Martin Randles

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this article, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN, and DT) using a standard security dataset (NSL-KDD).

With the recent prevalence of information networks, the topic of community detection has gained much interest among researchers. In real-world networks, node attribute (content information) is also available in addition to topology information. However, the collected topology information for networks is usually noisy when there are missing edges. Furthermore, the existing community detection methods generally focus on topology information and largely ignore the content information. This makes the task of community detection for incomplete networks very challenging. A new method is proposed that seeks to address this issue and help improve the performance of the existing community detection algorithms by considering both sources of information, i.e. topology and content. Empirical results demonstrate that our proposed method is robust and can detect more meaningful community structures within networks having incomplete information, than the conventional methods that consider only topology information.

Internet of Things (IoT) can help healthcare facilities to transform the experience for both the staff and patients. IoT can facilitate novel solutions for a classic problem, which is the real-time tracking of patients and staff. In this work, an architecture for real-time tracking using Bluetooth Low Energy (BLE) and iBeacons in hospitals is proposed. The proposed system is to locate not only humans but also other facilities such as medicines. It consists of four different frameworks: server communication, user interaction, cross-platform communication and indoor localization. A wearable BLE-enabled device or any smartphone can be used in our system. The first stage of the tracking system is setting up and calibrating the BLE iBeacons for initialization. Then the Received Signal Strength is collected from the BLE-enabled devices carried by users. These data are analyzed and calculated using our improved Least Square Estimation approach to estimate the actual location of users. Experimental results show that in a crowded environment, our tracking system achieves resolutions of the order of 12cm. This will be further integrated with other sensors such as accelerometer or heart rate monitor to improve the real-time healthcare monitoring experience.

People have always feared aging, and the increasing rate of dementia disease caused this fear to twofold. Dementia is irreversible, unstoppable and has no known cure. According to Alzheimer's Disease International 2015 and World Alzheimer Report 2015, the estimated financial cost for healthcare services of Alzheimer's Disease is $1 Trillion in 2018. This paper discusses the importance of investigating Alzheimer's Disease using machine learning, the need to use both behavioural and biological markers data, and a computational method to rank Alzheimer's Disease risk factors by importance using different machine learning models on Alzheimer's Disease clinical assessment data from ADNI. The dataset contains Alzheimer's Disease risk factors data related to medical history, family dementia history, demographical, and some lifestyle data for 1635 subjects. There are 387 normal control, 87 significant memory concerns, 289 early mild cognitive impairment, 539 late mild cognitive impairment and 333 Alzheimer's Disease subjects. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The results show that some risk factors in subjects genetically, demography and lifestyle are more important than some medical history risk factors. Having APOE4, education level, age, weight, family dementia history, and type of work rank as more influential among Alzheimer's Disease subjects.

This paper investigates data for 9 common Alzheimer's Disease risk factors, from three different categories; Medical History, Lifestyle, and Demography. The dataset used consists of 185 normal control, 177 early mild cognitive impairment, 161 late mild cognitive impairment and 127 Alzheimer's Disease subjects. The initial experiment had training results of 0.92 sensitivity, 0.935 specificity and 0.771 precision. However, during the test stage the final output was 0.741 sensitivity, 0.515 specificity and 0.286 precision. The results of this experiment did not give a clear classification or definite predictive value. Involving more variables and underlying data could provide a better outcome. This paper is a part of a long-term study that focuses on the classification and ranking the importance of Alzheimer's Disease risk factors using Machine Learning predictive models and classifications techniques.

Bluetooth Low Energy (BLE) technology and BLE-based devices such as iBeacons have become popular recently. In this work, an optimized indoor positioning approach using BLE for detecting a smart device's location in an indoor environment is proposed. The first stage of the proposed approach is a calibration stage for initialization. The Received Signal Strength Indicator (RSSI) is collected and pre-processed for a stable outcome, in the second stage. Then the distance is estimated by using the processed RSSI and calibrated factors in the third stage. The final stage is the position estimation using the outputs from the previous steps. The positioning technique, which is an improved Least Square estimation is evaluated against the other well-known techniques such as, Trilateration-Centroid, classic Least Square Estimation in estimating the user's location in the 2D plane. Experimental results show that our proposed approach has promising results by achieving an accuracy of positioning within 0.2 to 0.35m.

In this work, a novel Decentralized Iterative Community Clustering approach (DICCA) for detecting communities in complex networks is proposed. The DICCA approach is based on the random walk procedure. An important property of this approach is its ability to cluster the entire network without the global knowledge of the network topology. This ability means that this method could be easily adapted to any parallel/ distributed processing to find community clusters in big networks.

The objective of this study is to explore feature selection for the detection of internal intruders within a local network during the early stages of an attack. As the sophistication of attackers increase, current security systems have proven incapable of detecting advanced stealthy attackers whose aim is to compromise internal networks and remain undetected. We study the available features that are commonly used during network-layer attacker detection and propose two new features to model the extent to which a given networked endpoint conforms with network traffic norms. The proposed features are analysed using several attribute evaluation methods to compare the predictiveness of commonly used features. The results of the analysis show that the proposed features are highly predictive and work towards overcoming the identified deployability issues of previous systems.