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The Internet of Things (IoT) is a transformative technology marking the beginning of a new era where physical, biological, and digital worlds are integrated by connecting a plethora of uniquely identifiable smart objects. Although the Internet of terrestrial things (IoTT) has been at the center of our IoT perception, it has been recently extended t...
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... W ITH the proliferation of emerging services and the expansion of hyperscale interconnections, spectrum resources for traditional radio frequency (RF) networks are severely scarce [1], [2]. The visible spectrum, with wavelengths ranging from 380 nm to 780 nm, has emerged as a key enabler for the global Internet. ...
In this paper, we propose and experimentally demonstrate a rate-adaptive multiple-input-single-output (MISO) visible light communication (VLC) system, realized by superposition coded modulation (SCM) with hybrid geometric-probabilistic shaping (GPS). This approach ensures fine-grained net data rate (NDR) per user with flexibility, while simultaneously enhancing the overall NDR performance for all users. We design a hybrid GPS strategy for the proposed rate-adaptive MISO VLC system, which is robust to non-ideal factors such as high channel correlation, nonlinearity, and power competition, and is also insensitive to variations in the probabilistic shaping (PS) factor. Additionally, we propose a universal method for determining the optimal geometric shaping factor. A comprehensive experimental study of the proposed SCM-based hybrid GPS rate-adaptive MISO VLC system is conducted and compared with traditional rate-adaptive single-input-single-output (SISO) VLC systems. Experimental results demonstrate that the proposed system enhances the NDR by 19% for the near-end user and 105% for the far-end user compared to the rate-adaptive SISO VLC system with the PS quadrature amplitude modulation (QAM) signal. Furthermore, compared to the rate-adaptive SISO VLC system with the regular QAM signal, the proposed system not only significantly increases the NDR but also extends the maximum transmission distance by 111.1% under an NDR constraint of 30 Mbps.
... Optical wireless communication and, in particular, free-space optical (FSO) links are considered as a practical tool to bridge bandwidth gaps in the overall network infrastructure [1,2]. Towards this, first practical deployments of FSO links have demonstrated sustainable economic models [3]. However, many challenges exist when extending the bandwidth continuum of wired fiber networks over the air, such as a highly efficient optical coupling between FSO transceivers [4], the mitigation of atmospheric propagation effects [5], robust transmission under poor weather conditions [6], or the FSO link termination through single-mode fiber at the remote link end, to eventually enable coherent transmission over FSO communications [2,7]. ...
Terrestrial free-space optical (FSO) links are an ideal candidate to extend the bandwidth continuum offered by fiber networks, yet at the expense of unfavorable cost credentials due to highly complex opto-mechanical setups. As a response to this challenge, we present a simple fiber-based focal plane array (FPA) architecture which contributes beamforming functionality to an FSO link that bridges the gap between two single-mode fiber ports. Through use of space-switched and wavelength-routed beamforming networks, which together with a photonic lantern provide a compact arrangement of up to 61 fine-pitched optical antenna elements on a fiber tip, we experimentally demonstrate that our FPA can assist the channel optimization of an FSO link after rough initial beam pointing. We show that favorable coupling conditions can be accomplished between two standard single-mode fibers, which enables us to successfully retain the fiber continuum by achieving error-free 10 Gb/s/{\lambda} data transmission in a space-switched out-door fiber-wireless-fiber scenario.
... In fact, hand gesture recognition (HGR) has already become a common way of HCI (human-computer interaction) including, for example, smartphone control [1], [2], Virtual/Augmented Reality (VR/AR) [3], [4], and smart home [5]. Meanwhile, the fast development of visible light communication (VLC), one important branch of Light Fidelity (LiFi) technology in 6G [6], has created a large amount of VLC-ready products, such as Philips lamps Trulifi [7], LiFi desk lamps [8], [9] and smartphone VLC-capable antenna [10]. Though these devices are made for communication purposes"", we may enhance them towards an integrated sensing and communication (ISAC) design, so as to allow certain sensing functions to be piggybacked onto VLC-ready devices. ...
... Combining Eqns. (5), (6), and (7), the overall cross entropy loss function of the adversarial domain adaption scheme becomes: ...
As a main approach towards touch-free human-computer interaction, hand gesture recognition (HGR) has long been a research focus for both academia and industry. Meanwhile, visible light communication (VLC) has become increasingly popular with VLC-ready commercial products (e.g., Philips lamps) available on the market. These facts provoke us to ask: can we leverage a VLC-ready lamp to realize integrated sensing and communication (ISAC) by conducting both HGR and VLC simultaneously? To this end, we propose ReflexGest as our answer to this question. ReflexGest is implemented upon a table lamp for the sake of practicality; this VLC-ready lamp is equipped with a ring-shaped light-emitting diode (LED) array and a photodiode (PD, for light intensity sensing) originally aiming for up/down-link VLCs. Demanding hand gestures to be performed between the lamp and a table surface, ReflexGest exploits the variation of the reflection and their unique correlation with the corresponding hand gestures to achieve HGR. In particular, ReflexGest first handles the limited sensing ability of the PD by enhancing the LED lamp and thus diversifying the light emission patterns. Moreover, ReflexGest combats the reflection interference from varying table surfaces via an adversarial learning technique to distill only the features relevant to hand gestures. Our extensive evaluations demonstrate that ReflexGest is able to deliver accurate HGR under realistic VLC traffic. Index Terms-Hand gesture recognition, visible light sensing, visible light communication, integrated sensing and communication, human-computer interaction.
... This vision of interconnectedness was highlighted by Gartner as one of the top 10 pivotal technology trends of 2020 [4], predicting an exponential growth in smart devices, estimated to outnumber traditional IT devices by twentyfold by the year 2023 [5]. Furthermore, projections indicate an explosive growth in IoT connections, expecting over 41.6 billion linked devices by 2025 [6]. This proliferation of IoT technology employs smart devices and the internet to innovate solutions across a wide spectrum of challenges, thereby en-hancing efficiency and convenience in various sectors includ-ing business, government, and private industries worldwide [1]. ...
This The proliferation of Internet of Things (IoT) devices has led to a corresponding rise in cyberattacks targeting these interconnected devices. Among the most concerning threats are botnet-based attacks, known for their complexity and destructive potential within the IoT ecosystem. Researchers have explored various machine learning (ML) and deep learning (DL) techniques to detect and classify botnet attacks in IoT environments. This study introduces an effective approach for detecting botnet attacks on IoT devices using the N-BaIoT dataset. Leveraging DL and hybrid models, we develop methods to identify three prevalent and hazardous IoT threats. Our findings highlight the superiority of the CNN-BiGRU-BiLSTM model, achieving a remarkable 98.87% accuracy in detecting botnet assaults, surpassing existing models in the field. Our research contributes to the ongoing efforts to devise efficient and precise methodologies for detecting botnet-based attacks on IoT devices. The proposed method holds significant promise for enhancing IoT security and mitigating the adverse impacts of botnet attacks within the IoT ecosystem
... The modern IoT consists of several key components that are combined into local networks [32]. These components include: Autonomous microelectromechanical systems (MEMS): the basic elements that allow devices to collect, process, and transmit data [33]; radio communication technologies: provide wireless connectivity between IoT devices [34]; software products: software that controls IoT devices, including algorithms and interfaces [35]; electronic services and the Internet: provides connection of IoT devices to the global network and access to various online services [36]; industry and social information and communication hubs (e-ecosystems) [37]: combine various IoT devices into interconnected systems [38]. ...
... To quantify the benefits of AIoT edge nodes, this section introduces auction theory to model the system as a multi-round auction problem [10][11][12]. The auctioneer's agent is deployed in a cloud computing center with massive computing power, enabling quick transactions between buyers and sellers [13][14][15]. The auction compensation strategy continuously adjusts the task bids, and the specific design is detailed later. ...
Artificial Intelligence of Things (AIoT) edge computing has emerged as a critical enabler for consumer technology, easing the load on distribution grid networks through enhanced data transmission and processing capabilities. The Internet of Things (IoT), a modern network paradigm, connects devices using sensors and communication mediums to enable seamless information exchange. However, the limited computational capacity of edge nodes poses challenges in optimizing AIoT edge resources compared to cloud computing. To address this, a cloud-edge three-layer framework for task offloading and edge resource allocation is proposed in the context of consumer technology and distribution grids. This model incorporates random tasks, limited resources, unequal processing power, and high latency requirements. It features two key phases: a resource auction using a multi-round iterative process and compute offloading managed by a Deep Reinforcement Learning (DRL) approach. Enhanced algorithms such as Double DQN and Dueling DQN are integrated into the job offloading process to improve performance. Simulations validate the convergence of the task offloading algorithm and demonstrate that the proposed methods significantly enhance computational efficiency and resource utilization for edge nodes. These advancements offer promising solutions for the dynamic needs of consumer technology and future communication networks.
... It is to be noted that in this study, we have considered the position of the photodetector to be fixed while its orientation is random in a narrow FoV. Typically, a stationary photodetector position is practical in various indoor VLC systems where they are mounted on desks, walls, or static surfaces to maintain consistent communication links, and in VLC systems employing Internet of Things and sensor networks where devices like environmental sensors or appliances are deployed in fixed positions for extended periods [41], [45]- [47]. Thus, focusing on a fixed photodetector location allows the study to isolate the effects of random orientation effects and derive detailed statistical characterizations of the channel gain. ...
With the rapid increase in user demands, visible light communication (VLC) has been emerging as a promising technology for next-generation wireless communication networks to supplement the limited radio frequency spectrum. However, the reliability of VLC channels significantly depends on the orientation of the line-of-sight links, as the received signal strength is sensitive to the angular alignment between the transmitter and receiver. This paper studies this aspect by considering a VLC system where the light-emitting diode (LED) transmitter employs a binary modulation to transmit data to a photodetector receiver whose orientation with respect to the transmitter LED varies randomly within a narrow field-of-view (FoV). This random orientation of the receiver is modeled by a uniform distribution between θ1 and θ2 for which the probability density functions for the square of the channel gain are derived for various combinations of θ1 and θ2. Furthermore, employing optimal maximum likelihood receiver structures, series form expressions for the symbol error probabilities (SEPs) are derived for the cases when the VLC system has perfect knowledge of the channel gain and when the system utilizes the least-square technique for channel estimation prior to data transmission. Numerical results are presented to corroborate the analytical framework wherein the correctness of the statistical characterization is verified, and the variations of the SEPs are presented with the system parameters. It is observed that a narrow FoV plays a crucial role in the superior performance of the VLC system in terms of achieving a lower SEP value and mitigating saturation in the SEP by increasing the system’s signal-to-noise ratio.
... Optical wireless communication (OWC) leverages light waves to provide ultra-fast, energy efficient, interference-free, secured transmission capabilities with massive bandwidth within the unregulated optical spectrum [1]- [4]. It overcomes the challenges faced by conventional radio frequency (RF) technologies in achieving the rapidly growing data demands of modern concepts like smart cities, the internet of things (IoT), cloud computing, and Industry 4.0 [5], [6]. While showcasing applicability for diverse environments, OWC primarily focuses on indoor environments since 80% of wireless traffic is generated in such environments [7]. ...
... By including the initial arbitrary phase of the E m,q,n emitter as the φ m,q,n , we arrive at a more generalized electric field pattern, E m,q,n (r m,q,n ) = E 0 f (r m,q,n )θ(r m,q,n ) ∥r m,q,n ∥ e −j(km,q,n·rm,q,n+φm,q,n) . (6) For isotropic radiating elements, symmetric radiation pattern assumption results in f (r m,q,n ) = 1. We also require polarization matching at the intended receiver location. ...
Optical wireless networks emerge as a promising solution to the ever-growing data demand for user-centric indoor applications. This work demonstrates a novel approach to advance multi-beam radiation patterns in indoor optical wireless networks by utilizing a cluster-based optical aperture comprising IR radiative elements. Spatially distributed IR clusters permit a non-uniform spherical wave model to focus the radiation in the near-field regime. By executing sub-clusters within main clusters and assigning them to groups for phase delay compensation, we ensure the generation of independent narrow beams focused on each receiver simultaneously. To mitigate the grating lobe formation, we incorporate a dual-carrier framework that introduces an effective wavelength for the system. Based on this theoretical model, we examine multi-beam focusing with a systematic arrangement of clusters on a planar ceiling. It follows a phased array within a phased array structure and incorporates a sub-cluster segmentation algorithm. We suggest optimizing cluster excitation based on receiver positions to enhance power efficiency and safety. This involves selecting the optimal clusters from a uniform array by solving a multiobjective non-convex binary optimization problem, aiming to maximize receiver intensity, minimize intensity variations, and reduce side lobes level. Instead of stochastic algorithms, we adopt a sparse relaxation-based weighted sum method that convexifies the binary space with L1 norm regularization compensating for convexity. The Transformed problem is solved deterministically via Nelder-Mead simplex without gradients. Simulated results confirm a better multi-beam focusing pattern, effectively balancing three objectives. Our findings pave the way for sustainable indoor optical wireless networks in next-generation communication.
... This integration allows for real-time actions and decisions and enables smart cities, autonomous systems, and predictive analytics, among other applications. Thus, reliable wireless networks provide the backbone for implementing effective and scalable AI-IoT solutions [2,3]. ...
... Current wireless technologies, including Wi-Fi, 4G, 5G, B5G, and LPWAN, among others, rely on radio frequencies (RF) to transmit information. As a result of the overwhelming number of technologies that use RF for communication, the electromagnetic spectrum has become congested, particularly at frequencies below 6 GHz [1]. This necessitates the use of frequencies in higher frequency bands or attempting to reuse existing frequency bands. ...
... A subset of OWCs is visible light communication (VLCs), which uses visible light as a wireless medium to transmit information [5]. In VLC systems, a light emitting diode (LED), an array of LEDs, or lasers are commonly used as transmitters while photodetectors (PDs) and other types of optical detectors are commonly used as receivers [1]. ...
... VLC systems that achieve data rates suitable for internet of things (IoT) applications typically employ photodiodes as optical receivers [1]; nevertheless, recently, photovoltaic (PV) panels have been tested as optical receivers in IoT-aimed VLC systems [4]. Compared to photodiodes, PV panels have the advantage of being able to harvest energy and receive information, and have been proven to reach data rates up to Gbps [4]. ...
