Muscle activity-driven green-oriented random number generation mechanism to secure WBSN wearable device communications

Wireless body sensor networks (WBSNs) mostly consist of low-cost sensor nodes and implanted devices which generally have extremely limited capability of computations and energy capabilities. Hence, traditional security protocols and privacy enhancing technologies are not applicable to the WBSNs since their computations and cryptographic primitives are normally exceedingly complicated. Nowadays, mobile wearable and wireless muscle-computer interfaces have been integrated with the WBSN sensors for various applications such as rehabilitation, sports, entertainment, and healthcare. In this paper, we propose MGRNG, a novel muscle activity-driven green-oriented random number generation mechanism which uses the human muscle activity as green energy resource to generate random numbers (RNs). The RNs can be used to enhance the privacy of wearable device communications and secure WBSNs for rehabilitation purposes. The method was tested on 10 healthy subjects as well as 5 amputee subjects with 105 segments of simultaneously recorded surface electromyography signals from their forearm muscles. The proposed MGRNG requires only one second to generate a 128-bit RN, which is much more efficient when compared to the electrocardiography-based RN generation algorithms. Experimental results show that the RNs generated from human muscle activity signals can pass the entropy test and the NIST random test and thus can be used to secure the WBSN nodes

On the security of consumer wearable devices in the Internet of Things

Miniaturization of computer hardware and the demand for network capable devices has resulted in the emergence of a new class of technology called wearable computing. Wearable devices have many purposes like lifestyle support, health monitoring, fitness monitoring, entertainment, industrial uses, and gaming. Wearable devices are hurriedly being marketed in an attempt to capture an emerging market. Owing to this, some devices do not adequately address the need for security. To enable virtualization and connectivity wearable devices sense and transmit data, therefore it is essential that the device, its data and the user are protected. In this paper the use of novel Integrated Circuit Metric (ICMetric) technology for the provision of security in wearable devices has been suggested. ICMetric technology uses the features of a device to generate an identification which is then used for the provision of cryptographic services. This paper explores how a device ICMetric can be generated by using the accelerometer and gyroscope sensor. Since wearable devices often operate in a group setting the work also focuses on generating a group identification which is then used to deliver services like authentication, confidentiality, secure admission and symmetric key generation. Experiment and simulation results prove that the scheme offers high levels of security without compromising on resource demands

El uso de dispositivos inteligentes y "machine learning" para la predicción de enfermedades

En el presente trabajo se ha realizado una revisión bibliográfica sobre la predicción y monitorización de enfermedades debido a su gran importancia en la salud a nivel mundial, mejorando la calidad de vida de las personas, tanto sanas como ya enfermas, y disminuyendo el impacto económico que estas causan en la sanidad, ya que los tratamientos preventivos son menos costosos que el tratamiento de la enfermedad. Aun así, los métodos tradicionales usados para realizar estas actividades son costos y consumen mucho tiempo y personal cualificado, por lo que es necesario buscar alternativas más baratas, rápidas, y en muchos casos más exactas. Los métodos computacionales son una solución a este problema. Concretamente, en los últimos años, los métodos basados en “Machine Learning” y el uso de dispositivos inteligentes (wearables) han supuesto una revolución en este campo, acelerando y mejorando la predicción y tratamiento de un gran número de enfermedades. En primer lugar, se hará una introducción explicativa del concepto de “Big Data” y lo que representa, mostrando su gran importancia y su forma de utilización. A continuación, se llevará a cabo una breve introducción a los dispositivos inteligentes, citando algunos ejemplos, seguido de un resumen de los principios y técnicas más usadas en la utilización del “Machine Learning” como herramienta en la predicción de enfermedades (métodos Bayesianos, árboles de decisión, redes neuronales, vecinos cercanos o soporte vectorial). En los resultados se exponen diferentes ejemplos de cómo funcionan estas nuevas tecnologías en el ámbito de la salud, en comparación con los métodos tradicionales. Terminando con una breve conclusión acerca de cómo se debe seguir avanzando en este campo.Universidad de Sevilla. Grado en Farmaci

SafeCity: Toward Safe and Secured Data Management Design for IoT-Enabled Smart City Planning

The interaction among different Internet of Things (IoT) sensors and devices become massive and insecure over the Internet as we probe to smart cities. These heterogeneous devices produce an enormous amount of data that is vulnerable to various malicious threats. The generated data need to be processed and analyzed in a secure fashion to make smart decisions. The smart urban planning is becoming a reality through the mass information generated by the Internet of Things (IoT). This paper exhibits a novel architecture, SafeCity, that limelight the ecosystem of smart cities consists of cameras, sensors, and other real-world physical devices. SafeCity is a three-layer architecture, i.e., a data security layer, a data computational layer, and a decision-making layer. At the first layer, payload-based symmetric encryption is used to secure the data from intruders by exchanging only the authentic data among the physical devices. The second layer is used for the computation of secured data. Finally, the third layer extracts visions from data. The secured exchange of data is ensured by using Raspberry Pi boards while the computation of data is tested on trustworthy datasets, using the Hadoop platform. The assessments disclose that SafeCity presents precious insights into a secured smart city in the context of sensors based IoT environment

State of the Art, Trends and Future of Bluetooth Low Energy, Near Field Communication and Visible Light Communication in the Development of Smart Cities

The current social impact of new technologies has produced major changes in all areas of society, creating the concept of a smart city supported by an electronic infrastructure, telecommunications and information technology. This paper presents a review of Bluetooth Low Energy (BLE), Near Field Communication (NFC) and Visible Light Communication (VLC) and their use and influence within different areas of the development of the smart city. The document also presents a review of Big Data Solutions for the management of information and the extraction of knowledge in an environment where things are connected by an “Internet of Things” (IoT) network. Lastly, we present how these technologies can be combined together to benefit the development of the smart city

An ICMetric based multiparty communication framework

Cryptographic algorithms have always relied on stored keys for the provision of security services. Since these keys are stored on a system this makes them prone to attack. Efforts to increase the key size makes brute forcing difficult but does not eliminate key theft. This thesis proposes a comprehensive security framework for groups of devices. The research makes four major contributions to improve the security of devices in the multiparty environment. The proposed framework uses the novel Integrated Circuit Metric (ICMetric) technology which proposes utilizing measurable properties and features of a device to create a device identification. This device identification called the ICMetric is used to create cryptographic keys which are then used in the designed cryptosystems. The first contribution of the thesis is the creation of an ICMetric using sensors found in modern smart devices. The research explores both explicit and implicit features which can be used to generate of an ICMetric. The second contribution of this research is the creation of a group ICMetric which is computed using the device ICMetric. The computation of the device ICMetric is a particular challenge as it has to be computed without violating the properties of the ICMetric technology. The third contribution is the demonstration that an ICMetric can be used for the creation of symmetric key. The fourth contribution of this research is an efficient RSA based asymmetric key generation scheme for the multiparty environment. Designing a system using widely accepted cryptographic primitives does not guarantee a secure system therefore the security of proposed schemes has been studied under the standard model. The schemes presented in this thesis attempt to improve the security of devices in the group environment. The schemes demonstrate that key theft deterrent technologies can be incorporated into cryptographic schemes to offer higher levels of security and privacy

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond