International Journal of Wireless and Microwave Technologies @ijwmt
Journal articles - International Journal of Wireless and Microwave Technologies
All articles: 622
Adaptive HEC-VPS: The Real-time Reliable Wireless Multimedia Multicast Scheme
Scientific article
To satisfy the reliability of real-time wireless multimedia multicast services, the existing erasure error correction schemes usually assume that the packet size in transmissions is fixed. However, recent studies have shown that Variable Packet Size (VPS) can deeply influence the performance of unicast wireless services. Accordingly, using a delay-limited general architecture of EEC for real-time wireless multicast, this paper proposes an Adaptive Hybrid Error Correction (AHEC) scheme with VPS. Comparing with the AHEC schemes with fixed packet size, the analysis results show that the AHEC with VPS scheme can improve the throughput by about 10% in some cases.
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Adaptive Multi User Detection for FD-MC-CDMA in Presence of CFO
Scientific article
The main targets of multi-carrier direct sequence code division multiple access (MC-DS-CDMA) mobile communication systems are to overcome the multi-path fading influences as well as the near-far effect and to increase its capacity. Different types of optimal and suboptimal multi-user detection schemes have been proposed and analyzed in literature. Unfortunately, most of them share the drawback of requiring an efficient practical solution. Genetic algorithm provides a more robust and efficient approach for solving complex real world problem such as multi user detection, but genetic algorithms are not computationally efficient. Computational complexity and performance of the genetic algorithms depends on number of generations and/or the population size, schemes involving genetic algorithms would compromise in computational complexity or performance. In this paper we propose adaptive population sizing genetic algorithm based multi user detection algorithm and compare its performance with existing multi user detection algorithms in various channels. Simulation results confirmed that the proposed adaptive genetic algorithm assisted multi user detection algorithm performs better compared to the existing multi user detection algorithms.
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Adaptive Multi-Stage Fuzzy Logic Model of Student Knowledge Assessment
Scientific article
Accurate and objective assessment of students’ knowledge remains a challenging problem due to the inherent uncertainty and subjectivity of traditional evaluation systems. Conventional grading approaches often fail to account for task complexity, discrimination power, and variability in student responses, which leads to inconsistent and biased results. This study proposes a multi-stage fuzzy logic–based decision-making model for knowledge assessment. The model integrates several key evaluation indicators, including task difficulty, discrimination index, response value, and response weight, within a unified fuzzy inference framework. A structured multi-factor evaluation mechanism is developed, where fuzzy membership functions and rule-based inference are used to transform qualitative judgments into quantitative assessment measures. Furthermore, a defuzzification process based on the Center of Gravity (COG) method is applied to obtain final scores, and a correction mechanism is introduced to refine evaluation outcomes. A comparative analysis was conducted using assessment data from 100 students across 5 tasks evaluated on a [0–10] scale. The results suggest that the proposed approach provides a more differentiated and consistent interpretation of student performance than the traditional assessment method. The proposed model provides a reliable and interpretable framework for evaluating students’ knowledge and supports the development of adaptive and intelligent educational assessment systems.
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Adversarial Deep Learning in Anomaly based Intrusion Detection Systems for IoT Environments
Scientific article
Using deep learning networks, anomaly detection systems have seen better performance and precision. However, adversarial examples render deep learning-based anomaly detection systems insecure since attackers can fool them, increasing the attack success rate. Therefore, improving anomaly systems' robustness against adversarial attacks is imperative. This paper tests adversarial examples against three anomaly detection models based on Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), and Deep Belief Network (DBN). It assesses the susceptibility of current datasets (in particular, UNSW-NB15 and Bot-IoT datasets) that represent the contemporary network environment. The result demonstrates the viability of the attacks for both datasets where adversarial samples diminished the overall performance of detection. The result of DL Algorithms gave different results against the adversarial samples in both our datasets. The DBN gave the best performance on the UNSW dataset.
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Adversarial Machine Learning Attacks and Defenses in Network Intrusion Detection Systems
Scientific article
Machine learning is now being used for applications ranging from healthcare to network security. However, machine learning models can be easily fooled into making mistakes using adversarial machine learning attacks. In this article, we focus on the evasion attacks against Network Intrusion Detection System (NIDS) and specifically on designing novel adversarial attacks and defenses using adversarial training. We propose white box attacks against intrusion detection systems. Under these attacks, the detection accuracy of model suffered significantly. Also, we propose a defense mechanism against adversarial attacks using adversarial sample augmented training. The biggest advantage of proposed defense is that it doesn’t require any modification to deep neural network architecture or any additional hyperparameter tuning. The gain in accuracy using very small adversarial samples for training deep neural network was however found to be significant.
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Scientific article
Wireless Sensor Networks have emerged as a key technology enabling real time data collection and monitoring across various domains, including environmental monitoring, industrial control, healthcare, and security applications. However, despite their growing relevance, energy efficiency remains a fundamental design challenge due to the limited power supply of sensor nodes, which directly impacts overall network lifetime and reliability. This paper proposes an Adaptive Energy-Aware Clustering Protocol (EACP) designed to improve energy efficiency and extend the operational lifetime of homogeneous WSNs. The proposed protocol integrates three main mechanisms: Residual Energy-based Cluster Head Selection, to ensure balanced energy distribution; Mobility-Aware Cluster Head Reassignment, to maintain stable communication under node mobility; and Base Station Proximity Based Direct Transmission, which allows nodes near the BS to bypass CHs, thereby minimizing redundant energy use. These mechanisms allow the network to dynamically adapt to changing energy conditions and communication distances. The protocol was evaluated through extensive MATLAB simulations and compared with benchmark protocols including LEACH, HAC, and HSA. Simulation results demonstrate that the proposed EACP significantly improves network performance. Specifically, it achieves 50% to 94% improvement in network lifetime, reduces energy consumption by approximately 20% to 25%, and increases throughput by more than 2.5 times compared to the benchmark protocols. These results demonstrate that EACP offers a scalable, energy-efficient communication strategy well suited for large scale WSNs deployments.
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Scientific article
Wireless Sensor Networks play a vital role in the Internet of Things, smart cities, and industrial automation, yet there are open ended challenges in terms of efficient energy management and reliable data transmission. This paper presents a novel, two-phase routing framework comprising Dynamic Channel Selection and Energy-Efficient Routing Optimization to address these issues. In the first phase, Deep Q-Learning is utilized to identify stable communication channels, thereby enabling congestion-free data transfer across the network. The second phase implements Coral Reef Optimization to derive energy-efficient routing paths, significantly minimizing power consumption. Additionally, Adaptive Modulation and coding dynamically adjusts transmission parameters in real time to improve data throughput and reduce network delays. Existing solutions have been limited by network instability, poor scalability, and inefficient spectrum usage; In contrast, the integrated approach leverages Deep Q-Learning for intelligent channel allocation and Coral Reef Optimization for optimized route selection, while Adaptive Modulation and Coding fine-tunes the communication process to achieve optimal performance. Compared to existing models which shows high packet drop ratio and scalability constraints, our model achieves a 68% reduction in energy consumption, increases network lifetime by 82%, lowers error rate by 77%, enhances routing stability by 85%, and boosts overall throughput by 79%. These results highlight the proposed model’s potential as a highly adaptive, low-latency, and scalable solution for next-generation wireless sensor network applications.
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An Analysis of VoIP Application in Fixed and Mobile WiMAX Networks
Scientific article
WiMAX is a wireless broadband technology, which promises maximum coverage area and high data rates. WiMAX provides last mile connectivity. This network defines two working models such as fixed WiMAX and mobile WiMAX. The main aim of this paper is to compare and also analyze the performance of the VoIP application over fixed and mobile WiMAX, with respect to various codecs such as G.711, G.723.1ar5.3, G.726ar24, G.728ar16 and G.729. We have considered few QoS parameters such as average end-to-end delay, throughput, average jitter, average one-way delay and average MOS. From the obtained result, codec G.711 and G.726ar24 performs better when compared to other codecs in both fixed and mobile WiMAX network.
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An Analytical Study of Cloud Security Enhancements
Scientific article
Enhancements and extensions in pervasive computing have enabled penetration of cloud computing enabled services into almost all walks of human life. The expansion of computational capabilities into everyday objects and processes optimizes end users requirement to directly interact with computing systems. However, the amalgamation of technologies like Cloud Computing, Internet of Things (IoT), Deep Learning etc are further giving way to creation of smart ecosystem for smart human living. This transformation in the whole pattern of living as well as working in enterprises is generating high expectations as well as performance load on existing cloud implementation as well as cloud services. In this complete scenario, there are simultaneous efforts on optimizing as well as securing cloud services as well as the data available on the cloud. This manuscript is an attempt at introducing how cloud computing has become pivotal in the current enterprise setting due to its pay-as -you -use character. However, the allurement of using services without having to procure and retain involved hardware and software also has certain risks involved. The main risk involved in choosing cloud is compromising security concerns. Many potential customers avoid migrating towards cloud due to security concerns. Security concerns for the cloud implementations in the recent times have grown exponentially for all the varied stakeholders involved. The aim of this manuscript is to analyze the current security challenges in the existing cloud implementations. We provide a detailed analysis of existing cloud security taxonomies enabling the reader to make an informed decision on what combination of services and technologies could be used or hired to secure their data available on the cloud.
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An Approach Towards Dynamic Opportunistic Routing in Wireless Mesh Networks
Scientific article
Opportunistic routing (OR) for multi-hop wireless networks was first proposed by Biswas and Morris in 2004, but again as a modified version in 2005 as Extremely Opportunistic Routing (ExOR). A few other variants of the same were also proposed in the meanwhile time. In this paper we propose a Dynamic Opportunistic Routing (DOR) protocol which depends on network density and also provides spatial diversity. Our routing protocol is distributed in nature and provides partial 802.11 MAC layer abstraction. To verify the results of our protocol we took a network with light-density of nodes and bigger in size (as OR performs better in higher node density). A wireless mesh network in “QualNet network simulator” was created, where the average end-to-end delay and throughput at every node are compared with that of other standard routing protocol OLSR-INRIA.
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An Approach to Micro-blog Sentiment Intensity Computing Based on Public Opinion Corpus
Scientific article
Based on the analysis of the status of network public opinion, the features of short content and nearly real-time broadcasting velocity in this paper, it constructs a public opinion corpus on the content of micro-blog information, and proposes an approach to marking corpus on the basis of sentiment tendency from the semantic point of view; Furthermore, considering the characteristics of micro-blog, it calculates the sentiment intensity from three levels on words, sentences and documents respectively, which improves the efficiency of the public opinion characteristics analysis and supervision. So as to provide a better technical support for content auditing and public opinion monitoring for micro-blog platform.
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An Automated Optimization Workflow for HFSS Using GA and PSO for Circular Patch Antenna Design
Scientific article
This paper presents the automated design and optimization of a compact circular microstrip patch antenna for C-band applications using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Microstrip patch antennas inherently suffer from narrow impedance bandwidth, making systematic optimization essential for wideband wireless applications. The antenna is implemented on an FR4 substrate (24 × 24 mm2, εr = 4.4, h = 1.6 mm) and optimized through ANSYS HFSS using the PyAEDT Python interface. Three key design parameters were tuned to enhance impedance bandwidth and minimize return loss GA achieved the best performance among the considered optimization methods, with an optimized bandwidth of 3.74 GHz and a minimum S11 of –37 dB, while the optimized PSO method reduced computation time by approximately 49% compared to manual tuning and 31% compared to GA. The final optimized design exhibits consistent gain performance (2.3–2.8 dB) and stable radiation patterns across the operational band, confirming reliable C-band operation. The results demonstrate that metaheuristic optimization integrated with HFSS automation provides a powerful and efficient antenna design framework, which can be extended toward hybrid algorithms and intelligent machine-learning-assisted antenna prediction models.
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An Efficient and Cloud Based Architecture for Smart Grid Security
Scientific article
Due to explosive growth of users, increasing energy demand and also the need to improve efficiency and maintain the stability of the electricity grid, smart grid is the only option available to electrical industry engineers. In fact, the smart grid is a physical-cyber system that provides coherent and integrated communication, processing and control functions. The smart grid provides control and management of millions of devices in the electricity industry in a reliable, scalable, cost-effective, real time and two-sided manner. Given the increasing growth of cyber threats in the last decade, the need to protect the electricity industry and its critical systems seems essential. The slightest disruption to the power industry's systems results in disruption to other industries, reduced productivity, and discontent. Hence we proposed an efficient cloud based architecture to improve smart grid performance. Proposed architecture provides data security and privacy against different types of cyber threats such as replay attack, modification attack and so on.
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An Efficient and Comapct Design of Coupled-Line Unequal Wilkinson Power Divider
Scientific article
In this paper, 3:1 and 10:1 unequal Wilkinson power dividers are designed and investigated. Unequal power dividers are an integral part of the feeding network for antenna array where their performance affects a group of elements rather than an individual element. Thus, there is a need for an accurate design for such dividers. A coupled-line section with two grounding via has been used to attain the high characteristic impedance line. This is done in order to outplay the microstrip fabrication constraints of printing very thin/fine conductor lines. Further to reduce the size of the structure, meandering of the transmission line has been done which procreates three designs, namely: 00 Serpentine Flexure, 1800 Serpentine Flexure and Compact Meandered Flexure. Verification of the design methodology has been done by creating a 10:1 Unequal WPD. The structures are implemented on a high-resistive silicon substrate (HRS) for a centre frequency of 1.575 GHz. Further, their EM analysis is done in terms of S-parameters such as return loss and insertion loss using commercially available FEM solver. Satisfactory RF performance, with return loss better than -10 dB and required power split for all the structures, has been achieved.
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An Energy Efficient Routing Algorithm for Wireless Body Area Network
Scientific article
Wireless body area networks (WBANs) are emerging as one of the newest forms of Wireless Sensor Networks. In WBANs, sensor nodes accumulate human physiological data and transmit it to the sink node. However, transmission of physiological data to the sink node over a mobile route becomes a very daunting task for sensors due to their limited battery power. Moreover, replacement of critical sensor nodes is a major challenge in such scenarios. In order to increase network lifetime, some routing protocols have been proposed in the literature, but the majority of them are focused on coverage distance and residual energy of sensor nodes. In this work, we will propose an energy efficient routing algorithm for WBANs that will take into consideration two additional attributes (node criticality and communication count) along with the above stated attributes. These four parameters in the proposed algorithm will result in enhancement of working lifetime of the network via less energy consumption in comparison to existing routing protocols.
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An Energy-Efficient Wireless Sensor Network (EE-WSN) for Hazard and Crack Detection in Coal Mines
Scientific article
This paper presents a detailed study focused on the utilization of energy-efficient wireless sensor networks (WSNs) specifically designed for hazard and crack detection in coal mines. The primary objective of this research is to develop a WSN system that operates on low power consumption, enabling real-time monitoring of hazardous conditions and cracks within coal mines. The proposed system incorporates energy-efficient methods and protocols to minimize power usage and prolong the lifespan of sensor nodes. The study encompasses the complete design and implementation of a prototype WSN system, followed by a thorough evaluation of its performance within a simulated environment. The obtained results demonstrate the effectiveness of the proposed system in detecting hazards and cracks in real-time while consuming minimal power. Consequently, this research underscores the potential of energy-efficient WSNs to enhance the safety and efficiency of coal mining operations. Moreover, the findings of this study have broader implications, as they can serve as a foundation for the development of similar systems applicable to other hazardous environments, including oil rigs, nuclear power plants, and forest fires. By adopting the energy-efficient WSN approach outlined in this research, these industries can benefit from improved safety measures and enhanced operational efficiency.
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An Enhanced LEACH Routing Algorithm for Energy Conservation in A Wireless Sensor Network
Scientific article
It has been well established that the lifetime of a Wireless Sensor Network (WSN) depends on the energy of the sensor nodes which is limited by the battery capacity of the nodes. This challenge has led to research efforts towards developing a more efficient energy based routing algorithms and architectures. One of such popular algorithm is the Low-Energy Adaptive Clustering Hierarchy (LEACH). The LEACH approach adopts randomized rotation of local base stations (or cluster-heads (CH)) to evenly distribute energy load amongst the sensors in the network. A variant of LEACH known as Vice-LEACH (V-LEACH) introduces the concept of a vice-CH that takes over the role of the main CH in the event of CH death. Random selection of cluster- head node in both LEACH and V-LEACH ignore node's residual energy and the optimal number and distribution of cluster head is not ensured in all rounds. This work presents an enhanced cluster-based routing algorithm which took into consideration the residual energy of nodes in the network while maintaining the optimal number of CHs throughout the network lifetime. A sensor network was developed on OPNET Modeler software tool and simulation tests were performed to evaluate the performance of the enhanced routing algorithm on the network. Simulation results showed that this enhanced routing algorithm distributed network energy consumption across the network nodes hence significantly improving the lifetime of the sensor network.
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An Enhanced Method Utilizing Hopfield Neural Model for Mobile Agent Protection
Scientific article
Mobile agent is a piece of computer code that organically goes from one host to the another in a consistent or inconsistent environment to distribute data among users. An autonomous mobile agent is an operational programme that may migrate from one computer to machine in different networks under its own direction. Numerous health care procedures use the mobile agent concept. An agent can choose to either follow a predetermined course on the network or determine its own path using information gathered from the network. Security concerns are the main issue with mobile agents. Agent servers that provide the agents with a setting for prosecution are vulnerable to attack by cunning agents. In the same way agent could be carrying sensitive information like credit card details, national level security message, passwords and attackers can access these files by acting as a middle man. In this paper, optimized approach is provided to encrypt the data carried by mobile agent with Advanced Encryption Standard (AES) algorithm and secure key to be utilized by the AES Encryption algorithm is generated with the help of Hopfield Neural Network (HNN). To validate our approach, the comparison is done and found that the time taken to generate the key using HNN is 1101ms for 1000 iterations which is lesser than the existing models that are Recurrent Neural Networks and Multilayer Perceptron Network models. To add an additional level of security, data is encoded using hash maps which make the data not easily readable even after decrypting the information. In this way it is ensured that, when the confidential data is transmitted between the sender and the receiver, no one can regenerate the message as there is no exchange of key involved in the process.
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An Enhanced Reputation-based for Detecting Misbehaving Nodes in MANET
Scientific article
In this paper, we propose an enhanced approach based on first-hand reputation with allows to detected misbehavior node in Manet. The network's security is an important challenge in this kind of networks. The main objective of the misbehaving nodes in AODV routing protocol deletes all data packets that received thus, don’t transmit to their destination. In a reputation-based system, each node overhears the activity of its neighbors (transmit and receive) in first-hand or combined with second-hand. Our approach is composed of three-phase directly monitoring, calculating reputation value and node isolation. The reputation value is enhanced by the packet dropped due to other events such as overloading of queue and node availability. The node with a negative reputation will be isolated, and an alert packet will be distributed to neighboring nodes to inform, afterwards an improved local repair is started. The simulation results show that the proposed approach can detect and isolate a malicious node, which improves the packet delivery ratio and lowest increasing in the throughput, while reducing the success rate of the misbehaving nodes.
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An Epistemic Model Checking Approach to Web Service Compositions
Scientific article
Due to the dynamics of Web services, the openness and variability of Internet, and the loosely-coupled developing approach of Web services, the development and execution process of Web service compositions becomes uncertain, which imperils the trustworthy properties. In this paper we abstract Web service compositions as multi-agent systems, propose a formal model BSTS for modeling BPEL, develop and implement two translation algorithms B2S and S2I, to translate BPEL into BSTS and translate BSTS into the input language ISPL of the model checker MCMAS for multi-agent systems, respectively. The proposed method supports not only temporal properties, but also epistemic and cooperation properties, which are supported only in multi-agent systems. We implemented the prototype tool, called MCWS, for the proposed method. We modeled and verified an example of Web service compositions. The experimental results show the validity of MCWS.
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