The visual human perception of electromagnetic waves is limited to the so-called visible spectrum. Artificial extension of the human vision, e.g., via infrared cameras, is possible. Due to the expected low resolution at large wavelengths, however, this is rarely done in the low GHz regime. We investigate the quality of images obtained by simply turning a directive antenna across a scene. The procedure is straight forward and can be compared to how the lens in the human eye creates an image on the retina. Simulation data of an exemplary domestic indoor scenario illustrates how mono-frequency vision of WiFi signals might look like.

SCADA (Supervisory Control and Data Acquisition) systems play a pivotal role in managing critical industrial processes, extending from energy production to manufacturing. However, their widespread adoption and increased interconnectivity has exposed them to evolving cyber threats, demanding a thorough vulnerability assessment and effective defense mechanisms. This research focuses on revealing novel internal threats within SCADA systems capable of eluding conventional monitoring nodes. To simulate real-world scenarios, we've developed a virtualized SCADA testbed faithfully replicating Industrial Control Systems (ICS) complexities. Within this virtualized environment, we've introduced two groundbreaking covert attack scenarios. The SCADA Hijacking Attack illustrates an intruder manipulating process parameters deceptively to hijack the system without detection. Simultaneously, the SCADA Blackout discreetly halts the entire process. To fortify SCADA systems against these stealthier attacks, we propose a defense mechanism leveraging machine learning-based Network Intrusion Detection Systems (NIDS). These NIDS utilize meticulously crafted network features, discerning patterns indicative of covert infiltrations, surpassing traditional IDS approaches. Our research not only reveals potential threats within SCADA environments but also establishes the groundwork for enhancing the resilience of these critical systems against stealth and hijacking attacks.

This paper presents an in-depth examination of privacy-enhancing methodologies in machine learning. It highlights the integration of federated learning with cutting-edge encryption techniques and explores how blockchain architectures contribute to data privacy. A major focus is on federated learning, a decentralized model training strategy, and its combination with privacy-protecting technologies like Homomorphic Encryption, Differential Privacy, and Secure Multi-Party Computation. We emphasize that federated learning naturally improves data privacy and, when paired with cryptographic methods, increases resilience against data breaches and cyber-attacks. Additionally, this study explores the potential of blockchain in enhancing data privacy. Blockchain's immutable and transparent characteristics, supplemented with shuffling technology, zero-knowledge proofs, and ring signatures, improve the confidentiality and integrity of data transactions. The paper also emphasizes the critical need for transparency and explainability in machine learning, advocating for methods that demystify the decision-making processes of ML models. This transparency is crucial for building trust and is becoming a regulatory requirement in many industries. Furthermore, the paper discusses the importance of auditing in machine learning, highlighting the need for comprehensive model validation and ethical considerations. In conclusion, the paper argues that achieving a balance 1 between functionality and privacy in ML applications is essential. It suggests that a combination of federated learning, advanced cryptographic techniques, and explainable AI principles can create effective and privacy-respecting systems.

We always use timeline to describe time, but timeline unable to describe dynamic time. Because dynamic time is on changing, so timeline must be changing too, but it is impossible. Timeline includes layers, relationships existed between layers. If we changed a layer, the others need to be fixed too. Timeline can not make it up automatically, human being must take the work, therefore real-time-changing becomes impossible. It is no matter for film making, but internet needs instant responding, timeline can not cover it. Here we show a new structure called timetree, it is an auto-balanced hierarch structure. Its structure always be complete during changing without help from human being. It is a challenge for making dynamic interactive contents on internet, timetree is borned for it. We have tried timeline before, now it is the turn of timetree.

The performance of intensity modulation (IM) with direct detection (DD) transmission systems is enhanced through a novel combination of multidimensional coding, Nyquist pulse shaping, and electronic dispersion compensation (EDC) at the transmitter using a finite impulse response (FIR) filter. A 24dimensional (24D) extended Golay binary code effectively transforms each incoming 12-bit message into a 24-bit codeword, achieving a coding efficiency of 0.5 bits per symbol for a 56 Gb/s on-off keying (OOK) transmission over 80 km of single mode fiber. While this encoding process introduces a 50% overhead, the required bandwidth is maintained at 56 GHz through doubling the symbol rate and the application of Nyquist pulse shaping with a raised cosine (RC) profile and a roll-off factor of zero, resulting in a flat power spectral density. This flat distribution contrasts with standard OOK transmission at 56 Gb/s with a roll-off factor of 1.0, where signal power is predominantly concentrated in the lower frequency range. One of the key advantages of the 24D Golay code is its substantial error correction capability. However, the benefits of this multidimensional coding and Nyquist pulse shaping extend beyond error correction. It is shown that, while both the proposed and standard OOK methods exhibit comparable performance in a white Gaussian noise channel at back-to-back, they differ significantly under frequency selective power fading conditions caused by the interplay of chromatic dispersion (CD) and direct detection. The misalignment between the frequency notches introduced by the FIR pre-EDC and those inherent in the channel response, especially severe at lower frequencies, favors transmission schemes with a flat power spectral density, like the 24D Golay-coded Nyquist pulses.

The four-port couplers are the fundamental building blocks of large-scale passive beam-forming network design. They find application in the design of antenna feed networks, power combiners and dividers, balanced mixers and amplifiers. Conventional branch-line couplers (BLCs) were designed for quadrature-phase imbalance using four quarter-wavelength transmission lines (TLs). On the other hand, conventional rat-race couplers (RRCs) are designed for in-phased/out-of-phased imbalance using six identical quarter-wavelength TLs. To obtain a non-quadrature phase-shift, external phase shifters are required. Recently, BLCs with inherent non-quadrature phase imbalance properties have received attention among microwave researchers. There are several types of implementation such as lumped, distributed, and mixed with different topologies, that have been proposed by several researchers. It is quite challenging for young researchers to extract all possible design data from the design equations and simulate them to understand their figure of merits. This motivates the author to provide solutions for non-quadrature unequal power division BLCs. 

The use of unmanned aerial vehicles (UAVs) for a variety of commercial, civilian, and defense applications has increased many folds in recent years. While UAVs are expected to transform future air operations, there are instances where they can be used for malicious purposes. In this context, the detection, classification, and tracking (DCT) of UAVs (DCT-U) for safety and surveillance of national air space is a challenging task when compared to DCT of manned aerial vehicles. In this survey, we discuss the threats and challenges from malicious UAVs and we subsequently study three radio frequency (RF)-based systems for DCT-U. These RF-based systems include radars, communication systems, and RF analyzers. Radar systems are further divided into conventional and modern radar systems, while communication systems can be used for joint communications and sensing (JC&S) in active mode and act as a source of illumination to passive radars for DCT-U. The limitations of the three RF-based systems are also provided. The survey briefly discusses non-RF systems for DCT-U and their limitations. Future directions based on the lessons learned are provided at the end of the survey.

The sixth-generation wireless networks are required to satisfy the ever-increasing demands of diverse applications to guarantee power savings, energy efficiency, mass connectivity, and higher integration of devices. To accomplish these goals, in this paper, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-empowered cell-free (CF) network is proposed while leveraging integrated sensing and communication (ISAC). The proposed AI-based framework allocates the desired power for beamforming by activating the required number of grids from the serving HMIMO base stations (BSs) in the CF network to serve the users. An optimization problem is formulated that maximizes the sensing utility function, which in turn maximizes the signal-to-interference-plus-noise ratio (SINR) of the received signal, the sensing SINR of the reflected echo signal, as well as energy efficiency, ensuring efficient power allocation. To solve the optimization problem, an AI-based framework is proposed to enable a decomposition of the NP-hard problem into two subproblems: a sensing subproblem and a power allocation subproblem. Initially, a variational autoencoder (VAE)-based scheme is utilized to solve the sensing subproblem that identifies the current location of the users with the sensing information. Then, a transformer-based mechanism is devised to allocate the desired power to users by activating the required grids from the serving HMIMO BSs in the CF network based on the sensing information achieved with the VAE-based scheme. Simulation results demonstrate that the proposed AI-based framework performs better than the long short-term memory, gated recurrent unit-based mechanisms, with cumulative power savings of 8.64%, and 16.02%, and cumulative energy efficiency of 14.49%, and 16.61%, accordingly, taking the ground truth values into consideration. Therefore, the proposed AI-based framework ensures efficient power allocation for beamforming using ISAC to serve heterogeneous users.

Radio Access Networks (RANs) are responsible for the majority of the energy consumption of cellular networks, and their energy consumption has been growing at an unsustainable rate. Virtualized RANs (vRANs) are an alternative to monolithic RANs, which can adapt to ever-evolving traffic patterns in an agile way. Additionally, virtualization allows multiple radio sites to share the same computing infrastructure, which can lower energy consumption. However, the transition to vRANs is a slow process and there are many open questions about how to best manage vRANs to meet the requirements of cellular operators. In particular, it is unclear whether vRANs can reduce energy consumption compared to monolithic RANs and under which conditions. Energy models are crucial to address this question and identify opportunities for reducing energy consumption in vRANs. In this paper, we present an overview of the energy-modelling approaches that can be applied to RANs and we review models of monolithic and virtualized RANs. Additionally, we characterize the energy consumption of RANs at the network, node, component, and functional levels. Lastly, we examine the techniques that can be used to improve the performance of vRANs and highlight the challenges, unanswered questions, and opportunities in this field, concerning energy consumption.

In the context of India, a country with a rich tapestry of regional sign languages, effectively recognizing and interpreting Indian Sign Language (ISL) presents a formidable challenge for individuals with hearing and speaking impairments. This system introduces an innovative method for ISL recognition by leveraging the YOLOv5s (You Only Look Once version 5) object detection framework. Complementing the YOLOv5s, the project integrates Microsoft Azure’s cognitive app service, specifically the computer vision capabilities, and utilizes Mesa, a Python agent development framework. This comprehensive approach aims to enhance the expression and communication of individuals with hearing and speaking impairments in a predominantly spoken language-oriented world.

Underwater acoustic backscatter communication, which allows an underwater sensor node (USN) to communicate with the surface sink node (SN) by reflecting the acoustic signal from the SN, is a promising solution for enabling the Internet of Underwater Things. However, underwater backscattering is vulnerable to jamming attacks, especially from a gliding autonomous underwater vehicle (AUV) that can inject jamming signal to the underwater acoustic channel accordingly. This paper considers the attack-defense interactions between the AUV jammer and the SN defender in their transmit power allocations of jamming signals and acoustic signals over multiple USNs. We formulate the strategic power allocation problem for the SN and AUV as an asymmetric anti-jamming Colonel Blotto (CB) game model with a finite budget of total transmit powers. In particular, the competitive interactions between the SN and the AUV is modeled as the competition of two players in the game for limited resource budgets over the battlefield set. We analyze the mixedstrategy Nash equilibrium to the game, and obtain the closedform performance bound of the defense power allocation strategy. Numerical results show the superiority of the proposed CB game based multi-USN secure transmission scheme as compared to the benchmarks in terms of the expected sum-utility.

The increasing proliferation of internet-connected devices and the widespread reliance on wireless communication in daily activities are limiting the capabilities of the radio frequency (RF) spectrum to meet data traffic demands. The rise in connectivity requirements to realize sixth generation (6G) networks, coupled with the growth in utilization of artificial intelligence and its associated transmission of massive amounts of data, are contributing to the need to leverage the optical spectrum to complement the already congested RF bands. In general, optical wireless connectivity encompasses a range of solutions that can support data transfer in various outdoor and indoor use cases, benefiting from advantages in terms of operation within a broad unlicensed spectrum, minimization of RF interference, and inherent security. On the other hand, optical wireless communication (OWC) systems might experience a decline in performance as a result of factors like atmospheric conditions and link blockage. This requires customizing system designs according to the considered use cases and investigating new solutions for realizing optical wireless connectivity. In this article, we present an overview of OWC, encompassing both its conceptual frameworks and the scientific and technological advancements that hold potential for shaping its future in 6G networks and beyond.

Network slicing is a crucial enabler and a trend for the Next Generation Mobile Network (NGMN) and various other new systems like the Internet of Vehicles (IoV) and Industrial IoT (IIoT). Orchestration and machine learning are key elements with a crucial role in the network-slicing processes since the NS process needs to orchestrate resources and functionalities, and machine learning can potentially optimize the orchestration process. However, existing network-slicing architectures lack the ability to define intelligent approaches to orchestrate features and resources in the slicing process. This paper discusses machine learning-based orchestration of features and capabilities in network slicing architectures. Initially, the slice resource orchestration and allocation in the slicing planning, configuration, commissioning, and operation phases are analyzed. In sequence, we highlight the need for optimized architectural feature orchestration and recommend using ML-embed agents, federated learning intrinsic mechanisms for knowledge acquisition, and a data-driven approach embedded in the network slicing architecture. We further develop an architectural features orchestration case embedded in the SFI2 network slicing architecture. An attack prevention security mechanism is developed for the SFI2 architecture using distributed embedded and cooperating ML agents. The case presented illustrates the architectural feature’s orchestration process and benefits, highlighting its importance for the network slicing process.

Analyzing videos presents a unique challenge due to their rich content compared to images. Furthermore, processing lengthy videos efficiently necessitates segmenting them into scenes. Focusing on individual scene analysis offers an efficient alternative to analyzing entire videos. The application of this approach extends to a variety of Video Intelligence tasks, from surveillance applications to comprehensive video analytics. By capitalizing on open-source foundation models and leveraging audio and text features, our framework offers a versatile solution to the intricate task of video analysis, catering to a multitude of real-world applications.  

The two main issues in the area of the Internet of Things are low resources consumption and secure data transmission. Conjugating both is fairly hard on ensuring security, leading to great efforts in research. The standard cryptography methods currently proposed are based on simplifications of standard protocols, but are still demanding on resources. Chaotic encryption is a way to reduce this burden, while keeping an equivalent level of security. In this paper, we propose, for the first time, a purely digital scheme for chaotic secure communications, able to be implemented in hardware or software without occupying most of the available resources. Next to the delivered analysis of the system, the experimental demonstration on both FPGA and ESP32 Arduino platforms of chaotic synchronization between transmitter and receiver, included examples of applied encrypted communication in the case of consecutive picture and text transmissions.

Due to the ever increasing inter-dependency across a variety of diverse software and hardware components in ICT provisioning, supply chain vulnerabilities (SCVs) targeting such dependencies have evolved as a primary choice for malicious actors to initiate stealthy and complex cyber-attacks. The current modus operandi within the modern cyber threat spectrum is solely correlated with APTs that have shown to be quite prevalent across diversified attacks underpinning cyberwarfare, cyber terrorism and cybercrime in general. Thus, defense against such events is undoubtedly considered as a high priority on a global scale. Nonetheless, the integration of and dependence on third-party supply chain software and devices located at heterogeneous ICT ecosystems in parallel with the inability of current defense mechanisms to pinpoint nor consider enable a plethora of compromise entry points that consequently amplify APTs. Motivated by these challenges, this survey explores the state-of-the-art to (i) stratify and showcase the properties of supply chain-based APTs, (ii) elaborate on reported risks from such APTs, and, (iii) expand on existing defense methods as proposed until recently. The herein reported study aims to also relate academic research with industry practices having as a greater goal to expose this emerging issue and equip cybersecurity practitioners with the required knowledge for designing next generation APT defense mechanisms.

The presented study introduces a semi-analytical circuit model to fully characterize a dielectric unit cell structure made by blocks instead of applying the effective medium theory. A fitting strategy os used to a obtain a better understanding of the properties of the unit cell. Then, the approach is used to develop a semi-analytical model, demonstrating a good agreement with the scattering parameters extracted from a full-wave simulator. By tuning the dielectric material, our model can describe the behaviour of unit cells with different permittivities and electrical sizes. Finally, as an application for the analyzed dielectric unit cells, transmitarrays to generate orbital angular momentum (OAM) waves are designed and 3D-printed. The measured results show good performance for different number of OAM modes at 31 GHz.

The caching paradigm has been introduced to alleviate backhaul traffic load and to reduce latencies due to massive never ending increase in data traffic. To fully exploit the benefits offered by caching, unmanned aerial vehicles (UAVs) and device-to-device (D2D) communication can be further utilized. In contrast to prior works, that strictly limits the content delivery routes up to two hops, we explore a multi-hop communications scenario, where the UAVs, the UEs, or both can relay the content to individual users. In this context, we formulate the problem for joint route selection and power allocation to minimize the overall system content delivery duration. First, motivated by the limitations of existing works, we consider the case where the nodes may transmit content simultaneously rather than sequentially and propose simple yet effective approach to allocate the transmission power. Second, we design a low-complexity greedy algorithm jointly handling route selection and power allocation. The simulation results demonstrate that the proposed greedy algorithm outperforms the benchmark algorithm by up to 56.98% in terms of content delivery duration while it achieves close-to-optimal performance.

In this paper, we propose a novel self-optimization framework that incorporates an automatic weight function for determining Handover Control Parameters (HCP) and utilizes the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm for target cell selection. The proposed algorithm estimates suitable HCP values automatically based on key factors such as Signal-to-Interference-plus-Noise Ratio (SINR), traffic load, and user equipment (UE)'s speed. Additionally, TOPSIS algorithm is employed to select the most suitable target cell based on three evaluation criteria: SINR, Reference Signal Received Power (RSRP), and load level. The performance of the proposed algorithm is evaluated using load level, throughput, and Radio Link Failure (RLF) as key performance indicators (KPIs). Extensive simulations are carried out to assess the performance of the proposed approach. The findings indicate that the proposed algorithm outperforms the existing solutions in terms of load level, throughput, and RLF.

SpaceX's Starlink mission is a well known mission all around the world for its goal of providing a high-speed network with low-latency, to remote areas all around the Earth. As of November of 2023, there are 5,420 Starlink satellites in the Low Earth Orbit (LEO), which is around 200-1600 Km above the Earth. To be more precise, it orbits at exactly 550 Km above sea level, reducing the overall latency. In this study, we studied the patterns in the formation and placement of SpaceX's Starlink satellites so as to get an understanding of the reason behind it. We hypothesized that the Starlink satellites are going to be placed along the latitudinal planes of the Earth in order to maintain a precise and organized network of satellites. And since the gravitational field in the poles is more, we also hypothesized that there might not be as many satellites in the poles as there are in the equator and tropics. Supporting our hypothesis, we found out that there were not a lot of satellites near the poles, one of the reasons being gravity.

Artificial Intelligence-Generated Content (AIGC)- related network services, especially image generation-based services, have garnered notable attention due to their ability to cater to diverse user preferences, which significantly impacts the subjective Quality of Experience (QoE). Specifically, different users can perceive the same semantically informed image quite differently, leading to varying levels of satisfaction. To address this challenge and maximize network users’ subjective QoE, we introduce a novel interactive artificial intelligence (IAI) approach using Reinforcement Learning With Large Language Models Interaction (RLLI). RLLI leverages Large Language Model (LLM)-empowered generative agents to simulate user interactions, thereby providing real-time feedback on QoE that encapsulates a range of user personalities. This feedback is instrumental in facilitating the selection of the most suitable AIGC network service provider for each user, ensuring an optimized, personalized experience.

Predicting signal blockages in millimetre waves (mmWave) and terahertz (THz) networks is a challenging task that requires anticipating environmental changes. One promising solution is to use multi-modal data, such as vision and wireless inputs, and deep learning. However, combining these data sources can lead to higher communication costs, inefficient bandwidth usage, and undesirable latency, making it challenging. This paper proposes a semantic aware federated blockage prediction (SFBP) framework for a vision-aided next-generation wireless network. This framework uses computer vision techniques to extract semantic information from images and performs distributed on-device learning to enhance blockage prediction. Federated learning enables collaborative model training without exposing private data. Our proposed framework achieves 97.5% accuracy in predicting signal blockages, which is very close to the performance of centralised training. By using semantic information to train models, SFBP reduces communication costs by 88.75% and 57.87% compared to centralised learning and federated learning without semantics, respectively. On-device inference further reduces latency by 23% and 18% compared to centralised and federated learning without semantics, respectively.

Existing models for Radio Frequency (RF) and Free Space Optical (FSO) attenuations, such as the recommendations published by the International Telecommunication Union (ITU), require physical parameters along the communication channels. In practice, the weather parameters of the entire path are usually unavailable. This paper presents RF and FSO attenuation models built using machine learning algorithms and applied to empirical data. The empirical data consists of weather parameters collected at one end of the channel. Seven pairs of RF/FSO models are trained for specific weather conditions. The importance of each weather parameter is compared. RF attenuation is found to be sensitive to humidity, while FSO attenuation is closely related to scintillation. This paper shows how to obtain a pair of generic random forests that are applicable to seven specific weather conditions. The generic random forests predict the RF and FSO attenuations which have a joint distribution similar to the empirically observed distributions. They preserve the correlation between the RF and FSO attenuations as measured by the correlation coefficient and mutual information. When applied to empirical data, the generic random forests outperform the ITU models and models constructed by linear regression with interaction, both in terms of Root-Mean-Square-Error and R-squared.

Wireless Sensor Networks (WSN) are comprised of many sensing devices that can exchange data for monitoring and tracking. Data is exchanged over different paths where a few of malicious nodes may exist to capture the data. To handle this issue, several data security schemes have been presented that are discussed in the literature. In this paper, we perform a Systematic Literature Review (SLR) to analyse existing schemes for data security in WSN as per the research question. It involves an inclusion and exclusion criteria for article selection. In literature, we explore the energy efficient schemes in the category of elliptic curve, AES, RSA, chaotic maps, efficient block ciphers, and various other techniques. These schemes are also evaluated in terms of key sizes, plain text sizes, and dominating features of related techniques and results. We identified that most of the data security schemes are extensive due to extensive computations and delayed responses. This work focuses various types of data security approaches that are compared to examine which can be more suitable for secure data exchange.