International Journal of Communication Systems最新文献

This paper proposes the structured and investigation of multi-port ceramic-based aerial with filtering, high gain, and circular polarization features. The alumina ceramic is excited using a cross-shaped microstrip line. Defected ground structure (DGS) is utilized to provide good impedance matching within the operating band. Metasurface suspension over the two-port aerial improves the gain level by an amount of 10.2 dBi. The application of polarization diversity idea helps to improve separation between aerial ports by 25 dB. The prediction of |S₁₁| and axial ratio using random forest and XGBoost-built machine learning (ML) technique reduces the computational complexity. Fabricated prototype confirms that the designed antenna operates in between 2.55 and 3.45 GHz, and 3-dB axial ratio is found in between 2.75 and 3.15 GHz. Directive far-field attributes and suitable amount of diversity parameters confirm its applicability for sub-6 GHz-based 5G communication system.

The Internet of Things (IoT) has emerged as a revolutionary technology, connecting a vast number of people to the internet through various devices such as smartphones, laptops, sensors, and more. An essential component of IoT, the wireless sensor network (WSN), enables automation in processes like sensing, node monitoring, and data transmission. However, the full potential of IoT is hindered by cyber threats and unreliable communication. Although several security algorithms exist, they are not suitable for energy-constrained sensor nodes. To address this issue, this paper proposes an energy-efficient security mechanism called adaptive clustering and trust aware routing (ACTAR) for IoT-WSNs. ACTAR operates in three phases: adaptive and hybrid clustering (AHC), multiobjective function-based cluster head selection (MOCH), and trust aware routing (TAR). First, AHC utilizes a nonuniform clustering mechanism to categorize the network into nearby and distant clusters. Next, the selection of cluster heads is based on four metrics: coverage, communication cost, residual energy, and node proximity. Finally, TAR calculates the trust degree of sensor nodes by evaluating their direct and indirect behavior in terms of communication interactions and energy consumption. The node with the highest trust degree is selected as the next-hop forwarding node, followed by the route with the highest trust degree. Extensive simulations of ACTAR demonstrate its performance in terms of malicious detection rate, false-positive rate, residual energy, and packet delivery ratio. Comparative analysis shows that ACTAR outperforms existing methods, proving its superiority.

A cognitive radio ad hoc network (CRAHNs) is an infrastructure-free network containing a cognitive radio, which can use the spectrum resources effectively through an adaptive change of parameter settings and decision-making. Hence, it enhances the spectrum efficacy for satisfying the growing mobile traffic requests. In multihop CRAHNs, in order to ensure a reliable transmission and energy efficiency, the routing protocol should consider the metrics of channel quality, link stability, and energy consumption. In this paper, a Fuzzy-Markov routing policy and priority-based load balancing algorithm (FMRP-PLB) for CRAHNs is designed. In this protocol, a combined routing metric is derived using delay, energy rate, and link expiration time metrics. For modeling the channel state, a Fuzzy-Markov decision model is designed, in which the channel states and spectrum availabilities of SU are modeled as state transition matrices. Then, optimum routing decisions are made using the combined routing metric and the predicted transmission success probability. A load balancing metric (LBM) is derived in terms of available link capacity, remaining buffer size of node, and back-off delay. During data transmission, based on the traffic priority and measured levels of LBM, the optimum paths are selected. Simulation results using NS2 suggest that FMRP-PLB attains an increased packet delivery ratio with reduced delay and energy consumption.

In recent days, underwater exploration has emerged as one of the most predominant technologies for enhancing surveillance and early warning systems. Finding the location of the nodes placed in underwater is a difficult task owing to its harsh underwater environment. In large underwater wireless sensor networks (UWSNs), pinpointing the exact coordinates of sensor nodes may not be feasible or be incredibly expensive. In most of the applications, the coarse coordinate of the node is adequate. The primary technique used in UWSNs to determine the location of sensor nodes, based on the average distance between hops, is referred to as distance vector-hop (DV-Hop) localization. Nevertheless, the positioning accuracy in the classic DV-Hop technique is influenced by the average hop distance. To reduce the localization error, it is possible to create a distinct and optimized DV-Hop approach. To improve the effectiveness of the localization process, the average distance between hops is primarily used as an objective function. The optimization of this objective function is achieved by employing the Pelican Optimization Algorithm (POA). There is a noticeable decrease in the localization discrepancy if the optimized average hop distance is used to precisely determine the unidentified node to the anchor node distance among them. The factors used to evaluate the ability of the proposed methodology are the ratio of anchor, transmission range, and the density of the node. Compared to other localization procedures, the obtained outcomes demonstrate that the optimized approach that has been suggested achieves a low localization error of 0.3.

The fault node detection and recovery is essential in WSN for improving the network lifetime and node connectivity. Ensuring the tradeoff between energy consumption and node recovery is challenged due to redundant nodes. Hence, a novel approach is developed based on the objective of automatic recovery from failure, redundant node elimination, fault node replacement, and minimalizing energy consumption. In this approach, the fuzzy boosted sooty tern optimization (FBSTO) technique is proposed for fault node detection and replacement. Based on the node density and distance, the nodes are clustered. Then, the cluster head selection process can be accomplished with a fuzzy logic approach based on distance calculation, energy consumption, and Quality of service (QoS) nodes. The node replacement is carried out through cascaded movement, which minimizes energy utilization. The efficiency of the FBSTO approach is estimated with packet delivery ratio, packet loss ratio, end-to-end delay, and energy consumption. The proposed approach reduces the end-to-end delay to 10 ms for random deployment of 100 nodes. Also, the packet delivery ratio performance of the proposed approach is 143 for 100 SNs. For existing Multi-objective Cluster Head Based Energy-aware Optimized Routing (MCH-EOR), Ant Lion Optimization (ALO), Particle swarm Optimization (PSO), Gray Wolf Optimization (GWO), and Genetic approach (GA), the packet delivery ratio is reduced to 130, 60, 50, and 100. Compared with the existing approaches, the proposed approach provides better performance.

In this article, a compact triple-band frequency-tunable (FT) hexagonal-shaped graphene antenna through a machine learning (ML) approach for terahertz (THz) application is presented. The proposed THz antenna is designed on a polyamide ($��{\in }_r�=�3.5�$) substrate with a thickness of 10 μm, and graphene is used as an antenna radiator. The size of the substrate is 38 × 46 μm². The FT is achieved by changing the chemical potential of graphene material. The performance of the proposed THz antenna has been investigated, and the impacts of several conducting materials like gold, aluminum, copper, and graphene and dielectric materials like Rogers RT/duroid 5880, polyamide, quartz, and SiO₂ are explored. The proposed THz antenna provides three operating bands. The frequency of operation in Band-1 is 2.51–5.05 THz, Band-2 is 5.99–7.43 THz, and Band-3 is 7.94–9.63 THz. The bandwidth in Band-1, Band-2, and Band-3 are 2.54, 1.44, and 1.69 THz, respectively. The % of impedance bandwidth in Band-1, Band-2, and Band-3 are 67.19%, 24.02%, and 21.28% respectively. The proposed antenna has a maximum peak gain of 5 dBi. The proposed antenna is optimized through various ML algorithms like random forest (RF), extreme gradient boosting (XGB), K-nearest neighbor (KNN), decision tree (DT), and artificial neural network (ANN). The RF algorithm gives more than 99% accuracy compared to other ML algorithms and accurately predicts the S₁₁ of the proposed antenna. The proposed THz antenna would be suitable for applications related to imaging, medical, sensing, and ultra-speed short-distance communication applications in the THz region.

The spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection-based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED-NAQ-AOA-SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro-fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

The majority of wireless sensor network (WSN) systems include multiple data traffic with different service requirements. Small batteries are used to supply energy to the sensor nodes. This research work explores a new optimal hybrid MAC protocol for heterogeneous WSN to carry out efficient routing. The major intention of the designed protocol is the incorporation of features of both IEEE 802.15.4 and Low-Energy Adaptive Clustering Hierarchy (LEACH) to solve the challenges. The energy-saving circuit is adopted by predetermining the cluster heads (CHs), whereas the usual nodes are powered by a battery. Thus, it is suggested to extend the lifespan of network operation, where the designed hybrid protocol is intended to transfer the essential activities to the elected cluster heads when reducing the activity of the nodes. Here, a new optimizer known as the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is suggested for selecting the cluster heads and also for promoting the IEEE 802.15.4 protocol. This protocol is assisted to ensure performance regarding self-healing, scalability, self-reconfigurability, and energy efficiency. Thus, the performance evaluation is conducted in terms of various performance measures like throughput and energy consumption over the Adaptive Leach Protocol and multi-channel MAC protocol with IEEE 802.15.4.