International Journal of Wireless and Microwave Technologies @ijwmt
Статьи журнала - International Journal of Wireless and Microwave Technologies
Все статьи: 596
A Weighed Least Square TDOA Location Algorithm for TDMA Multi-target
Статья научная
In order to improve the location precision of multiple targets in a time division multiple address (TDMA) system, a new weighed least square algorithm is presented for multi-target ranging and locating. According to the time synchronization of the TDMA system, the range difference model between multiple targets is built using the time relations among the slot signals. Thus, the range of one target can be estimated by the other one's, and a group of estimated value can be acquired for every target. Then, the weighed least square algorithm is used to estimate the range of every target. Due to the time differences of arrival (TDOA) of all targets are used in one target's location, the location precision is improved. The ambiguity and non-solution problems in the traditional TDOA location algorithm are avoided also in the presented algorithm. At the end, the simulation results illustrate the validity of the proposed algorithm.
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A Wireless Solution to Collecting and Displaying Oil Temperature Data Based on Zigbee Network
Статья научная
In some oil fields of China, monitoring oil temperature during producing almost relies on manual operation. According to this, we designed a solution based on Zigbee network and sensor technology. In our design of sampling nodes, with JN5139 module as the core, uses sensor PT100 to collect temperature data and LCD module LCM141C-01 to display the final result. The whole solution has an advantage of a simple structure, low power consumption etc, therefore, it has a positive meaning in improving monitoring work efficiency in oil fields.
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A double auction scheme based on secret sharing and safe comparing protocol
Статья научная
At present most practical electrical auction schemes that based on secret sharing, group signature and hash chain assume the existence of a trusted thid party, while the schemes that based on commitment, zero knowledge or homomorphic encryption without the TTP model face the problem of high cost of communication and computation. The double auction scheme proposed by Bogetoft is based on secure multiparty computation without a TTP and get very high efficiency. In this article, we improve the above scheme. Based on secret sharing and constant round safe comparing protocol, we reduce the requirement of honest majority in the original scheme. We improve the security of the scheme while maintain the high efficiency.
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A hybrid cryptosystem to enhance security in IoT health care system
Статья научная
Internet of Things (IoT) based health care system provides an essential interface between tiny devices and customers, who require customary checking by remedial focus. The technological innovation is important to guarantee the data protection and security among customer and devices, though it is vulnerable by various security assaults. The cryptographic technique is a prominent method for data protection in a healthcare management system. Single cryptographic algorithm based solution suffered to provide efficient security as its high probability of attacks. So this paper proposes a hybrid cryptographic algorithm that secures the sensitive medicinal information in IoT health care system. The proposed hybrid algorithm is dealing with various security attacks in a proficient way, and also increases its performance when compared to other cryptographic algorithms.
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A modified semi-supervised color image segmentation method
Статья научная
The paper proposed a modified color image segmentation method basing on semi-supervised hidden Markov random fields (HMRF) with constraints. Making use of MeanShift algorithm to get supervision information and, cluster number and initial values for cluster centroids, color images can be segmented effectively with the method in this paper by K-Means algorithm. The experimental results are very encouraging.
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A novel energy efficient cluster head selection method for wireless sensor networks
Статья научная
Wireless Sensor Networks are becoming a worldwide sensational topic with recent advances in wireless communications and digital electronics. It serves as the backbone for controlling real-life applications. It consists of group of sensor nodes that sense the information from an event area and passes it to the base station which reacts according to the environment. There are number of cluster based routing protocols, in which a region is divided into number of clusters and within each cluster, a cluster head is elected based on some parameter. So, a novel selection method for the cluster head having efficiency in energy is based on Flower Pollination Algorithm (FPA) is proposed in this paper. The performance of our proposed scheme is being analyzed and is compared with the already existing protocols like LEACH, C-LEACH and K-Means in terms of energy efficiency, number of alive nodes, packet drop ratio and energy dissipation etc
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A power-based method for improving the ODMRP protocol performance in mobile ad-hoc networks
Статья научная
Mobile AD hoc Networks as a special type of wireless networks have received special attention due to having special features such as no need for central management, no need for infrastructure and high mobility capability and can be used in cases where creating an effective communication infrastructure is not cost-effective or is practically impossible, such as conferences, such as battles and communications after natural disasters. Several routing protocols are proposed for these networks. ODMRP protocol is one of the most famous and used protocols in Mobile AD hoc networks. This study was carried out aimed to discuss this routing protocol and then provide a new routing method for this protocol for increasing its efficiency. In the ODMRP protocol, the optimal route is selected based on the shortest route. In wireless communications and getting the nodes away from each other, the received signal levels are weakened and may result in loss of data, and in practice, the shortest path that works based on the number of hops loses its effectiveness. In the proposed protocol, the route is selected based on the received signal strength level. According to the simulation results, the performance of the proposed protocol increases by decreasing control overhead and increasing the packet delivery rate compared to the original protocol.
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Статья научная
Blockchain’s vast applications in different industries have drawn several researchers to pursue extensive research in securing blockchain technologies. In recent times we could see several institutions coming together to create consortium based blockchain networks such as Hyperledger. Although for applications of blockchain such as Bitcoin, Litcoin, etc. the majority-attack might not be a great threat but for consortium based blockchain networks where we could see several institutions such as public, private, government, etc. are collaborating, the majority-attack might just prove to be a prevalent threat if collusion among these institutions takes place. This paper proposes a methodology where we can use intelligent software agents to monitor the activity of stakeholders in the blockchain networks to detect anomaly such as collusion, using supervised machine learning algorithm and algorithmic game theory and stop the majority attack from taking place.
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A stable routing protocol based on DSR protocol for mobile ad hoc networks
Статья научная
Mobile ad hoc network is a wireless non-centralized network. This network includes a set of distributed nodes with any base or central management, develop a temporary network. Such a way, routing is of the basic challenges of these kinds of networks. Till now, various protocols have been provided for routing in these kinds of networks. One of the optimum routing techniques in these networks is stable routing. In the stable routing, the target is to use paths for send and receive that probably have a higher life period. This causes the data to be sent from a more confident path and the need to new routing will be less, due to liquidation of the current path. In this paper, the target is to provide a stable routing protocol with high efficiency for these kinds of networks, by improving the DSR routing protocol. In the provided protocol, beside the path stability, the energy of the path nodes and path length will be considered too, in order to discover a path with higher quality and use it. The provided protocol will be called as ST-DSR. The result of stimulation in the NS-2 environment shows that the ST-DSR has a better operation toward the base protocol, meaning DSR.
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A study on Need of Adaptation Layer in 6LoWPAN Protocol Stack
Статья научная
Emergence of wireless embedded applications is resulting in the communication among sensor nodes connected in a wireless personal area network. Sensor nodes gather the real time information and transmit it to the desired application. This requires transmission of IPv6 packets over Low-power wireless personal area network and is called 6LoWPAN. IPv6 is resource intensive protocol whereas 6LoWPAN is resource constraint due to small packet size, limited device memory, short transmission range, and less data rate of sensor nodes. Also these nodes in 6LoWPAN are mainly battery operated hence minimum power consumption is also a major constraint. To make the efficient transmission of information in such a resource constraint network, an adaptation layer was suggested and implemented by Internet Engineering Task Force (IETF). The placing of this additional layer is in between network layer and data link layer of TCP/IP protocol stack. This paper contributes in the detailed analysis of need of adaptation layer in 6LoWPAN protocol stack. The necessity of this additional layer is justified by explaining the major functions like header compression, fragmentation and reassembly of packets and packet routing handled by it.
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Статья научная
The optimal capacity expansion of base station subsystems in Next Generation Wireless Network (NGWN) problem with respect to multi-demand type and system capacity constraints is known to be NP-complete. In this paper, we propose a novel ant colony optimization algorithm to solve a network topology has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. There are two important aspects of upgrading to NGWN. The first importance of backward compatibility with pre-existing networks, and the second is the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. Our objective function is the sources to concentrators connectivity costas well as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. We evaluate the performance of our algorithm with a set of real world and data randomly generated. Numerical results show that our algorithms is a promising approach to solve this problem.
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Статья научная
This research presents a pioneering investigation into the tangible outcomes of implementing an Artificial Intelligence (AI) driven network security strategy, with a specific emphasis on dynamic threat landscape adaptation and the integration of time series analysis algorithms. The study focuses on the innovative fusion of adaptive mechanisms to address the ever-evolving threat landscape, coupled with the application of the Autoregressive Integrated Moving Average (ARIMA) time series analysis algorithm. Real-world case studies are employed to provide concrete evidence of the efficacy of these strategies in fortifying network defenses and responding dynamically to cyber threats. Novelty is introduced through the unified integration of dynamic threat landscape adaptation mechanisms that continuously learn and evolve. The paper details adaptive access controls, showcasing how the security system dynamically adjusts permissions in real time to respond to emerging threats. Additionally, the application of the ARIMA time series analysis algorithm represents a pioneering contribution to the field of cybersecurity. By unveiling temporal patterns in security incidents, ARIMA adds a predictive element to network defense strategies, offering valuable insights into potential future threats and enabling a proactive response. The findings underscore the practical impact of the applied strategies, with real-world case studies demonstrating substantial improvements in threat detection rates, the effectiveness of adaptive responses, and the predictive capabilities facilitated by ARIMA. This research contributes to the advancement of AI in network security by providing tangible evidence of the innovative and effective nature of the integrated approach. The outcomes bridge the gap between theoretical concepts and practical applications, offering valuable insights for organizations seeking adaptive and predictive strategies to enhance their cybersecurity resilience in dynamic threat environments.
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Статья научная
All new mobile radio communication systems undergo a cautious cellular network planning and re-planning process in order to resourcefully utilize the allotted frequency band and also ensure that the geographical area of focus is adequately fortified with integrated base stations transmitters. To this end, efficient radio propagation model prediction and tuning is of huge importance, as it assists radio network engineers to effectively assess and plan the cellular network signal coverage area. In this research work, an adaptive least absolute deviation approach is proposed and verified to fine-tune the parameters of Ericsson propagation model. The adaptive tuning technique have been verified experimentally with field propagation loss data acquired over three different suburban locations of a recently deployed LTE radio cellular network in Waterlines area of Port Harcourt City. In terms of the mean absolute percentage error and coefficient of efficiency, the outcomes of the proposed adaptive tuning approach show a higher degree of prediction performance accuracy on the measured loss data compared to the commonly applied least squares regression tuning technique.
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Статья научная
One of the key aspects of 5G networks is the implementation of massive MIMO (Multiple Input Multiple Output) technology combined with adaptive beamforming. This study explores the use of a linear array antenna to manage and reduce unwanted signals such as jamming, interference, and noise, while also boosting the signal strength towards the intended user or device. The main challenge lay in optimizing the weights of the antenna elements, which was tackled by employing adaptive algorithms like LCMV (Linearly Constrained Minimum Variance) and RLS (Recursive Least Squares). To simplify the optimization process, two soft computing techniques—Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)—were utilized. The performance of the beamforming weights and radiation patterns was assessed in terms of minimizing unwanted signals and maximizing the desired signal. To check how well the proposed methods work, some commonly used algorithms like MVDR (Minimum Variance Distortionless Response) and LCMV are also applied. The outcomes were compared to those from other algorithms. A Differential Beamforming method is applied to examine how effectively the system can focus the signal in the target direction while minimizing unwanted interference from other directions. Additionally, the fminsearch algorithm, which is a basic local search method, is used to compare how well it can adjust the beamforming weights compared to the more advanced global optimization techniques. The results indicate that PSO and GA produce highly similar performance levels.
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Adaptive Cross-layer Resource Allocation by HNN in OFDM-MISO System
Статья научная
This paper presents an adaptive cross-layer resource allocation problem with the fairness in multi-user OFDM-MISO communication systems, and provides two solutions with Hopfield Neural Network (HNN) and Genetic Algorithm (GA) for the problem. We utilized HNN’s characteristics such as parallel processing, fast convergence speed and easy convergence to the optimum, to solve this problem under the conditions of proportional fairness for satisfying system performances and users’ requirements. The method is simplified in the computation by dividing the bit-loading matrix into three matrixes. The simulation results show that HNN and GA can effectively solve optimization problems of resource allocation in such system, and results of selected HNN and GA methods are more effective than that of the traditional method.
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Adaptive HEC-VPS: The Real-time Reliable Wireless Multimedia Multicast Scheme
Статья научная
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
Статья научная
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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Adversarial Deep Learning in Anomaly based Intrusion Detection Systems for IoT Environments
Статья научная
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
Статья научная
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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Статья научная
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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