The localization of nodes plays a fundamental role in Wireless Sensor and Actors Networks (WSAN) identifying geographically where an event occurred, which facilitates timely response to this action. This article presents a performance evaluation of multi-hop localization range-free algorithms used in WSAN, such as Distance Vector Hop (DV-Hop), Improved DV-Hop (IDV-Hop), and the Weighted DV-Hop (WDV-Hop). In addition, we propose a new localization algorithm, merging WDV-Hop, with the weighted hyperbolic localization algorithm (WH), which includes weights to the correlation matrix of the estimated distances between the node of interest (NOI) and the reference nodes (RN) in order to improve accuracy and precision. As performance metrics, the accuracy, precision, and computational complexity are evaluated. The algorithms are evaluated in three scenarios where all nodes are randomly distributed in a given area, varying the number of RNs, the density of nodes in the network, and radio coverage of the nodes. The results show that in networks with 100 nodes, WDV-Hop outperforms the DV-Hop and IDV-Hop even if the number of RNs is reduced to 10. Moreover, our proposal shows an improvement in terms of accuracy and precision at the cost of increased computational complexity, specifically in the algorithm execution time, but without affecting the hardware cost or power consumption.

Diabetes mellitus (DM) is a metabolic disorder characterized by blood glucose levels above normal limits. The impact of this disease on the population has increased in recent years. It is already a public health problem worldwide and one of the leading causes of death. Recently, several proposals have been developed for better and regular monitoring of glucose. However, theses proposals do not discard erroneous readings and they are not able to anticipate a critical condition. In this work, we propose an algorithm based on the double moving average supported by an IoT architecture to prevent possible complications in elderly patients. The algorithm uses historical readings to construct a series. Given a number of periods, it is possible to calculate averages of different subsets and trends for the next periods and, in this way, the prognosis is obtained. With the prognosis, it is possible to notify the doctor and relatives in advance about a possible critical condition in the patient. The aim of our work is to validate the architecture and prognosis algorithm used for elderly persons. Tests of the algorithm and the architecture were performed with different readings and it was shown that the system generated corresponding notifications before the glucose values were higher than those defined by the WHO (World Health Organization), thus avoiding unnecessary alarms.

Protecting sensitive data, such as data collected from sensors, is crucial for ensuring the accurate assessment of sensing devices and preventing unauthorized access. In this regard, Internet of Things (IoT) devices offer a promising alternative for in situ monitoring. However, IoT sensing devices are often constrained by limited processing power and memory. Therefore, lightweight and efficient security algorithms are essential. This review paper assesses the implementation of lightweight cryptographic algorithms for power-constrained microcontrollers in IoT systems using symmetric cryptography. The implemented algorithms analyze the resource constraints of the IoT devices and compare their performance for the efficient implementation of secure monitoring systems. Experimental results demonstrate the performance of various lightweight encryption algorithms on low-power microcontrollers. The analysis reveals the comparative performance of these algorithms in terms of average power and energy consumption, memory usage, latency, and throughput.

Long power wide area networks (LPWAN) systems play an important role in monitoring environmental conditions for smart cities applications. With the development of Internet of Things (IoT), wireless sensor networks (WSN), and energy harvesting devices, ultra-low power sensor nodes (SNs) are able to collect and monitor the information for environmental protection, urban planning, and risk prevention. This paper presents a WSN of self-powered IoT SNs energetically autonomous using Plant Microbial Fuel Cells (PMFCs). An energy harvesting device has been adapted with the PMFC to enable a batteryless operation of the SN providing power supply to the sensor network. The low-power communication feature of the SN network is used to monitor the environmental data with a dynamic power management strategy successfully designed for the PMFC-based LoRa sensor node. Environmental data of ozone (O₃) and carbon dioxide (CO₂) are monitored in real time through a web application providing IoT cloud services with security and privacy protocols.

Protecting sensitive data, such as data collected from sensors, is crucial for ensuring the accurate assessment of sensing devices and preventing unauthorized access. In this regard, Internet of Things (IoT) devices offer a promising alternative for in-situ monitoring. However, IoT sensing devices are often constrained by limited processing power and memory. Therefore, lightweight and efficient security algorithms are essential. This review paper assesses the implementation of lightweight cryptographic algorithms for power-constrained microcontrollers in IoT systems using symmetric cryptography. The implemented algorithms analyze the resource constraints of the IoT devices and compare their performance for efficient implementation of secure monitoring systems. Experimental results demonstrate the performance of various lightweight encryption algorithms on low-power microcontrollers. The analysis reveals the comparative performance of these algorithms in terms of average power and energy consumption, memory usage, latency, and throughput.

Today, embedded systems (ES) tend towards miniaturization and the carrying out of complex tasks in applications such as the Internet of Things, medical systems, telecommunications, among others. Currently, ES structures based on artificial intelligence using hardware neural networks (HNNs) are becoming more common. In the design of HNN, the activation function (AF) requires special attention due to its impact on the HNN performance. Therefore, implementing activation functions (AFs) with good performance, low power consumption, and reduced hardware resources is critical for HNNs. In light of this, this paper presents a hardware-based activation function-core (AFC) to implement an HNN. In addition, this work shows a design framework for the AFC that applies a piecewise polynomial approximation (PPA) technique. The designed AFC has a reconfigurable architecture with a wordlength-efficient decoder, i.e., reduced hardware resources are used to satisfy the desired accuracy. Experimental results show a better performance of the proposed AFC in terms of hardware resources and power consumption when it is compared with state of the art implementations. Finally, two case studies were implemented to corroborate the AFC performance in widely used ANN applications.