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Age of Information-based Performance of Ultrasonic Communication Channels for Nanosensor-to-Gateway Communication

May, 2023

We investigate the timely detection of abnormalities using nanosensors flowing in the blood vessels and reporting to external wearable devices. We develop analytic solutions to evaluate the information freshness using the average peak age of information (PAoI) metric. We model the mobility of nanosensors in the blood flow as a random process through a Markov chain. The communication capabilities with external devices to report detected abnormalities are modeled as a linear time-variant (LTV) channel. Besides, for communication with external devices, we study the impact of the mobility of nanosensors when using ultrasonic waveforms and integrate it with the PAoI formulation. As primary metrics, we use the bit error rate (BER), the packet error rate (PER), and the average PAoI. The results give clear insights into the impact of the position of the external monitor. We also illustrate that local communication performance almost does not influence the average PAoI.

Data Sharing in Virtual Edge Computing using Coded Caching

April, 2023

Multi-access edge computing (MEC) has been identified as a powerful concept for offloading computational tasks and for storing popular data in close proximity of end users; avoiding frequent communication to a back-end cloud server. In the context of vehicular applications, similar functionality can be provided by vehicles collaboratively offering storage and computational resources on-board, i.e., a virtual MEC concept. Data management in a virtual edge is particularly challenging due to the high degree of mobility. Coded caching is a concept to store data on distributed systems in form of fragments. When needed, these fragments are transmitted to the requesting node in a coded form so that the total number of transmissions is reduced (i.e., optimizing for reduced download times and reduced resource utilization). In this paper, we introduce a new protocol for data sharing among vehicles participating in virtual edge computing using coded caching. Our results show that coded caching improve the efficiency of data sharing by up to 50% in a virtual edge computing environment.

Digital Communication Techniques in Macroscopic Air-Based Molecular Communication

March, 2023

An air-based macroscopic molecular communication testbed exploiting fluorescence properties of water-based solutions of Uranine and Rhodamine 6G dyes is presented in this work. The testbed comprises of an industrial sprayer as its transmitter, a 2 m long tube as the transmission channel, and a high-speed camera-based detector. The considered transmission distances cover a range over several tens of centimeters to meters. The analytical end-to-end system model is extended to include the noise model in this work. Various modulation schemes exploiting the concentration levels of the sprayed dyes as the information source have been implemented and their performances are compared to the standard on-off keying. Spatial domain, along with the concentration of the dyes has been introduced as an additional degree of freedom into the testbed to improve the bit rate, and the performance is compared with respect to those modulation schemes using color and concentration levels of the dyes as degrees of freedom. Various equalization techniques and detection algorithms have also been implemented and compared. All comparative analyses are performed with respect to the measurements obtained from the testbed, the analytical models, and the particle-based Pogona simulator.
  • Sunasheer Bhattacharjee, Martin Damrath, Lukas Stratmann, Peter Adam Hoeher and Falko Dressler, "Digital Communication Techniques in Macroscopic Air-Based Molecular Communication," IEEE Transactions on Molecular, Biological and Multi-Scale Communications (T-MBMC), vol. 8 (4), pp. 276–291, December 2022. [DOI, BibTeX, PDF and Details...]

ViTaLS - A Novel Link-Layer Scheduling Framework for Tactile Internet over Wi-Fi

February, 2023

The pioneering field of Tactile Internet (TI) will enable the transfer of human skills over long distances through haptic feedback. Realizing this demands a roundtrip latency of sub-5 ms. we propose Video-Tactile Latency Scheduler (ViTaLS) - a novel link layer framework for tuning the video-tactile frame transmissions to suit their heterogeneous QoS requirements. We also present ViTaLS-optimal - a variant of ViTaLS, for further reducing the tactile latency. Objectively, we show that ViTaLS-optimal yields a latency improvement of up to 82 %. Based on experiments conducted on a real TI testbed, we subjectively demonstrate that ViTaLS-optimal outperforms the VH-multiplexer.
  • Vineet Gokhale, Kees Kroep, R. Venkatesha Prasad, Boris Bellalta and Falko Dressler, "ViTaLS - A Novel Link-Layer Scheduling Framework for Tactile Internet over Wi-Fi," IEEE Internet of Things Journal, vol. 10 (11), pp. 9917–9927, June 2023. [DOI, BibTeX, PDF and Details...]

When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worlds

January, 2023

This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence.
  • Kai Li, Yingping Cui, Weicai Li, Tiejun Lv, Xin Yuan, Shenghong Li, Wei Ni, Meryem Simsek and Falko Dressler, "When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worlds," IEEE Internet of Things Journal, vol. 10 (5), pp. 4148–4173, March 2023. [DOI, BibTeX, PDF and Details...]

Improving IEEE 802.11ax UORA Performance: Comparison of Reinforcement Learning and Heuristic Approaches

December, 2022

In the Wi-Fi domain, machine learing (ML) is applied to solve challenges such as efficient channel access and fair coexistence with other technologies in unlicensed bands. In this paper, we address the performance of uplink orthogonal frequency division multiple random access (UORA) in IEEE 802.11ax networks. Optimization of UORA is a good case for applying ML because of its inherent complexity and dependence on situation and time-dependent parameters. In particular, we use deep reinforcement learning to tune UORA parameters. Our simulation results show that even though the ML-based solution leads to close to optimal results, its operation is comparable to a much simpler, non-ML heuristic.
  • Katarzyna Kosek-Szott, Szymon Szott and Falko Dressler, "Improving IEEE 802.11ax UORA Performance: Comparison of Reinforcement Learning and Heuristic Approaches," IEEE Access, vol. 10, pp. 120285–120295, November 2022. [DOI, BibTeX, PDF and Details...]

Nanosensor Location Estimation in the Human Circulatory System using Machine Learning

November, 2022

The human body can be considered a complex natural network due to the variety of interconnections between the different body regions. One example is the network of blood vessels, where artificial communication channels can be rendered using nanosensors that travel in the bloodstream as collectors and carriers of information. Further advancing this vision, in this work we investigate the detection and localization capabilities of flowing nanosensors in the blood flow to report abnormalities in the human body. Specifically, we target the detection of quorum sensing molecules and provide a methodology to evaluate its performance. The methodology consists of modeling the traveling path of nanosensors along the vessels through a Markov chain, and the use of machine learning (ML) models to compute their transition probabilities.
  • Jorge Torres Gómez, Anke Kuestner, Jennifer Simonjan, Bige Deniz Unluturk and Falko Dressler, "Nanosensor Location Estimation in the Human Circulatory System using Machine Learning," IEEE Transactions on Nanotechnology (TNANO), vol. 21, pp. 663–673, October 2022. [DOI, BibTeX, PDF and Details...]

Low-power and Low-delay WLAN using Wake-up Receivers

October, 2022

Energy-efficient communication technologies are a key enabler for many IoT applications. We investigate the use of wake-up receivers in combination with IEEE 802.11 WLAN. We extend the protocol used to communicate between the access point and the client to introduce a wake-up signal. This can be implemented in a way that is fully compatible with existing WLAN standards, thus, it can be deployed gradually with little effort and no need to change existing systems. As a proof of concept and to perform first lab experiments, we developed a hardware prototype using a selective wake-up receiver and off-the-shelf USB-WLAN dongles.

Asynchronous Background Processing for Accelerated Simulation of Wireless Communication on Multi-Core Systems

September, 2022

Most popular discrete event simulation software packages run single-threaded. Thus, they achieve only limited performance improvements from more modern multi-core CPUs. At the same time, existing approaches for parallel simulation of networks do not perform well on wireless systems or require complex paradigm shifts in simulation models. In this paper, we propose Asynchronous Background Processing (ABP) to accelerate the simulation of wireless communication on multi-core systems. By moving expensive computation from the main thread into asynchronous tasks computed by background threads, it accelerates the progression of events and thus reduces response time. Tasks are started as early as possible to exploit the time the main thread spends processing other events, ideally providing results before they are needed in the simulation.

Adaptive Blind MPSK Constellation Recovery and Equalization for Cognitive Radio Applications

August, 2022

This work proposes a novel single-step blind adaptive filter solution, inspired by an adaptive interference canceler, for joint equalization and constellation symbol recovery from received phase shift keying (PSK) waveforms. Furthermore, we propose new coefficients update mechanisms based on the constant amplitude of PSK signals. The proposed solution exhibits reduced computational complexity compared to the state of the art and a reduced time of convergence. Additionally, the proposed scheme does not require a training sequence to operate properly. The obtained results clearly show that the proposed scheme significantly improves accuracy regarding phase symbol estimation and ISI reduction.
  • Liset Martínez Marrero, Jorge Torres Gómez, Falko Dressler and Maria Julia Fernández-Getino García, "Adaptive Blind MPSK Constellation Recovery and Equalization for Cognitive Radio Applications," IEEE Transactions on Vehicular Technology (TVT), vol. 71 (11), pp. 11988–12000, November 2022. [DOI, BibTeX, PDF and Details...]

Siting and Sizing Charging Infrastructure for Electric Vehicles with Coordinated Recharging

July, 2022

We analyze daily schedules of drivers to find suitable locations for slow and fast charging stations. In simulation, we test how many charge points to assign to each charging station. Vehicles can be charged with either en-route or destination charging using a realistic model for charging and energy consumption for five electric vehicle models of different car segments. To reduce waiting times at charging stations, we use a centralized charging station database (CSDB), that coordinates charging between vehicles. We found that a combination of a few centralized fast charging stations and many distributed slow charging stations is the best option to improve the average extra time spent with charging for all vehicle types.

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

June, 2022

The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we investigate proposed solutions and highlight open challenges in the field of ML for Wi-Fi.

V-Edge: Virtual Edge Computing as an Enabler for Novel Microservices and Cooperative Computing

May, 2022

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. In this position paper, we discuss a way forward, namely the virtual edge computing (V-Edge) concept. V-Edge helps bridging the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available. Thus, V-Edge acts as an enabler for novel microservices as well as cooperative computing solutions in next-generation networks. We introduce the general V-Edge architecture and we characterize some of the key research challenges to overcome in order to enable wide-spread and intelligent edge services.
  • Falko Dressler, Carla Fabiana Chiasserini, Frank H. P. Fitzek, Holger Karl, Renato Lo Cigno, Antonio Capone, Claudio Ettore Casetti, Francesco Malandrino, Vincenzo Mancuso, Florian Klingler and Gianluca A. Rizzo, "V-Edge: Virtual Edge Computing as an Enabler for Novel Microservices and Cooperative Computing," IEEE Network, vol. 36 (3), pp. 24–31, May 2022. [DOI, BibTeX, PDF and Details...]

Hybrid-Fidelity: Utilizing IEEE 802.11 MIMO forPractical Aggregation of LiFi and WiFi

March, 2022

This paper introduces Hy-Fi, a system that aggregates light fidelity (LiFi) and radio frequency (RF)-based communication on the 802.11 (WiFi) physical layer by utilizing the MIMO capabilities in IEEE 802.11-compliant commodity WiFi chips. Hy-Fi is based on two key ideas. First, we use inexpensive commodity hardware to facilitate direct transmission of WiFi waveforms over the optical wireless channel, as this is proposed in the IEEE P802.11bb task group. Second, we use the MIMO signal processing from WiFi to aggregate LiFi and radio signals directly at the physical layer.

Multi-Technology Cooperative Driving: An Analysis Based on PLEXE

February, 2022

This work tackles the fallback and recovery mechanisms that the longitudinal controlling system of a platoon of vehicles can implement as a distributed system with multiple communication interfaces. We present a protocol and procedure to correctly compute the safe transition between different controlling algorithms, down to autonomous (or manual) driving when no communication is possible. To empower the study, we also develop a new version of PLEXE, which is an integral part of this contribution as the only Open Source, free simulation tool that enables the study of such systems with a modular approach, and that we deem offers the community the possibility of boosting research in this field.

Reducing Waiting Times at Charging Stations with Adaptive Electric Vehicle Route Planning

January, 2022

Electric vehicles are becoming more popular all over the world. With increasing battery capacities and a growing fast-charging infrastructure, they are becoming suitable for long-distance travel. However, queues at charging stations could lead to long waiting times, making efficient route planning even more important. We propose a central charging station database (CSDB) that helps estimating waiting times at charging stations ahead of time. This enables our adaptive charging and routing strategy to reduce these waiting times.
  • Sven Schoenberg and Falko Dressler, "Reducing Waiting Times at Charging Stations with Adaptive Electric Vehicle Route Planning," IEEE Transactions on Intelligent Vehicles (T-IV), vol. 8 (1), pp. 95–107, January 2023. [DOI, BibTeX, Details...]

Dwell Time Estimation at Intersections for Improved Vehicular Micro Cloud Operations

December, 2021

The idea of vehicular micro clouds is to turn cars into (virtual) edge computing infrastructure. One of the challenging questions in this domain is to maintain data within and among such micro clouds. In this paper, we focus on this task and present a novel solution for such data exchange between vehicular micro clouds. For efficient operation, the dwell times of cars in such a micro cloud need to be known or accurately predicted. In an extensive study based on trace data, we investigate the distribution of dwell times of cars at intersections.

Vehicular Visible Light Communications: A Survey

November, 2021

In this survey article, we study the state of the art of Vehicular Visible Light Communication (V-VLC) and identify open issues and challenges. We study the V-VLC communication system as a whole and also dig into the characteristics of the VLC channel. For the beginner in the field, this review acts as a guide to the most relevant literature to quickly catch up with current trends and achievements. For the expert, we identify open research questions and also introduce the V-VLC research community as a whole.

LSTM-characterized Deep Reinforcement Learning for Continuous Flight Control and Resource Allocation in UAV-assisted Sensor Network

October, 2021

This paper proposes a new deep reinforcement learning based flight resource allocation framework (DeFRA) to minimize the overall data packet loss in a continuous action space. DeFRA is based on Deep Determin- istic Policy Gradient (DDPG), optimally controls instantaneous headings and speeds of the UAV, and selects the ground device for data collection. Furthermore, a state characterization layer, leveraging long short-term memory (LSTM), is developed to predict network dynamics, resulting from time-varying airborne channels and energy arrivals at the ground devices.
  • Kai Li, Wei Ni and Falko Dressler, "LSTM-characterized Deep Reinforcement Learning for Continuous Flight Control and Resource Allocation in UAV-assisted Sensor Network," IEEE Internet of Things Journal, August 2021. (online first) [DOI, BibTeX, Details...]

Duality between Coronavirus Transmission and Air-based Macroscopic Molecular Communication

September, 2021

This paper exploits the duality between aviral infection process and macroscopic air-based molecularcommunication. Airborne aerosol and droplet transmission through human respiratory processes is modeled as an instance of a multiuser molecular communication scenario employing respiratory-event-driven molecular variable-concentration shift keying. Modeling is aided by experiments that are motivated by a macroscopic air-based molecular communication testbed. In artificially induced coughs, a saturated aqueous solution containing a fluorescent dye mixed with saliva is released by an adult test person. We confirmed the experimental data by simulation.
  • Max Schurwanz, Peter Adam Hoeher, Sunasheer Bhattacharjee, Martin Damrath, Lukas Stratmann and Falko Dressler, "Duality between Coronavirus Transmission and Air-based Macroscopic Molecular Communication," IEEE Transactions on Molecular, Biological and Multi-Scale Communications (T-MBMC), Infectious Diseases Special Issue, vol. 7 (3), pp. 200–208, September 2021. [DOI, BibTeX, PDF and Details...]

Towards an IEEE 802.11 Compliant System for Outdoor Vehicular Visible Light Communications

August, 2021

This paper introduces a complete IEEE 802.11 compliant V-VLC system. The system relies on USRP software defined radios programmed using the GNURadio framework, a typical car headlight plus a custom driver electronics for the high-power car LEDs (sender), and a photodiode (receiver). Our system also supports OFDM with a variety of Modulation and Coding Schemes (MCS) up to 64-QAM and is fully compliant with IEEE 802.11. We, for the first time, experimentally explore the communication performance in outdoor scenarios, even in broad daylight, and show that rather simple optical modifications help to reduce the ambient noise to enable long distance visible light communication.

Low-Power Downlink for the Internet of Things using IEEE 802.11-compliant Wake-Up Receivers

July, 2021

We go beyond classic wake-up system concepts and show how wake-up receivers can be used for an efficient and multi-purpose low power downlink (LPD) communication channel. We demonstrate how to (a) extend the wake-up signal to support low-power flexible and extensible unicast, multicast, and broadcast downlink communication and (b) utilize the WuRx-based LPD to also improve the energy efficiency of uplink data transfer.
  • Johannes Blobel, Vu H. Tran, Archan Misra and Falko Dressler, "Low-Power Downlink for the Internet of Things using IEEE 802.11-compliant Wake-Up Receivers," Proceedings of 40th IEEE International Conference on Computer Communications (INFOCOM 2021), Virtual Conference, May 2021. [DOI, BibTeX, PDF and Details...]

Hy-Fi: Aggregation of LiFi and WiFi using MIMO in IEEE 802.11

June, 2021

We present Hy-Fi, a system which combines light fidelity (LiFi) and radio based on the WiFi physical layer waveform by using the MIMO features available in IEEE 802.11-compliant commodity chip sets. Hy-Fi is based on two key ideas. First, we use inexpensive COTS hardware to facilitate direct transmission of WiFi waveforms over the optical wireless channel, as this is proposed in the IEEE P802.11bb task group. Second, we use the MIMO signal processing to aggregate LiFi and radio signals at the physical layer.

Age of Information in Molecular Communication Channels

May, 2021

This paper introduces the age of information (AoI) concept in molecular communication channels. We derive a theoretical equation to compute the average peak AoI. This allows analyzing the trade-off between an increased rate of transmission (thereby a reduced latency) and the produced inter-symbol interference (ISI) (thereby an increased error rate).

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