Presently, the Internet of Things (IoT) concept involves a scattered collection of different multipurpose sensor networks that capture information, which is further processed and used in applications such as smart cities. These networks can send large amounts of information in a fairly efficient but insecure wireless environment. Energy consumption is a key aspect of sensor networks since most of the time, they are battery powered and placed in not easily accessible locations. Therefore, and regardless of the final application, wireless sensor networks require a careful energy consumption analysis that allows selection of the best operating protocol and energy optimization scheme. In this paper, a set of performance metrics is defined to objectively compare different kinds of protocols. Four of the most popular IoT protocols are selected: Zigbee, LoRa, Bluethooth, and WiFi. To test and compare their performance, multiple sensors are placed at different points of a university campus to create a network that can accurately simulate a smart city. Finally, the network is analyzed in detail using two different schemes: collaborative and cooperative.

Epilepsy is a chronic brain disorder that affects the quality of life of many patients even when this disease is being controlled. If we want to improve those lives affected, we need to perform real-time epilepsy detection with constant monitoring of the electroencephalogram (EEG) signal. Typically, the statistical behavior of the EEG signals presents heavy-tail phenomena, therefore their analysis must be particular in order to define a strong framework based on statistical parameters to detect seizures. In this article, the heavy-tail characterization of EEG signals is studied, a simple real-time epilepsy detection using an alpha-stable estimator is proposed, and the false-positive rate is analyzed. The performance of the proposed estimator is compared to others previously reported in the literature, and we show that one of the signal parameters characterized as an alpha-stable distribution, serves as an indicator of epilepsy episodes more efficiently. Furthermore, the proposed algorithm presents low sensitivity to noise below the 3.8 dB.

A class AB two stage op-amp with accurate static current control, symmetric signal paths to the output transistors, and suitable for environments with uncertain or variable supply voltages is presented. The implementation of the conceptual floating voltage source for the push pull operation in the output stage has the ability to adopt positive and negative values indistinctively by means of a resistor with bidirectional currents driven by a control circuit. The latter requires very few additional devices and power consumption compared to the conventional scheme and is capable of operating with supply voltages as low as a gate-source voltage and a saturation voltage of a transistor, and as high as permitted by design and technology. Experimental results from a fabricated prototype in 0.5lm technology show the class AB operation of the amplifier driving a load capacitance of 16 pF.

Long-memory processes, in particular fractional Gaussian noise processes, have been applied as models for many phenomena occurring in nature. Non-stationarities, such as trends, mean level-shifts, etc., impact the accuracy of long-memory parameter estimators, giving rise to biases and misinterpretations of the phenomena. In this article, a novel methodology for the detection and location of mean level-shifts in stationary long-memory fractional Gaussian noise (fGn) signals is proposed. It is based on a joint application of the wavelet-Tsallis q-entropy as a preprocessing technique and a peak detection methodology. Extensive simulation experiments in synthesized fGn signals with mean level-shifts confirm that the proposed methodology not only detects, but also locates level-shifts with high accuracy. A comparative study against standard techniques of level-shift detection and location shows that the technique based on wavelet-Tsallis q-entropy outperforms the one based on trees and the Bai and Perron procedure, as well.

Position Location Information (PLI) has become a crucial requirement for the provision of multiple location based services in cellular and wireless ad-hoc networks. As sensor networks and internet of things scenarios proliferate, three dimensional (3D) position location becomes more relevant as a solution enabler for location based services and applications. Nevertheless, PLI acquisition techniques in 3D ad-hoc and sensor networks present several challenges. For instance, conventional triangulation algorithms may not be applicable as direct line of sight between concerned nodes and fixed references cannot be guaranteed. In this paper, a PLI acquisition algorithm suitable for 3D ad-hoc environments is proposed based on space discretization. Feasibility of the algorithm is examined through analysis and simulation. Algorithm improvements are also proposed.