This study introduces PEFT-DML, a parameter-efficient deep metric learning framework for robust multi-modal 3D object detection in autonomous driving. Unlike conventional models that assume fixed sensor availability, PEFT-DML maps diverse modalities (LiDAR, radar, camera, IMU, GNSS) into a shared latent space, enabling reliable detection even under sensor dropout or unseen modality–class combinations. By integrating Low-Rank Adaptation (LoRA) and adapter layers, PEFT-DML achieves significant training efficiency while enhancing robustness to fast motion, weather variability, and domain shifts. Experiments on benchmarks nuScenes demonstrate superior accuracy.

Advancements in high-frequency communication technologies, including millimeter-wave (mmWave), terahertz (THz), and optical wireless bands, play a crucial role in extending wireless connectivity beyond 5 G. These bands provide ultra-wide bandwidths that enable very high data rates, support dense device deployments, and precise positioning. However, their performance critically depends on maintaining clear Line-of-Sight (LoS) conditions, since Non-Line-of-Sight (NLoS) components suffer from strong attenuation and reflection losses. While mmWave links may provide limited connectivity through NLoS reflections, LoS propagation remains the main factor governing the link budget and reliability of the target applications. In contrast, THz and optical wireless links are almost completely blocked by opaque materials, making LoS assurance essential. This paper tackles the issue of LoS coverage by determining the minimum number and optimal placement of Access Points (APs) required to ensure LoS connectivity in stochastic environments with random obstacles. The environment is modeled as a visibility graph whose nodes represent sub-polygons and edges denote visibility overlaps. Using maximal-clique clustering and maximum-clique packing algorithms, the proposed deterministic framework ensures LoS coverage under all realizations within the modeled stochastic ensemble, achieving up to a 50% reduction in the number of required APs while maintaining over 90% LoS coverage for every realization of obstacle locations.

Future wireless networks are anticipated to evolve by aerial communication platforms. Nonetheless, the operational lifespan and efficacy of transceivers such as unmanned aerial vehicle (UAVs) and Internet of Things (IoT) devices are strictly prohibited by their constrained onboard power sources. This paper focuses on an aerial network configuration where a UAV harvests solar power to serve energy-limited IoT devices through simultaneous wireless information and power transfer. In this setup, the UAV and the IoT devices, each are equipped with energy and data buffers. This system also benefits from rate splitting multiple access for efficient interference management. Upon optimizing the system efficacy, we formulate a long-term resource allocation problem to maximize the time-averaged energy efficiency. To address this stochastic and non-convex optimization problem, we propose a multi-stage solution strategy. Firstly, by leveraging Lyapunov optimization theory, we transform the long-term stochastic problem into an equivalent deterministic short-term form. Next, by recasting this equivalent problem into Markov decision process, we propose a resource allocation mechanism based on actor-critic hindsight experience replay (AC-HER), tailored to capture the problem dynamics and optimize its variables. Moreover, given the UAV high mobility and the system reconfigurations, we fortify the trained AC-HER model with meta-learning strategy, enhancing its adaptability to system variations. Simulations verified that the proposed resource allocation strategy considerably outperforms its counterparts.

This study investigates the application of non-orthogonal multiple access (NOMA) to enable massive connectivity for a stacked intelligent metasurface (SIM) system that performs signal processing in the electromagnetic wave domain. To realize the full potential of NOMA assisted SIM systems, a radio resource allocation problem is accordingly formulated to jointly optimize the key decision variables, including the decoding order of users, the transmit power at the base station, as well as the phase shift at the SIM. By adhereing to the users’ quality-of-service (QoS) requirements, as well as the power budget of the base station, the problem is aimed at maximizing the admission rate of the system. Due to the problem’s inherent non-convexity and complexity, we recast it as a Markov decision process and employ a quantile regression deep Q-network (QRDQN) agent to optimize the decision variables. Recognizing the mobility of users and the dynamic reconfiguration of the system, we further enhance the QR-DQN model’s adaptability and generalization capabilities by incorporating a meta-learning strategy. Simulation results demonstrate that integrating NOMA with SIM systems yields a significant increase of 39% in the average number of served users compared to the conventional orthogonal multiple access based approach. The proposed resource allocation mechanism additionally improves deep deterministic policy gradient (DDPG) in literature by 27% in the number of served users.

Stacked intelligent metasurface (SIM) as an advanced signal processing paradigm enables real-time processing of electromagnetic waves at the speed of light. Benefiting from this technology, the current paper studies the downlink transmission of a wireless network, where a SIM-deployed base station (BS) serves two disjoint sets of energy- and information-oriented terminals via simultaneous wireless information and power transfer (SWIPT). Toward optimizing the performance of this system, a resource allocation problem is formulated for characterizing the fundamental trade-off between the aggregate information rate and the overall harvested energy. By virtue of its tightly-coupled and non-convex nature, we equivalently transform this problem to a Markov decision process (MDP) form. Next, we train an asynchronous advantage actor critic (A3C) agent on the MDP-reformulated problem for optimizing the transmit power of the BS and the electromagnetic response of the SIM, in a joint fashion. Subsequently, by taking into account the mobility of terminals, we further enrich the adaptability of the trained A 3 C agent via meta-learning. It is numerically revealed that incorporating SIM leads to an approximate 30 % enhancement in the energy efficiency of existing SWIPT systems.

This paper centers around a multi-antenna unmanned aerial vehicle (UAV) which leverages stacked intelligent metasurface (SIM) as a green technology for signal processing at the electromagnetic wave domain. To assess the performance of this system, a radio resource allocation problem is accordingly formulated for jointly optimizing the motion trajectory and transmit power at the UAV, as well as the electromagnetic response at the SIM as decision variables. Since the problem is non-convex and challenging to solve, we reformulate it in Markov decision process form and train a distributed distributional deep deterministic policy gradient (D4PG) agent to optimize its decision variables. Concerning the significant mobility of the UAV and thus remarkable rearrangement of the system, we enhance the adaptability and generalization of the trained D4PG model by integrating meta-learning strategy. According to simulations, wave-domain beamforming via SIM at UAV leads to 40% and 22% reduction in average energy consumption, compared fullydigital and beamspace beamforming, respectively.

This study focuses on downlink transmissions of a low earth orbit (LEO) satellite, assisted by a beyond diagonal reconfigurable intelligent surface (BD-RIS) to serve ground terminals. Toward optimizing the performance of this system, we formulate the minimization of the average age of information (AoI) achieved at ground terminals. Our formulation respects the power budget of the LEO satellite and guarantees the quality-of-service of ground terminals by optimizing the downlink transmit power at the LEO satellite and reflection coefficients at the BD-RIS as decision variables. Owing to its non-convex and tightly-coupled nature, we reformulate the problem as a Markov decision process which effectively captures its dynamics. Next, a Q-learning propagation (Q-Prop) agent is trained to optimize the decision variables. In light of the mobility of ground terminals as well as LEO satellite, this communication system is highly dynamic. Therefore, we enhance the trained Q-Prop model with meta-learning strategy, which augments its adaptability and generalization to system variances. Numerical results indicate that, in comparison to RIS-lacking and RIS-assisted counterparts, our optimised solution achieves 38% and 26% lower average AoI, respectively.

Unmanned aerial vehicles (UAVs) with multiple antennas have recently been explored to improve capacity in wireless networks. However, their strict energy constraint for simultaneously flying and communication tasks renders the exploration of energy-efficient multi-antenna techniques indispensable. Meanwhile, lens antenna subarrays (LASs) emerge as a promising energy-efficient multi-antenna structure that have not been previously harnessed for this purpose. In this paper, we propose a LAS-aided UAV to serve ground users in downlink transmission. We formulate a resource allocation problem aimed at initiating a trade-off between aggregate data rate of ground users and the power consumption of the UAV (energy efficiency) by optimizing the lens-based beamforming and flight trajectory of the UAV. To address this non-convex problem, we recast it in Markov decision process that captures its dynamic features and provides a framework to train an actor-critic agent. This agent is fine-tuned via hindsight experience replay for enhanced stabilization. As well, given the frequent mobility of the UAV, we fortify the trained agent with a meta-learning strategy, enhancing its adaptability to system variations. Numerically, more than 20% energy efficiency gain is achieved by incorporating a 4lens LAS for UAV, compared to its single-lens architecture in literature. Simulations also demonstrate that the proposed resource allocation strategy achieves significant superiority over counterparts in literature.

This endeavor centers around downlink transmission of a base station (BS), outfitted with a stacked intelligent metasurface (SIM) that realizes an energy-efficient wave-domain multi-user beamforming. The underlying network includes mobile devices with multimedia service requirements, including web surfing, HTTP live video streaming and voice-over-LTE (VoLTE). Rather than conventional quality-of-service (QoS) metrics, we assess the satisfaction level of users relying on quality-of-experience (QoE) criteria. Invoking mean opinion score (MOS) as the subjective measurement of QoE, we also evaluate the overall efficacy of this system by posing a resource allocation optimization problem aimed at maximizing the achievable MOS of all users, while adhering to their minimum MOS requirements and the maximum transmit power budget of the BS. Due to the interdependency of optimization variables and non-convex nature of the problem, we first reformulate it in Markov decision process, which captures its dynamic traits. Relying on the MDP model, a conservative Q-learning (CQL) agent is trained for jointly designing the optimization variables, including the BS downlink transmit power, as well as the electromagnetic response of the SIM. In light of real-time mobility of users and the resulting non-trivial network dynamism, we further utilize meta-learning technique to enhance the adaptability and generalization of the CQL agent. Numerically, it is demonstrated that, respectively, 31%, 44% and 26% superior average MOS is achieved, for web, video and audio services, compared to traditional QoS-driven resource allocation.

This research investigates a wireless system in which a base station (BS), outfitted with a stacked intelligent meta-surface (SIM), performs wave-domain multi-user beamforming in downlink transmission. The communication benefits from the assistance of an active reconfigurable intelligent surface (RIS) that amplifies incoming signal strength to extend the network coverage. System performance is evaluated through the formulation of a resource allocation optimization problem aimed at maximizing the number of served users while adhering to their quality-of-service demands and the maximum transmit power budget of the BS. Due to the complex interdependencies among optimization variables and the non-convex nature of the problem, we first reformulate it in Markov decision process, which captures its dynamic traits. Subsequently, a maximum a posteriori policy optimization (MPO) agent is trained for jointly designing the optimization variables, including the BS transmit power, the electromagnetic response of the SIM, as well as the amplitude/phase of the active RIS. In light of real-time mobility of users and non-trivial network dynamism, we invoke the integration of meta-learning technique to enhance the adaptability and generalization of the MPO model. Numerically, it is demonstrated that incorporating an active RIS upscales the number of served users by 57% and 113%, on average, in comparison with existing passive RIS-assisted and conventional SIM-enabled systems, respectively.

Reconfigurable intelligent surface (RIS) has emerged as a groundbreaking paradigm in shaping the future of wireless systems in late years. Derived from RISs, stacked intelligent metasurface (SIM), by enabling the real-time modulation of electromagnetic waves at the speed of light, and simultaneous transmitting and reflecting RIS (STAR-RIS), by drastically enhancing the coverage extension of cellular networks, appear to revolutionize the future of wireless communication. This letter delves into the synergization of SIM and STAR-RIS in a wireless system, where a base station (BS), outfitted with a SIM, benefits from a STAR-RIS to serve downlink receivers. The performance of the system is examined through the formulation of a resource allocation optimization problem, which seeks to maximize the system’s data rate, subject to constraints on receivers’ quality-of-service and the BS’s transmit power budget. Given the interdependency of variables and its inherently non-convex nature, the problem is firstly transformed into a Markov decision process form, which encapsulates its dynamic characteristics. Thereafter, a natural actor critic (NAC) agent is employed to holistically optimize the problem variables, including the transmit power at the BS, the electromagnetic response at the SIM and the reflection coefficients at the STAR-RIS. Furthermore, to account for the real-time mobility of receivers and thus the dynamism of the network, we enhance the adaptability of the trained NAC model via meta-learning technique. Simulation results reveal that the introduction of a STAR-RIS improves the system data rate, by 21% and 38% on average, compared to existing RIS-enabled and conventional SIM-based systems, respectively.

Composed of an array of low-cost radiating elements, flexible intelligent metasurfaces (FIMs) can adaptively morph their surface shapes by adjusting the positions of elements along the direction perpendicular to the surface. This adaptive morphing, not only enhances wireless channel conditions, but also significantly curtails power consumption. This paper conducts an adaptive performance analysis of a wireless system, in which a FIM-equipped base station (BS) leverages the presence of a reconfigurable intelligent surface (RIS) to facilitate downlink transmission. To rigorously evaluate the system’s efficiency, we formulate an optimization problem centered on resource allocation, with the primary objective of maximizing the network achievable data rate. This maximization is subject to multiple constraints, especially on stringent quality-of-service (QoS) requirements of users and the BS finite power budget. Due to the highly intricate interdependencies among optimization variables and the inherent non-convexity of the problem, we strategically reformulate it as a Markov decision process (MDP), encapsulating its dynamic characteristics. To derive an optimal solution, we train a deep deterministic policy gradient (DDPG) agent, relying on MDP, which simultaneously optimizes the decision variables: the BS transmit beamforming, the morphology of the FIM and the reflection coefficient matrix of the RIS. Furthermore, to accommodate the real-world challenges imposed by user mobility, we enhance the generalization of the DDPG model through the integration of meta-learning technique, thereby significantly improving its adaptability to system variations. Numerical evaluations affirm that incorporating an RIS yields a pronounced improvement in achievable network data rate, particularly when the BS transmit power budget is maintained within a moderate operational range.

As the next-generation battery substitute for IoT system, energy harvesting (EH) technology revolutionizes the IoT industry with environmental friendliness, ubiquitous accessibility, and sustainability, which enables various self-sustaining IoT applications. However, due to the weak and intermittent nature of EH power, the performance of EH-powered IoT systems as well as its collaborative routing mechanism can severely deteriorate, rendering unpleasant data package loss during each power failure. Such a phenomenon makes conventional routing policies and energy allocation strategies impractical. Given the complexity of the problem, reinforcement learning (RL) appears to be one of the most promising and applicable methods to address this challenge. Nevertheless, although the energy allocation and routing policy are jointly optimized by the RL method, due to the energy restriction of EH devices, the inappropriate configuration of multi-hop network topology severely degrades the data collection performance. Therefore, this article first conducts a thorough mathematical discussion and develops the topology design and validation algorithm under energy harvesting scenarios. Then, this article develops DeepIoTRouting , a distributed and scalable deep reinforcement learning (DRL)-based approach, to address the routing and energy allocation jointly for the energy harvesting powered distributed IoT system. The experimental results show that with topology optimization, DeepIoTRouting achieves at least 38.71% improvement on the amount of data delivery to sink in a 20-device IoT network, which significantly outperforms state-of-the-art methods.

In recent years, using the Unmanned Aerial Vehicle (UAV) as a Base Stations (BS) to cover users in wireless networks has increased dramatically. One of the main goals of integrating UAVs into wireless networks is to deploy UAVs in such a way that user expectations are met with the fewest number of UAVs. To achieve this aim, the coverage area of each UAV should include as many users as possible. Furthermore, the resources assigned to the backhaul links for such UAV deployments must fulfill the requirements of users served by each UAV. In this paper the goal is to position the least number of UAVs in a 3D position to cover cellular network users. To provide appropriate quality of service, we consider a maximum path loss allowed for the network. The path loss of potential links is affected by the propagation environment and might vary depending on network structure. To reflect this uncertainty, path loss is expressed as a random variable with a probability distribution based on environmental characteristics. As a result, we're dealing with an optimization problem with uncertain information. We use stochastic programming to work with uncertain information and formulate the UAV positioning and data rate assignment problem. The implementation results of our proposed mixed-binary linear mathematical model and Monte Carlo simulation in various scenarios show its optimum performance in different dimensions.

Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing and collecting of IoT data, and defend against cyber threats. To address these issues, the authors present a distributed and decentralized blockchain-software-defined networking (SDN)-based energy-aware architecture for IoT in smart cities. Thus, SDN is continuously observing, controlling, and managing IoT devices activities and detects possible attacks in the network; blockchain provides adequate security and privacy against cyber attacks, and reduces the central point of failure issues; network function virtualization (NFV) is used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a cluster head selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g., throughput, response time, gas consumption, and communication overhead) and demonstrate the result that provides higher throughput, lower response time, and lower gas consumption than existing works for smart cities.

In recent years, we have witnessed tremendous advances in cloud data centres (CDCs) from the point of view of the communication layer.A recent report from Cisco Systems Inc. demonstrates that CDCs, which are distributed across many geographical locations, will dominate the global data centre traffic flow for the foreseeable future.Their importance is highlighted by a top-line projection from this forecast that by 2019, more than four-fifths of total data centre traffic will be Cloud traffic.The geographical diversity of the computing resources in CDCs provides several benefits, such as high availability, effective disaster recovery, uniform access to users in different regions, and access to different energy sources.Although Cloud technology is currently predominant, it is essential to leverage new agile software technologies, agile processes and agile applications near to both the edge and the users; hence, the concept of Fog has been developed.Fog computing (FC) has emerged as an alternative to traditional Cloud computing to support geographically distributed, latency-sensitive and QoS-aware IoT applications while reducing the burden on data centres used in traditional Cloud computing.In particular, FC with features that can support heterogeneity and real-time applications (e.g.low latency, location awareness, and the capacity to process a large number of nodes with wireless access) is an attractive solution for delay-and resource-constrained large-scale applications.The distinguishing feature of the FC paradigm is that a set of Fog nodes (FNs) spreads communication and computing resources over the wireless access network to provide resource augmentation to resource-and energy-limited wireless (possibly mobile) devices.The joint management of Fog and Internet of Technology (IoT) paradigms can reduce the energy consumption and operating costs of state-of-the-art Fog-based data centres (FDCs).An FDC is dedicated to supervising the transmission, distribution and communication of FC.As a vital component of the Internet of Everything (IoE) environment, an FDC is capable of filtering and processing a considerable amount of incoming data on edge devices, by making the data processing architecture distributed and thereby scalable.An FDC therefore provides a platform for filtering and analysing the data generated by sensors utilising the resources of FNs.Increasing interest is emerging in FDCs and CDCs that allow the delivery of various kinds of agile services and applications over telecommunication networks and the Internet, including resource provisioning, data streaming/transcoding, analysis of high-definition videos across the edge of the network, IoE application analysis etc. Motivated by these issues, this special section solicits original research and practical contributions that advance the use of CDCs/FDCs in new technologies such as IoT, edge networks and industries.Results obtained from simulations are validated in terms of their boundaries by experiments or analytical results.The main objectives of this special issue are to provide a discussion forum for people interested in Cloud and Fog networking, and to present new models, adaptive tools and applications specifically designed for distributed and parallel on-demand requests received from (mobile) users and Cloud applications.The papers presented in this special issue provide insights in fields related to Cloud and Fog/edge architecture, including parallel processing of Cloudlets/Foglets, the presentation of new emerging models, performance evaluation and improvements, and developments in Cloud/Fog applications.We hope that readers can benefit from the insights in these papers, and contribute to these rapidly growing areas.

Cloud computing has arisen as the mainstream platform of utility computing paradigm that offers reliable and robust infrastructure for storing data remotely, and provides on demand applications and services. Currently, establishments that produce huge volume of sensitive data, leverage data outsourcing to reduce the burden of local data storage and maintenance. The outsourced data, however, in the cloud are not always trustworthy because of the inadequacy of physical control over the data for data owners. To better streamline this issue, scientists have now focused on relieving the security threats by designing remote data checking (RDC) techniques. However, the majority of these techniques are inapplicable to big data storage due to incurring huge computation cost on the user and cloud sides. Such schemes in existence suffer from data dynamicity problem from two sides. First, they are only applicable for static archive data and are not subject to audit the dynamic outsourced data. Second, although, some of the existence methods are able to support dynamic data update, increasing the number of update operations impose high computation and communication cost on the auditor due to maintenance of data structure, i.e., merkle hash tree. This paper presents an efficient RDC method on the basis of algebraic properties of the outsourced files in cloud computing, which inflicts the least computation and communication cost. The main contribution of this paper is to present a new data structure, called Divide and Conquer Table (D&CT), which proficiently supports dynamic data for normal file sizes. Moreover, this data structure empowers our method to be applicable for large-scale data storage with minimum computation cost. The one-way analysis of variance shows that there are significant differences between the proposed method and the existing methods in terms of the computation and communication cost on the auditor and cloud.