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The rapid advancements in wireless communications have prompted a surge in mobile data traffic, necessitating innovative solutions for 5G and beyond. This paper introduces a two-tier Heterogeneous Cloud Radio Access Network (HC-RAN) model leveraging millimeter Wave (mmWave) and sub-6 GHz frequencies to address this need. It integrates User-RRH associations to mitigate interference, enhance network throughput (via Heuristic Algorithm) and RRH-BBU clustering (via k-means) to manage resources in the network. The study evaluates SINR and rate coverage probabilities across various deployment scenarios, including Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions, as well as random and edge-based deployments. Results demonstrate that strategic placement of Remote Radio Heads (RRHs) and efficient clustering significantly improve network efficiency and user connectivity. In LOS conditions, random RRH deployments deliver superior coverage and throughput due to spatial diversity and reduced path loss. Conversely, edge-based deployments necessitate more resources to handle traffic demands but can excel in controlled scenarios. The proposed joint User-RRH association with RRH-BBU k-means clustering algorithm effectively manages interference, also maintains a balance between quality of service and efficient resource management. The proposed User-RRH association sub problem scheme that based on minimum path loss as a basic criterion outperforms on Limited Capacity User-RRH Association scheme (LC UA) in both the random and edge deployment scenarios and yield increasing in average throughput by approximately 38% and 27%, respectively. In other hand, the adaptive solution of RRH-BBU k-means clustering sub problem depend on actual load and number of active RRHs in the network to find the number of k RRH-BBU clusters, which manage resource consumption. This highlights the challenges in resource allocation and management with and without clustering. This paper concludes that optimized cell site deployment combined with association and clustering algorithms can significantly enhance 5G network performance, particularly in dense urban environments. These insights help network operators balance high service quality with efficient resource utilization.

Tor (The Onion Routing) network was designed to enable users to browse the Internet anonymously. It is known for its anonymity and privacy security feature against many agents who desire to observe the area of users or chase users’ browsing conventions. This anonymity stems from the encryption and decryption of Tor traffic. That is, the client’s traffic should be subject to encryption and decryption before the sending and receiving process, which leads to delay and even interruption in data flow. The exchange of cryptographic keys between network devices plays a pivotal and critical role in facilitating secure communication and ensuring the integrity of cryptographic procedures. This essential process is time-consuming, which causes delay and discontinuity of data flow. To overcome delay or interruption problems, we utilized the Software-Defined Network (SDN), Machine Learning (ML), and Blockchain (BC) techniques, which support the Tor network to intelligently speed up exchanging the public key via the proactive processing of the Tor network security management information. Consequently, the combination network (ITor-SDN) keeps data flow continuity to a Tor client. We simulated and emulated the proposed network by using Mininet and Shadow simulations. The findings of the performed analysis illustrate that the proposed network architecture enhances the overall performance metrics, showcasing a remarkable advancement of around 55%. This substantial enhancement is achieved through the seamless execution of the innovative ITor-SDN network combination approach.

Distributed denial of service (DDoS) attacks continue to be a major security concern, threatening the availability and reliability of network services. Software-defined networking (SDN) has emerged as a promising solution to address this issue, enabling centralized network control and management. However, conventional SDN-based DDoS mitigation techniques often struggle to detect and mitigate sophisticated attacks due to their limited ability to analyze complex traffic patterns. This paper proposes an innovative and optimized approach that effectively combines mininet, Ryu controller, and one dimensional-convolutional neural network (1D-CNN) to detect and mitigate DDoS attacks in SDN environments. The proposed approach involves training the 1D-CNN model with labeled network traffic data to effectively identify abnormal patterns associated with DDoS attacks. Furthermore, seven hyperparameters of the trained 1D-CNN model were tuned using non-dominated sorting genetic algorithm II (NSGA-II) to achieve the best accuracy with minimum training time. Once the optimized 1D-CNN model detects an attack, the Ryu controller dynamically adapts the network policies and employs appropriate mitigation techniques to protect the network infrastructure. To evaluate the effectiveness of the optimized 1D-CNN model, extensive experiments were conducted using a simulated SDN environment with a realistic DDoS attack dataset. The experimental results demonstrate that the developed approach achieves significantly improved detection accuracy of 99.99% compared to other machine learning (ML) models. The NSGA-II enhances the optimized model accuracy with an improvement rate of 9.5%, 8%, 5.4%, and 2.6% when it is compared to logistic regression (LR), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) optimized models respectively. This research paves the way for future developments in leveraging deep learning (DL) driven techniques and SDN architectures to address evolving cybersecurity challenges.

Fifth-generation (5G) empowered internet of things (IoT) edge networks suffer from latency in delay-sensitive applications. To fulfil the low latency requirements of beyond fifth-generation (B5G)-IoT applications and provide quality of service (QoS) to IoT-edge communication, it is important to minimize server delay. Furthermore, 5G-IoT systems consume more power than their predecessors, which is a concern given the growing size of future IoT networks. This research presents a hybrid latency and power-aware approach for B5G-IoT networks (HLPA B5G-IoT) that minimizes latency with minimum overhead on battery-constrained IoT nodes while simultaneously providing a power-efficient solution for B5G-IoT-edge networks. HLPA B5G-IoT has a novel algorithm classifier tool (ACT) for selecting appropriate optimization algorithms based on the characteristics and requirements of B5G-IoT systems. The ACT matrix not only parametrically compares HLPA B5G-IoT with existing approaches but also identifies crucial parameters that enable algorithm selection for load balancing and energy efficiency. In this paper, metaheuristic algorithms, i.e., biogeography-based optimization (BBO) and grey wolf optimization (GWO), are tailored to meet the requirements of load balancing and power efficiency in IoT-edge systems. The proposed load-balancing algorithm reduces latency and improves overall network performance by 33.33%, 27.45%, 23.52%, 21.56%, 13.72%, 11.76%, and 7.84% compared with simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), bacteria foraging algorithm (BFA), ant colony optimization (ACO), bat algorithm (BA), and genetic SA PSO (GSP), respectively. The power-efficiency algorithm consumes 46.6%, 40%, 32.2%, 27.7%, 15.5%, 11.1%, and 6.6% less energy compared with SA, GA, PSO, BFA, ACO, BA, and GSP, respectively.

In recent years, with the advent of 6G and intelligent devices, sensors, and new applications such as virtual reality and autonomous driving, user data traffic has exploded, especially video traffic and small IoT packets. These bandwidth-hungry applications require an increased network capacity and user access. The 6G network may use nonorthogonal multiple access (NOMA) instead of orthogonal multiple access (OMA) to maintain higher data rates, throughput, and lower latency. On the other hand, choosing the channel coding method for future 6G mobile communication is critical for maintaining the high demand for 6G. This paper proposes two-channel coding structures to achieve higher data rates with a lower error rate floor; these structures are polar convolutional serial code (PCSC) and polar convolutional parallel code (PCPC); these structures can achieve a larger channel capacity and reduced bit error rates when used with NOMA. The obtained simulation results showed that bit error rate (BER) performance improves the overall coding gain by 1.2 dB compared to polar code in fifth-generation (5G). PCSC surpasses PCPC with a 1.5dB coding gain. This performance ranged from 4 to 6.25dB with higher system settings. The obtained throughput results showed an improvement of 56-60%, in which the enhancement percentage depended on the modulation method used in a direct proportion manner.

Recently, the increasing demand to transfer data through the Internet has pushed the Internet infrastructure to the final edge of the ability of these networks. This high demand causes a deficiency of rapid response to emergencies and disasters to control or reduce the devastating effects of these disasters. As one of the main cornerstones to address the data traffic forwarding issue, the Internet networks need to impose the highest priority on the special networks: Security, Health, and Emergency (SHE) data traffic. These networks work in closed and private domains to serve a group of users for specific tasks. Our novel proposed network flow priority management based on ML and SDN fulfills high control to give the required flow priority to SHE data traffic. The proposal relies on selected header bits from the traffic class field of a packet using the ML to prioritize traffic flows according to the precedence levels by governing the Differentiated Services Code Point (DSCP) bits in keeping with network administrator policies. The proposed network has been evaluated and performed utilizing the MATLAB platform and the Mininet simulator. The results of extensive testing show enhancement by applying our forcing priority algorithm obtained an efficient reduction in queuing delay and lost packets. The average waiting time in queue was reduced by around 61%, and the lost packets hit 0.005% when adopting the SDN-based ML network traffic priority management.

Due to the huge variety of 5G services, Network slicing is promising mechanism for dividing the physical network resources in to multiple logical network slices according to the requirements of each user. Highly accurate and fast traffic classification algorithm is required to ensure better Quality of Service (QoS) and effective network slicing. Fine-grained resource allocation can be realized by Software Defined Networking (SDN) with centralized controlling of network resources. However, the relevant research activities have concentrated on the deep learning systems which consume enormous computation and storage requirements of SDN controller that results in limitations of speed and accuracy of traffic classification mechanism. To fill this gap, this paper proposes Intelligent SDN Multi Spike Neural System (IMSNS) by implementing Moderately Multi-Spike Return Neural Networks (MMSRNN) controller with time based coding achieving remarkable reduction on energy consumption and accurate traffic identification to predict the most appropriate network slice. In addition, this paper proposes another intelligent Recurrent Neural Network (RNN) controller for load balancing and slice failure condition. The current researchers have adopted the: accuracy, precision, recall and F1-Score, the simulation results revealed that the proposed model could provide the accurate 5G network slicing as compared with a convolutional neural network (CNN) by 5%.

Recently, wireless sensor networks (WSNs) were perceived as the foundation infrastructure that paved the way to the emergence of the Internet of Things (IoT). However, a challenging issue exists when WSNs are integrated into the IoT because of high energy consumption in their nodes and poor network lifespan. Therefore, the elementary discussions in WSN are energy scarcity in sensor nodes, sensors’ data exchange, and routing protocols. To address the aforesaid shortcomings, this paper develops an optimized energy-efficient path planning strategy that prolongs the network lifetime and enhances its connectivity. The proposed approach has four successive procedures: initially, the sensing field is partitioned into equal regions depending on the number of deployed mobile sinks that eliminate the energy-hole problem. A new heuristic clustering approach called stable election algorithm (SEA) is introduced to minimize the message exchange between sensor nodes and prevent frequent cluster heads rotation. A sojourn location determination algorithm is proposed based on the minimum weighted vertex cover problem (MWVCP) to find the best position for the sinks to stop and collect the data from cluster heads. Finally, three optimization techniques are utilized to evaluate the optimized mobile sinks’ trajectories using multi-objective evolutionary algorithms (MOEAs). Whilst the performance of the developed work was evaluated in terms of cluster heads number, network lifetime, the execution time of the sinks’ sojourn locations determination algorithm, the convergence rate of optimization techniques, and data delivery. The simulation scenarios conducted in MATLAB and the obtained results showed that the introduced approach outperformed comparable existing schemes. It succeeded in prolonging the network lifetime up to 66% compared to existing routing protocols.

An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to change its affiliation with other clusters based on a deep learning modified Element-wise Attention Gate. The modified Element-wise Attention Gate has the ability to handle the buffer capacity in all the network, thereby enriching the Quality of Service. A deep learning modified training algorithm is proposed to learn the artificial intelligent system allowing the neurons to have greater concentration ability. The simulation results demonstrate that the Root Mean Square error is minimized by 37.14% when using modified Element-wise Attention Gate when compared with a Deep Learning Recurrent Neural Network. Also, the Quality of Service of the network is improved, for example, the network lifetime is enhanced by 12.7% more than with Deep Learning Recurrent Neural Network.

Quantum computing can provide a solution for many problems that classical communication networks encounter, but the knowledge gap between these two is not bridged yet. Accordingly, this paper has proposed directions, evaluations, and futuristic quantum solutions to cloud-based cellular communications. This paper shows the effect of using quantum entanglement phenomena upon the classical cloud network, particularly using an entanglement theory to decrease the enlarged signaling cost delay that the traditional cloud network faces. Through modeling the latency of both paradigms, this paper promises a delay reduction when adapting a quantum method into the mobile cloud networks. This paper also models the power consumption (PC) and energy efficiency of the traditional and quantum-based cloud networks. This paper also shows, via modeling the PC, that installing a quantum-based paradigm is not a power costly method; rather, it shows identical power and energy efficiency figures with a possibility of improvement.

The utilization of conventional modeling strategies in the identification and control of a nonlinear dynamical system suffers from some weaknesses. These include absence of precise, conventional knowledge about the system, a high degree of uncertainty, strongly nonlinear and time-varying behavior. In this paper, a modified training algorithm for the identification and control of a nonlinear system using a soft-computing approach is proposed. Specifically, a modified structure of the Elman neural network with spike neural networks is proposed. This modified structure includes self-feedback, which provides a dynamic trace of the training algorithm. This self-feedback has weights, which can be trained during the training process. The simulation results show that the modified structure with the modified training algorithm is capable of the identification and control of a dynamic system in a more robust manor than when solely applying the other types of neural networks by 70% in terms of minimization of the percentage of error.

This paper presents a proposal for an innovative base station for 5G and beyond for a mobile network system based on the evolved packet system (EPS). Functions of EPS elements can be achieved by the SVeNB, according to the type of function or service and the device that is used to achieve that function or service. The current system, even if these functions and services have been virtualized by the system, it may put them away from the users. Consequently, this causes a delay in completing the establishment of a connection. Since these functions and services can be virtualized and programmed in virtual machines (VMs), we have been simulated the kernel of those functions and services executed by the SVeNB through its VMs. Moreover, The SVeNB position is located close to the users, which helps to reduce the End-to-End delay. Our proposed approach gives the SVeNB the ability to share and contribute in making decisions with the core network (CN) and BaseBand Unit (BBU) pool to initiate a connection, further in making determinations within its vision regarding its users. This procedure leads to a reduction in the load on the BBU pool and the CN, thus lessening the latency, and to increase the data rate that is delivered to the users. That means the SVeNB can regionally decide for all local users that are covered by the SVeNB to connect to each other or the Internet. The MATLAB platform has been used to evaluate the SVeNB performance, with the results demonstrating that it surpasses. The obtained results point that the SVeNB passes the C-RAN system by 68% in terms of user profiles processing, whilst also and it reducing the End-to-End delay by 62%.

Keeping a connection continuity during the movement of a mobile node (MN) between access points without any suspension of provided services is one of the most pressing issues should be solved. Long handover processing causes interruptions in session connection, high rate of data loss, and long end-to-end delay time. Smart virtualization means the cooperation of different virtualization technologies with novel ideas. In this paper, we proposed a mobile network architecture compatible with cloud computing of 5G and beyond networks. We invented a new idea to create a tag to be used as an MN's identity, which consists of the standard E.164 numbering and MAC address. Based on the uniqueness of E.164 numbering and MAC which are processed together to generate the MN tag (TH). TH is used to handle the packets inside the mobile networks. The software-defined networking (SDN) provides a capability of separating the control plane from the data plane. This decoupling is a suitable candidate to exploit it in our proposed system that uses the SDN and other virtualization technologies. The requirements of the 5G and beyond for future mobile communications encouraged us to think in a novel packet forwarding during the handover to keep real-time connection continuity for an MN. Our proposed system has been simulated and performed by MATLAB and Mininet platforms. The results showed that the packet loss rate decreased to 4% of the date that was lost during the handover delay time or packets re-direction mechanism. At the same time, the MN could receive 96.4% of the data that were lost during the handover process.

The Internet of Things (IoT) comprises a large number of sensor nodes with limited processing, storage, and battery abilities. The IoT has to operate in a constrained environment with specific challenges, such as hardware malfunctions, battery depletion, and harsh wireless environmental conditions. Deploying a reliable IoT is especially important for critical IoT applications such as smart cities. To ensure the quality of service requirements of these applications, the IoT needs to provide specific reliability guarantees. There are several strategies to ensure energy efficient and reliable transport of data in the IoT. However, there is an inherent conflict between power consumption and reliability: an increase in reliability usually leads to an increase in power consumption as in traditional retransmission-based reliability. To solve this problem, we present four scenarios of optimization using a mixed integer linear programming (MILP) model. First, we used a standby routes selection scheme (SBRS) to replace node failures and achieve reliability with minimum traffic power consumption. Second, we used a desired reliability level scheme (DRLS), which minimizes the traffic power consumption of the IoT devices while considering the desired reliability level as a key factor. We also propose a reliability-based sub-channel scheme (RBS) to avoid overhead on busy reliable routes while mitigating interference. Moreover, we present a reliability-based data compression scheme (RBDS) to overcome the capacity limits of the links. The results show that our proposed schemes reduce the negative effect between reliability and total traffic power consumption with an average power saving of 57% in SBRS and 60% in RBDS compared to DRLS.

Analysing data centers energy consumption is the main step toward building a reliable infrastructure. Evidently, data centers consume a large number of billions of gigabytes information to the point that putting tremendous pressure on energy suppliers. Every internet activity involves a huge amount of data that need to be stored in a cloud data center somewhere, not forgetting the Internet of Things applications and other social media services that produce an extraordinarily large scale of big data that require high processing and analysis. Moreover, current data centers consume about 3% of global electricity supply, which is about 416.2 TWh of power that world data centers consumed in 2016. In this paper, we have developed an intelligent power reduction decision routing protocol (iPRDR) in a medium scale hybrid-software-defined network data center environment. The proposed iPRDR protocol approach is to dynamically segregate big traffic and route it to a high index processing devices with a power-optimal selected path. The protocol approach is to decrease the overall power consumption of the whole network, as well as to reduce the failure rate in each device that may occur due to a high level of link congestion and elevated temperature. The experimental results show that uplink utilization has been reduced by 8.33% and power consumption levels reduced by 0.85kw/day, which is equivalent to 34.9% of the operational power. In Addition, the high-raised temperatures have been dropped from high range 50+ C° (high critical) to mid 40+ C° (high warning). The effectiveness of the proposed approach was verified experimentally using a virtualized testbed platform.

Although wind-generated power was estimated to be 4% of the entire world electricity usage, wind turbines are considered to be a growing technology with many experts considering new approaches to wind turbine design and farmland selection to increase the wind turbine output efficiency. When implementing a new wind turbine install on a farm, many concerns are taken into consideration such as environmental challenges and cost. Thus, the power of the wind turbine can be increased at least 10 times when the most efficient wind power location is selected before the wind turbine is placed. In this paper, we propose a novel software-defined quadcopter-6 wheeled industrial IoT (SDQ-6WI) architecture that is based on a developed quadcopter system to collect wind speed data from a mobile IoT vehicle base station that is based on a developed open flow (DOV) protocol operation. The mobile ground vehicle act as a wind speed measurement system that travels on a given set of waypoints to measure the best optimal wind speed quality and send the collected information to the quadcopter-based SDN controller then to the cloud for further processing. Our proposed system can handle a heterogeneous environment that lacks Wi-Fi and cellular coverage and uses the minimum total transmission power when sending data. The experiential results showed that the measured wind speed data could be collected in a time-efficient manner compared to a traditional process which is considered to be costly, time wasting, and non-effective. Our extensive testing showed that about 23.19% in power was reduced in the wind measurement process in comparison with the fixed sensor nodes. In essence, the proposed architecture help reduce the high cost of relocating wind turbines to an efficient location and increase the generated power by selecting the best optimal windy location.

Fairness, low latency, and high throughput with low energy consumption are desired attributes for medium access control (MAC) protocols. The IEEE 802.15.4 standard defines the MAC and physical layers standard for IPv6 over low power personal area network (6LoWPAN). When a non-appropriate parameter setting is used, the default MAC parameters generate excessive collisions, packet losses, and high latency under high traffic when a large number of 6LoWPAN nodes being deployed. A search of the literature revealed few studies that investigate the impact of optimizing these parameters to achieve high throughput with minimum latency. This paper proposes a new intelligent approach to selecting the optimal 6LoWPAN MAC layer parameters set; the introduced mechanism depends on artificial neural networks, genetic algorithm, or particles swarm optimization to select and validate the optimized MAC parameters. The obtained simulation results showed that utilizing the optimal MAC parameters improved 6LoWPAN network throughput by 52-63% and reduced the end-to-end delay by 54-65% in which the enhancement percentage depends on the number of deployed sensor nodes in the network.

Collision recovery is considered one of the main potentials for improving bulk reading speed in the UHF radio identification (RFID) system. The collision occurs when two or more tags reply at the same time. State-of-the-art collision recovery algorithms rely on perfect channel state information (CSI), which is unrealistic. Moreover, these algorithms neglect the rate tolerance, that is, the uncertainty in the bit rate of the tag responses. This paper presents a novel algorithm to estimate all tag parameters, such as tag data rate, number of tags per slot, and CSI, using the Matrix Pencil Method (MPM). The proposed system is compatible with the existing EPCglobal Class-1 Gen-2 RFID standard. The accuracy of the proposed algorithm is evaluated with the different encoding schemes of the tag. The estimated parameters are forwarded to MIMO linear receivers to recover the colliding slots to speed up the identification process. The performance of the three linear receivers; Zero Forcing (ZF), Minimum Mean Square Error (MMSE), and Maximum Likelihood (ML) receivers, is evaluated when the CSI is estimated using the proposed algorithm. All receivers perform similarly due to their different sensitivity to the CSI estimation error. The proposed collision recovery system reduces the time for reading a bulk by at least 13% compared to the literature.

This paper presents a linear-to-circular polarization converter (LCPC) used for multi-band functionality. The LCPC surface features symmetrically printed copper strips on a single-layer substrate and is surrounded by a square substrate integrated waveguide (SIW) cavity arranged periodically in two dimensions. The study successfully utilizes the LCPC surface to achieve a desirable axial ratio (AR) band width (BW) below 3 dB in multiple frequency ranges, specifically: 1) 4.51-4.80 GHz, 2) 6.51-6.80 GHz, 3) 9.01-9.20 GHz, 4) 10.90-12.40 GHz, and 5) 17.05-17.49 GHz. The LCPC induces a change in polarization as the wave propagates through it. In the first ( $f_{31}$ ) and fourth ( $f_{34}$ ) frequency bands, the initially linearly polarized (LP) wave transforms into a left-hand circularly polarized (LHCP) wave. Conversely, in the second ( $f_{32}$ ), third ( $f_{33}$ ), and fifth ( $f_{35}$ ) frequency bands, the wave converts into a right-hand circularly polarized (RHCP) wave. Measurement results indicate that the LCPC provides circularly polarized (CP) wave bandwidths of 12.72%, 4.8%, 2.4%, 11.39%, and 3.15% across frequency ranges of 3.9 - 4.43 GHz, 6.21- 6.52 GHz, 8.9-9.08 GHz, 10.51-11.78 GHz, and 16.85-17.39 GHz, respectively. This characteristic renders the LCPC suitable for utilization in the C (4-8 GHz), X (8-12 GHz), and Ku (12-18 GHz) frequency bands.