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.

Cooperative, connected and automated mobility (CCAM) is revolutionizing the future of transportation by promising substantial improvements in road safety, traffic efficiency, and driver comfort. Central to CCAM is the concept of collective perception, where vehicles exchange sensor data via vehicle-to-everything (V2X) communication to overcome individual perception limitations. However, communication disruptions, data privacy, and reliability issues persist, particularly when obstacles interfere with the exchange of collective perception messages (CPMs). This paper investigates the potential of reconfigurable intelligent surface (RIS) in addressing these challenges by enhancing V2X communication channels. For the first time, we explore the integration of RIS in a simulation framework designed to model the exchange of CPM messages in cooperative driving environments. Additionally, we perform object detection on a static vehicle’s field of view and transmit the video stream with detection results to a moving vehicle, further enhancing situational awareness. We also present a novel analysis of the tradeoff between cooperative perception quality and latency under RIS-assisted communication by evaluating the impact of frame compression on packet loss and information freshness. Using the CoopeRIS platform integrated with CARLA, we demonstrate how RIS-assisted communication can mitigate the impact of obstacles and improve the reliability of CPM exchange, providing a robust foundation for the future of collective perception services in CCAM. Our results show significant gains in communication reliability, detection precision, and the effectiveness of CPM exchange in obstructed environments.

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.

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.

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.

There has been widespread adoption of blockchain in the wireless domain, especially with the growing interest and potential of Internet of Things (IoT). While the properties of blockchain namely trustworthy transactions and decentralization of authority have opened up numerous opportunities, new challenges pertinent to security and performance have emerged in the presence of wireless communications and mobility. Although current literature provides extensive analysis of blockchain networks when connectivity is offered by static and wired links, there has been little attention in the wireless space. Wireless blockchain analysis has been pursued under specific and simplified assumptions which are insufficient for generalized blockchain performance in real world scenarios. Our work tries to fill this gap by covering a large space of blockchain and network based parameters such as the block interval, the number of miners, node mobility, transmission power and network density and present comprehensive performance results obtained using the NS-3 simulator. Among a number of interesting results, our experiments reveal that transaction throughput is greatly influenced by node mobility patterns, a higher percentage of miners is detrimental for blockchain security and wireless network density and power transmission values beyond particular thesholds are required for block transmission behavior similar to the one observed in wired-static blockchain networks preventing randomness due to packet losses.

In this paper, we present a distributed machine learning based intrusion detection system in Internet of Things (IoT) utilizing Blockchain technology. In particular, spectral partitioning is proposed to divide the IoT network into autonomous systems (AS) enabling traffic monitoring for intrusion detection (ID) to be performed by the selected AS border area nodes in a distributed manner. The ID system is based on machine learning, where a support-vector machine algorithm is trained using prominent IoT data sets and detection of the attackers is provided. Furthermore, the integrity of the attackers' list is offered by utilizing Blockchain technology, which enables a distributed sharing of the attackers' information among the AS border area nodes of the Blockchain network. Simulations are performed to evaluate different aspects of the proposed IoT system and demonstrate the potential of integrating machine learning based ID to a distributed spectral partitioned Blockchain network.

Video share of the Internet traffic is increasing day by day. This includes streaming on the go to/from mobile devices. These trends necessitate dynamic and robust resource allocation at Internet Exchange Point level to provide good quality of services to mobile video users. Any effective solution to this problem requires accurate predictions of the video traffic coming from or delivered to mobile devices. In this paper, we propose a framework to correctly identify and accurately predict the live streaming and video traffic. Deep packet inspection is used to identify the 23 most common live streaming and video traffic protocols. Subsequently Long Short-Term Memory neural network is used to predict the live streaming and video traffic over a prediction horizon of 6 hours with an average accuracy of 97.24% thus outperforming previous frameworks in both the accuracy and the prediction horizon. This technique can be used as a baseline towards a more effective application of traffic engineering techniques.

Cyber-physical Systems (CPS) comprise of a network of physically distributed embedded sensors and actuators equipped with computational and communication capability. In CPS, Internet of Things (IoT) devices communicate in a trustless environment as the data can be compromised due to the centralized database, limited power and computational constraints. At the same time, reliability and resiliency are key concerns in CPS in the face of unforeseen circumstances, often emanating from disaster based failures. In this paper, critical issues of centralized database security in CPS are addressed via a distributed blockchain based solution. The proposed system encompasses a smart contract based framework in Ethereum blockchain. It further explores the potential of blockchain in securing and offering a distributed network for the CPS in a closed loop manner. To demonstrate the realizability of the proposed framework, a testbed implementation for the proposed idea is provided. A desktop computer, a laptop, a simple temperature sensor and a Light Emitting Diode (LED) are interfaced in a Peer-to-Peer (P2P) network using Ethereum. The speedy transaction of sensor data in the blockchain at various difficulty levels and actuation through smart contracts enhance the usability of the platform for CPS and various IoT applications.

The Tactile Internet is envisioned to be characterized by ultra low latency, ultra reliability with high availability, and security. The next fifth generation is expected to underpin this emerging technology at the wireless edge. The stringent low latency and high reliability requirements of the Tactile Internet render the design more challenging in the presence of energy constraints on Internet of Things device in the underlying device-to-device network. In this paper, to deal with the stringent delay requirements of the tactile communication in the presence of energy constraints on devices and nodes, we pose the combined energy management and rate control problem as a queue control problem, where the objective is to control the energy and data queues to predefined reference levels. The intelligently controlled energy queues make the communication reliable and guarantee that nodes of the network are always alive and have some amount of energy close to predefined reference values for emergency and critical operations. In addition, tight control of the data queue ensures low queuing delays. We have considered both linear and nonlinear model of queues and have designed controllers based on model predictive control and nonlinear control theory. Convergence properties of the controllers are established analytically and the effectiveness of the proposed methods is also demonstrated through simulations.

Probabilistic flooding is commonly considered in vehicular ad-hoc networks (VANETs) to alleviate the celebrated broadcast storm problem. Heuristics and simulations have so far proved to be an efficient way with which to design probabilistic information dissemination solutions with demonstrated effectiveness. Supplementing with mathematical analysis for both design and evaluation is expected to generalize the results, and increase the confidence in the proposed solutions. So far, this has been limited due to the difficulties associated with the underlying stochastic dependencies. In this paper, we introduce simple models of single and multiple lane roads to design effective probabilistic flooding schemes for VANETs. The proposed analytical framework offers a novel and generic tool which can cover any number of lanes, vehicle transmission range, and density. We derive difference equations whose solutions yield the probability of all vehicles receiving the emergency warning message as a function of the rebroadcast probability, the number of neighbors of each vehicle, and the dissemination distance. The models are validated using simulations, realistically representing both the traffic and networking aspects, and are used as a baseline to design two schemes for information dissemination which employ probabilistic flooding. The one attempts direct calculation of the required parameters whereas the other adaptively regulates the rebroadcast probability based on the vehicle speed. The schemes are evaluated using simulations and are found to achieve significant performance improvements compared to blind flooding with respect to the achieved reachability, end-to-end delay, and number of rebroadcasts, comparable to the performance achieved by optimal flooding, obtained via brute force.

Due to scarce energy resources and large power consumptions, Wireless Power Transfer (WPT) technology is gaining increasing interest in 5G networks and the Internet of Things (IoT). However, when data communication is conducted concurrently with WPT, large interference signals may be induced which degrade performance significantly. In LoRaWAN networks, a popular IoT technology, the problem is further exacerbated by the potential of the interference signals to jam the join procedure by flattening the Received Signal Strength (RSS), thus compromising system security. In this work, we develop a mathematical framework which allows the characterization of the join procedure and communication in LoRaWAN networks as a result of the interference effects induced by WPT devices. The conducted analysis and the relevant numerical results are used to characterize the interference effects and join procedure failure ratio with respect to the power, distance and number of WPT devices. The flattening effects of RSS are shown via simulations and its implications on the network security are also demonstrated. The presented work aims at providing design guidelines for the system, so that communication performance degradation as well as network security deterioration as a result of the WPT effects is kept within acceptable levels.

Message congestion control in vehicular ad hoc networks is a major issue and several algorithms have been proposed in literature. The linear adaptive approach is represented by the linear message rate integrated control (LIMERIC) algorithm, which integrates the error in order for the message generation rate to converge to a desired value. The stability of this algorithm has been established in the case of repetitive transmission of the basic safety messages in a single-hop communication setting. However, the communication might be realized in a multi-hop fashion, so, in this work we adopt simple models of single and multiple lane roads to investigate the stability of the LIMERIC algorithm in a multi-hop communication setting. Our results indicate that the stability of the system depends on the vehicle density, which dictates appropriate choice of the algorithm parameters for each vehicle density to avoid instability. To this end, we propose the use of certainty equivalent controllers, which adapt the parameters based on vehicle density estimates. Inspired by recent works on adaptive probabilistic flooding, we propose that these density estimates are obtained using speed measurements via a nonlinear relationship of the traffic flow equations whose parameters can be tuned using on-line parameter identification techniques.

MAC layer adaptation is very crucial for supporting dense and diverse data requirements of sensor networks in smart cities, powered by energy harvesting. In this paper, we perform MAC layer optimization for maximizing throughput subject to application-specific needs and energy availability in Solar Energy Harvesting Wireless Sensor Networks (EH-WSNs). In contrast to previous schemes that limit energy consumption based on current availability only, we propose Energy Prediction based Energy Management algorithm (EPEM). This algorithm exploits energy prediction and sets threshold rate of energy consumption to ensure accumulation of sufficient energy for non- energy harvesting period. Our analysis shows that MAC optimization (MO) along with EPEM algorithm not only improves performance by 72% but also avoids energy scarcity during non-energy harvesting period.

Molecular communications with MIMO transmissions based on Brownian diffusion have been previously explored to improve the network performance and achieve higher data rates. However, diffusion based molecular communications suffer from the slow transmission rates for longer separations of transmitters and receivers. Molecular motors are another communication mechanism in which chemical energy is converted into mechanical transport of molecules over microtubules walkways. Molecular motors provide low delay, bidirectional and controllable communication and can carry significant number of molecules compared to diffusion or flow based communications especially in larger networks. In this paper, we propose a novel communication paradigm which involves the use of the MIMO technique over molecular motors communication channels for nanonetworks. The system performance and throughput have been investigated under various MIMO configurations and the improvement in performance compared to the corresponding SISO system is demonstrated.

We review harvested energy prediction schemes to be used in wireless sensor networks and explore the relative merits of landmark solutions. We propose enhancements to the well-known Profile-Energy (Pro-Energy) model, the so-called Improved Profile-Energy (IPro-Energy), and compare its performance with Accurate Solar Irradiance Prediction Model (ASIM), Pro-Energy, and Weather Conditioned Moving Average (WCMA). The performance metrics considered are the prediction accuracy and the execution time which measure the implementation complexity. In addition, the effectiveness of the considered models, when integrated in an energy management scheme, is also investigated in terms of the achieved throughput and the energy consumption. Both solar irradiance and wind power datasets are used for the evaluation study. Our results indicate that the proposed IPro-Energy scheme outperforms the other candidate models in terms of the prediction accuracy achieved by up to 78% for short term predictions and 50% for medium term prediction horizons. For long term predictions, its prediction accuracy is comparable to the Pro-Energy model but outperforms the other models by up to 64%. In addition, the IPro scheme is able to achieve the highest throughput when integrated in the developed energy management scheme. Finally, the ASIM scheme reports the smallest implementation complexity.

A number of probabilistic flooding schemes have been recently considered in VANETs to address problems of information dissemination in safety applications. The design approach has so far been simulative, a method which does not guarantee that the derived protocols will work when the simulation settings are violated in practice. In this paper, motivated by the need to design information dissemination protocols with verifiable properties prior to implementation, we employ mathematical models of single and multiple lane roads to investigate probabilistic flooding in VANETs using mathematical analysis. We demonstrate that the system can be described by linear difference equations the solutions of which yield the probability of all vehicles receiving the critical message as a function of the rebroadcast probability, the number of neighbors of each vehicle and the number of vehicles. We utilize the obtained solutions to derive the desired rebroadcast probabilities as a function of the transmission range, the vehicle density and the dissemination distance. The obtained results are in line with results obtained using extensive simulations and can be used as a baseline to develop information dissemination protocols with verifiable properties.

Finding critical nodes in a network is a significant task, highly relevant to network vulnerability and security. We consider the node criticality problem as an algebraic connectivity minimization problem where the objective is to choose nodes which minimize the algebraic connectivity of the resulting network. Previous suboptimal solutions of the problem suffer from the computational complexity associated with the implementation of a maximization consensus algorithm. In this work, we use spectral partitioning concepts introduced by Fiedler, to propose a new suboptimal solution which significantly reduces the implementation complexity. Our approach, combined with recently proposed distributed Fiedler vector calculation algorithms enable each node to decide by itself whether it is a critical node. If a single node is required then the maximization algorithm is applied on a restricted set of nodes within the network. We derive a lower bound for the achievable algebraic connectivity when nodes are removed from the network and we show through simulations that our approach leads to algebraic connectivity values close to this lower bound. Similar behaviour is exhibited by other approaches at the expense, however, of a higher implementation complexity.

Critical node discovery plays a vital role in assessing the vulnerability of a network to an abrupt change, such as an adversarial attack or human intervention. In this paper, we propose a new metric to characterize the criticality of a node in an arbitrary network which we refer to as the Combined Banzhaf & Diversity Index (CBDI). The metric utilizes a diversity index which is based on the variability of a node's attributes relative to its neighbors and the Banzhaf Power Index which characterizes the degree of participation of a node in forming shortest paths. The Banzhaf power index is inspired from the theory of voting games in game theory. We evaluate the performance of the new metric using simulations. Our results indicate that in a number of network topologies, the proposed metric outperforms other proposals which have appeared in the literature. The proposed CBDI index chooses more critical nodes which, when removed, degrade network performance to a greater extent than if critical nodes based on other criticality metrics were removed.

A significant issue in vehicular ad hoc networks is the design of an effective broadcast scheme which can facilitate the fast and reliable dissemination of emergency warning messages in the vicinity of an expected event, such as a car accident. In this work we propose a novel solution to this problem, which we refer to as Speed Adaptive Probabilistic Flooding. The scheme employs probabilistic flooding to mitigate the effects of the broadcast storm problem, typical when using blind flooding, and its unique feature is that the rebroadcast probability is regulated adaptively based on the vehicle speed to account for varying traffic densities within the transportation network. The protocol enjoys a number of benefits relative to other approaches: it is simple to implement, it does not introduce additional communication burden, as it relies on local information only and it does not rely on the existence of a positioning system which may not always be available. The scheme is evaluated on different sections of the highway system in the City of Los Angeles using an integrated platform combining the OPNET Modeler and the VISSIM simulator. Simulation results indicate that the proposed scheme fulfills its design objectives as it achieves high reachability and low latency of message delivery in a number of scenarios. Its robustness with respect to changing number of hops and transmission ranges is also demonstrated.

Many congestion control protocols have been recently proposed in order to alleviate the problems encountered by TCP in high-speed networks and wireless links. Protocols utilizing an architecture that is in the same spirit as the ABR service in ATM networks require estimates of the effective number of users utilizing each link in the network to maintain stability in the presence of delays. In this paper, we propose a novel estimation algorithm that is based on online parameter identification techniques and is shown through analysis and simulations to converge to the effective number of users utilizing each link. The algorithm does not require maintenance of per-flow states within the network or additional fields in the packet header, and it is shown to outperform previous proposals that were based on pointwise division in time. The estimation scheme is designed independently from the control functions of the protocols and is thus universal in the sense that it operates effectively in a number of congestion control protocols. It can thus be successfully used in the design of new congestion control protocols. In this paper, to illustrate its universality, we use the proposed estimation scheme to design a representative set of Internet congestion control protocols. Using simulations, we demonstrate that these protocols satisfy key design requirements. They guide the network to a stable equilibrium that is characterized by high network utilization, small queue sizes, and max-min fairness. In addition, they are scalable with respect to changing bandwidths, delays, and number of users, and they generate smooth responses that converge quickly to the desired equilibrium.

The growing demand of computer usage requires efficient ways of managing network traffic in order to avoid or at least limit the level of congestion in cases where increases in bandwidth are not desirable or possible. In this paper we developed and analyzed a generic Integrated Dynamic Congestion Control (IDCC) scheme for controlling traffic using information on the status of each queue in the network. The IDCC scheme is designed using nonlinear control theory based on a nonlinear model of the network that is generated using fluid flow considerations. The methodology used is general and independent of technology, as for example TCP/IP or ATM. We assume a differentiated-services network framework and formulate our control strategy in the same spirit as IP DiffServ for three types of services: Premium Service, Ordinary Service, and Best Effort Service. The three differentiated classes of traffic operate at each output port of a router/switch. An IDCC scheme is designed for each output port, and a simple to implement nonlinear controller, with proven performance, is designed and analyzed. Using analysis performance bounds are derived for provable controlled network behavior, as dictated by reference values of the desired or acceptable length of the associated queues. By tightly controlling each output port, the overall network performance is also expected to be tightly controlled. The IDCC methodology has been applied to an ATM network. We use OPNET simulations to demonstrate that the proposed control methodology achieves the desired behavior of the network, and possesses important attributes, as e.g., stable and robust behavior, high utilization with bounded delay and loss, together with good steady-state and transient behavior.