which make it easy to be utilized by MiTM attack. In this paper, Man in The Middle (MiTM) attack was conducted to examine the security of different variations provided by the SSH service, and various hardening approaches were proposed to resolve the issue of SSH weak implementation and weak keys

An RFID solution for the monitoring of storage time and localization of perishable food in a distribution center

© 2015 IEEE. In this research we adopt a design-science approach to propose an RFID-based solution that addresses the issue of short life-time of perishable food in the distribution center of a Logistics company. Various design alternatives are explored and assessed against the system requirements and constraints to illustrate how an RFID-based solution can effectively tackle the short storage-life problem. A prototype system is developed and evaluated. The proposed system monitors the storage time of perishable food items that are placed on tagged pallets in the warehouse and triggers alerts before the maximum storage-time is reached. We also discuss a solution for the localization of a pallet whose food content has nearly reached it maximum storage time. Design principles for developing appropriate systems to minimize the loss of perishable food during their storage at the warehouse are proposed and these can act as best practices for practitioners

Dynamic and efficient brokering of energy suppliers and consumers in a smart grid

© 2016 IEEE. One of the fundamental tasks of a smart-grid is achieving an optimal balance between the supplied and consumed energy in the grid. The optimal balance avoids underutilisation as well as overloading of energy sources; minimises the cost of energy transportation and storage; and reduces the price of energy. In this paper we propose a stochastic model for associating energy-suppliers with consumers having matching characteristics in a probabilistic sense. The optimal number of users a particular supplier can serve is described in terms of the probability density functions of its energy production and the demand of consumers. We shall demonstrate both analytically and numerically that an optimal balance can be achieved when the supplied energy, the demand for energy, and the number of users associated with a particular supplier, all, have a normally distributed probability distribution function (pdf)