Intelligent reflective surface (IRS) technologies help mitigate undesirable effects in wireless links by steering the communication signal between transmitters and receivers. IRS elements are configured to adjust the phase of the reflected signal for a user’s location and enhance the perceived signal-to-noise ratio (SNR). In this way, an IRS improves the communication link but inevitably introduces more communication overhead. This occurs especially in mobile scenarios, where the user’s position must be frequently estimated to re-adjust the IRS elements periodically. Such an operation requires balancing the amount of training versus the data time slots to optimize the communication performance in the link. Aiming to study this balance with the age of information (AoI) framework, we address the question of how often an IRS needs to be updated with the lowest possible overhead and the maximum of freshness of information. We derive the corresponding analytical solution for a mobile scenario, where the transmitter is static and the mobile user (MU) follows a random waypoint mobility model. We provide a closed-form expression for the average peak age of information (PAoI), as a metric to evaluate the impact of the IRS update frequency. As for the performance evaluation, we consider a realistic scenario following the IEEE 802.11ad standard, targeting the mmWave band. Our results reveal that the minimum achievable average PAoI is in the microsecond range and the optimal IRS update period is in the seconds range, causing 9% overhead in the link when the MU moves at a velocity of 1m/s.

Technologies developed at universities are direct means to conceive a teaching plan for promoting science, technology, engineering and mathematics (STEM) at the K-12 level of education. This work-in-progress develops this notion: We elaborate a teaching plan for K-12 students based on technologies for recording plant bioelectrical activity. The teaching plan sketches hands-on activities, where pupils can self-assemble the electronic components such as the electro-potential sensor, analog-to-digital (ADC) converter, and Arduino board for processing, as designed by research students at Politecnico di Milano. Using our project as a blueprint, we aim to support other educators at universities to promote further STEM and expose pupils to technical developments at universities.

Many emerging applications in edge computing require processing of huge volumes of data generated by end devices, using the freshest available information. In this paper, we address the distributed optimization of multi-user long-term average Age-of-Information (AoI) objectives in edge networks that use NOMA transmission. This poses a challenge of non-convex online optimization, which in existing work often requires either decision making in a combinatorial space or a global view of entire network states. To overcome this challenge, we propose a reinforcement learning-based framework that adopts a novel hierarchical decomposition of decision making. Specifically, we propose three different types of distributed agents to learn with respect to efficiency of AoI scheduling, fairness of AoI scheduling, as well as a high-level policy balancing these potentially conflicting design objectives. Not only does the proposed decomposition improve learning performance due to disentanglement of different design objectives/rewards, but it also enables the algorithm to learn the best policy while also learning the explanations – as actions can be directly compared in terms of the design objectives. Our evaluations show that the proposed algorithm improves the long-term average AoI by$200\%{-}300\%$and 400% compared to prior works with NOMA and the optimal solution without NOMA, respectively.

In this paper, we study a stable Age-of-Information (AoI) scheduling problem to handle the massive packet aggregation from arbitrary total ofnend devices to an edge device via a single hop wireless channel when information constantly arrives at the end side. Specifically, we consider the arrivals of the information with anonlineinjection mode, i.e., information is injected to end devices with an unknown injection rate in each time slot. After the information is injected, the AoI with respect to those end devices constantly increases until their fresh messages are received by the edge device, which characterizes the freshness of the information at destination. Based on the online injection mode, we propose the first distributed stable AoI scheduling algorithm combining NOMA (Non-Orthogonal Multiple-Access) technique in this paper, to minimize the expected average peak AoI (EAP-AoI) of our end-to-edge information system. Adopting NOMA technique enableskmessages decoded from a mixed signal by the edge device in our algorithm, with parameter$k\gt 1$. We prove that our algorithm is stable even under the asymptotically maximum injection rate of$O(k/n)$that any stable AoI scheduling algorithm may handle, and the EAP-AoI of our information system is bounded by$O(\sqrt [{3}]{nk})$time slots under the injection rate of$O(k/n)$. Comparing with two existing results, the EAP-AoI in our algorithm is$O\left ({{\frac {n^{2/3}}{k^{4/3}}}}\right)$and$O\left ({{\frac {n^{2/3}}{k^{1/3}}}}\right)$times smaller. Numerical results also verify the stability and efficiency of our algorithm.

Promoting science, technology, engineering and mathematics (STEM) education at K-12 level motivates pupils to pursue technical careers at universities, which is of high importance due to skilled labor shortage and other factors. In particular, in Germany the population with a tertiary education level is less than the average in the European Union (35–41 %. Motivating the study of technical careers, this paper features a model for promoting STEM at the K-12 education level by developing joint teaching platforms with students at technical universities. The model is derived from hands-on experience at TU Berlin targeting a climate change topic: As part of student semester projects, Bachelor students integrated a humidity sensor into a drone and conducted atmospheric measurements. The resulting hard and software components provided by the students allowed the preparation of a lesson plan to promote STEM at the K-12 level. In this way, we synergically connect the means from the university with hands-on activities as means to promote STEM topics with K-12 students. The resulting benefits are manifold; students at the university develop technical skills and become part of a social intention to promote STEM topics on the one hand. On the second hand, pre-university students might have first-hand experiences with hardware prototypes and their applicability to relevant topics like climate change surveillance. The developed model may illustrate straightforward means for educators to design lesson plans, including actual practices at university and pre-university levels.

Precision medicine applications supported by nanotechnologies enforce designing a communication interface between in-body nanosensors and external gateways. Such a communication interface will enable both a data and a control channel between nanodevices operating within the human body and external control units. In this direction, recent literature focuses on deriving analytic channel models for intra-body links through the human tissues, including the analysis of achievable communication capacities in the terahertz band. A yet missing component, however, is a synchronization module to implement communication schemes in the intra-body link. Such synchronization module will ultimately bound the communication performance regarding the perceived signal to noise ratio (SNR) and bit error rate (BER), for instance. This paper contributes to the state of the art in two directions: (a) evaluating the bounds on the communication performance with the Cramer-Rao lower bound (CRLB) for the synchronization symbol timing offset (STO) and (b) designing a low-complex mechanism to synchronize communication. This analysis considers a communication link between external gateways located on the skin and nanosensor devices flowing in the human vessels. Using envelope and slope detectors, we devise a low-complex solution that relies on the received signal strength (RSS) metric to trigger data emissions. The method estimates the peak of the received RSS metric to ignite communication in the most favorable location, i.e., when the nanosensor is located at the shortest distance in the communication range with external gateways. Our findings illustrate the feasibility of such a low-complex synchronization method. Performance illustrates a BER less than 1×10−5for those nanosensors traveling close to the upper vessel wall.

What are recent topics in engineering education research addressed by the German scientific community? What are current trends, loose ends, and open directions? Is the research conducted locally or in networks across institutions? Following these questions, we aim to seize the German research community's trends, research directions, and contexts. We survey a systematic literature review covering publications of German researchers published in education-related conferences and jour-nals affiliated to IEEE between 2019 and 2022, resulting in 205 contributions in total. We analyze these works and illustrate results as contributions per topic, authors per institution, and the cross-work between institutions. The most prevalent topics turned out to be educational research, followed by curriculum design and remote instruction. Distinguishable, the German community also rehearse research for the K-12 level and develop Gamification techniques with the 18 % of contributions. Furthermore, we observe teams of three authors per contribution on average, and approximately 15 % of contributions are in collaboration among different institutions. On the basis of the literature review, we additionally provide our in-depth viewpoint discussing potential directions for German researchers and open a debate for best practices in supporting research on educational topics. We elaborate on effective means for promoting educational research in the community, more accessibility to learning technologies, and promotion of cross-work among institutions for further steeping the teaching research community within the IEEE.

Molecular communication (MC) is getting closer to becoming a next-generation communication technology with many applications in life sciences and other industrial applications. Multiple techniques have been proposed on how to design MC receivers depending on the channel characteristics. Experimentally, first testbeds also demonstrate the potentialities for communication using molecules as carriers. In this paper, we focus on developing a reinforcement learning (RL)-based receiver, targeting a realistic scenario with testbed measurements, and addressing transmitter mobility. Leveraging on reported solutions for machine learning (ML) methods, we demonstrate the usability of an RL agent to synchronize the receiver to the received signal. We evidence the learning capabilities of the agent to compensate for the impact of mobility, achieving a low probability of missed detection and small misalignment with the symbol time.

Ultrasonic communication is one of the frequently discussed communication technologies for connecting in-body nanosensors with out-of-body gateway systems. Such communication has even been explored in various experimental setups. Yet, the impact of mobility remains primarily unexplored, which is critical for in-body applications. This paper aims to fill this gap by studying the impact of mobility on ultrasonic communication channels as the link between traveling nanosensors in the blood flow and external gateways fixed to the skin. We study both Doppler shift as well as signal gain both in an analytical model as well as in an extensive set of simulations. Our results indicate a significant impact of Doppler shift that needs to be compensated for real communication. We also show that the position of the nanosensors with respect to the gateway plays a very important role. Thus, we open the path for new research on protocol design for in-body to out-of-body communication using ultrasound.

The age of information (AoI) has been proposed as a metric for evaluating freshness of information; recently also within the context of intelligent transportation systems (ITS). The most frequently used definition of AoI, however, does only account for the generation time of the data but not for application-specific aspects. In ITS, for example, the distance of vehicles is not considered and nodes farther away may experience an increased AoI due to effects of the wireless communication channel. We propose a new way of interpreting the AoI in such a context, also considering the location of the transmitting vehicle as a metric of importance to the information. In particular, we introduce a weighting coefficient used in combination with the peak age of information (PAoI) metric to describe the AoI requirement, emphasizing on packets from more important neighbors. As an example, we characterize such importance using the orientation and the distance of the involved vehicles. We use the derived model to focus on timely updates of relevant vehicles for meeting a given AoI requirement, which can save resources on the wireless channel while keeping the AoI minimal.

Nanomachines are envisioned for a variety of applications in the industry and health sectors operating as sensors and actuators. Considering their potential mobility, it is relevant to study the capability of nanomachines to cooperate in molecular communication scenarios. To this end, we provide new insights into the leader-follower dynamics when a mobile leader node moves randomly in three-dimensional space and emits molecules into a diffusive environment to send information about its position to a follower node. In this paper, we investigate the random distance between the two nodes due to decision errors at the follower and analyze an upper bound for the average distance as a function of time. Simulations are provided to validate our analytical results. Moreover, by comparing to the benchmark scenario of uncoordinated movement of leader and follower, we investigate for which parameters the follower can reliably follow the leader.

The implementation of accurate solutions to synchronize the transceiver's clocks in real-time operations plays a major role in the current development of communication systems. Considering the orthogonal frequency division multiplexing (OFDM)-based systems, adopted for the deployment of fifth- generation mobile networks (5G), industrial networks, and the wireless local area network (WLAN) standard, several reported solutions focus on field-programmable gate array (FPGA) designs for the clock synchronization in real-time operations. However, reported solutions describe with minor details the impact of the introduced quantization noise error (QNE) based on the implemented bit-width. Their studies provide insights to implement digital solutions of reduced complexity. The current article provides theoretical expressions to characterize accuracy and precision while synchronizing the transmitter's clock. The presented analysis is intended to support designers to evaluate the clock synchronization design considering the bit-width for implementing the given system.

Early detection of diseases in the human body is of utmost importance for the diagnosis and medical treatment of patients. Supported by recent advancements in nanotechnology, diseases may be detected by patrolling nanosensors, even before symptoms appear. This paper explores the detection capabilities of nanosensors flowing through the human circulatory system (HCS). We model the HCS through a Markov chain and propose the use of machine learning (ML) methods to learn the corresponding transition probabilities. Doing so, we propose a methodology to develop an early detection mechanism of quorum sensing (QS) molecules released by bacteria. Simulation results indicate the suitability of our machine learning approach as a basis for in-body precision medicine.

Energy harvesting (EH) emerges as a novel technology to promote green energy policies. Based on Cognitive Radio (CR) paradigm, nodes are designed to operate with harvested energy from radio frequency signals. CR-EH systems state several strategies based on sensing and access policies to maximize throughput and protect primary users from interference, simultaneously. However, reported solutions do not consider to maximize detection performance to detect spectrum holes which represent a major drawback whenever available energy is not efficiently used. In this concern, this paper addresses optimal sensing policies based on energy harvesting schemes to maximize probability of detection of available spectrum. These novel policies may be incorporated to previous reported solutions to maximize performance. Optimal processing scheduling schemes are proposed for offline and online scenarios based on convex optimization theory, Dynamic Programming (DP) algorithm and heuristic solutions (Constant Power and Greedy policies). Performance of proposed policies are validated by simulations for common detection techniques such as Matched Filter (MF), Quadrature Matched Filter (QMF) and Energy Detector (ED). As a result, it is shown that the best detection scheme theoretically addressed by MF, does not always perform better than the poorest detection scheme, given by the ED, in an energy harvesting scenario.