Wireless sensor networks (WSNs) have been adopted in many fields of application, such as industrial, civil, smart cities, health, and the surveillance domain, to name a few. Fateway and sensor nodes conform to WSN, and each node integrates processor, communication, sensor, and power supply modules, sending and receiving information of a covered area across a propagation medium. Given the increasing complexity of a WSN system, and in an effort to understand, comprehend and analyze an entire WSN, different metrics are used to characterize the performance of the network. To reduce the complexity of the WSN architecture, different approaches and techniques are implemented to capture (model) the properties and behavior of particular aspects of the system. Based on these WSN models, many research works propose solutions to the problem of abstracting and exporting network functionalities and capabilities to the final user. Modeling an entire WSN is a difficult task for researchers since they must consider all of the constraints that affect network metrics, devices and system administration, holistically, and the models developed in different research works are currently focused only on a specific network layer (physical, link, or transport layer), making the estimation of the WSN behavior a very difficult task. In this context, we present a systematic and comprehensive review focused on identifying the existing WSN models, classified into three main areas (node, network, and system-level) and their corresponding challenges. This review summarizes and analyzes the available literature, which allows for the general understanding of WSN modeling in a holistic view, using a proposed taxonomy and consolidating the research trends and open challenges in the area.

Organic electrochemical devices are an emerging class of devices with synaptic properties that might allow for the implementation of next‐generation neuromorphic circuits for power‐efficient computing. Here, a brain‐inspired neural network approach, namely reservoir computing, which relies on a nonlinear transformation of a low‐dimensional input signal onto a high‐dimensional output space for information processing is utilized. The implementation of reservoir computing using dendritic networks of polymeric fibers is demonstrated and the nonlinear response of the polymer networks are analyzed and the sources of nonlinearity are identified. Furthermore, by adding a delayed feedback loop to the reservoir, it is proven that such a network can undergo a bifurcation into a chaotic state, proving sufficient complexity of the system for advanced classification tasks with time‐dependent data. Ultimately, a classification task is carried out and the accuracy is compared of the classification of different degrees of complexity of the system, showing an increase in accuracy from 60% for the base network to 80% when the delayed feedback loop is incorporated.

This letter presents a differential transimpedance (TI) amplifier with a maximum gain of 71 dB Ω and a bandwidth (BW) of 65 GHz, including the effect of a photodetector with 65-fF capacitance. The implemented circuit in a 130-nm SiGe BiCMOS process with f _T = 300 GHz has an averaged input-referred noise current of 7.2 pA /√{Hz}, while the total harmonic distortion is better than 1.5% for peak-to-peak differential input currents lower than 0.8 mA at a differential output voltage of 0.8 V. Time-domain measurements show an open eye at 112 and 100 Gb/s for a non-return-to-zero and pulse-amplitude-modulation 4-level signals, respectively. The presented design, to the best of our knowledge, shows the lowest noise and one of the highest gain-bandwidth (GBW) products compared to the state-of-the-art. The improvement of the TI limit is achieved by optimizing the open-loop GBW product of the core amplifier in a shunt-feedback structure. This includes tradeoffs associated with the selection of a high value for the feedback resistor to keep the input-referred noise low. The BW is further increased by using a series inductor at the input and a T-coil at the output.

Today's organic electronic devices, such as the highly successful OLED displays, are based on disordered films, with carrier mobilities orders of magnitude below those of inorganic semiconductors like silicon or GaAs. For organic devices such as diodes and transistors, higher charge carrier mobilities are paramount to achieve high performance. Organic single crystals have been shown to offer these required high mobilities. However, manufacturing and processing of these crystals are complex, rendering their use outside of laboratory‐scale applications negligible. Furthermore, doping cannot be easily integrated into these systems, which is particularly problematic for devices mandating high mobility materials. Here, it is demonstrated for the model system rubrene that highly ordered, doped thin films can be prepared, allowing high‐performance organic devices on almost any substrate. Specifically, triclinic rubrene crystals are created by abrupt heating of amorphous layers and can be electrically doped during the epitaxial growth process to achieve hole or electron conduction. Analysis of the space charge limited current in these films reveals record vertical mobilities of 10.3(49) cm² V⁻¹ s⁻¹. To demonstrate the performance of this materials system, monolithic pin‐diodes aimed for rectification are built. The of these diodes is over 1 GHz and thus higher than any other organic semiconductor‐based device shown so far. It is believed that this work will pave the way for future high‐performance organic devices based on highly crystalline thin films.

In this article, we present a smart glove for Tactile Internet applications. The individual finger motions are measured via resistive strain sensors. The strain sensors are directly integrated with the textile glove and are produced in an automated process. The sensor glove is integrated with sensor conditioning, controller, wireless frontend and battery. We investigate the measured sensor data for a variety of gestures, demonstrating the good quality of the data allowing for easy and low-energy gesture recognition.