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Population protocols are a model of distributed computation intended for the study of networks of independent computing agents with dynamic communication structure. Each agent has a finite number of states, and communication opportunities occur nondeterministically, allowing the agents involved to change their states based on each other's states. Population protocols are often studied in terms of reaching a consensus on whether the input configuration satisfied some predicate. A desirable property of a computation model is modularity, the ability to combine existing simpler computations in a straightforward way. In the present paper we present a more general notion of functionality implemented by a population protocol. This notion allows to design multiphase protocols as combinations of independently defined phases. The additional generality also increases the range of behaviours that can be captured in applications.

As spiking-based deep learning inference applications are increasing in embedded systems, these systems tend to integrate neuromorphic accelerators such as $\mu$Brain to improve energy efficiency. We propose a $\mu$Brain-based scalable many-core neuromorphic hardware design to accelerate the computations of spiking deep convolutional neural networks (SDCNNs). To increase energy efficiency, cores are designed to be heterogeneous in terms of their neuron and synapse capacity (big cores have higher capacity than the little ones), and they are interconnected using a parallel segmented bus interconnect, which leads to lower latency and energy compared to a traditional mesh-based Network-on-Chip (NoC). We propose a system software framework called SentryOS to map SDCNN inference applications to the proposed design. SentryOS consists of a compiler and a run-time manager. The compiler compiles an SDCNN application into subnetworks by exploiting the internal architecture of big and little $\mu$Brain cores. The run-time manager schedules these sub-networks onto cores and pipeline their execution to improve throughput. We evaluate the proposed big little many-core neuromorphic design and the system software framework with five commonlyused SDCNN inference applications and show that the proposed solution reduces energy (between 37% and 98%), reduces latency (between 9% and 25%), and increases application throughput (between 20% and 36%). We also show that SentryOS can be easily extended for other spiking neuromorphic accelerators.

The Controller Area Network (CAN) is the most common protocol interconnecting the various control units of modern cars. Its vulnerabilities are somewhat known but we argue they are not yet fully explored -- although the protocol is obviously not secure by design, it remains to be thoroughly assessed how and to what extent it can be maliciously exploited. This manuscript describes the early steps towards a larger goal, that of integrating the various CAN pentesting activities together and carry them out holistically within an established pentesting environment such as the Metasploit Framework. In particular, we shall see how to build an exploit that upsets a simulated tachymeter running on a minimal Linux machine. While both portions are freely available from the authors' Github shares, the exploit is currently subject to a Metasploit pull request.

The Internet of Things (IoT) comprises of a heterogeneous mix of smart devices which vary widely in their size, usage, energy capacity, computational power etc. IoT devices are typically connected to the Cloud via Fog nodes for fast processing and response times. In a rush to deploy devices quickly into the real-world and to maximize market share, the issue of security is often considered as an afterthought by the manufacturers of such devices. Some well-known security concerns of IoT are - data confidentiality, authentication of devices, location privacy, device integrity etc. We believe that the majority of security schemes proposed to date are too heavyweight for them to be of any practical value for the IoT. In this paper we propose a lightweight encryption scheme loosely based on the classic one-time pad, and make use of hash functions for the generation and management of keys. Our scheme imposes minimal computational and storage requirements on the network nodes, which makes it a viable candidate for the encryption of data transmitted by IoT devices in the Fog.

Unmanned aerial vehicles (UAVs) are envisioned to be extensively employed for assisting wireless communications in Internet of Things (IoT) applications. On the other hand, terahertz (THz) enabled intelligent reflecting surface (IRS) is expected to be one of the core enabling technologies for forthcoming beyond-5G wireless communications that promise a broad range of data-demand applications. In this paper, we propose a UAV-mounted IRS (UIRS) communication system over THz bands for confidential data dissemination from an access point (AP) towards multiple ground user equipments (UEs) in IoT networks. Specifically, the AP intends to send data to the scheduled UE, while unscheduled UEs may pose potential adversaries. To protect information messages and the privacy of the scheduled UE, we aim to devise an energy-efficient multi-UAV covert communication scheme, where the UIRS is for reliable data transmissions, and an extra UAV is utilized as a cooperative jammer generating artificial noise (AN) to degrade unscheduled UEs detection. We then formulate a novel minimum average energy efficiency (mAEE) optimization problem, targetting to improve the covert throughput and reduce UAVs' propulsion energy consumption subject to the covertness requirement, which is determined analytically. Since the optimization problem is non-convex, we tackle it via the block successive convex approximation (BSCA) approach to iteratively solve a sequence of approximated convex sub-problems, designing the binary user scheduling, AP's power allocation, maximum AN jamming power, IRS beamforming, and both UAVs' trajectory planning. Finally, we present a low-complex overall algorithm for system performance enhancement with complexity and convergence analysis. Numerical results are provided to verify our analysis and demonstrate significant outperformance of our design over other existing benchmark schemes.

The fifth-generation (5G) mobile system is now being deployed across the world and the scale of 5G subscribers is growing quickly in many countries. The attention of academia and industry is increasingly shifting towards the sixth generation (6G) and many pioneering works are being kicked off, indicating an important milestone in the history of 6G. At this juncture, an overview of the current state of the art of 6G research and a vision of future communications are of great interest. This paper thus investigates up-to-date 6G research programs, ambitions, and main viewpoints of representative countries, institutions, and companies worldwide. Then, the key technologies are outlined and a vision on ``What 6G may look like?" is provided. This paper aims to serve as an enlightening guideline for interested researchers to quickly get an overview when kicking off their 6G research.

The Electric Field Integral Equation (EFIE) is a well-established tool to solve scattering problems. But the development of efficient and easy to implement preconditioners remains an active research area. In recent years, operator preconditioning approaches have become popular for the EFIE, where the electric field operator is regularised by multiplication with another convenient operator. A particularly intriguing choice is the exact Magnetic-to-Electric (MtE) operator as regulariser. However, evaluating this operator is as expensive as solving the original EFIE. In a work by El Bouajaji, Antoine and Geuzaine approximate local Magnetic-to-Electric surface operators for the time-harmonic Maxwell equation were proposed that can be efficiently evaluated through the solution of sparse surface problems. This paper demonstrates the preconditioning properties of these approximate MtE operators for the EFIE. The implementation is described and a number of numerical comparisons against other preconditioning techniques for the EFIE are presented to demonstrate the effectiveness of this new technique.

Numerous communications and networking challenges prevent deploying unmanned aerial vehicles (UAVs) in extreme environments where the existing wireless technologies are mainly ground-focused; and, as a consequence, the air-to-air channel for UAVs is not fully covered. In this paper, a novel spatial estimation for beamforming is proposed to address UAV-based joint sensing and communications (JSC). The proposed spatial estimation algorithm relies on using a delay tolerant observer-based predictor, which can accurately predict the positions of the target UAVs in the presence of uncertainties due to factors such as wind gust. The solution, which uses discrete-time unknown input observers (UIOs), reduces the joint target detection and communication complication notably by operating on the same device and performs reliably in the presence of channel blockage and interference. The effectiveness of the proposed approach is demonstrated using simulation results.

In this paper, we present a novel and practical joint hybrid beamforming (HYBF) and combining scheme to maximize the weighted sum-rate (WSR) in a single-cell massive multiple-input-multiple-output (MIMO) millimeter-wave (mmWave) FD system. All the multi-antenna users and the base station (BS) are assumed to be suffering from the limited dynamic range (LDR) noise due to non-ideal hardware, and we adopt an impairment-aware HYBF approach. To model the non-ideal hardware of a hybrid FD transceiver, we extend the traditional LDR noise model to mmWave. We also present a novel interference and self-interference (SI) aware optimal power allocation scheme for the uplink (UL) users and the BS. The analog processing stage is assumed to be quantized, and we consider both the unit-modulus and unconstrained cases. Compared to the traditional designs, our design also considers the joint sum-power and the practical per-antenna power constraints. It relies on alternating optimization based on the minorization-maximization method. We investigate the maximum achievable gain of a hybrid multi-user FD system with different levels of the LDR noise variance and with different numbers of the radio-frequency (RF) chains. We also show that amplitude manipulation at the analog stage is beneficial for a hybrid FD BS when the number of RF chains is small. Simulation results show that the proposed HYBF design significantly outperforms the fully digital HD system with only a few RF chains at any LDR noise level.

This paper presents a formally verified quantifier elimination (QE) algorithm for first-order real arithmetic by linear and quadratic virtual substitution (VS) in Isabelle/HOL. The Tarski-Seidenberg theorem established that the first-order logic of real arithmetic is decidable by QE. However, in practice, QE algorithms are highly complicated and often combine multiple methods for performance. VS is a practically successful method for QE that targets formulas with low-degree polynomials. To our knowledge, this is the first work to formalize VS for quadratic real arithmetic including inequalities. The proofs necessitate various contributions to the existing multivariate polynomial libraries in Isabelle/HOL. Our framework is modularized and easily expandable (to facilitate integrating future optimizations), and could serve as a basis for developing practical general-purpose QE algorithms. Further, as our formalization is designed with practicality in mind, we export our development to SML and test the resulting code on 378 benchmarks from the literature, comparing to Redlog, Z3, Wolfram Engine, and SMT-RAT. This identified inconsistencies in some tools, underscoring the significance of a verified approach for the intricacies of real arithmetic.