Evidencias de las causas del agravamiento de la pobreza

The research methodologies in this thesis are summarized below:

• Design a pervasive health monitoring wireless sensor network archi- tecture and assess the usability of two wireless communication tech- nologies in the presented context. For the health monitoring platform, we used IEEE 802.11 WLAN and ZigBee wireless technologies. The experimental setup to compare both architectures consisted of a hospi- tal room with 20 patient nodes reading a patient’s medical data from various sensors. The employed sensors were a two-lead ECG, SpO2, Blood Pressure, Heart Rate, Temperature, Respiration, and Glucose level. There was one sink node for the ZigBee based architecture or an IEEE 802.11 WLAN access point for the IEEE 802.11 WLAN based architecture to collect data from all the patient nodes in the respec- tive setup. The distance between the adjacent patient nodes in the same column was two meters, and the distance between the adjacent patient nodes in the different columns was six meters. Every patient

node transmitted approximately 8.7 kbits of data per second.

• Evaluate the proposed secure elliptic curve-based mutual authentica- tion scheme for RFID implant systems that are used in healthcare IoT applications. In this work, we mainly focused on the performance analysis of implantable tags because RFID readers are known to be ro- bust devices [26]. As a common cryptographic primitive, we exploited standardized 163-bit elliptic curve domain parameters recommended by NIST. The parameters were defined over the binary finite field F(2163_{). We utilized the Elliptic Curve Digital Signature Algorithm} (ECDSA) algorithm having the coordinate (x, y). As a reminder, the elliptic curve domain parameters over F(2m_{) were specified by the tu-} ple T = (m, f (x), a, b, G, n, h), where m = 163 and the representation of F(2163_{) is defined by f (x) = x}163_{+ x}7_{+ x}6_{+ x}3_{+ 1 [27]. As an} environment to measure the computational time for the mentioned cryptography algorithms, we used an Intel Core2 CPU T5500 1.66 GHz having 1GB RAM. In the proposed scheme, we outlined the stor- age requirement by considering the tag’s memory, including its public key and private key. The private key is denoted as the tag’s secret keys s1 and s2 and the public key is the tag’s public key IDt. In the proposed scheme, the required memory consists of (IDt,s1,s2). • Evaluate the security level and performance of the proposed ECG-

based cryptographic key generation approaches in terms of distinc- tiveness, a test of randomness, temporal variance, and key generation execution time. We conducted the experiments on both normal and abnormal ECG signals obtained from the publicly available and widely used database, that is, Physiobank [28]. PhysioBank is comprised of databases of multi-parameter neural, cardiopulmonary, and other biomedical signals from patients and healthy subjects with a variety of conditions. Subject conditions may include sudden cardiac death, irregular heartbeat (arrhythmia), congestive heart failure, sleep ap- nea, and epilepsy. The experiments were carried out on both normal and abnormal. ECG signals which, were obtained from 239 subjects studied by the Beth Israel Hospital Laboratory in Boston and the Na- tional Metrology Institute of Germany (Physikalisch-Technische Bun- desanstalt (PTB)).

The employed ECG signals included: (1) ECG signals of 18 subjects (five men, aged 26 to 45; 13 women, aged 20 to 50) with Normal Sinus Rhythm. The recordings were digitized at 128 samples per second with a 11-bit resolution over a 10 mV range. (2) ECG signals of 48 subjects with Arrhythmia (22 women aged 23 to 89; 26 men aged 32 to 89) were recorded using two-channel ambulatory ECG system.

The recordings are digitized at 360 samples per second with an 11-bit resolution over a 10 mV range per patient. (3) ECG signals of 25 men with Atrial Fibrillation were recorded for 10 hours and contained two ECG signals, each digitized at 250 samples per second with 12- bit resolution over a range of 10 mV. (4) ECG signals of 148 subjects with Myocardial Infarction (89 men aged 17 to 87; 59 women aged 19 to 83). Each signal was digitized at 1000 samples per second, with 16-bit resolution over a range of 16 mV. We captured 100 different samples of 5-minute long ECG data for each subject and evaluated the efficiency of the approach. The collected ECG signals were filtered using a low-pass filter with a 30 Hz threshold frequency. Such a filter reduces environmental noise and provides a smoother signal for further analysis. For the experiment, we generated 128-bit cryptographic keys using the approaches mentioned above. We implemented and analyzed the key generation approaches utilizing MATLAB [29].

• The system architecture of distributed end-to-end communication sup- porting mobility was implemented for experimental evaluation. To Implement the architecture, we set up a platform that consisted of medical sensors, UT-GATE smart e-health gateways, a remote server, and end-users. A UT-GATE was constructed from the combination of a PandaBoard [30] and a Texas Instruments (TI) SmartRF06 board that was integrated with a CC2538 module [31]. The PandaBoard is a low-power and low-cost single-board computer development plat- form based on the TI OMAP4430 System-on-chip (SoC) following the OMAP architecture and fabricated using 45 nm technology. The OMAP4430 processor is composed of a Cortex-A9 Microprocessor Unit (MPU) subsystem including dual-core ARM cores with symmetric multiprocessing at up to 1.2 GHz each. In the configuration, UT- GATE used 8GB of external memory and was powered by Ubuntu OS, which allowed for controlling devices and services, such as local storage and notification. To investigate the feasibility of the proposed architec- ture, the Wismote [32] platform, which is a common resource-limited sensor, was utilized in Contiki’s network simulation tool Cooja [33]. Wismote is equipped with a 16MHz MSP430 micro-controller, an IEEE 802.15.4 radio transceiver, 128KB of ROM, 16KB of RAM, and sup- ports 20-bit addressing. For the evaluation, we used the open source tool OpenSSL version 1.0.1.j to create elliptic curve public and pri- vate keys from the NIST P-256 (prime256v1) and X.509 certificates. The prevailing form of certificates are X.509 and are employed in the certificate-based mode of DTLS [34]. The server association to the end-user was created using Open Secure Sockets Layer (SSL) Appli- cation Programming Interface (API). It provided all necessary func-

tions related to end-users, including configuration, certificate, hand- shake, session state, and cipher suites to support session resumption.

TinyDTLS [35] was used as the code-base of the proposed scheme,

in this work. TinyDTLS is an open-source implementation of DTLS in symmetric key-based mode. We extended it with support for the certificate-based DTLS as well as session resumption. For the public- key functions, we utilized the Relic-toolkit [36] that is an open source cryptography library tailored for specific security levels with an empha- sis on efficiency and flexibility. The My Structured Query Language (SQL) database was set up for static and non-static records. Static records that are managed by system administrators include white ta- bles, essential data required by the DTLS handshake, and an end-user authentication mechanism. Non-static records store up-to-date bio- signals that are synchronized between the PandaBoard database and a cloud server database. The cloud server database was processed using xSQL Lite, which is a third party tool for data synchroniza- tion. Concerning the cryptographic primitives and to make a fair comparison, we followed similar cipher suites as employed in the most recently proposed authentication and authorization architecture for IP-based IoT [36]. In this regard, we utilized elliptic curve NIST-256 for public-key operations, AES 128 CCM 8 (with an IV of 8 bytes) for symmetric-key, and SHA256 for hashing operations.