Programmable wireless environments (PWEs) have emerged as a key paradigm for next-generation communication networks, aiming to transform wireless propagation from an uncontrollable phenomenon into a reconfigurable process that can adapt to diverse service requirements. In this framework, pinching-antenna systems (PASs) have recently been proposed as a promising enabling technology, as they allow the radiation location and effective propagation distance to be adjusted by selectively exciting radiating points along a dielectric waveguide. However, most existing studies on PASs rely on the idealized assumption that pinching-antenna (PA) positions can be continuously adjusted along the waveguide, while realistically only a finite set of pinching locations is available. Motivated by this, this paper analyzes the performance of two-state PASs, where the PA positions are fixed and only their activation state can be controlled. By explicitly accounting for the spatial discreteness of the available pinching points, closed-form analytical expressions for the outage probability and the ergodic achievable data rate are derived. In addition, we introduce the pinching discretization efficiency to quantify the performance gap between discrete and continuous pinching configurations, enabling a direct assessment of the number of PAs required to approximate the ideal continuous case. Finally, numerical results validate the analytical framework and show that near-continuous performance can be achieved with a limited number of PAs, offering useful insights for the design and deployment of PASs in PWEs.

As 6G wireless networks seek to enable robust and dynamic programmable wireless environments (PWEs), reconfigurable intelligent surfaces (RISs) have emerged as a cornerstone for controlling electromagnetic wave propagation. However, realizing the potential of RISs for demanding PWE applications depends on precise and real-time user localization, especially in scenarios with random receiver orientations and inherent hardware imperfections. To address this challenge, we propose a novel optical localization framework that integrates conventional ceiling-mounted LEDs with light-emitting reconfigurable intelligent surfaces (LeRISs). By leveraging the spatial diversity offered by the LeRIS architecture, the framework introduces robust signal paths that improve localization accuracy and reduce errors under varying orientations. To this end, we derive a system of equations for received signal strength-based localization that accounts for random receiver orientations and imposes spatial constraints on LED placement, ensuring unique and reliable solutions. Finally, our simulation results demonstrate that the proposed framework achieves precise beam control and high spectral efficiency even for RISs with large number of reflecting elements by tightly coupling the localization process with the beamforming configuration, allowing accurate direction estimation and robust PWE operation.

Programmable Wireless Environments (PWEs) leverage Reconfigurable Intelligent Surfaces (RISes) to actively shape electromagnetic (EM) propagation, enabling advanced control over wireless channels. Beyond improved performance in B5G/6G networks, this control also introduces new security capabilities. Exploiting this, we propose RF-Fencing: a service that selectively suppresses EM signals toward eavesdroppers while preserving reliable communication for legitimate users, thereby significantly enhancing network covertness. Building on that, in this paper, we introduce SHIELD, the first RF-Fencing algorithm that partitions the PWE into Signal Suppression Areas (SSAs) and Signal Delivery Areas (SDAs) through on-the-fly merging of RIS configurations. Extensive EM analysis confirms SHIELD’s effectiveness in preventing wardens from intercepting critical information and achieving covert communications with minimal impact on legitimate users. Moreover, SHIELD can serve also as a jamming-mitigation mechanism and is applicable across various frequency bands and RIS designs.

The evolution toward sixth-generation wireless networks introduces the concept of intelligent and reconfigurable environments designed to support advanced services. Achieving this paradigm shift requires addressing the limitations of traditional wireless systems, particularly their inability to effectively counteract path loss or adapt to diverse user scenarios. Pinching antenna systems (PASs) have emerged as a promising solution, enabling dynamic control over path loss by leveraging dielectric waveguides to support low-loss transmission at high frequencies. This work presents an analytical framework for assessing the reliability of PASs through the derivation of closed-form expressions for the outage probability under both free-space and waveguide attenuation. In addition, a rigorous formulation is provided for the optimal positioning of the pinching antennas to maximize signal reception, taking into account the trade-off between waveguide losses and spatial separation. Simulation results validate the impact of waveguide attenuation on performance and show that PASs consistently outperform conventional architectures in terms of outage behavior, confirming their suitability for next-generation wireless networks.

Cooperative, Connected, and Automated Mobility (CCAM) is based on fast, secure, and reliable Vehicle-to-Everything (V2X) communication to enable collective perception and maneuver coordination in autonomous driving. However, high-frequency wireless communication, particularly in the millimeter wave (mmWave) and terahertz (THz) bands, is highly susceptible to environmental obstacles, leading to severe signal attenuation and communication delays. This study investigates the integration of Reconfigurable Intelligent Surfaces (RIS) into vehicular networks to mitigate these challenges and analyzes their impact on Age of Information (AoI) and Peak AoI (PAoI) metrics. The latter metrics are highly relevant to feedback delays introduced in the Cooperative Control Schemes of Autonomous Vehicles, which are well known to compromise their stability. Their characterization is thus crucial for the performance assessment of the automatic controllers with this characterization in the presence of RIS not investigated as of now in the literature. Using the All-or-Nothing Receiver Model (ANRM) and Distance-Dependent Propagation Model (DDPM), we extend AoI formulations by incorporating RIS path loss characteristics revealing that RIS-assisted networks significantly reduce AoI and PAoI values, enabling reliable cooperative control in vehicular systems. We consider a cooperative merging maneuver on an intersection as our test case and demonstrate that the effect of the introduced delays on the cooperative control scheme performance is minimal. Passive RISs are assumed throughout the study, due to their energy efficient operation as compared to active metasurfaces.

Accurate indoor localization is crucial for enabling 6G applications, such as smart homes, augmented reality, and advanced healthcare systems. Optical wireless systems utilizing Light-Emitting Diodes (LEDs) offer centimeter-level accuracy due to their dominant line-of-sight (LoS) characteristics. However, most existing methods assume fixed and known user orientations, limiting their practical applicability in real-world scenarios with random orientations. In this paper, we propose an LED-based visible light positioning (VLP) scheme that accurately, through closed form equations, localizes users with arbitrary orientations using optical received signal strength (RSS) measurements. The proposed method achieves high localization accuracy, without requiring hardware for orientation measurements. Finally, an analytical expression for the error is derived, while Monte Carlo simulations validate the scheme's performance, highlighting the critical role of the parameters of the system in achieving accurate localization.

Programmable Wireless Environments aim to render the communication environment a controllable, software-defined medium. Reconfigurable Intelligent Surfaces (RISes) are the key enabling technology, which can offer the real-time capability to manipulate impinging waves. RISes are expected to be widely deployed in B5G/6G networks to serve a large number of users simultaneously. Despite numerous analyses highlighting the benefits of utilizing previously unexploitable propagation factors through the use of RISes, there is a lack of analysis regarding their relation to the concept of network resource, their allocation to users/stakeholders and their fair pricing. Thus, this paper models RISes as networked resources. Based on this definition, the PRIME algorithm is proposed, the first algorithm for RIS resource allocation and joint pricing. PRIME strives for proportionality between the offered end-user performance level and the corresponding resource pricing, promoting fairness. The algorithm is validated via full-wave electromagnetic simulations and applies to multiple RIS functionalities and frequency bands.

Unmanned aerial vehicles (UAVs) equipped with full-duplex relays (FDRs) are pivotal in overcoming connectivity challenges by dynamically establishing effective communication channels. However, despite their potential in network performance via trajectory optimization, integrating energy consumption models for UAV-mounted FDRs remains unexplored, crucial for trajectory design adhering to existing energy constraints. To this end, we introduce an energy-aware trajectory optimization framework to maximize network performance and user fairness within the UAV’s energy constraints. Specifically, we present a detailed energy consumption model describing the operational needs of UAV-mounted FDRs and formulate a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem considering the power dynamics of UAV-mounted FDRs. Finally, our simulation results highlight the role of energy awareness in achieving optimal trajectory and scheduling, contributing to UAV-mounted FDRs’ performance in future networks.

Future connected vehicles will require high-performance communication technologies for advanced cooperative driving applications such as maneuvering and cooperative perception. mmWave communications can meet the bandwidth requirements of such applications, but the typically harsh propagation conditions of vehicular environments hinder the broad adoption of mmWave devices on cars. Reconfigurable intelligent surfaces (RISs) can help mitigate this problem by enabling the reflection of signals in a configurable direction. In turn, this can result in more stable non-line of sight (NLoS) links whenever a LoS path is not available. RISs have recently gained attention in the vehicular domain but, while providing benefits, they also introduce a lot of research challenges. To measure their effectiveness at scale, it is necessary to develop simulation tools that can reproduce their characteristics with high fidelity and federate them with existing cooperative driving simulation frameworks. In this work we present CoopeRIS, an open-source simulation framework federated within the Plexe/Veins/SUMO ecosystem, capable of modeling and simulating RIS-based mmWave communications in a vehicular environment. We exploit CoopeRIS to perform an initial feasibility study, highlighting the challenges ahead and the performance RISs need to deliver in order to enable this type of communication. In addition we propose a method to combine multiple RIS configurations into a single one to enable multi-user service delivery, showing its performance via CoopeRIS. The insights we presents within this work show the potential of such simulation framework and thus how the community can build further research work on top if it.

In the evolving landscape of sixth-generation (6G) wireless networks, unmanned aerial vehicles (UAVs) have emerged as transformative tools for dynamic and adaptive connectivity. However, dynamically adjusting their position to offer favorable communication channels introduces operational challenges in terms of energy consumption, especially when integrating advanced communication technologies like reconfigurable intelligent surfaces (RISs) and full-duplex relays (FDRs). To this end, by recognizing the pivotal role of UAV mobility, the paper introduces an energy-aware trajectory design for UAV-mounted RISs and UAV-mounted FDRs using the decode-and-forward (DF) protocol, aiming to maximize the network’s minimum rate and enhance user fairness, while taking into consideration the available on-board energy. Specifically, this work highlights their distinct energy consumption characteristics and their associated integration challenges by developing appropriate energy consumption models for both UAV-mounted RISs and FDRs that capture the intricate relationship between key factors such as weight, and their operational characteristics. Furthermore, a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem is formulated, considering the power dynamics of both systems, while assuring that the UAV energy is not depleted mid-air. Finally, simulation results underscore the importance of energy considerations in determining the optimal trajectory and scheduling and provide insights into the performance comparison of UAV-mounted RISs and FDRs in UAV-assisted wireless networks.

A primary objective of the forthcoming sixth generation (6G) of wireless networking is to support demanding applications, while ensuring energy efficiency. Programmable wireless environments (PWEs) have emerged as a promising solution, leveraging reconfigurable intelligent surfaces (RISs), to control wireless propagation and deliver exceptional quality-of-service. In this paper, we analyze the performance of a network supported byzero-energy RISs (zeRISs), which harvest energy for their operation and contribute to the realization of PWEs. Specifically, we investigate joint energy-data rate outage probability and the energy efficiency of a zeRIS-assisted communication system by employing three harvest-and-reflect (HaR) methods, i) power splitting, ii) time switching, and iii) element splitting. Furthermore, we consider two zeRIS deployment strategies, namely BS-side zeRIS and UE-side zeRIS. Simulation results validate the provided analysis and examine which HaR method performs better depending on the zeRIS placement. Finally, valuable insights and conclusions for the performance of zeRIS-assisted wireless networks are drawn from the presented results.

Sliding Window Random Linear Network Coding (RLNC) offers a clear path towards achieving ultra-high reliability and low latency at the same time. Such requirements are pivotal for a wide range of applications in the future Internet as well as in 5G and beyond networks. While traditional RLNC has been extensively used for some years now, its Sliding Window flavor is rather recent and extremely promising because of its implementation advantages and the high degree of customization. Probably the most essential parameter of Sliding Window RLNC is the coding depth, i.e., the extent of non-coded packets protected by a coded one. In this work, for the first time, we elaborate on properly choosing the coding depth and shed light on the related trade-offs. We, first, show, experimentally, that significant performance gains can be obtained by fine-tuning the coding depth. Then, we propose and validate an analytical framework that allows us to decide the coding depth based on a channel’s reliability profile. Finally, we introduce a dynamic algorithm that, based on our analytical findings, can improve the performance of sliding window RLNC in the presence of bursts of errors.

Data collection in massive Internet of Things networks requires novel and flexible methods. Unmanned aerial vehicles (UAVs) are foreseen as a means to collect data rapidly even in remote areas without static telecommunication infrastructure. In this direction, UAV-mounted reconfigurable intelligent surfaces (RISs) aid in reducing the hardware requirements and signal processing complexity at the UAV side, while increasing the network’s energy efficiency and coverage. Hence, in this paper, we propose the utilization of a UAV-mounted RIS for data collection and study the coverage probability in such networks. Additionally, we propose a novel medium access control protocol based on slotted ALOHA and Code Combining to handle the communication of multiple sensors. To account for the crucial energy issue in UAVs, we devise an energy model that considers both the UAV and the RIS weight, as well as the environmental conditions and the UAV’s velocity. Finally, we characterize the performance of the proposed data collection scheme by analyzing the average throughput and the average collected data per flight, while providing useful insights for the design of such networks.

Reconfigurable Intelligent Surfaces (RIS) are well established as a promising solution to the blockage problem in millimeter-wave (mm-wave) and terahertz (THz) communications, envisioned to serve demanding networking applications, such as 6G and vehicular. HyperSurfaces (HSF) is a revolutionary enabling technology for RIS, complementing Software Defined Metasurfaces (SDM) with an embedded network of controllers to enhance intelligence and autonomous operation in wireless networks. In this work, we consider feedback-based autonomous reconfiguration of the HSF controller states to establish a reliable communication channel between a transmitter and a receiver via programmable reflection on the HSF when Line-of-sight (LoS) between them is absent. The problem is to regulate the angle of reflection on the metasurface such that the power at the receiver is maximized. Extremum Seeking Control (ESC) is employed with the control signals generated mapped into appropriate metasurface coding signals which are communicated to the controllers via the embedded controller network (CN). This information dissemination process incurs delays which can compromise the stability of the feedback system and are thus accounted for in the performance evaluation. Extensive simulation results demonstrate the effectiveness of the proposed method to maximize the power at the receiver within a reasonable time even when the latter is mobile. The spatiotemporal nature of the traffic for different sampling periods is also characterized.

Programmable metasurfaces constitute an emerging paradigm, envisaged to become a key enabling technology for Reconfigurable Intelligent Surfaces (RIS) due to their powerful control over electromagnetic waves. The HyperSurface (HSF) paradigm takes one step further by embedding a network of customized integrated circuit (IC) controllers within the device with the aim of adding intelligence, connectivity, and autonomy. However, little is known about the traffic that the network needs to support as the target electromagnetic function or boundary conditions change. In this paper, the framework of a methodology is introduced to characterize the workload of programmable metasurfaces which is then used to analyze the beam steering HSFs. The workload characterization leads to many useful insights into traffic behavior, including the spatio-temporal load incurred and the HSF limitations in terms of fine-grained tracking of moving targets. It is observed that the traffic is inherently bursty with an uneven spatial distribution of load and that finer resolution comes at the cost of an increased but less bursty load. An indoor mobility model indicates reasonable signaling load on the deployed surfaces. Finally, a statistical analysis on the traffic patterns is performed, showing that the incoming traffic can be well represented by an ON-OFF model.

Reconfigurable Intelligent Surfaces (RIS) constitute a promising technology that could fulfill the extreme performance and capacity needs of the upcoming 6G wireless networks, by offering software-defined control over wireless propagation phenomena. Despite the existence of many theoretical models describing various aspects of RIS from the signal processing perspective (e.g., channel fading models), there is no open platform to simulate and study their actual physical-layer behavior, especially in the multi-RIS case. In this paper, we develop an open simulation platform, aimed at modeling the physical-layer electromagnetic coupling and propagation between RIS pairs. We present the platform by initially designing a basic unit cell, and then proceeding to progressively model and simulate multiple and larger RISs. The platform can be used for producing verifiable stochastic models for wireless communication in multi-RIS deployments, such as vehicle-to-everything (V2X) communications in autonomous vehicles and cybersecurity schemes, while its code is freely available to the public.

Wireless communications are subject to the chaotic scattering of wireless waves. However, the recently-proposed software-defined metasurfaces (SDMs) can transform the wireless propagation into a programmable and even fully deterministic phenomenon. Unnatural aerial paths can be created by program-ming multiple SDMs deployed in an environment, to alter the direction, phase, amplitude and polarization of waves impinging upon them. Optimizing this programming is an open challenge as parasitic effects, e.g. parasitic reflection lobes, can appear and deteriorate the quality of the received power delay profile. This paper proposes a novel optimization algorithm based on the ant colony framework, through which the SDM programming options are explored, gradually converging to a significantly improved received power delay profile. The study first presents a novel graph-based model of SDM-enabled environments, which ab-stracts the underlying physics, facilitating their cross-disciplinary understanding. Simulation results showcase the flexibility of the model and optimization algorithm in multiple settings.

Software-Defined Metasurfaces (SDMs) have ad-vanced from basic theoretical ideas to a key enabler for next generation wireless systems. When massively deployed in a wireless environment, they can realize precise, software-defined propagation of signals and wireless channel customizations. A plethora of studies have thoroughly investigated the physical design of the SDMs and their wireless channel engineering capa-bilities. This work introduces a novel architecture and associated processes for integrating SDMs into existing networked wireless systems. First, the integration architecture and the according complex underlying physics of the SDMs are presented. Then the software abstractions are proposed that enable the interaction of the surfaces in a physics-agnostic manner. These software abstractions require an SDM profile to operate in real-time, i.e., a database that accurately describes the capabilities of a given metasurface. Thus, the manufacturing time workflows are suggested to produce such a profile. The proposed solutions are evaluated in an actual physical setup/testbed.

Metasurfaces constitute a revolutionary technology for the realization of intelligent reflecting surfaces (IRS) which can alleviate the blockage problem in mmWave and Thz communications in the absence of Line of Sight (LOS). In this work, we consider the use of multiple IRSs to provide LOS paths between a sender and a receiver via reflection. Unlike previous work, we use the directivity as a means to incorporate the metasurface reflection behavior in the channel model and parameterize with respect to the design parameters. The design problem considered is the choice of the “best” IRSs for consecutive reflection of the transmitted signal to optimize the communication channel. The problem is formulated as an optimization problem which is challenging to solve due to the dependence of each link cost on the previous link. We consider a relaxation which decouples the link costs, we apply Dijkstra’s algorithm for the solution and we show that the performance degradation as compared to the original problem which is solved using exhaustive search is not significant.

Massive IoT deployments in smart cities pose a significant challenge to the data collection due to the harsh wireless channel conditions of dense urban environments. Aerial reconfigurable intelligent surfaces (RIS) carried by Unmanned Aerial Vehicles (UAVs) can improve the communication thanks to their high mobility that provides line-of-sight propagation. In this paper, we investigate the impact of the aerial RIS in the data collection by deriving the outage probability of the randomly-deployed devices, while taking into account the imperfect channel state and the UAV fluctuations. Furthermore, we study the effects on the network reliability of two hybrid automatic repeat request protocol types, i.e., incremental redundancy and code combining, as well as on the average throughput. Finally, we provide useful insights regarding the RIS characteristics that guarantee the optimal network performance.

Programmable wireless environments (PWEs) utilize internetworked intelligent metasurfaces to transform wireless propagation into a software-controlled resource. In this article, the interplay is explored between the user devices, the metasurfaces, and the PWE control system from the theory to the end-to-end implementation. This article first discusses the metasurface hardware and software, covering the complete workflow from the user device initialization to its final service via the PWE. Furthermore, to be compatible with the 5G and 6G wireless systems, the software-defined networking (SDN) paradigm is extended to achieve scalable internetworking and central control in PWE deployments with multiple metasurfaces and multihop communication. Subsequently, the set of SDN foundations is exploited in order to abstract the physics behind PWEs and a theoretical framework is established to describe and manipulate them in an algorithmic form. This can lead to smart radio environments that are readily accessible from various engineering disciplines, facilitating their integration into existing networks, wireless systems, and applications. This article is concluded by outlining strategies for the optimal placement of metasurfaces within a PWE-controlled space, open challenges in PWE security, specialized SDN integration issues, and theoretical problems toward the graph-driven modeling of PWEs.

Programmable wireless environments (PWEs) utilize electromagnetic metasurfaces to transform wireless propagation into a software-controlled resource. In this work we study the effects of user device mobility on the efficiency of PWEs. An analytical model is proposed, which describes the potential misalignment between user-emitted waves and the active PWE configuration, and can constitute the basis for studying queuing problems in PWEs. Subsequently, a novel, beam steering approach is proposed which can effectively mitigate the misalignment effects. Ray-tracing-based simulations evaluate the proposed scheme.

Intelligent surfaces exert deterministic control over the wireless propagation phenomenon, enabling novel capabilities in performance, security and wireless power transfer. Such surfaces come in the form of rectangular tiles that cascade to cover large surfaces such as walls, ceilings or building facades. Each tile is addressable and can receive software commands from a controller, manipulating an impinging electromagnetic wave upon it by customizing its reflection direction, focus, polarization and phase. A new problem arises concerning the orchestration of a set of tiles towards serving end-to-end communication objectives. Towards that end, we propose a novel intelligent surface networking algorithm based on interpretable neural networks. Tiles are mapped to neural network nodes and any tile line-of-sight connectivity is expressed as a neural network link. Tile wave manipulation functionalities are captured via geometric reflection with virtually rotatable tile surface norm, thus being able to tunable distribute power impinging upon a tile over the corresponding neural network links, with the corresponding power parts acting as the link weights. A feedforward/backpropagate process optimizes these weights to match ideal propagation outcomes (normalized network power outputs) to wireless user emissions (normalized network power inputs). An interpretation process translates these weights to the corresponding tile wave manipulation functionalities.

The traditional cycle of industrial products has been linear since its inception. Raw resources are acquired, processed, distributed, used and ultimately disposed of. This linearity has led to a dangerously low efficiency degree in resource use, and has brought forth serious concerns for the viability of our natural ecosystem. Circular economy is introducing a circular workflow for the lifetime of products. It generalizes the disposal phase, reconnecting it to manufacturing, distribution and end-use, thus limiting true deposition to the environment. This process has not been extended so far to software. Nonetheless, the development of software follows the same phases, and also entails the use-and waste-of considerable resources. This include human effort, as well as human and infrastructure sustenance products such as food, traveling and energy. This paper introduces circular economy principles to the software development, and particularly to network management logic and security. It employs a recently proposed concept-the Socket Store-which is an online store distributing end-user network logic in modular form. The Store modules act as mediators between the end-user network logic and the network resources. It is shown that the Socket Store can implement all circular economy principles to the software life-cycle, with considerable gains in resource waste.

Programmable, intelligent surfaces can manipulate electromagnetic waves impinging upon them, producing arbitrarily shaped reflection, refraction and diffraction, to the benefit of wireless users. Moreover, in their recent form of HyperSurfaces, they have acquired inter-networking capabilities, enabling the Internet of Material Properties with immense potential in wireless communications. However, as with any system with inputs and outputs, accurate sensing of the impinging wave attributes is imperative for programming HyperSurfaces to obtain a required response. Related solutions include field nano-sensors embedded within HyperSurfaces to perform minute measurements over the area of the HyperSurface, as well as external sensing systems. The present work proposes a sensing system that can operate without such additional hardware. The novel scheme programs the HyperSurface to perform compressed sensing of the impinging wave via simple one-antenna power measurements. The HyperSurface can jointly be programmed for both wave sensing and wave manipulation duties at the same time. Evaluation via simulations validates the concept and highlight its promising potential.

Summary Networked Music Performance (NMP) systems involve musicians located in different places who perform music while staying synchronized via the Internet. The maximum end‐to‐end delay in NMP applications is called Ensemble Performance Threshold (EPT) and should be less than 25 milliseconds. Due to this constraint, NMPs require ultra–low‐delay solutions for audio coding, network transmission, relaying, and decoding, each one a challenging task on its own. There are two directions for study in the related work referring to the NMP systems. From the audio perspective, researchers experiment on low‐delay encoders and transmission patterns, aiming to reduce the processing delay of the audio transmission, but they ignore the network performance. On the other hand, network‐oriented researchers try to reduce the network delay, which contributes to reduced end‐to‐end delay. In our proposed approach, we introduce an integration of dynamic audio and network configuration to satisfy the EPT constraint. The basic idea is that, the major components participating in an NMP system, the application and the network interact during the live music performance. As the network delay increases, the network tries to equalize it by modifying the routing behavior using Software‐Defined Networking principles. If the network delay exceeds a maximum affordable threshold, the network reacts by informing the application to change the audio processing pattern to overcome the delay increase, resulting in below EPT end‐to‐end delay. A full prototype of the proposed system was implemented and extensively evaluated in an emulated environment.

Programmable wireless environments enable the software-defined propagation of waves within them, yielding exceptional performance. Several building-block technologies have been implemented and evaluated at the physical layer in the past. The present work contributes a network-layer solution to configure such environments for multiple users and objectives, and for any underlying physical-layer technology. Supported objectives include any combination of Quality of Service and power transfer optimization, eavesdropping, and Doppler effect mitigation, in multi-cast or uni-cast settings. In addition, a graph-based model of programmable environments is proposed, which incorporates core physical observations and efficiently separates physical and networking concerns. The evaluation takes place in a specially developed simulation tool, and in a variety of environments, validating the model and reaching insights into the user capacity of programmable environments.

Metasurfaces, ultrathin and planar electromagnetic devices with sub-wavelength unit cells, have recently attracted enormous attention for their powerful control over electromagnetic waves, from microwave to visible range. With tunability added to the unit cells, the programmable metasurfaces enable us to benefit from multiple unique functionalities controlled by external stimuli. In this review paper, we will discuss the recent progress in the field of programmable metasurfaces and elaborate on different approaches to realize them, with the tunability from global aspects, to local aspects, and to software-defined metasurfaces.

Tunable metasurfaces are ultra-thin, artificial electromagnetic components that provide engineered and externally adjustable functionalities. The programmable metasurface, the HyperSurFace, concept consists in integrating controllers within the metasurface that interact locally and communicate globally to obtain a given electromagnetic behaviour. Here, we address the design constraints introduced by both functions accommodated by the programmable metasurface, i.e., the desired metasurface operation and the unit cells wireless communication enabling such programmable functionality. The design process for meeting both sets of specifications is thoroughly discussed. Two scenarios for wireless intercell communication are proposed. The first exploits the metasurface layer itself, while the second employs a dedicated communication layer beneath the metasurface backplane. Complexity and performance trade-offs are highlighted.

Wireless communication environments are unaware of the ongoing data exchange efforts within them. Moreover, their effect on the communication quality is intractable in all but the simplest cases. The present work proposes a new paradigm, where indoor scattering becomes software-defined and, subsequently, optimizable across wide frequency ranges. Moreover, the controlled scattering can surpass natural behavior, exemplary overriding Snell's law, reflecting waves towards any custom angle (including negative ones). Thus, path loss and multi-path fading effects can be controlled and mitigated. The core technology of this new paradigm are metasurfaces, planar artificial structures whose effect on impinging electromagnetic waves is fully defined by their macro-structure. The present study contributes the software-programmable wireless environment model, consisting of several HyperSurface tiles controlled by a central, environment configuration server. HyperSurfaces are a novel class of metasurfaces whose structure and, hence, electromagnetic behavior can be altered and controlled via a software interface. Multiple networked tiles coat indoor objects, allowing fine-grained, customizable reflection, absorption or polarization overall. A central server calculates and deploys the optimal electromagnetic interaction per tile, to the benefit of communicating devices. Realistic simulations using full 3D ray-tracing demonstrate the groundbreaking potential of the proposed approach in 2.4GHz and 60GHz frequencies.

New strains of distributed denial-of-service (DDoS) attacks have exhibited potential to disconnect communication networks, even cutting off entire countries from the Internet. The “crossfire” is a new, indirect DDoS link-flooding attack, which masks itself as natural congestion, making it very hard to counter. Several studies have proposed online attack detection schemes, whose efficiency has been shown to vary in different network topologies. However, the topology/detection relation has been studied qualitatively, without formal proof or quantification metric. This paper is motivated by the fact that network topology changes are generally expensive and slow. Therefore, network designers should be provided with means of evaluating the effects of topology modifications to the attack detection efficiency. This paper fills this gap by contributing a formal proof for the topology-detection efficiency relation, as well as a novel off-line metric that quantifies it. Full attack prototypes are implemented and evaluated in real-Internet topologies, validating the analytical findings. It is shown that the novel metric expresses the topology-detection relation efficiently, while existing and widely used metrics do not constitute good choices for this task.

Networked Music Performance (NMP) constitutes a class of ultra-low delay sensitive applications, allowing geographically separate musicians to perform seamlessly as a tele-orchestra. For this application type, the QoS indicator is the mouth-to-ear delay, which should be kept under 25 milliseconds. The mouth-to-ear delay comprises signal processing latency and network delay. We propose a strong collaboration between the network and NMP applications to actively keep the to mouth-to-ear delay minimal, using direct state notifications. Related approaches can be characterized as passive, since they try to estimate the network state indirectly, based on the end application performance. Our solution employs Software Defined Networking (SDN) to implement the network-to-application collaboration, being facilitated by the well-defined network interface that SDN offers. Emulation results show that the proposed scheme achieves an improvement of up to 59% in mouth-to-ear delay over the existing passive solutions.

A developer of mobile or desktop applications is responsible for implementing the network logic of his software. Nonetheless: i) Developers are not network specialists, while pressure for emphasis on the visible application parts places the network logic out of the coding focus. Moreover, computer networks undergo evolution at paces that developers may not follow. ii) From the network resource provider point of view, marketing novel services and involving a broad audience is also challenge for the same reason. Moreover, the objectives of end-user networking logic are neither clear nor uniform. This constitutes the central optimization of network resources an additional challenge. As a solution to these problems, we propose the Socket Store. The Store is a marketplace containing end-user network logic in modular form. The Store modules act as intelligent mediators between the end-user and the network resources. Each module has a clear, specialized objective, such as connecting two clients over the Internet while avoiding transit networks suspicious for eavesdropping. The Store is populated and peer-reviewed by network specialists, whose motive is the visibility, practical applicability and monetization potential of their work. A developer first purchases access to a given socket module. Subsequently, he incorporates it to his applications under development, obtaining state-of-the-art performance with trivial coding burden. A full Store prototype is implemented and a critical data streaming module is evaluated as a driving case.

The present study proposes a novel collaborative traffic engineering scheme for networks of autonomous systems (ASes). Backpressure routing principles are used for deriving priority routing rules that optimally stabilize a network, while maximizing its throughput under latency considerations. The routing rules are deployed to the network following simple software-defined networking principles. The proposed scheme requires minimal, infrequent interaction with a central controller, limiting its imposed workload. Furthermore, it respects the internal structure of the ASes and their existing peering relations. In addition, it co-exists smoothly with underlying distance vector-based routing schemes. The proposed scheme combines simplicity with substantial gains in served transit traffic volume, as shown by simulations in realistic setups and proven via mathematical analysis.

Nanonetworks comprise numerous wireless nodes, assembled at micro-to-nano scale. The unique manufacturing challenges and cost considerations of these networks make for minimal complexity solutions at all network layers. From a networking aspect, packet retransmissions should be kept minimal, while ensuring communication between any two nanonodes. In addition, assigning unique addresses to nanonodes is not straightforward, since it can entail a prohibitively high number of packet exchanges. Thus, efficient data routing is considered an open issue in nanonetworking. The present paper proposes a routing system which can be dynamically deployed within a nanonetwork. Static, dense topologies with numerous, identical nodes are examined. These attributes are especially important in the context of recently proposed applications of nanonetworks. The proposed scheme incurs a trivial setup overhead and requires integer processing capabilities only. Once deployed, it operates efficiently, inducing lower packet retransmission rates than related schemes.

Distributed link-flooding attacks constitute a new class of attacks with the potential to segment large areas of the Internet. Their distributed nature makes detection and mitigation very hard. This work proposes a novel framework for the analytical modeling and optimal mitigation of such attacks. The detection is modeled as a problem of relational algebra, representing the association of potential attackers (bots) to potential targets. The analysis seeks to optimally dissolve all but the malevolent associations. The framework is implemented at the level of online Traffic Engineering (TE), which is naturally triggered on link-flooding events. The key idea is to continuously re-route traffic in a manner that makes persistent participation to link-flooding events highly improbable for any benign source. Thus, bots are forced to adopt a suspicious behavior to remain effective, revealing their presence. The load-balancing objective of TE is not affected at all. Extensive simulations on various topologies validate our analytical findings.

Software-Defined Networking enables the centralized orchestration of data traffic within a network. However, proposed solutions require a high degree of architectural penetration. The present study targets the orchestration of network elements that do not wish to yield much of their internal operations to an external controller. Backpressure routing principles are used for deriving flow routing rules that optimally stabilize a network, while maximizing its throughput. The elements can then accept in full, partially or reject the proposed routing rule-set. The proposed scheme requires minimal, relatively infrequent interaction with a controller, limiting its imposed workload, promoting scalability. The proposed scheme exhibits attracting network performance gains, as demonstrated by extensive simulations and proven via mathematical analysis.

Wireless networks of nano-nodes will play a critical role in future medical, quality control, environmental monitoring and military applications. Nano-nodes are invisible/marginally visible to the human eye, ranging in size from approximately 100 μm to few nanometers. Nano-networking poses unique challenges, requiring ground-breaking solutions. First, the nano-scale imposes severe restrictions to the computational and communication capabilities of the nodes. Second, nano-nodes are not accessible for programming, configuration and debugging in the classical sense. Thus, a nano-network should be self-configuring, resilient and adaptive to environmental changes. Finally, all nano-networking protocols should be ultra-scalable, since a typical nano-network may comprise billions of nodes. The study contributes a novel paradigm for data dissemination in networking nano-machines, addressing these unique challenges. Relying on innovative analytical results on lattice algebra and nature-inspired processes, a novel data dissemination method is proposed. The nano-nodes exploit their environmental feedback and mature adaptively into network backbone or remain single network users. Such a process can be implemented as an ultra-scalable, low complexity, multi-modal nano-node architecture (physical layer), providing efficient networking and application services at the same time. Requiring existing manufacturing technology, the proposed architecture constitutes the first candidate solution for realizable nano-networking.

The Web offers abundant amounts of information anytime, anyplace. Multimedia provision companies bloom and die, seeking to attract a viable share of the data dissemination market. More often than not, failure is attributed to unsuitable content orientation. The present work contributes a novel content specialization scheme for periodic, push-based data streaming services. Its goal is to maximize the ratio of user requests served within their respective deadlines. In this sense, optimality is proven to be achievable only by discarding all requests for certain data items, focusing solely on the streaming of the remaining ones. Thus, the service achieves the content specialization that yields the most clients. The methodology workflow is as follows. By means of mathematical analysis we quantify data compatibility in terms of boosting the system’s service ratio. A data selection algorithm is then proposed, which follows a sort-and-segment approach. Through simulations, the algorithm is shown to surpass related solutions, while coinciding with bruteforce results and analytical expectations.

In broadcast systems a central server collects client queries for a given set of data items and schedules the next broadcast accordingly. Since anonymity has been shown to promote user participation, the client queries do not contain a client ID. However, this choice leaves ample room for service misuse. This paper identifies a number of prospective attacks and quantifies their impact on the performance of the system. A novel query filtering mechanism named Sensor Swarm is introduced to tackle these attacks. Extensive simulations show that the proposed scheme can ensure smooth system operation even under heavy attacks. Thus, a broadcast system that employs the Sensor Swarm filtering scheme can sustain excessive client misbehavior before resolving to Turing tests or compromising user anonymity.

The present paper proposes a generalization of the square root rule for optimal periodic scheduling. The rule defines a ratio of item occurrences in a schedule, which minimizes the mean serving time. However, the actual number of each item's occurrences must be an integer. Therefore, the square root rule assumes large schedules, in order for the ratio to hold with acceptable precision. The present paper introduces an analysis-derived formula which connects the mean serving time and the size of the schedule. The relation shows that small schedules can also achieve near-optimal serving times. The analysis is validated through comparison with simulation and brute force-derived results. Finally, it is shown that minimizing the size of the schedule is also an efficient way of optimizing the aggregate scheduling cost.

The present work demonstrates a novel, free and open-source educational software package on wireless communications. Targeting graduate and post-graduate studies, the package covers issues of antennas and propagation, wireless channel modeling (fading, shadowing, path loss, Doppler effect), static and adaptive modulation, client mobility and network planning applied to DVB/T and indoor networks (WiFi, femtocell) settings. The corresponding packages are fully interactive and parametric, offering 3D GUIs, ray traced maps and connection to field measurements. Being extensible via the addition of simple text files, the presented package constitutes a concrete approach that was missing from the related approaches that follow the virtual laboratories paradigm.

Knowledge of the clients' preferences in communication systems enables adaptive, precise resource allocation in real-time. The present work introduces a novel scheme for estimating the clients' preferences in wireless data broadcast systems. The proposed scheme relies on the principle of maximum entropy, with statistical moments of the clients' overall waiting time serving as inputs, and the probability distribution of the broadcast data items being the output. Observations regarding periodic broadcast scheduling enabled the reduction of the convergence time well below that of related well-known approaches, in both noisy and noiseless environments. Simulation results showed that the technique can be used for accurate adaptation in minimal times, compared to the alternatives.

A novel periodic scheduling scheme for wireless environments is presented. The proposed scheme enables quality of service (QoS) agreements per data item, contrary to the per-dataset approach of existing solutions. Constraints abiding by any of the existing models (impatience, utility, and waiting time) are allowed to exist concurrently. The impact of individual agreements on the system's performance as a whole is analytically studied and quantified. Comparison with related approaches rendered the proposed scheme optimally efficient and flexible enough to serve as a basis for the implementation of specialized QoS metrics as well.

Wireless data broadcasting systems are typically assigned limited operational bandwidth due to their usually wide spatial coverage. This limitation raises issues of increased client waiting times, especially as the total size of the data to be disseminated increases. This problem could be partially or fully resolved by offloading part of the data to local mobile ad hoc subsystems. To this end, a novel merging of wireless data broadcasting and information hovering is proposed. The information hovering subsystem handles the least popular data items, allowing the most popular ones to be efficiently disseminated through the central wireless broadcasting. Both bandwidth-greedy and bandwidth-conserving cooperation algorithms are proposed. The validity of the proposed merged scheme is evaluated through simulation in a realistic MANET with locality of data demand. The results show considerably decreased mean waiting times in any case.

A main design issue in a wireless data broadcasting system is to choose between push-based and pull-based logic: The former is used as a low-cost solution, while the latter is preferred when performance is of utmost importance. Therefore, the most significant advantage of a push system is the minimal cost. This fact implies that hardware limitations do exist in the case of push systems. As a consequence, every related proposed algorithm should primarily be cost-effective. This attribute, however, has been overlooked in related research. In this paper, popular broadcast scheduling approaches are tested from an implementation cost aspect, and the results render them only conditionally realizable. Moreover, a new, cost-effective, adaptivity oriented schedule constructor is proposed as a realistic, minimal-cost solution.

The Broadcast Disks method is commonly used to schedule the data transmission in wireless push based networks. The performance of a system that utilizes this method depends upon several parameters. This paper presents the Optimization- Based Procedure (OBP), an analytical approach for estimating the optimal values of these parameters. The validity of the analysis is verified through simulation and comparison with other popular and traditional approaches. The new approach was found to be accurate and dominant in the vast majority of a wide set of test cases. It also provided a thorough insight of the system's stability and behavior in general.