Barkhausen Institut

Equality saturation has gained significant interest as a powerful optimization and reasoning technique. At its heart is the e-graph data structure, that space-efficiently represents equal sub-terms uniquely. An important open problem in this context is extending this efficient representation to languages featuring (bound) variables. Independent of how we represent variables in e-graphs, either as names or nameless (using de Bruijn indices), sharing is broken as sub-terms that differ only in the names of their variables are represented separately. This results in aggressive e-graph growth, bad performance, as well as reduced expressiveness. In this paper, we present a novel approach to representing bound variables in e-graphs by making them a first-class built-in feature of the data structure. Our slotted e-graph represents terms that differ only by (bound or free) variable names uniquely. To do so, e-classes that represent equivalent terms via e-nodes are parameterized by slots , abstracting over free variables of the represented terms. Referring to an e-class from an e-node now requires relating the variables from its context to the slots of the e-class. Our evaluation of slotted e-graph uses two case studies from compiler optimization and theorem proving to show that performing equality saturation for languages with bound variables is greatly simplified and that we can solve practically relevant problems that cannot be solved with e-graphs using de Bruijn indices.

This paper addresses the issue of high cost and fabrication complexity associated with isolating circularly polarized (CP) waves in a compact multiple-input-multiple-output (MIMO) antenna for joint communications and sensing (JC&S) in IEEE 802.11ad spectrum. A printed circuit board (PCB)based dual-port antenna engaging 3.83λ×3.83λ×0.04λ area at the center frequency of 57.55 GHz is proposed. The antenna comprises of a microstrip patch attached to a grounded coplanar waveguide (CPW), with a signal track designed to enable CP radiations. A metallic grid-based electromagnetic bandgap (EBG) structure was engineered and positioned between the two antenna elements to attain a peak isolation of 50.83 dB. The distance between each pair of microstrip parts was subjected to the fabrication capabilities of conventional PCB manufacturers. The final arrangement yielded a gain of ≈10dBi and efficiency of >85%. The simulation results have been validated through cost-effective fabrication and measurements. Compared to the latest designs, the proposed antenna is fairly competitive.

The distributed Internet of Things (IoT) systems facilitate real-time services through the composition of loosely coupled microservices. The composed IoT service is the output of multiple microservices, executed at computationally capable edge or fog nodes, which we consider as the facility nodes (FNs). However, the service composition process in IoT microservice architectures is abstracted from the users that gives freedom to the FNs to act maliciously and provide low-quality IoT services. Further, the service composition needs to be transparent so that the FNs involved in a service cannot repudiate their involvement at a later time. In this article, we propose a novel, lightweight, trustworthy, and verifiable service composition framework for IoT-based systems that adopt microservice architecture. First, we propose a dynamic programming approach to select trustworthy FNs for each user request, while considering the trust scores of the FNs and the delay requirements of the users. Next, we propose a transparent service composition framework that uses lightweight cryptography functions to generate the proof-of-involvement for the FNs in each service. With the help of a trust controller, we verify the proofs generated by the FNs and update the trust scores of the FNs. Considering the user traces from Berlin city in the simulation of urban mobility tool, we show the efficacy of the proposed framework in maximizing user trust and detecting malicious FNs involved in user services. Further, we show that the delay and communication overhead of the proposed framework are very low compared to the state-of-the-art methods.

In this paper, we study IoT domain names, the domain names of backend servers on the Internet that are accessed by IoT devices. We investigate how they compare to non-IoT domain names based on their statistical and DNS properties, and the feasibility of classifying these two classes of domain names using machine learning (ML). By surveying past studies that used testbeds with real IoT devices, we construct a dataset of IoT domain names. For the non-IoT dataset,We use two lists of top-visited websites. We study the statistical properties of the domain name lists and their DNS properties. We also leverage machine learning and train six machine learning models to perform the classification between the two classes of domain names. The word embedding technique we use to get the real-value representation of the domain names is Word2vec. Our statistical analysis highlights significant differences in domain name length, label frequency, and compliance typical to domain name guidelines, while our DNS analysis reveals notable variations in resource record availability and configuration between IoT and non-IoT DNS zones. As for classification of IoT and non-IoT domain names using machine learning, among the models we train, Random Forest achieves the highest performance, yielding the highest accuracy, precision, recall, and F ₁ score. Our work offers novel insights to IoT, potentially informing protocol design and aiding in network security and performance monitoring.

Drones are evolving into highly capable and adaptable devices, prompting the development of advanced control frameworks. This paper introduces a novel online control framework tailored for a multi-task drone, explicitly addressing the simultaneous execution of in-situ sensing and goods delivery. To tackle this complex scenario, a finite-horizon Markov decision process (FH-MDP) is formulated to ensure not only the prompt delivery of goods but also the minimization of energy consumption and the maximization of the drone's reward for in-situ sensing. A significant contribution lies in establishing the monotonicity and subadditivity of the FH-MDP. This mathematical foundation provides evidence for the existence of an optimal, monotone, deterministic Markovian policy. The crux of the optimal policy revolves around flight distance- and time-related thresholds, determining the precise points at which the drone should switch its optimal action. This unique feature empowers the multi-task drone to make real-time decisions, such as adjusting flight speed or engaging in in-situ sensing, by comparing its current state with these predefined thresholds. This process can be accomplished with a linear complexity, ensuring efficiency in decision-making. The optimality of our approach is rigorously demonstrated through numerical validation, where it is compared against a computationally expensive, dynamic programming-based alternative. Under the considered simulation settings, our approach reduces drone energy consumption by a substantial 19.8% compared to existing benchmarks. This not only highlights the practical effectiveness of the proposed framework but also underscores its potential for significant advancements in the field of drone operations and energy efficiency.

Joint Communication and Sensing (JCAS) systems are emerging as a core technology for next-generation wireless systems due to the potential to achieve higher spectral efficiency, energy savings, and new services beyond communications. This paper provides a review of the state-of-the-art in JCAS systems by focusing on obtrusive passive sensing capabilities and inherent security and privacy challenges that arise from the integration of communication and sensing. From this point of view, we discuss existing techniques for mitigating security and privacy issues, as well as important aspects for the designing of secure and privacy-aware JCAS systems. Additionally, we discuss future research directions by emphasizing on new enabling technologies and their integration on JCAS systems along with their role in privacy and security aspects. We also discuss the required modifications to existing systems and the design of new systems with privacy and security awareness, where the challenging trade-offs between security, privacy and performance of the JCAS system must be considered.

This paper contributes to the consolidation of networked isac, emphasizing essential aspects, technologies, and strategies for the design of this new paradigm. Herein, we outline our vision for networked isac and its architectural components. Our work stands out by addressing key aspects from the physical to network layers, as well as security, privacy, and resilience, which we believe are pivotal in shaping the design of networked isac systems and assessing the viability of use cases with strict requirements. Our discussion includes aspects regarding advanced antenna technologies, challenges regarding synchronization and interference management, resource allocation and cooperation strategies, and procedures and protocols at the network level. Additionally, we discuss new threat vectors in the context of networked isac, considering ethical and legal implications, and potential solutions. Given the criticality and safety-threatening nature of isac scenarios, it is crucial to design systems that meet stringent requirements under failures and malfunctions. Thus, we raise the main considerations for resilience design in networked isac, and identify opportunities to enhance resilience through cooperation. Finally, we offer insights on emerging research directions, pinpointing key areas that deserve focused research.

While engaging with the unfolding revolution in autonomous driving, a challenge presents itself, how can we effectively raise societal awareness about this transformative trend? While full-scale autonomous driving vehicles often come with a hefty price tag, the emergence of scaled-down, small-scale car platforms offers a compelling alternative. These miniature vehicles are designed to perform predefined tasks and challenges, equipped with onboard sensors, processing units, and control actuators. These platforms not only serve as valuable educational tools for the broader public and young generations but also function as robust research platforms, contributing significantly to the ongoing advancements in autonomous driving technology. This survey outlines various small-scale car platforms, categorizing them and detailing the research advancements accomplished through their usage. The conclusion provides proposals for promising future directions in the field.