The advent of next-generation Internet of Things (IoT) networks necessitates communication capabilities characterized by ultra-reliability and low latency (URLLC) and energy efficiency requirements that traditional wireless designs frequently struggle to satisfy under practical constraints. In this context, realizing symbiotic IoT networks using mutualism backscattering technologies with short-packet transmissions has emerged as a viable solution to achieve the goals of net zero for sustainable IoT network deployment while satisfying URLLC conditions. Following that, this paper first develops theoretical frameworks for evaluating the performance of short packet transmissions in uplink symbiotic systems by deriving formulas for the block error rate (BLER) and total symbiotic ergodic rate under two diversity techniques, namely selection combining with maximal-ratio combining (SC-MRC) and full maximal-ratio combining (Full-MRC). Then, we scale up this considered system for large-scale IoT networks, where we propose an energy-aware resource allocation method using successive convex approximation (SCA) techniques for multi-user scenarios while adhering to URLLC conditions. Finally, simulation results are provided to validate the developed theoretical analyses as well as demonstrate how our proposed optimization solution achieves substantial improvements in energy efficiency performance. These findings lay a robust groundwork for developing scalable and dependable communication systems in future energy-efficient IoT eras.

The evolution from fifth-generation (5G) to sixth-generation (6G) networks is driving an unprecedented demand for advanced machine learning (ML) solutions. Deep learning has already demonstrated significant impact across mobile networking and communication systems, enabling intelligent services such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. At the same time, the rapid proliferation of resource-constrained Internet-of-Things (IoT) devices has accelerated the adoption of tiny machine learning (TinyML) for efficient on-device intelligence, while large machine learning (LargeML) models continue to require substantial computational resources to support large-scale IoT services and ML-generated content. These trends highlight the need for a unified framework that integrates TinyML and LargeML to achieve seamless connectivity, scalable intelligence, and efficient resource management in future 6G systems. This survey provides a comprehensive review of recent advances enabling the integration of TinyML and LargeML in next-generation wireless networks. In particular, we(i)provide an overview of TinyML and LargeML,(ii)analyze the motivations and requirements for unifying these paradigms within the 6G context,(iii)examine efficient bidirectional integration approaches,(iv)review state-of-the-art solutions and their applicability to emerging 6G services, and(v)identify key challenges related to performance optimization, deployment feasibility, resource orchestration, and security. Finally, we outline promising research directions to guide the holistic integration of TinyML and LargeML for intelligent, scalable, and energy-efficient 6G networks and beyond.

In this paper, we investigate the performance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled short-packet communication (SPC) systems that employ covert rate-splitting (RS) to facilitate applications of Internet-of-Things (IoT). Under the generalized model of the α-η-κ-μ fading, we analyze covertness by first deriving a closed-form expression for the warden’s detection error probability (DEP) and then deducing the optimal detection threshold at which the DEP is minimized. From this optimal DEP, we guide the choice of the feasible power-allocation (PA) region at which the warden’s produced DEP is always beyond the minimal acceptable covertness. On the other hand, we develop mathematical frameworks for evaluating the system’s block-error rate (BLER) and the ergodic rate (ER), as well as providing guidelines on how to access their performance limits at high signal-to-noise ratio, especially the diversity orders and ergodic slopes of the users. Furthermore, we also propose to enhance the system performance by jointly optimizing the PA and RS coefficients in order to: (1) minimize the maximum BLER across private users subject to a minimum covertness requirement and (2) maximize the minimum ER across users subject to covertness and decoding constraints. Numerical results confirm the analytical expressions and show that the proposed optimization efficiently tunes the PA and RS coefficients to achieve the BLER and ER fairness objectives.

The spectral efficiency of sixth-generation (6G) wireless networks is anticipated to experience large improvements through the implementation of non-orthogonal multiple access (NOMA) technology. The integration of short-packet communications (SPC) into NOMA networks enables low-latency operation and high spectral efficiency. The present study investigates the performance of multiple users in a NOMA downlink SPC system operating over an$\alpha - \kappa - \mu$shadowed fading channel. Precise and asymptotic closed-form approximations for the average block error rate (BLER), reliability, throughput, goodput, and overall BLER were derived using approximate Gaussian-Chebyshev quadrature. The analytical results were validated through numerical simulations, providing insights into the impact of fading parameters on system performance. The study's findings indicate that the proposed downlink NOMA SPC system is highly suitable for ultra-reliable and low-latency communications (URLLC), achieving reliability levels of 99.99% for multiple users. The study also determined the optimal transmission bit rate required to maximize throughput and goodput while minimizing the BLER. The proposed downlink NOMA SPC system operating with an$\alpha - \kappa - \mu$shadowed fading channel demonstrates high potential for improving Internet of Things (IoT) network performance over conventional downlink orthogonal multiple access (OMA) approaches. Most of the analysis adopts perfect successive interference cancellation (pSIC) and perfect channel state information (pCSI) as theoretical benchmarks, while additional results explicitly quantify the performance degradation caused by residual interference and channel estimation errors (CEE). The results reveal that imperfect SIC (ipSIC) dominates the BLER floor at high signal-to-noise ratio (SNR), whereas imperfect CSI (ipCSI) primarily affects the moderate-SNR regime, highlighting distinct impairment-driven performance bottlenecks. Therefore, we have extended the study by including two additional scenarios: pCSI combined with ipSIC, and ipCSI combined with ipSIC. This extension allows us to compare the performance of these systems and clearly shows that the system with pSIC and pCSI achieves the best performance. Finally, the results were validated with Monte Carlo simulations.

This paper presents the application of the improved Self-Organizing Migrating Algorithm (iSOMA) to the synthesis and optimization of quantum circuits. We develop a comprehensive method to evolve candidate quantum circuits with minimal cost by integrating iSOMA with circuit evaluators in Qiskit. Experimental evaluation across 100 independent runs demonstrates a 90% success rate in synthesizing Toffoli gates, with an 80% circuit uniqueness rate indicating diverse solution exploration. The algorithm achieves a median cost of 0.000 and determinism = 8/8 for successful runs, confirming its effectiveness for quantum circuit synthesis. This research is important for future 6G networks and beyond, as quantum computing has the potential to be more efficient, especially in the physical layer of the Radio Access Network (RAN), where quantum-supported optimization mechanisms are able to process a large number of tasks faster. The proposed synthesis of Toffoli gates controlled by iSOMA technology contributes to quantum computing by supporting the efficient design of quantum circuits, which is a prerequisite for the deployment of quantum-native functions, such as the Quantum Fourier Transform, in future communications and wireless infrastructures.

This paper presents a vendor-agnostic architecture for secure pre-shared key (PSK) exchange between Quantum Key Distribution (QKD) nodes, leveraging post-quantum cryptography (PQC) tools. The proposed system combines PQC-OpenVPN and OQS-OpenSSH with USB mass storage emulation and single-board computers (SBCs) to automate the transfer of initial authentication secrets. This design significantly reduces manual intervention and mitigates risks associated with physical key handling. The solution was experimentally validated on IDQ Clavis3 and Cerberis3 devices and is broadly applicable to other QKD platforms that support only USB-based key input. Integration of lattice-based algorithms such as Kyber, Dilithium, and ML-DSA enables encapsulation and authentication of quantum-safe keys. Furthermore, a layered design using VPN and SSH channels provides robust cryptographic isolation for authentication material in transit. The work contributes a reproducible and cost-effective testbed for post-quantum hardened QKD deployments and demonstrates the practical feasibility of combining PQC mechanisms with QKD systems to enhance trust in future quantum-safe infrastructures.

In this article, we describe a novel active reconfigurable repeater-aided nonorthogonal multiple access networks within the context of the Internet of Things. The study focuses on a scenario, where a source simultaneously transmits public information to an untrusted user and a covert signal to a legitimate user in the surveillance of an external warden or eavesdropper. We develop comprehensive analytical and optimization frameworks to evaluate the reliability, security, and covertness of the proposed system’s performance, measuring three respective key metrics: 1) outage probability (OP); 2) secrecy OP (SOP); and 3) detection error probability (DEP). First, we derive exact closed-form and asymptotic expressions for OP, SOP in internal and external eavesdropping scenarios, and DEP in external monitoring situations. Based on an asymptotic analysis of the OP, we propose two optimization methods for power allocation (PA) to achieve fairness in outage among users: 1) a convex approximation method and 2) an approximate closed-form solution. We then introduce an alternative method for optimizing PA to improve SOP in both eavesdropping scenarios while maintaining minimal OP requirements. In addition, we propose an effective approach for determining the warden’s detection threshold to minimize the DEP, with low complexity and fast convergence, thereby improving communications covertness. Finally, we validate the theoretical and optimization frameworks through extensive Monte Carlo simulations, exploring the impact of key system parameters on each performance metric.

This work investigates the covertness and security performance of Internet-of-Things (IoTs) networks under Rayleigh fading environments. Specifically, a cellular source transmits covert information to cell-edge users with the assistance of an IoT master node, employing a coordinated direct and relay transmission strategy combined with non-orthogonal multiple access (NOMA). This approach not only enhances spectrum utilization but also generates friendly interference to complicate a warden’s surveillance or an eavesdropper’s decoding efforts. From a covertness perspective, we derive exact closed-form expressions for the detection error probability (DEP) under arbitrary judgment thresholds. We then identify the optimal judgment threshold for the worst-case scenario, at which the warden minimizes its DEP performance. Accordingly, we determine the effective region for user power allocation (PA) in NOMA transmission that satisfies the DEP constraint. From a security perspective, we derive analytical expressions for the secrecy outage probability under two eavesdropping strategies using selection combining and maximal ratio combining. Based on this analysis, we propose an adaptive PA scheme that maximizes covert rate while ensuring the quality-of-service (QoS) requirements of legitimate users, the system’s minimum covertness requirements, and supporting successive interference cancellation (SIC) procedures. Furthermore, we design an adaptive PA scheme that maximizes the secrecy rate while ensuring the QoS requirements of legitimate users and SIC conditions. Numerical results demonstrate the accuracy of the analytical framework, while the proposed optimization strategies effectively adjust PA coefficients to maximize either the covert rate or the secrecy rate.

Mobility prediction has been a popular research topic for many decades. With the advent of new generation technologies (5G and beyond) and smaller coverage cells, hand-over operations have become more frequent. Cellular system companies are therefore taking increasing interest in using the available predictive information on node movements to optimize and manage their bandwidth resources. In particular, the main challenging scope of our contribution consists in solving the issue of reliable next-cell prediction, aimed to call dropping probability minimization. In addition, our proposal is based on the innovative concept of mobility data to image encoding. The scheme is able to a-priori determine the next visited cells during host movements by applying a convolutional neural approach to mobility images. The power of machine learning is used to advantage, and highly accurate image classification is achieved for mobility prediction. We performed numerous simulation campaigns related to next-cell prediction in mobile cellular environments, obtaining very satisfactory results by the application of convolutional neural networks, which have an impressive history of effectiveness with image classification problems. The trained network has been associated to each coverage cell and the prediction accuracy has been evaluated.

With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real-time and based on the Non-Intrusive Load Monitoring (NILM) paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own dataset, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study’s simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances.

This article investigates the combination of active reconfigurable intelligent surfaces (aRISs) with cognitive radio networks (CRns) enabled by nonorthogonal multiple access (NOMA) to enhance the capability of aRIS-based Internet of Things (IoT) systems. The proposed system model enhances overall performance and energy allocation by combining aRIS and NOMA. In this proposed system paradigm, the secondary source (SS) controls the information transmission to two secondary users (SUs) via the aRIS element. Transmission power limitations are put in place to lessen the impression that base stations are interfering with the main purpose. This article evaluates critical system performance indicators, such as outage probability (OP), achievable ergodic rate (AER), throughput, and energy efficiency (EE). Specifically, the impact of the distance between the aRIS unit and the base station on AER is explored. The examination of the correlation among SS-aRIS-Users in the presence of the Nakagami-m fading scenario is further investigated. Such discoveries offer significant perspectives for the enhancement and configuration of aRIS-NOMA frameworks in CRn, contributing to the advancement of adaptable communication infrastructures. In contrast to the traditional method that uses orthogonal multiple access (OMA), the aRIS-NOMA system proposed in CRn appears to be a promising way to improve the performance of IoT networks based on aRIS. Simulations have demonstrated significant improvements in spectral efficiency, with gains as high as 10%–20% observed. This suggests that performance has significantly improved, particularly for OP and AER. Finally, the Monte Carlo simulation confirms and strengthens these findings.

In this study, we investigate the trade-off between reliability and security in unmanned aerial vehicle (UAV) communications systems, considering a UAV-terrestrial network aided by a relay powered by a dedicated power beacon. For this system, we derive the outage probability (OP) under both exact and approximate frameworks and compute the approximations in the closed-form expressions. For the security aspect, we also derive the intercept probability (IP) under exact and approximated frameworks. To minimize the IP, a friendly jamming technique is employed whereby the power beacon constantly broadcasts artificial noise (AN) toward an eavesdropper. Based on the derived mathematical framework, we then formulate a bi-objective optimization problem by jointly minimizing the OP and IP with respect to the UAV’s position and the time-switching (TS) ratio. A suitable algorithm, named non-dominated sorting genetic algorithm version II (NSGA-II), is deployed to obtain the sub-optimal solution. Finally, numerical results are presented to verify the accuracy of the proposed mathematical framework and the superiority of jointly minimizing both the OP and IP using only the channel statistics.

In the last decade, the investigation of mobility features has gained enormous significance in many scenarios as a result of the significant diffusion and deployment of mobile devices covered by high-speed technologies (e.g., 5G). Many contributions in the literature have attempted to discover mobility properties, but most studies are based on the time features of the mobility process. No study has yet considered the effects of setting a proper sampling frequency (generally set to 1 s), in order to avoid information loss. Following our previous works, we propose a novel predictive spectral approach for mobility sampling based on the concept of a predictive wavelet. With this method, the choice of sampling frequency is governed by the current spectral components of the mobility process and derived from an analysis of future, predicted components. To assess whether our proposal may yield a helpful method, we conducted several simulation campaigns to test sampling accuracy and obtained results that confirmed our expectations.

This study investigates a two-way relaying non-orthogonal multiple access (TWR-NOMA) enabled Internet-of-Things (IoT) network, in which two NOMA users communicate via an IoT access point (IAP) relay using a decode-and-forward (DF) protocol. A power beacon (PB) is used to power the IAP to address the IAP's limited lifetime due to energy constraints. Since co-channel interference (CCI) is inevitable in IoT systems, this effect is also studied in the proposed system to improve practicality. Based on the proposed system model, the closed-form equations for the exact and asymptotic outage probability (OP) and ergodic data (ED) of the NOMA users' signals are first derived to describe the performance of TWR-NOMA systems. The system's diversity order and throughput are then evaluated according to the derived results. To further improve the system's performance, a low-complexity strategy 2D golden section search (GSS) is performed, subject to power allocation (PA) and time-switching (TS) factors, to optimize the outage performance. Finally, a deep learning design with minimal computing complexity and precision OP prediction is established for a real-time IoT network configuration. The numerical results are discussed and analyzed in terms of the effects of the CCI, the TS ratio, the PA factor, the fading parameter on the OP, system throughput, and ED.

Free Space Optics (FSO) represent a promising technology for secure communications in several types of architectures: from Quantum Key Distribution Networks (QKDNs) to satellite communications. In this paper, in particular, we take into account terrestrial point-to-point laser communications and evaluate the performance in terms of Signal-to-Noise Ratio (SNR) and Bit Error Rate (BER), taking into account different scenarios, that can reflect real situations in which long distances can be reached in a secure way, guaranteeing an acceptable level of BER. So, after a huge campaign of simulations, we would like to let the scientific community know which are the theoretical limits that such kind of communications can reach. We take into account standard telescopes parameters (available today in the market), while configuring several real situations, in function of, for example, bit-rate, visibility, link distance, etc. A brief survey of the existing works is given, then a clearer performance evaluation of terrestrial FSO links is proposed.

Nowadays, mobile computing has become a key component of telecommunication systems, and the Open Systems Interconnection (OSI) layer operations are affected by the effects of node movements along the roads, from the physical to the routing/transport layers. In particular, routing approaches have been investigated from many years, trying to optimize the performance of the whole considered system, under different points of view. In this paper we are focusing the attention on the analysis of the mobility grade trend for a mobile ad-hoc network environment, as well as on the way it can be a-priori known, in order to have the possibility to study how the dynamics of mobile nodes can be described and in-advance known, with a predicted knowledge of nodes stability (in terms of mobility). Our simulations considered mobility in real geographical maps, and the obtained results confirmed the goodness of our proposed study.

With the rapid growth of vehicles, the vehicular networks meet main challenges such as dynamic, heterogeneous, and large scaled. In addition, the cellular-based vehicular networks must satisfy further strict requirements, including ultralow latency, high reliability, high spectrum efficiency, and massive connections of the next-generation (6G) network. Recently, by exploiting vehicle clustering utilized for reducing the complexity of vehicle-to-everything (V2X) systems, it could ultimately improve road traffic efficiency. In some specific scenarios related to Internet of Things, a group of vehicles can be served effectively in terms of spectrum efficiency when two key techniques are enabled, i.e., nonorthogonal multiple access and cognitive radio (CR) schemes are joint deployed. These techniques certainly benefit to 6G V2X services to reduce specific challenges, such as traffic congestion and massive connections. Different from existing works, we propose wireless power transfer applied to the roadside unit to improve the situation that energy shortening in small devices deployed in V2X communications. In particular, we derive expressions of throughput to exhibit performance of the two grouped vehicles. To further indicate advantages of spectrum efficiency, we compare two schemes of V2X systems with and without CR schemes. The numerical results demonstrate that the non-CR NOMA-V2X scheme outperforms the CR-based NOMA-V2X scheme with fixed power allocation, but the non-CR NOMA-V2X scheme costs higher spectrum resource compared with the counterpart. Besides, by comparing with orthogonal multiple access (OMA)-assisted V2X, NOMA-V2X schemes demonstrate superiority in terms of throughput performance whilst achieving the benefits of both NOMA and CR schemes.

Mobility is a key aspect of modern networking systems. To determine how to better manage the available resources, many architectures aim toa prioriknow the future positions of mobile nodes. This can be determined, for example, from mobile sensors in a smart city environment or wearable devices carried by pedestrians. If we consider infrastructure networks, frequently changing the coverage cell may lead to service disruptions if a predictive approach is not deployed in the system. All predictive systems are based on the storage of old mobility samples to adequately train the model. Our focus is based on the possibility to determine an approach for adaptively sampling mobility patterns based on the intrinsic features of the human/node behavior. Several works in the literature examine mobility prediction mobile networks, but all of them are dedicated to the study of time features in mobility traces: none took into account the spectral content of historical mobility patterns for predictive purposes. In contrast, we take into account this spectral content in mobility samples. Through a set of wavelet transforms, we adapted the sampling frequency dynamically and obtained a considerable set of advantages (space, energy, accuracy, etc.). In fact, this issue covers an important role in the IoT paradigm, where energy consumption is one of the main variables requiring optimization (frequent and unnecessary mobility samplings can disrupt battery life). We performed several simulations using real-world traces to confirm the merit of our proposal.

Radio-frequency (RF) energy harvesting (EH) in wireless relaying networks has attracted considerable recent interest, especially for supplying energy to relay nodes in the Internet of Things (IoT) systems to assist the information exchange between a source and a destination. Moreover, limited hardware, computational resources, and energy availability of IoT devices have raised various security challenges. To this end, physical-layer security (PLS) has been proposed as an effective alternative to cryptographic methods for providing information security. In this study, we propose a PLS approach for simultaneous wireless information and power transfer (SWIPT)-based half-duplex (HD) amplify-and-forward (AF) relaying systems in the presence of an eavesdropper. Furthermore, we take into account both static power splitting relaying (SPSR) and dynamic power splitting relaying (DPSR) to thoroughly investigate the benefits of each one. To further enhance secure communication, we consider multiple friendly jammers to help prevent wiretapping attacks from the eavesdropper. More specifically, we provide a reliability and security analysis by deriving closed-form expressions of outage probability (OP) and intercept probability (IP), respectively, for both the SPSR and DPSR schemes. Then, simulations are also performed to validate our analysis and the effectiveness of the proposed schemes. Specifically, numerical results illustrate the nontrivial tradeoff between reliability and security of the proposed system. In addition, we conclude from the simulation results that the proposed DPSR scheme outperforms the SPSR-based scheme in terms of OP and IP under the influences of different parameters on system performance.

Simultaneous wireless information and power transfer (SWIPT) and full-duplex (FD) communications have emerged as prominent technologies in overcoming the limited energy resources in Internet of Things (IoT) networks and improving their spectral efficiency (SE). This article investigates the outage and throughput performance for a decode-and-forward (DF) relay SWIPT system, which consists of one source, multiple relays, and one destination. The relay nodes in this system can harvest energy from the source’s signal and operate in the FD mode. A suboptimal, low-complexity, yet efficient relay selection scheme is also proposed. Specifically, a single relay is selected to convey information from a source to a destination so that it achieves the best channel from the source to the relays. An analysis of outage probability (OP) and throughput performed on two relaying strategies, termed static power splitting-based relaying (SPSR) and optimal dynamic power splitting-based relaying (ODPSR), is presented. Notably, we considered independent and nonidentically distributed (i.n.i.d.) Rayleigh fading channels, which pose new challenges in obtaining analytical expressions. In this context, we derived exact closed-form expressions of the OP and throughput of both SPSR and ODPSR schemes. We also obtained the optimal power splitting ratio of ODPSR for maximizing the achievable capacity at the destination. Finally, we present extensive numerical and simulation results to confirm our analytical findings. Both simulation and analytical results show the superiority of ODPSR over SPSR.

This article describes the methodology and the possibilities of collecting operation data in a mobile network provider. First, the architecture and the principles used in the system are described. The precision analysis of the population commuting in the region and during the pandemic and nonpandemic times. Moreover, several ideas about further utilization of the data will be formulated and described. Finally, a graph-based approach that describes the creation of the community structure between the people and the means of its analysis.

This paper proposes a novel method for video quality evaluation based on machine learning technique. The current research deals with the correct interpretation of objective video quality evaluation (Quality of Service – QoS) in relation to subjective end-user perception (Quality of Experience – QoE), typically expressed by mean opinion score (MOS). Our method allows us to interconnect results obtained from video objective and subjective assessment methods in the form of a neural network (computing model inspired by biological neural networks). So far, no unified interpretation scale has been standardized for both approaches, therefore it is difficult to determine the level of end-user satisfaction obtained from the objective assessment. Thus, contribution of the proposed method lies in description of the way to create a hybrid metric that delivers fast and reliable subjective score of perceived video quality for internet television (IPTV) broadcasting companies.

In the modern telecommunication systems, mobility is one of the key advantage of wireless communications, given that it is possible to transmit/receive data, without caring of having a static position into the network. Of course, mobility poses special issues such as degradations, channel quality fluctuations, fast topology changes, and so on. Modern researches focus their attention on predicting mobile future node positions, in order to a-priori know, for example, what the evolution of the network topology will be or which level of stability each node will reach. Each prediction scheme is based on the storage and analysis of several historical mobility trajectories, in order to train the proper prediction algorithm. In this paper, we focus our attention on the optimization of the space needed to store historical mobility samples, encoding their values and evaluating the conversion error, comparing different encoding functions. Several simulation campaigns have been carried out in order to evaluate the goodness and feasibility of our proposal.

With the proliferation of connected vehicles, new coverage technologies and colossal bandwidth availability, the quality of service and experience in mobile computing play an important role for user satisfaction (in terms of comfort, security and overall performance). Unfortunately, in mobile environments, signal degradations very often affect the perceived service quality, and predictive approaches become necessary or helpful, to handle, for example, future node locations, future network topology or future system performance. In this paper, our attention is focused on an in-depth stochastic micro-mobility analysis in terms of nodes coordinates. Many existing works focused on different approaches for realizing accurate mobility predictions. Still, none of them analyzed the way mobility should be collected and/or observed, how the granularity of mobility samples collection should be set and/or how to interpret the collected samples to derive some stochastic properties based on the mobility type (pedestrian, vehicular, etc.). The main work has been carried out by observing the characteristics of vehicular mobility, from real traces. At the same time, other environments have also been considered to compare the changes in the collected statistics. Several analyses and simulation campaigns have been carried out and proposed, verifying the effectiveness of the introduced concepts.

In recent years, noticeable progress has been made in the development of quantum equipment, reflected through the number of successful demonstrations of Quantum Key Distribution (QKD) technology. Although they showcase the great achievements of QKD, many practical difficulties still need to be resolved. Inspired by the significant similarity between mobile ad-hoc networks and QKD technology, we propose a novel quality of service (QoS) model including new metrics for determining the states of public and quantum channels as well as a comprehensive metric of the QKD link. We also propose a novel routing protocol to achieve high-level scalability and minimize consumption of cryptographic keys. Given the limited mobility of nodes in QKD networks, our routing protocol uses the geographical distance and calculated link states to determine the optimal route. It also benefits from a caching mechanism and detection of returning loops to provide effective forwarding while minimizing key consumption and achieving the desired utilization of network links. Simulation results are presented to demonstrate the validity and accuracy of the proposed solutions.

Myriads of new devices take their places around us every single day, making a decisive step towards bringing the concept of the Internet of Things (IoT) in reality. The Low Power Wide Area Networks (LPWANs) are today considered to be one of the most perspective connectivity enablers for the resource and traffic limited IoT. In this paper, we focus on one of the most widely used LPWAN technologies, named LoRaWAN. Departing from the traditional data-focused security attacks, in this study we investigate the robustness of LoRaWAN against energy (depletion) attacks. For many IoT devices, the energy is a limited and very valuable resource, and thus in the near future the device's energy may become the target of an intentional attack. Therefore, in the paper, we first define and discuss the possible energy attack vectors, and then experimentally validate the feasibility of an energy attack over one of these vectors. Our results decisively show that energy attacks in LoRaWAN are possible and may cause the affected device to lose a substantial amount of energy. Specifically, depending on the device's SF (Spreading Factor), the demonstrated attack increased the total energy consumption during a single communication event 36% to 576%. Importantly, the shown attack does not require the attacker to have any keys or other confidential data and can be carried against any LoRaWAN device. The presented results emphasize the importance of energy security for LPWANs in particular, and IoT in general.

It is not an entirely trivial matter to ensure the security of VoIP services and analyze attacks on telecommunication solutions the possible gains of which are attracting a growing number of active attackers. In many situations, it is necessary to detect and analyze these attacks, monitor their progress and then prepare an effective defence against them. The best way how to detect attacks on VoIP infrastructure is by implementing VoIP honeypot. To attract the highest number of attackers possible, our VoIP honeypots create fake VoIP traffic among themselves. This feature is based on a Markov chains principle. In this paper, we provide a complete implementation of a SIP emulation model which ensures the exchange process of SIP signaling messages between the honeypots.

High utility pattern mining (HUPM) has become a key issue in knowledge discovery since it provides retailers and managers with useful information for making decisions efficiently. However, previous studies most focused on mining the high-utility patterns (HUPs) from a single database. In this paper, we present a framework to incorporate the weighted model for parallel synthesis of the discovered HUPs from various databases. The pre-large concept was also used as a buffer here in order to provide more prospective HUPs, thus providing higher accuracy of the synthesized patterns. From our experiments, the developed model exceeds existing works, in particular the designed model has increased precision and recall on knowledge synthesization compared to the previous works.

In this paper, we evaluate and compare performance of multi-hop relaying (MR) protocols under impact of hardware impairments, in terms of outage probability (OP) and throughput (TP). By applying non-orthogonal multiple access (NOMA) technique at each hop, the end-to-end data rate/throughput of the MR protocol can be enhanced, as compared with the conventional one. Particularly, the transmitter at each hop combines two signals, and forwards the combined signal to the receiver which uses successive interference cancelation (SIC) to extract the data. For performance evaluation and comparison, we derive exact closed-form expressions of OP and TP for the considered protocols over Nakagami-m channel. Monte Carlo simulations are then performed to verify the theoretical derivations.

This paper provides an overview of the port container inspection techniques and procedures (standardized security procedures) relating to the detection of illicit material in containers. These procedures affect the duration of the containers transportation periods in different parts of the transport chain, according to the 2002 Container Security Initiative (CSI) regulations. The main object of this work – to demonstrate the inability of standard systems and associated technologies to deal with current threats and to propose solutions that are in line with the “intelligent containers” worldwide initiative.

In this paper, we derived the analytical expressions of the system performance (in term outage probability and throughput) of the power splitting half-duplex power beacon-assisted energy harvesting relay network in both amplify-and-forward and decode-and-forward modes. Moreover, the analytical results are also demonstrated and convinced by using Monte-Carlo simulation. The numerical results demonstrated and convinced the analytical and the simulation results are matched well with each other in connection with all possible system parameters.

Thanks to the benefits of deploying non-orthogonal multiple access (NOMA) in wireless communications, i.e, wireless sensor networks, we evaluate an energy harvesting wireless sensor network (EH-WSN) deploying NOMA, where the destination can receive two data symbols the whole transmission process with two time slots. To be more clear, we derive expressions for the achievable data rate and outage probability. In addition, we present Monte-Carlo simulations to prove the performance and the correctness of the obtained numerical results.

The energy harvesting amplify-and-forward full-duplex relaying network over the dissimilar fading environments in imperfect CSI condition is investigated. In this system model, the energy, and information are transferred from the source to the relay nodes by the power splitting protocol with helping of the full-duplex relay node. Firstly, the outage probability, achievable throughput, and the optimal power splitting factor in terms of the analytical mathematical expressions were proposed, analyzed, and demonstrated. Furthermore, the system performance of the proposed model on the connection with all system parameters is rigorously studied. Finally, the numerical results demonstrated and convinced one that the analytical and the simulation results are matched well with each other for all system parameter values using Monte-Carlo simulation. The results show that the system performance degrades significantly but is still in a permissible interval while the channel estimation error increases and the system performance of the mixing scenarios is better in comparison with the Rayleigh-Rayleigh scenario.

We investigate the system performance of a two‐way amplify‐and‐forward (AF) energy harvesting relay network over the Rician fading environment. For details, the delay‐limited (DL) and delay‐tolerant (DT) transmission modes are proposed and investigated when both energy and information are transferred between the source node and the destination node via a relay node. In the first stage, the analytical expressions of the achievable throughput, ergodic capacity, the outage probability, and symbol error ratio (SER) were proposed, analyzed, and demonstrated. After that, the closed‐form expressions for the system performance are studied in connection with all system parameters. Moreover, the analytical results are also demonstrated by Monte Carlo simulation in comparison with the closed‐form expressions. Finally, the research results show that the analytical and the simulation results agree well with each other in all system parameters.

In wireless networking, the main desire of end-users is to take advantage of satisfactory services, in terms of QoS, especially when they pay for a required need. Many efforts have been made to investigate how the continuity of services can be guaranteed in QoS networks, where users can move from one cell to another one. The introduction of a prediction scheme with passive reservations is the only way to face this issue; however, the deployment of in-advance bandwidth leads the system to waste resources. This work consists of two main integrated contributions: a new pattern prediction scheme based on a distributed set of Markov chains, in order to handle passive reservations, and a statistical bandwidth management algorithm for the reduction of bandwidth wastage. The result of the integration is the Distributed Prediction with Bandwidth Management Algorithm (DPBMA) that is independent from the considered technology and the vehicular environment. Several simulation campaigns were conducted in order to evaluate the effectiveness of the proposed idea. It was also compared with other prediction schemes, in terms of system utilization, accuracy, call dropping, and call blocking probabilities.