Transactions on Emerging Telecommunications Technologies最新文献

The security principles of the fifth generation (5G) are anticipated to include robust cryptography models, information security models, and machine learning (ML) powered Intrusion Detection Systems (IDS) specifically designed for Internet of Things (IoT) based wireless sensor networks (WSNs). Nevertheless, the existing security models fall short in addressing the dynamic network characteristics of WSNs. In this context, the suggested system introduces a secure and collaborative multi-watchdog system through the implementation of deep convolutional neural network (DCNN) and distributed particle filtering evaluation scheme (DPFES). The proposed system utilizes deep learning (DL) techniques to create a dynamic multi-watchdog system that safeguards each sensor node by monitoring its transmissions. Furthermore, the proposed approach includes secure data-centric and node-centric evaluation methods that are crucial for enhancing the security of 5G-based IoT-WSN networks. The network evaluation processes based on DL facilitate the creation of a secure multi-watchdog system within dense IoT-WSN environments. This system enables the deployment of active watchdog IDS agents as needed. The proposed approach includes various components such as a system dynamics model, cooperative watchdog model, Dual Line Minimum Connected Dominating Set (DL-MCDS), and DL-based event analysis procedures. From a technical perspective, the system is driven by the implementation of DPFES, which utilizes particle filtering frameworks to analyze network events and establish a secure 5G environment. The system has been successfully implemented, and its results have been compared with those of other similar works. The performance of the proposed cooperative multi-watchdog system demonstrates a significant improvement of $$ and $$ compared to other techniques.

Moving target detection in a heavy clutter area is a challenging problem in the field of radar automatic target detection. Within the framework of track posture hypothesis testing, the radar-blips-oriented target detection algorithms named ErgSad and RanSaD are proposed for single target and formation targets detection, respectively in this paper. With the assumption that target motion is a linear Gaussian process in a short time, the algorithms use dual blips in different scans to generate the track hypothesis and test the hypothesis with residual blips in other scans to determine the existence of moving targets. To reduce time consumption and minimize the error of model estimation, the sampling rules and the supporting domain related to timestamps of track hypothesis models were designed. Simulation data experiments show that the proposed algorithms have more superior performance to detect targets than the state-of-the-art algorithms in the serious clutter environment.

Managing energy consumption poses a substantial challenge within Wireless Sensor Networks (WSN) due to frequent communication among sensor nodes. A reliable scheduling framework presents a promising solution for maximizing WSN lifetime, minimizing energy consumption, and ensuring robust communication. Despite various proposed methods for reliable scheduling, energy consumption remains high. To address this, a spiking quantum fire hawk network-based reliable scheduling in WSN is introduced. This research incorporates clustering, duty-cycle management, and reliable routing to enhance energy efficiency. An improved Nutcracker Optimization-based Cluster Head (CH) election and Spiking Quantum Fire Hawk Network duty cycling contribute to optimal CH selection and increased network lifetime. Additionally, a Link Quality-based Energy Aware Proficient Trusted Routing Protocol (LQEAP-TRP) minimizes data transmission delay, offering reliable routing. Finally, the data communication is done in the reliable route. The developed approach is executed in Network Simulator and validated with the existing protocols. The results of the simulations indicate that the proposed approach achieves a network lifetime of 99.34% and a packet delivery ratio of 99.95%.

The advancement of Intelligent Transport Systems (ITSs) and the Internet of Vehicles (IoVs) introduces significant security challenges, particularly in ensuring secure communication between vehicles and vehicles (V2V), vehicles and Road Side Units (V2RSU). These vulnerabilities could potentially compromise the integrity of vehicular networks, making robust security measures essential. This paper aims to develop a secure communication framework for IoV environments that addresses these security concerns by enhancing authentication and encryption processes. The proposed approach combines the Ring-Based Degree Truncated Polynomial Cryptosystem (RNTPC) with a Blockchain framework incorporating an enhanced proof-of-authentication (BEPoAuth) consensus algorithm. The approach is initiated from the registration phase and managed by a Trusted Authority (TA), ensuring secure vehicle and RSU registration. Authentication is carried out in two ways: V2V authentication through vehicle signatures and V2RSU authentication via batch verification of cluster members. Group keys are generated using RNTPC for secure communication, and blockchain technology is integrated to secure transactions within the IoV environment. The proposed methodology is evaluated using various performance metrics, demonstrating a 15%–20% improvement in throughput compared to existing techniques such as Practical Byzantine Fault Tolerance (PBFT), Secure and Highly Efficient Blockchain PBFT Consensus Algorithm (SG-PBFT), credit-based PBFT consensus algorithm (CPBFT) and Geographic-PBFT (G-PBFT). The system also exhibited minimal computational and communication latency while enhancing V2V and V2RSU communication efficiency. The RNTPC-BEPoAuth framework offers a robust and secure solution for IoV communications, significantly outperforming existing methods in terms of throughput and efficiency. This approach provides a reliable foundation for secure ITSs, addressing critical security concerns in vehicular networks.

Cloud computing (CC), in contrast to traditional high-performance computing environments, is a group of imaginary and networked resources of computing that are controlled by one unified maximum-performance computing power. Here, this work aims to develop a novel data replication method in the cloud. The data replication is carried out with a new multi-objective technique that considers constraints like cost, the distance between data centers, trust, and risk. Moreover, for optimal data replication, a new hybrid algorithm termed poor rich strategy assisted grasshopper optimization (PRS-GO) is introduced. To increase the accessibility of the system, the data used continuously should be duplicated in various areas. A minimal mean value of 0.66 is gained with the PRS-GO scheme, whereas Particle Swarm Optimization-Tabu Search (PSO + TS), Receding Horizon Control (RHC), Sun Flower Optimization (SFO), Cat Mouse-Based Optimization (CMBO), Hunger Games Search Optimization (HGSO), Seagull Optimization (SGO), Poor And Rich Optimization (PRO), and Grasshopper Optimization Algorithm (GOA) have got a high mean value of 0.722, 0.71, 0.71, 0.71, 0.7, 0.7, 0.7, and 0.69.

Compressed sensing is used for channel estimation in Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, but large-scale networks face challenges regarding the antenna elements and spatial non-stationarities. To enhance spectral efficiency in Multi User-MIMO (MU-MIMO) systems, essential signals are collected by Quadrature Phase Shift Keying (QPSK) in the transformer phase. Further, the modulated signals are provided to the “Pulse Shaping Algorithm (PSA)” for neglecting the inter-symbol interference rate along with inter-carrier interference. Subsequently, in the transmitter phase, the “Inverse Fast Fourier Transforms (IFFT)” technique is performed to map the symbols and then provide the signal to the receiver side. The spare channel is validated using a semi-blind sparse algorithm, with parameters tuned using the Opposition Mud Ring Algorithm (OMRA). Then, the estimated sparse channel values are used for generating the new data, and the Adaptive Extreme Learning Model (AELM) is trained to predict the spare channel outcome. The main objective is to reduce the Minimum Mean Square Error (MMSE) in the channel. Thus, the spare channel outcome is predicted automatically using the AELM. Then, diverse evaluations are executed in the suggested spare channel estimation approach over the different mechanisms to observe their effectualness rate.

In multi-access edge computing (MEC), computational task offloading of mobile terminals (MT) is expected to provide the green applications with the restriction of energy consumption and service latency. Nevertheless, the diverse statuses of a range of edge servers and mobile terminals, along with the fluctuating offloading routes, present a challenge in the realm of computational task offloading. In order to bolster green applications, we present an innovative computational task offloading model as our initial approach. In particular, the nascent model is constrained by energy consumption and service latency considerations: (1) Smart mobile terminals with computational capabilities could serve as carriers; (2) The diverse computational and communication capacities of edge servers have the potential to enhance the offloading process; (3) The unpredictable routing paths of mobile terminals and edge servers could result in varied information transmissions. We then propose an improved deep reinforcement learning (DRL) algorithm named PS-DDPG with the prioritized experience replay (PER) and the stochastic weight averaging (SWA) mechanisms based on deep deterministic policy gradients (DDPG) to seek an optimal offloading mode, saving energy consumption. Next, we introduce an enhanced deep reinforcement learning (DRL) algorithm named PS-DDPG, incorporating the prioritized experience replay (PER) and stochastic weight averaging (SWA) techniques rooted in deep deterministic policy gradients (DDPG). This approach aims to identify an efficient offloading strategy, thereby reducing energy consumption. Fortunately, $$ algorithm is proposed for each MT, which is responsible for making decisions regarding task partition, channel allocation, and power transmission control. Our developed approach achieves the ultimate estimation of observed values and enhances memory via write operations. The replay buffer holds data from previous $$ time slots to upgrade both the actor and critic networks, followed by a buffer reset. Comprehensive experiments validate the superior performance, including stability and convergence, of our algorithm when juxtaposed with prior studies.

In the Internet of Things (IoT) network, within a period of time it constructs a huge network of millions and billions of things that incorporate with each other, leading to various technical and application problems. Also, on-time delivery of packets is significant in software-defined networks. In the current software-defined network, the bandwidth overhead is not considered when an enormous amount of traffic enters the network; this may lead to network congestion. To overcome this issue, a novel method is proposed to detect network congestion based on hybrid optimization, namely, the Hybrid Gannet with Pelican Optimization (HGPO) algorithm. The node-level congestions and the link-level congestions, which means the buffer overflow and the multiple nodes trying to utilize the channel at the same time, must be controlled efficiently. Once the network congestion gets controlled, the shortest route path selection and congestion prevention are performed perfectly with the aid of the proposed Deep Spiking Equilibrium Neural Network (DSENN). A few route discovery frequency vectors, such as the interroute discovery time and route discovery time of each node, are determined to prevent congestion. Finally, it is implemented in the Python platform successfully, and the achieved throughput, delay, packet loss ratio, packet delivery ratio, end-to-end delay, and performance measures of the proposed method are 140 Mbit/s, 17 ms, 3.8%, 0.35 s, and 100%, respectively, which outperforms the other compared traditional algorithms.

In this article, we investigate the successful transmission probability of an aerial cellular network in which an unmanned aerial vehicle (UAV), as a macrocell base station (UAV-BS) serves other UAVs as aerial users. The antennas with beamforming capability are mounted on the UAVs, to increase the throughput of the network. The random effects of inner forces such as controlling errors or outer forces like the air conditions, result in the random fluctuations in the hovering UAVs. We assume Rician fading distribution over the links between the UAV-BS and other UAVs. Then, we calculate the distribution of the corresponding channel gain under hovering fluctuations and we derive the closed form expressions for successful transmission probability for each user. Defining an optimization problem on the average successful transmission probability of the network, we obtain the best placement of UAV-BS along with the resource allocation. The problem turns out to be a non-convex problem and time consuming via numerical exhaustive search methods. We obtain a lower bound for the average successful transmission probability and we solve the optimization problem for this lower bound. Maximization problem for the achieved lower bound is equivalent to maximize the main problem. Using some approximations, we convert the problem to a low complex one, then, the problem becomes convex which is solved by KKT conditions and yields the location of UAV-BS. The theoretical results reveal the influence of fluctuations on the throughput of the network and show that optimizing the lower bound probability achieves the suboptimal solution for power assignment and placement problem, which is verified by simulation results.