Forthcoming and Online First Articles

International Journal of Wireless and Mobile Computing

International Journal of Wireless and Mobile Computing (IJWMC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Wireless and Mobile Computing (58 papers in press)

Regular Issues

  •   Free full-text access Open AccessFracture constitutive study of spring beam model applied to slurry anchor connection.
    ( Free Full-text Access ) CC-BY-NC-ND
    by Wenjun Zhou, Shipian Shao, Hongxin Nie, Li Ma 
    Abstract: In order to prove the feasibility of selecting the beam element attributes of the spring-beam model by parameter prediction algorithm. The study proposes beam elements fracture constitutive model that simulates the fracture of mortar and concrete at the slurry-anchor connection. By adjusting the elastic modulus, plastic strain, fracture modulus and section properties of beam elements, the results of literature tests are compared with the finite element calculation, and analyze the effects of key parameters in the beam elements fracture constitutive calculation model. The results show that the calculated slip deformation of the slurry anchor connection is controlled by the plastic strain and fracture modulus of beam elements. The mortar enters into the plasticity calculation stage is controlled by section properties of beam elements, the overall deviation of calculation from experimental results is controlled by the elasticity modulus of beam elements, and the parameter optimal solution has uniqueness.
    Keywords: spring-beam; beam element fracture; slurry anchor; mortar; concrete.
    DOI: 10.1504/IJWMC.2024.10061994
     
  • Multi-objective workflow scheduling in the cloud environment based on NSGA-II   Order a copy of this article
    by Tingting Dong, Chuangbai Xiao 
    Abstract: The emergence of cloud computing offers a novel perspective to solve large-scale computing problems. Workflow scheduling is a major problem in the cloud environment, and parallelism and dependency are two important characteristics of tasks in a workflow, which increases the complexity of problem. Workflow scheduling is also a multi-objective scheduling problem, and task execution time and cost are the two extremely significant goals for users and providers in the cloud environment. Existing heuristic algorithms are popular, but they lack of robustness and need to be revised when the problem statement changes. Evolutionary algorithms have a complete algorithm system, which is widely used in the multi-objective scheduling problem. In this paper, Nondominated Sorting Genetic Algorithm-II (NSGA-II) is utilized to solve the workflow scheduling problem aiming at minimizing the task execution time and cost. Some real-world workflows are used to make simulation expriments, and comparative simulations with genetic algorithm are given. Results show that NSGA-II is effective for the workflow scheduling.
    Keywords: cloud computing; workflow scheduling; non-dominated sorting genetic algorithm-II; multi-objective scheduling.

  • Enhancing artificial bee colony algorithm with depth-first search and direction information   Order a copy of this article
    by Xinyu Zhou, Hao Tang, Shuixiu Wu, Mingwen Wang 
    Abstract: In recent years, artificial bee colony (ABC) algorithm has been criticized for its solution search equation, which makes the search capability bias towards exploration at the expense of exploitation. To solve the defect, many improved ABC variants have been proposed aiming to use the elite individuals. Although these related works have shown effectiveness, they rarely take the factor of search direction into account. In fact, the search direction has an important role in determining the performance of ABC. Thus, in this work, we are motivated to investigate how to combine the idea of using the elite individuals with the search direction, and a new ABC variant, called DDABC, is designed. In the DDABC, the depth-first search (DFS) mechanism and direction information learning (DIL) mechanism are introduced, and the former mechanism is to allocate more computation resources to the elite individuals, while the latter mechanism aims to adapt the search to the promising directions. To verify the effectiveness of the DDABC, experiments are carried out on 22 classic test functions, and three relative ABC variants are included as the competitors. The comparison results show the competitive performance of our approach.
    Keywords: artificial bee colony; exploration and exploitation; depth-first search; direction information learning.

  • Machine learning-based approach for the detection of phishing websites   Order a copy of this article
    by Yaqin Wang, Jingsha He, Nafei Zhu 
    Abstract: Compared with traditional forms of crime, cyber-attacks and cyber-crimes have removed the limitation on distance and speed. With very low cost, phishing is a very effective way of launching network attacks with the purpose of obtaining sensitive information about users, such as username, password and payment voucher, through counterfeiting regular websites so as to steal users private information and personal property using the obtained information. Both the trust that internet users have and the development of the internet itself can be affected by this kind of attack, making it imperative to detect this type of attack. Many methods have been proposed for the detection of phishing websites in the literature in recent years based on techniques ranging from conventional classifiers to complex hybrid classifiers. Meanwhile, although convolutional neural networks (CNNs) can achieve very high accuracy in classification tasks, not much research has been done on the use of CNNs for the detection of phishing websites. This paper proposes a CNN-based scheme for the detection of phishing websites in which four dimensions of the features of phishing websites are defined and CNN is used to extract local features. The proposed CNN-based scheme is compared with several machine learning-based methods on the effectiveness of detecting phishing websites, which shows that the proposed scheme can achieve the accuracy rate of 97.39% and is better than the other classification methods in terms of accuracy, recall and F1-score.
    Keywords: convolutional neural network; classification; machine learning; phishing website detection.

  • Cold-start recommendation algorithm based on user preference estimation   Order a copy of this article
    by Biao Cai, Jiahui Xin, Xu Ou 
    Abstract: In order to improve the dilemma of collaborative filtering in the face of cold start and achieve a better balance between accuracy and diversity, this paper considers the influence of user characteristics on recommendation results and proposes a Preference Estimation Network (PEN) based on maximum likelihood. PEN uses the user's characteristic information to estimate the user's preference information, and represents the user's preference vector with the item's label system. On this basis, PEN-Rec, an improved version of the traditional recommendation algorithm based on preference vector estimation and particle swarm optimisation, is proposed. Finally, the PEN-Rec algorithm is compared with the benchmark algorithm on six public evaluation indicators using open datasets, and the experimental results show that the accuracy, diversity and novelty of the PEN-Rec algorithm are all improved.
    Keywords: recommendation; feature impact; preference estimation; label vector.

  • Link prediction with Fusion of DeepWalk and node structural information   Order a copy of this article
    by Xinhui Xiang, Biao Cai, Yunfen Luo 
    Abstract: The existing link prediction algorithms are mainly based on structural information or network embedding, but minimal research has been conducted on the fusion of these algorithms. It is found that the structure-based algorithms have high accuracy, but the complexity is higher owing to the introduction of high-order structural information while the network embedding algorithms have low complexity, but because the structural information of the node is not fully used, the accuracy is not as good as some structure-based algorithms. Therefore, by combining the structural attributes of nodes and the degree of convergence between node pairs, this paper proposes two new improved similarity algorithms the similarity algorithm based on edge-degree DeepWalk cosine (EDDWC) and the similarity algorithm based on preferential attachment mechanism DeepWalk cosine (PADWC). Experiments show that the performances of the proposed algorithms are greatly improved over that of the DeepWalk algorithm, and they are also better than other link prediction algorithms.
    Keywords: link prediction; DeepWalk; edge-degree; preferential attachment mechanism; cosine similarity.

  • Hop count, ETX and energy selection based objective function for image data transmission over 6LoWPAN in IoT   Order a copy of this article
    by Archana Bhat, Geetha V 
    Abstract: Internet of things (IoT) is technology that connects millions of things to the internet for collecting data and controlling things. 6LoWPAN looks promising for future IoT networks as it works with IPv6, which is essential to address millions of things. However, as the 6LoWPAN devices are resource constrained with payload constraint at the data link layer, it needs efficient mechanisms to send packets over IEEE 802.15.4 MAC layer. The challenge increases when the sensors used in the devices are camera or audio recordings. Multimedia data transmission over 6LoWPAN is great challenge, and this paper addresses the same with respect to selection of Objective Function (OF) for multimedia data traffic. A new hop count, ETX and energy selection based OF is proposed in this work. The proposed technique is compared with existing OF, and the simulation results shows that the proposed technique provides better performance.
    Keywords: 6LoWPAN; objective function; IPv6; multimedia; RPL; IEEE 802.15.4.

  • Poor and rich squirrel algorithm-based Deep Maxout network for credit card fraud detection   Order a copy of this article
    by Annu Paul, Varghese Paul 
    Abstract: This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation phase, transforming data using Yeo-Johnson (YJ) transformation. Then, the feature selection procedure is done by the Fisher score for creating the unique and significant features. Next, based on the selected textures, the data augmentation mechanism is done using the oversampling model. At last, the fraud detection is carried out by the Deep Maxout network, which is trained by the proposed PRSA optimisation algorithm, derived by integrating Poor and Rich Optimisation (PRO) and Squirrel Search Algorithm (SSA). The integration of parametric features of the PRSA algorithm trained the classifier to update weights to generate the best solution by considering fitness measures. The proposed method achieved the best accuracy, sensitivity, and specificity measures of 0.96, 0.95, and 0.94, respectively.
    Keywords: credit card; deep learning; fraud detection; data augmentation; data transformation.

  • A study on dual-sense broadband circularly polarised monopole antenna for UWB applications   Order a copy of this article
    by Umesh Singh, Kalyan Mondal, Rajesh Mishra 
    Abstract: The proposed work is designed with the embedded of stubs and Parasitic Strips (PSs) under the radiator. An FR4 substrate is used to design the antenna (?_r= 4.4, h = 1.6 mm). The overall size of the antenna is 0.8?_0
    Keywords: monopole; CP; dual-sense; stubs; ARBW; satellite;.

  • A novel trust-based approach for intrusion detection architecture in wireless sensor networks   Order a copy of this article
    by Mr. Jeelani, Kishan Pal Singh, Aasim Zafar 
    Abstract: Wireless sensor networks (WSNs) is a new technology that can be used to monitor the environment. Because sensor nodes in wireless sensor networks are installed in an open environment, they are more vulnerable to attacks. The sensor network lifetime improvement is dependent on minimum energy use. Protection is also a major concern when it comes to designing protocols for multi-hop secure routing. The results based on trust have proven to be more effective in addressing malicious node attacks. In this article, we propose a novel trust-based approach for intrusion detection architecture (IDA) in a wireless sensor network that is called the trust-based approach for varying nodes with energy (TBNE) model. TBNE finds the misbehaving nodes in the network. The structure is based on the trust model for secure communication in WSN and improves the performance of nodes. The simulation has been done with QualNet 5.0 simulator.
    Keywords: wireless sensor network; throughput; packet delivery ratio.

  • Data sharing with privacy protection based on blockchain and federated learning in edge computing enabled IoT   Order a copy of this article
    by Shiqiang Zhang, Zhenhu Ning 
    Abstract: Data sharing of Internet of things devices is a powerful means and technology to break the data island in the era of big data. However, frequent privacy leaks indicate that privacy protection has become one of the most urgent problems in data sharing. The existing data sharing schemes usually provide data to the data demanders through access control authorisation through a third-party organization. This way can protect the privacy of data to a certain extent. But the biggest problem is that the data owner will lose control of the data, which increases the risk of privacy disclosure. In this paper, we proposed a new data sharing scheme based on blockchain and federated learning. The data sharing problem is transformed into a machine learning problem. The IoT devices train the model locally and use differential privacy technology to avoid privacy leakage, and ensures its security through the blockchain network aggregation model.
    Keywords: data sharing; blockchain; federated learning; differential privacy; edge computing; IoT.

  • A survey of lung nodule computer-aided diagnostic system based on deep learning   Order a copy of this article
    by Tongyuan Huang, Yuling Yang 
    Abstract: With the development of machine learning, especially deep learning, the research of pulmonary nodules based on deep learning has made great progress, which has important theoretical research significance and practical application value. Therefore, it is necessary to summarise the latest research in order to provide some reference for researchers in this field. In this paper, the related research, typical methods and processes in the field of pulmonary nodules are analysed and summarised in detail. Firstly, the background knowledge in the field of pulmonary nodules is introduced. Secondly, the commonly used data sets and evaluation indexes are summarised and analysed. Then, the computer-aided diagnostic system related processes and key sub problems are summarised and analysed. Finally, the development trend and conclusion of pulmonary nodule computer-aided diagnostic system are prospected.
    Keywords: machine learning; deep learning; pulmonary nodule; CAD system.

  • Technology adoption of enablers of 5G networks for m-learning: an analysis with interpretive structural modelling and MICMAC   Order a copy of this article
    by L. Kala, Hameed T. A. Shahul, V.R. Pramod 
    Abstract: Mobile learning (m-learning) is one of the real-time applications of 5G technology with an impulsive future. COVID-19 pandemic enhanced the adoption of m-learning over wireless networks by facilitating continued formal education or work from home. This research aims to analyse enablers of 5G networks that enhance real-time m-learning by applying Interpretive Structural Modelling (ISM), a set-theory-based structural modelling method widely employed in many engineering and technology related research fields. Data was collected through questionnaire-based information gathering and from one-to-one discussions with experts. Modelling was performed to identify the correlations among system parameters through a hierarchically structured model. Further, the enablers were classified into different clusters based on their driving powers and dependency with MICMAC analysis, by which the results were validated. The study shows that enablers of 5G will undoubtedly support and uphold the system performance for future real-time scenarios of m-learning by eliminating all the inhibiting parameters of former 4G wireless networks.
    Keywords: 5G; wireless networks; enablers; mobile learning; ISM; MICMAC; driving power dependence.

  • Aggregation techniques in wireless communication using federated learning: a survey   Order a copy of this article
    by Gaganbir Kaur, Surender K. Grewal 
    Abstract: With the recent explosive rise in mobiles, IoT devices and smart gadgets, the data generated by these devices has grown exponentially. Given that the data generated by these devices is private, transmitting large amount of private data is not practical. So a new learning paradigm has been introduced known as federated learning, which is a machine learning technique. In this technique, user data is not transmitted to the base server as in centralized approach but only the locally updated model is transmitted. These model updates generated by the devices are aggregated at the server which updates its global model according to the local models and transmits back to the devices for next round. This technique reduces the privacy risk and also decreases the communication overhead. Various aggregation schemes are proposed in literature for increasing the performance and accuracy of the system while also increasing the security and reliability. This paper presents a survey of the latest advances in research of such aggregation techniques.
    Keywords: federated learning; machine learning; stochastic gradient descent; aggregation techniques; federated averaging.

  • Two-phase approach for the detection and isolation of black hole attack in mobile ad hoc network   Order a copy of this article
    by Pankaj Khuresha, Sonal Sood, Mandeep Sandhu, Anurag Dixit 
    Abstract: A mobile ad hoc network (MANET) is an infrastructure-less network in which no central controller is present and nodes can communicate with each other independently. Owing to unique nature of the network, malicious nodes can enter the network which triggers various types of attack. The black hole is the attack in which the malicious node does not forward any packets and all the packets will be dropped in the network. In this research work, an approach is proposed for the detection and isolation of black hole attack in MANET. The proposed approach works in two phases: in the first phase the malicious node will be detected and in the second phase the malicious node will be isolated from the network. The proposed methodology is implemented in network simulator version 2 and results are analysed in terms of throughput, delay and packet loss.
    Keywords: MANET; black hole; malicious nodes; clustering; trust.

  • Optimised recurrent neural network based localisation in wireless sensor networks: a composite approach   Order a copy of this article
    by Shivakumar Kagi, Basavaraj S. Mathapati 
    Abstract: Localisation is one of the key techniques in the wireless sensor network. The location estimation methods can be classified into target/source localisation and node self-localisation. There are several challenges in some special scenarios. Therefore, the anchor node-based distance estimation scheme is used in this research work. In the anchor-based localisation technique, the unknown node uses the position of the anchor node to estimate its location. The trained Recurrent Neural Network (RNN) with the extracted Angle Of Arrival (AoA) and RSSI features of the anchor node and the estimated nodes makes the localisation of the unknown node more precise. Further, to lessen the localisation errors in RNN, its weights are fine-tuned by an Improved Whale optimisation Algorithm (IWOA).
    Keywords: WSN; node localisation; AoA and RSSI based feature computation; RNN; IWOA.

  • Research on employment quality evaluation system of skilled talents   Order a copy of this article
    by Guojun Zheng 
    Abstract: In the new development stage of China, skilled talents shoulder the important mission of in-depth implementation of innovation-driven development strategy, which is an important basis for enterprises to enhance competitiveness and improve economic benefits, and also the key to stabilise and expand employment and achieve common prosperity. The employment quality of skilled talents should actively adapt to the needs of economic restructuring and industrial upgrading to achieve higher quality and fuller employment. This paper constructs an indicator system of employment quality and skilled talents supply from the macro level, evaluates the employment quality and skilled talents supply in the two years before and after the outbreak of COVID-19 by using the entropy method, and calculates the coupling coordination and correlation degree between the two systems. The research shows that the level of economic development is an important dimension affecting the employment quality, and the education level has the least influence on the employment quality of skilled talents. After the outbreak of the epidemic, employment training and employment opportunities have a greater impact on the quality of employment, and lead to a more serious shortage of skilled talents. The antagonistic coupling between the quality of employment and the supply of skilled talents has become more serious due to the impact of the epidemic.
    Keywords: employment quality evaluation; skilled talents; COVID-19; economic development.

  • LTE 2100 MHz band half-wave two element rectifier array for wireless electromagnetic energy harvesting   Order a copy of this article
    by Pradeep Chandrakant Dhanawade, Shivajirao M. Sangale 
    Abstract: In this manuscript, a two-element half-wave rectenna array for wireless energy harvesting from LTE2100 MHz band is presented. The 2100 MHz band is chosen based on the spectrum survey in the locality. An outdoor peak power of -15.8 dBm is sensed using a 1.5 dBi gain wideband antenna and spectrum analyser. A half-wave rectifier circuit using two different Schottky diodes and a capacitor filter is developed and connected in mirror image form. The proposed structure combines the direct current power of individual elements using two series capacitors improving the rectenna efficiency. The reported full-wave rectifier array has 19.95% and 63.01% radio-frequency to direct current efficiency for conventional and high-performance Schottky diodes respectively. A detailed analysis of major design parameters have been performed and presented in the manuscript which will help researchers to choose a suitable operating band and design components for rectenna design. The presented half-wave-rectifier rectenna has a comparable conversion efficiency with the full-wave-rectifier rectennas resulting in improved throughput wireless energy harvesting systems.
    Keywords: rectifier; rectenna; Schottky diode; wireless energy harvesting; rectenna array.

  • Signal strength and energy based efficient AODV routing algorithm in MANET   Order a copy of this article
    by Priyanks Pandey, Raghuraj Singh 
    Abstract: In recent years, Mobile Ad Hoc Network (MANET) has become one of the most popular research areas in the wireless networking domain. However, one of the major challenges remains to develop an efficient routing algorithm which depicts par excellence performance on all performance parameters even under highly dynamic network. Ad Hoc On-Demand Distance Vector (AODV) is a generalized routing protocol which establishes routes to destinations on demand in MANET environment and supports unicast as well as multicast routing. Many enhancements have also been proposed in AODV from time to time. These enhancements are based on various features which define a specific environment. But, these enhancements do not perform well on all considered performance metrics such as packet delivery ratio, delay, normalized routing load and throughput in highly dynamic network environment. In this paper, we have proposed an Enhanced version of AODV, namely (ENAODV) algorithm considering two important and additional stability parameters i.e. energy and signal strength along with hop count and sequence number in route selection process. Algorithm has been simulated using NS2 simulator and evaluated under different network conditions with varying maximum speed. Performance of the algorithm has been evaluated to be better on all parameters like throughput, normalized routing load, packet delivery ratio, control overhead and end to end delay than the AODV algorithm.
    Keywords: MANET; signal strength; RWP; AODV.

  • QoS-based handover approach for 5G mobile communication system   Order a copy of this article
    by Amina Gharsallah, Nouri Omheni, Faouzi Zarai, Mahmoud Neji 
    Abstract: 5G mobile communication systems are in-depth fusions of multi-radio access technologies characterised by frequent handover between cells. Handover management is a particularly challenging issue for 5G networks development. In this article, a novel optimised handover framework is proposed to find the optimal network to connect with a good quality of service in accordance with the users preferences. This framework is based on an extension of IEEE 802.21 standard with new components and new service primitives for seamless handover. Moreover, the proposed vertical handover process is based on an adaptive heuristic model aimed at achieving an optimised network during the decision-making stage. Simulation results demonstrate that, compared to other existing works, the proposed framework is capable of selecting the best network candidate accurately based on the quality of service requirements of the application, network conditions, mobile terminal conditions and user preferences. It significantly reduces the handover delay, handover blocking probability and packet loss rate.
    Keywords: 5G mobile network; ultra-dense network; media independent handover; vertical handover optimisation; fast handover.

  • A feature fusion pedestrian detection algorithm   Order a copy of this article
    by Nan Xiang, Lu Wang, Xiaoxia Ma, Chongliu Jia, Yuemou Jian, Lifang Zhu 
    Abstract: When pedestrians are in different angles and positions, The feature extraction and fusion capabilities are often limited of YOLO series model. Aimed at this problem, we propose an improved feature fusion pedestrian detection algorithm YOLO-SCr. To enhance the ability of cross-scale feature extraction and detection speed, we reconstruct the network structure of the YOLO algorithm in the backbone part and convolution layer part, respectively. Then, to strengthen the feature fusion ability of pedestrians at different scales ,we introduce the spatial pyramid pooling module and shuffle & CBAM(Convolutional Block Attention Module) attention mechanisms in different positions before YOLO layers. The experimental results show that compared with the detection algorithm such as YOLOv3, YOLO-SCr can performance effectively improve the detection accuracy , Recall and speed.
    Keywords: YOLO series ; feature extraction ; feature fusion ;spatial pyramid pooling; pedestrian detection ; shuffle & CBAM attention;.

  • Research on a laser cutting path planning method based on improved ant colony optimisation   Order a copy of this article
    by Naigong Yu, Qiao Xu, Zhen Zhang 
    Abstract: Laser cutting path planning for fabric patterns is critical to cutting efficiency. The ant colony optimisation algorithm commonly used in this field is constrained by the complete cutting and cannot plan a true global optimal path, resulting in large empty strokes. To solve this problem, this paper proposes an ant colony optimisation method based on virtual segmentation of multiple feature points for path planning of laser cutting. The method first changes the feature point selection strategy of traditional ant colony optimisation and increases the number of feature points in a single pattern. Then the single closed pattern is virtually divided into multiple open contours. Finally, the optimal cutting path is planned based on the solution of the travelling salesman problem. Experiments show that the cutting planning path obtained by the proposed method has a higher degree of compression on the idle stroke and significantly improves the laser cutting efficiency.
    Keywords: laser cutting; path planning; ant colony optimisation; virtual segmentation.

  • Two novel blind CFO estimation techniques for CP-OFDM   Order a copy of this article
    by Mohammadreza Janbazi Roudsari, Javad Kazemitabar, Hossein Miar-Naeimi 
    Abstract: In this paper, two new cyclic prefix (CP) based blind carrier frequency offset (CFO) estimation methods for orthogonal frequency division multiplexing (OFDM) transmission over multipath channels are proposed. In doing so, we first estimate the maximum delay of the fading channel. We borrow the concept of remodulation introduced in earlier works and use the repetitive structure of CP to calculate a maximum-likelihood based measure. In the first proposed method we use particle swarm optimisation aided search on all possible samples to find the optimal set. This technique provides performance improvement at the expense of more complexity. Then, in a second proposed method, we average over the optimal set of samples to estimate CFO. The second technique provides a major improvement over previous works while offering less complexity. Simulation results corroborate that both our proposed methods significantly decrease the mean square error.
    Keywords: orthogonal frequency division multiplexing; carrier frequency offset; cyclic prefix.

  • Towards energy-efficient 5G heterogeneous networks through dynamic small cell zoom and sleep control algorithm   Order a copy of this article
    by Janani Natarajan, Rebekka B 
    Abstract: The tremendously escalating mobile traffic and bandwidth hungry applications is challenging the network operators to provide guaranteed quality of service (QoS) over wider coverage and effective network resource usage. One of the effective solutions is heterogeneous network (HetNet) comprising an overlay of small cells (SCs) within a macrocell coverage. For enhancement in network energy efficiency (EE), we propose a joint small cell zoom and sleep strategy. The small cell zoom technique involves load-aware adaptive power control of the SCs for optimum network power consumption through lower SBS use together with appropriate user load balance. The small cell sleep method switches the SBSs with higher interference to sleep mode, thereby improving the network capacity as well as power saving. Simulation results show an EE improvement of the proposed sleep and zoom scheme by 25%, 26% and 28%, respectively, compared with three similar benchmark schemes in the literature.
    Keywords: heterogeneous networks; small cells; energy efficiency; small cell zoom; small cell sleep; adaptive power control.

  • Deep reinforcement learning multi-robot cooperative scheduling based on service entity network   Order a copy of this article
    by Xueguang Jin, Chengrui Wu, Yan Yan, Yingli Liu 
    Abstract: Multi-robots are increasingly deployed with the development of automation in agriculture, industry, and warehousing logistics. With the help of CPS virtualisation technology, services or tasks can be decomposed into a network with capability or function entity nodes and edges connecting nodes. In this paper, the service entity network is extended with human, robot, and IT resources as a task-decomposed network with public entities, private entities, and links. Based on the service entity network virtualisation architecture, it is possible to form a global service entity network corresponding to the correlated tasks. Meanwhile, deep reinforcement learning multi-robot cooperative scheduling based on a service entity network framework is studied, which makes it possible to jointly optimise the deployment of multi-robot tasks with multi-service entity networks. The results show that the model based on the artificial intelligence virtualisation architecture achieves a better performance.
    Keywords: service entity network; virtualisation technology; multi-robot cooperative scheduling.

  • SBER: Stable and Balance Energy Routing Protocol to Enhance the Stability and Energy for WBANs   Order a copy of this article
    by Sara Raed, Salah Abdulghani Alabady 
    Abstract: Stability and reduced energy consumption are essential in the design requirements of Wireless Body Area Network (WBAN) routing protocols. For instance, many energy-efficient routing protocol solutions have been suggested for WBANs; however, the significant feature of stability in these existing solutions has not been effectively addressed. In this paper, we propose a Stable and Balance Energy Routing (SBER) protocol to improve the stability period and manage the limited power of the WBAN network efficiently. SBER consists of two solutions, namely, the next-hop node selection and adding awareness to the transmission of control packets techniques. For analysis of the performance of the SBER protocol, MATLAB has been used. The average improvements rate of the SBER in terms of network residual energy over ERRS, M-ATTEMPT, and SIMPL protocols are 35%, 52%, and 100% respectively, which proves SBER to be a more efficient and reliable approach for WBANs.
    Keywords: WBANs; stability period; routing protocol; SBER; ERRS; M-ATTEMPT; SIMPL.

  • Research on fire alarm system of the intelligent building based on information fusion   Order a copy of this article
    by Sun Xuejing 
    Abstract: In order to effectively reduce the hazards caused by fire and improve the accuracy of fire alarm systems, this paper proposes to use STM32 microcontroller as the control core, use the communication method of Zigbee wireless communication technology combined with CAN bus technology, apply the QPSO-BP neural network algorithm based on multi-sensor information fusion method to fire alarm judgment, and use the fire protection partition in the building as the basis for the distributed intelligent building fire alarm system. The results show that the distributed intelligent building fire alarm system designed in this paper meets the design requirements of the system while fully considering the economic benefits and makes up for the shortcomings of the traditional fire alarm system. The algorithm output results are accurate and reliable, providing a reference for the design of building fire alarm systems.
    Keywords: intelligent building fire alarm; information fusion; QPSO-BP neural network algorithm; Zigbee technology.

  • A hybrid meta-heuristic algorithm to detect malicious activity based on dynamic ON VANET environmental information   Order a copy of this article
    by Gagan Preet Kour Marwah, Anuj Jain 
    Abstract: VANET has the characteristics of self-organisation, rapid topology changes, and frequent link disconnection that perhaps led to challenging issues. In order to mitigate these issues, a highly effective technology is required; therefore, this work has adopted a Hybrid Firefly Optimisation Algorithm (FOA) and a Whale Optimisation Algorithm (WOA) named as HFWOA-VANET. The HFWOA-VANET has the features of both meta-heuristic algorithms and is implemented to enhance the performance of VANET. This process is mainly based on consideration of Quality of Service (QoS) parameters of each vehicle. Therefore, the performance of vehicle can be determined and the better service in VANET platform is enabled. The implementation of this work is done on NS2 platform and the obtained results are analysed for ensuring the performance of the proposed model. Moreover, the performance of the model is compared with the existing technology; therefore, the proposed model can be ensured as a more effective technique than the existing technique in terms of performance metrics.
    Keywords: VANET; firefly optimisation algorithm; whale optimisation algorithm; QOS; QMM-VANET; HFWOA-VANET.

  • Performance analysis of downlink precoding techniques in massive MIMO under perfect and imperfect channel state information in single and multi-cell scenarios   Order a copy of this article
    by Chanchal Soni, Namit Gupta 
    Abstract: The novel Optimised Max-Min Zero forcing precoder (OM2ZFP) scheme is proposed in this work. The optimization is incorporated with the chimp optimization strategy (CPO) to maximise the spectral efficiency, achievable sum rate, max-min rate, and minimise BER. The designed precoder model is contemplated under single cell perfect CSI, single-cell imperfect CSI and multiple cells perfect CSI, multi-cell imperfect CSI. Three pre-coding schemes, zero forcing (ZF), Maximum Ratio Pre-coding (MRT) and Minimum Mean Square Error (MMSE) precoder techniques, are implemented in the Matlab platform to manifest the effects of the novel designed precoder. The performance of the achievable sum rate is analysed under three cases, namely case I (fixed users and varying antenna), case II (fixed and varying) and case III (varying channel estimation error). The results show that the increasing number of antenna and users enhance the spectral efficiency, downlink transmits power and achievable sum rate performance.
    Keywords: massive MIMO; precoder; downlink transmission; antenna; optimisation; spectral efficiency; achievable sum rate.

  • Preoperative staging of endometrial cancer based on decision tree model   Order a copy of this article
    by Jun Xu, Hao Zeng, Shuqian He, Lingling Qin, Zhengjie Deng 
    Abstract: Endometrial cancer is extremely common in gynaecological tumours. Ultrasound technology has become an important detection method for endometrial cancer, but the accuracy of ultrasound diagnosis is not high. Therefore, using data-driven methods to accurately predict the preoperative staging of endometrial cancer has important clinical significance. To build a more accurate diagnosis model, this paper uses a decision tree model to analyse the preoperative staging diagnosis indicators of endometrial cancer. Experimental results show that the three-detection data of tumour-free distance (TFD), ca125, and uterine to endometrial volume ratio are of high value for the diagnosis of endometrial cancer. The accuracy, sensitivity and specificity of the random forest (RF) model based on decision tree for preoperative staging of endometrial cancer were 97.71%, 94.11% and 100.00%, respectively. The comprehensive predictive ability based on the RF model has good application value for the prediction of preoperative staging of endometrial cancer.
    Keywords: random forest; decision tree; machine learning; endometrial cancer; preoperative staging.

  • RPL-OFs analysis and dynamic OF selection for QoS optimisation of RPL protocol   Order a copy of this article
    by Sharwari Solapure, Harish Kenchannvar, Umakant Kulkarni 
    Abstract: Quality of Service (QoS) requirements differ for various IoT applications, such as smart health reliability is the need, for industry delay is essential etc. The Routing-Protocol-for-Low-Power-Lossy-Network (RPL) with Objective Function (OF) is used for routing in an IoT application. Default RPL-OF is deficient to fulfil the QoS requirements of different IoT applications. Hence, several OF designs were proposed as per the QoS need in the earlier research. The work presented in this paper is the extension of previous research work. The analysis of these OF designs is carried out with the parameters such as number of nodes, simulation-time, data-rate, Media Access Control (MAC) protocols, communications ranges and different topologies. This analysis resulted in a dataset that addresses most of the QoS-requirements and it is used to optimise the RPL protocol QoS performance. Decision tree algorithm is used to predict a suitable RPL-OF design. The accuracy achieved using Gini and Entropy method of decision tree is 87.14% and 88.57% respectively. Thus, the contribution of this research is to prepare the dataset using comprehensive analysis and use the same for predicting suitable RPL-OF design according to QoS-requirements of an IoT application. The proposed methodology is useful in IoT applications where dynamic-OF selection as per QoS requirements is needed.
    Keywords: IoT; RPL; OF; LLN; QoS.

  • An improved fuzzy clustering log anomaly detection method   Order a copy of this article
    by Shuqian He, WenJuan Jiang, Zhengjie Deng, Xuechao Sun, Chun Shi 
    Abstract: Logs are semi-structured text data generated by log statements in software code. Owing to the relatively small amount of abnormal data in log data, there is a situation of data imbalance, which causes a large number of false negatives and false positives in most existing log anomaly detection methods. This paper proposes a fuzzy clustering anomaly detection model for unbalanced data, which can effectively deal with the problem of data imbalance and can effectively detect singular anomalies. We introduce an imbalance compensation factor to improve the fuzzy clustering method, and use this method to build an anomaly detection model for anomaly detection of real log data. Experiments on real data sets show that our proposed method can be effectively applied to log-based anomaly detection. Furthermore, the proposed log-based anomaly detection algorithms outperform other the state-of-the-art algorithms in terms of the accuracy, recall and F1 measurement.
    Keywords: distributed information system; log data; anomaly detection; artificial intelligence for IT operations; fuzzy clustering; imbalanced datasets; unsupervised learning; machine learning.

  • Research on system parameter optimisation in electromagnetic tomography technology   Order a copy of this article
    by Liu Li, Yue Luo, Yao Huang, Lijuan Wu 
    Abstract: Electromagnetic tomography technology (EMT) based on the principle of electromagnetic induction is a multi-phase flow detection technology. It is reconstructed without contact and intervention. The development process of EMT is presented in this paper. The basic physical model is constructed. The internal sensitive field equation is given. The detection values are analysed by the numerical calculation method. It is mainly to establish the sensitivity model and the detection value matrix. By using the control variable method, the effects of the excitation current frequency, current strength on the detection values and phases are compared and analysed. Under the same parameter setting conditions, different imaging algorithms are used to reconstruct the images for models. In the inverse problem, Tikhonov regularization, LBP methods and conjugate gradient algorithm are introduced. The optimal parameters are determined by using parameters of IE (Image Error) and CC (Correlation Coefficient) to evaluate the reconstructed image.
    Keywords: electromagnetic tomography technology; image reconstruction; Ccnjugate gradient algorithm; inverse problem.

  • OLSR-ETX: a parameterised solution for oscillatory network packet losses   Order a copy of this article
    by Kifayat Ullah, Ihtisham Ali 
    Abstract: Expected Transmission Count (ETX) has gained popularity due to identifying a high-throughput path in the multihop wireless network. However, the oscillatory network may not work correctly with a high traffic load; the probe packets may be lost or queued. This paper proposes a parameterized solution (data rate tuning and packet size adjustment) to minimize packet losses. Experimental results indicate that the network's performance has improved using ETX as a routing metric by tuning data rates and adjusting packet size. The results show that by keeping the Data rate under 200kbps and a Packet size of 256 bytes, the performance of the OLSR-ETX routing protocol has improved in the oscillatory network. Finally, we have evaluated the OLSR-ETX parameterized-based solution with OLSR-ETX in oscillation scenarios concerning packet loss ratio. The results show that a parameterized-based solution improves the functionality of the routing protocols in the oscillatory network.
    Keywords: ETX; OLSR-ETX; OLSR; oscillatory network; packet loss ratio.

  • An efficient blockchain model for improving data transmission rate in ad hoc networks   Order a copy of this article
    by Lucky Narayana 
    Abstract: A Mobile Ad hoc Network (MANET) is an infrastructure-less network that can be established dynamically whenever and wherever required for establishing communication. The MANET is a series of nodes with capabilities in wireless communication and networking. A temporary network that is possible without an already-oriented network or centralised supervisor is linked by an ad hoc network to its mobile hosts as required. The topology of an ad hoc network is different for node mobility. The function of the ad hoc network needs its own solutions and should be different from the static networks to build applications. Radio nodes are immediately established to communicate with each other. With the help of intermediate nodes, nodes not within each other\'s radio range can be transmitted from source to destination. As ad hoc networks are dynamic in nature, they frequently undergo several attacks that reduces the data transmission rate. In the proposed work, an efficient blockchain model is used in ad hoc networks for improving the data transmission rate by analysing the cause for packet loss. In the proposed model, a Malicious Task Identification Head Node (MTIHN) is selected from the network that analyse the blocks generated after every transaction for checking the cause of packet drops. The blockchain is a modern data storage platform. In the various systems with different operating principles this does not operate in the same way. The proposed work explores network security using the blockchain framework to make it easier to send messages and information without loss that improves system performance. The proposed model is compared with the traditional methods and the results show that the proposed model exhibits better performance in improving Data Transmission Rate.
    Keywords: data transmission rate; malicious actions; blockchain; security; ad hoc networks; block generation.

  • Research on wireless routing problem based on dynamic polycephalus algorithm   Order a copy of this article
    by Zhang Yi, Yang Zhengquan 
    Abstract: The efficiency of the traditional Physarum Polycephalum Model (PPM) is low for wireless planning problems. Also, other heuristic algorithms are easy to fall into local optimum and usually require a large training set to find the optimal parameter combination. Aiming at these problems, we propose a new dynamic model of Physarum Polydynia (DMOP2) algorithm combined with PPM in this paper. This algorithm can judge the irrelevant nodes according to the traffic matrix after each iteration and then delete them and re-establish a new distance matrix when solving the routing problem. The improvements not only reduce the time consumed by calculation but also improve the accuracy of calculation pressure. Simulation experiments in random network and real road network prove the feasibility and effectiveness of the proposed algorithm in solving the path planning problem, and the experimental results show that the efficiency is significantly improved compared with PPM.
    Keywords: wireless planning; Physarum Polycephalum model; dynamic model.

  • Energy-efficient dynamic load balanced clustering for MANET   Order a copy of this article
    by Naghma Khatoon, Vinay Singh, Prakash Kumar 
    Abstract: In mobile ad-hoc network (MANET), enhancing network lifetime is a challenging issue. Clustering is proved to be a suitable solution to increase scalability and MANET lifetime. In this paper, we present an energy-efficient dynamic load-balanced clustering for MANET. For cluster formation, nodes are divided into open set and restricted set. Depending upon the weight of Cluster Head (CH), node join them to form a cluster which make it load balanced. We use technique for self-adjustment of role of CHs dynamically based on fitness factor which is derived from remaining energy and weight of nodes to increase CH lifetime. The proposed method is experimented extensively and compared with related existing algorithms to demonstrate its ascendancy related to various performance metrics like packet delivery ratio, network lifetime, average number of clusters formed and re-clustering required. Also, we demonstrate that the work proposed accomplishes persistent messages and the time complexity is linear.
    Keywords: MANET; cluster head; fitness factor; remaining battery energy; packet delivery ratio.

  • Research on facial expression recognition based on multimodal data fusion and neural network   Order a copy of this article
    by Yi Han, Xubin Wang, Zhengyu Lu 
    Abstract: Facial expression recognition is a challenging task when a neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes a convolutional neural network designed to extract features from facial image, a neural network designed to extract features from facial landmarks, and a neural network designed to extract features from histogram of gradient, and three sub-neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experiment results show that, the algorithm has a great improvement in accuracy, robustness and detection speed.
    Keywords: multimodal data; deep learning; neural network; facial expression recognition; data fusion.

  • A hybrid malicious node detection approach based on fuzzy trust model and Bayesian belief in wireless sensor networks   Order a copy of this article
    by Wuchao Shi 
    Abstract: With the wide range of wireless sensor network (WSN) applications including environmental monitoring and healthcare, the sensor nodes in WSN are susceptible to security threats including dishonest recommendation attacks from malicious nodes, which could disrupt communications integrity. Thus, malicious node detection in WSN is essential. In recent years, several malicious node detection approaches based on trust management were proposed to protect the WSN against dishonest recommendation attacks. However, the existing approaches ignore data consistency and re-evaluation of participating nodes in trust evaluation, which seriously undermine their effectiveness. To address these limitations, we propose a hybrid malicious node detection technique for WSN based on the fuzzy trust model (FTM) algorithm and the Bayesian belief estimation (BBE) approach. The key idea in the proposed approach is to determine direct trust values through the FTM algorithm using the correlation of data collected over time and ascertain the trustworthiness of indirect trust values from recommendation nodes via the BBE approach.
    Keywords: wireless sensor network; dishonest recommendation attacks; fuzzy trust model; Bayesian belief.

  • A trusted management mechanism based on trust domain in hierarchical internet of things   Order a copy of this article
    by Mingchun Wang, Jia Lou, Yedong Yuan, Chunzi Chen 
    Abstract: Existing trusted models usually authenticate the identity and behaviour of sensing nodes, without considering the role of sensing nodes in the process of interaction and transmission of information. Therefore, in view of the hierarchical wireless sensor network architecture of the internet of things, this paper proposes a new hierarchical trusted management mechanism based on trusted domain. The mechanism abstracts different nodes in the hierarchical structure of the internet of things, gives them different identities, and calculates the trust value of the sensing nodes by using similarity weighted reconciliation method. The experimental results show that the proposed scheme is feasible and effective.
    Keywords: trusted domain; trusted management; similarity weighted reconciliation; trust value; hierarchical structure.

  • Task scheduling in multi-cloud environment via improved optimisation theory   Order a copy of this article
    by Prashant Balkrishna Jawade, S. Ramachandram 
    Abstract: One of the most popular technologies nowadays, cloud computing has a big demand in the distributed software space. It is highly difficult for CSPs to work together in a multi-cloud context, and contemporary literature does not adequately address this issue. Throughout this work, a protected TS paradigm in a multi-cloud environment is introduced. The suggested scheme mainly focuses on the optimal scheduling of tasks by considering a Modified Deep Neural Network (DNN) as a task scheduler. Accordingly, the task is allotted based upon makespan, execution time, security constraints (risk assessment), utilisation cost, maximal service level agreement adherence, and power usage effectiveness. Moreover, the weights of DNN are tuned optimally by self-improved aquila optimisation technique. The developed model has a lowMAE value of 0.052581, which is 46.67%, 90.85%, 89.29%, and 86.43% better than DNN, NN, RNN, and LSTM, respectively.
    Keywords: task scheduling; execution time; modified DNN; risk assessment; SI-AO model.

  • A modified advanced encryption standard-based model for secure data transmission in cognitive radio with multi-channels   Order a copy of this article
    by Kiran P. More, Rajendrakumar A. Patil 
    Abstract: Cognitive Radio Network (CRN) is said to be a capable mechanism for boosting the advancement of 5G networks. Designing of a Medium Access Control (MAC) protocol for CRN is demanding owing to the complexity concerned with accessing and sensing the channel. Our previous work focused on predicting the channel states using an optimisation-based DBN prediction model. In this research, the channel state is predicted using a novel Cat and Mouse Based Optimiser (I-CMBO)-based LSTM. Moreover, it is aimed to perform secure data transmission via the Modified Advanced Encryption Standard (M-AES) protocol and secure communication through the predicted available spectrum channels. An investigation was done to depict the enhancement of the presented model. From the scrutiny, it is noticed that the LSTM scheme has obtained a negligible sensing delay of 0.11391 for PU count = 100 for experimentation 2.
    Keywords: cognitive radio; spectrum efficiency; secondary user; LSTM approach; M-AES concept.
    DOI: 10.1504/IJWMC.2023.10062514
     
  • A new time-frequency synchronisation algorithm based on preamble sequence in OFDM system   Order a copy of this article
    by Weimin Hou, Yan Wang, Yanli Hou 
    Abstract: Aiming at the problems of high computational complexity in the timing synchronization phase and poor frequency offset estimation performance of existing time-frequency synchronization algorithms, this paper proposed an improved time-frequency synchronization algorithm based on preamble sequence for OFDM systems. The preamble sequence is designed by using the property that the cross-correlation value of the Constant Amplitude Zero Auto Correlation (CAZAC) sequence with different root values is close to zero. Based on its features, a timing metric function and the frequency offset estimation function are designed. The frequency offset estimation function is used to obtain the coarse fractional frequency offset, and the fine fractional frequency offset is obtained by combining cyclic prefix (CP) and cyclic suffix (CS). Then the time domain sliding correlation between receiving sequence and the local preamble sequence is used to estimate the integer frequency offset. The results indicate that the proposed method has better synchronization capability than existing algorithms.
    Keywords: OFDM system; timing synchronization; frequency offset estimation; preamble sequence; CAZAC sequence.

  • Optimised design of cross shaft parameters based on response surface optimisation model with MOGA   Order a copy of this article
    by Sijie Xiong, Yuanmin Xie, Chunlong Zou, Yanfeng Mao, Yongcheng Cao 
    Abstract: The cross shaft is the core component of the cross-type universal coupling and has a vital transmission function. This paper proposes Sparse Grid and the Kriging interpolation to construct a response surface model to solve the problem of long design cycles, low reliability, and high susceptibility to cross-shaft fatigue deformation. The critical dimensions of the cross shaft are used as design variables, and the maximum equivalent force and deformation are reduced as the optimization objective. Then experimental points are obtained by Sparse Grid Initialization and then the response surface model is obtained with high accuracy by Kriging interpolation, and finally, the optimized design of the cross-shaft is completed using MOGA in this paper. Compared with the original structural solution, the maximum deformation of the cross shaft is reduced by 0.4717mm (22.35%), the maximum equivalent force is reduced by 130.35Mpa (17.21%), and the mass increased by only 4.17%.
    Keywords: cross shaft; Kriging interpolation; MOGA; multi-objective optimisation.
    DOI: 10.1504/IJWMC.2023.10060440
     
  • Multi-objective optimisation design of cross shaft based on Kriging response surface optimisation model   Order a copy of this article
    by Yuanmin Xie, Sijie Xiong, Juntong Yun, Yanfeng Mao, Boao Li, Xinjie Tang, Yongcheng Cao 
    Abstract: Cross universal coupling is a key component of the mechanical transmission system, and the cross shaft is the core component of the coupling for torque transmission. Under normal circumstances, cross shafts are most susceptible to fatigue and deformation, mainly due to the large torques they carry and the irrationality of their structure. Traditional design methods rely on practical experience to determine the key dimensions of the cross shaft, resulting in long design cycles and low reliability. To address this problem, parametric modelling of the cross shaft is carried out in this paper and imported into ANSYS Workbench; In addition, static and finite element analyses are carried out to find the weak parts of the cross shaft as the objective function; Finally, sensitivity analysis is carried out using the main structural parameters of the cross-shaft as design variables. Based on the linear correlation matrix and sensitivity graph, the three design variables that have the greatest impact on the objective function, Journal height, Thickness, and Body length, are selected.
    Keywords: cross shaft; sensitivity analysis; multi-objective optimisation; ANSYS Workbench.
    DOI: 10.1504/IJWMC.2023.10060441
     
  • Low-complexity PAPR reduction in FBMC/OQAM communication channels using hybrid trellis coded-based SLM with momentum search algorithm   Order a copy of this article
    by Sudarshani Kataksham, P. Siddaiah 
    Abstract: One of the essential multicarrier modulation methods in the 5G communication system is the Filter Bank Multicarrier (FBMC) based Offset Quadrature Amplitude Modulation (OQAM) (FBMC/OQAM) process. Due to the overlapping nature of the signal in the FBMC system, traditional Peak to Average Power Ratio (PAPR) reduction approaches are ineffective. Thus, Hybrid Trellis Coded based Selective Level Mapping (TCSLM) with Momentum Search Algorithm (MSA) in FBMC/OQAM communication channels is proposed in this manuscript. Initially, Hybrid TCSLM is applied to FBMC/OQAM communication channel for reducing the PAPR. Then, to avoid overlapped features and minimizes the complexity in the communication channel, the TCSLM parameters are optimized using MSA method. To fulfill the objective of minimum PAPR value, the phase rotation and overlapping factors are optimized by integrating the MSA into TCSLM. Finally, the efficiency of the proposed approach is calculated and it achieves 5.56 dB PAPR reduction, which is better than existing methods.
    Keywords: filter bank multicarrier; hybrid trellis coded-based selective level mapping; offset quadrature amplitude modulation; momentum search algorithm.
    DOI: 10.1504/IJWMC.2023.10061078
     
  • Automatic modulation recognition based on channel and spatial attention mechanism   Order a copy of this article
    by Tianjun Peng, Guangxue Yue 
    Abstract: With the complexity of the wireless communication environment, automatic modulation recognition (AMR) of wireless communication signals has become a significant challenge. Most existing researches improve the model recognition performance by designing high-complexity architectures or providing supplementary feature information. This paper proposes a novel AMR framework named CCSGNet. The convolutional neural network (CNN) and bidirectional gate recurrent unit (BiGRU) are employed in CCSGNet to reduce the spectral and time variation of the signals, furthermore, the channel and spatial attention are employed to fully extract local and global features of signals. In order to reduce the training time cost of the model, we propose a piecewise adaptive learning rate tuning method to improve the training of the model. The comparisons with several common learning rate tuning methods on CCSGNet show that the proposed method achieves convergence in 25 training epochs, reducing the training time cost of the model. Moreover, CCSGNet improves the recognition accuracy of 16QAM and 64QAM by 6.47%-50.95% and 4.54%-25.66%, respectively.
    Keywords: automatic modulation recognition; attention mechanism; learning rate; deep learning.

  • Finger vein recognition based on efficient channel attention and GhostNet   Order a copy of this article
    by Yintao Ke, Hui Zheng, Jing Jie, Beiping Hou, Yuchuan Chen 
    Abstract: The deep convolutional network has the disadvantages of large computational complexity, long training time and slow recognition speed. In order to better deploy deep convolutional neural networks in embedded devices such as finger vein locks, a finger vein recognition method based on lightweight efficient channel attention (ECA) mechanism and GhostNet is proposed. This method combines the G-bneck in GhostNet with the ECA attention mechanism to form a new ECAGhostNet, which effectively improves the model accuracy without increasing the number of parameters and with only a small amount of calculation. At the same time, a more realistic FV-UST data set is established for the application scenario of finger vein door lock, which includes finger vein images with rotation, stain, skin damage, hand sweat, different temperatures, and different illumination. The results show that on the public data set FV-USM, ECAGhostNet improves the accuracy by 0.82% compared with GhostNet when the number of parameters is almost not increased and the calculation amount is only increased by 1.9M.
    Keywords: deep convolutional neural network; efficient channel attention mechanism; finger vein recognition; GhostNet.
    DOI: 10.1504/IJWMC.2023.10061410
     
  • Effective IoT-based crop disease prediction using localised search traversing coupled with deep convolutional neural network classifier   Order a copy of this article
    by B.V. Vani, C.D. Guruprakash 
    Abstract: Predicting crop disease based on the image obtained from the effected crop has been a potential research topic. Deep-learning-based models are broadly used to extract significant crop features for prediction. In this research, the deep convolutional neural network (deep CNN) is used to predict the disease in the crops. The local search traversing optimization -based deep convolutional neural network is proposed for the effective prediction of crop disease. The most dominant statistical and texture features are used for training the deep CNN model, in which the hyperparameters are effectively tuned by the LSTO algorithm. The experimental result show that the proposed model attains higher accuracy than other conventional models.
    Keywords: deep convolutional neural network; optimisation; IoT sensor; wireless sensor network; smart irrigation.
    DOI: 10.1504/IJWMC.2023.10061465
     
  • A scalable multimodal ensemble learning framework for automatic modulation recognition   Order a copy of this article
    by Jian Shi, Guangxue Yue, Shengyu Ma, Tianjun Peng, Bolin Ma 
    Abstract: Most automatic modulation recognition (AMR) researches have designed complex structures or supplemented feature information to achieve the recognition of modulation signals, which cannot fully combine the advantages of different models to extract features, resulting in poor recognition accuracy of modulated signals To solve the problem, we propose a scalable multimodal ensemble learning framework (SMELF), which trains various models with multimodal information including In-phase Quadrature (I/Q) and Amplitude Phase (A/P) information to supplement feature information The meta-model is used as a combined strategy to correlate the feature extraction advantages of each model The simulation results show that SMELF not only achieves superior classification accuracy, but also is the most robust under different signal-to-noise ratios (SNRs) environments and the training sample sizes In addition, our method can further improve the classification accuracy by combining more diverse and better performance models, which reflects the great potential of the framework.
    Keywords: automatic modulation recognition; multimodal information; ensemble learning; vision transformer.
    DOI: 10.1504/IJWMC.2023.10061678
     
  • Data survivability in unattended wireless sensor networks with optimal clustering and hybrid encryption algorithm   Order a copy of this article
    by Nischaykumar Hegde, Linganagouda Kulkarni 
    Abstract: This research proposes an energy- and security-aware data survivability solution for Unattended Wireless Sensor Networks operating in hostile environments. The multi-objectives, such as energy consumption, delay, distance, communication overhead, inter-cluster distance, and intra-cluster distance, are taken into consideration. The projected hybrid optimisation approach is referred to as Aquila Updated Candidate Selection Optimiser. To solve this issue, a novel hybrid cryptographic model denoted as Two-Fold Advanced Triple Data Encryption Standard (TF-A3DES) is developed. The TF-A3DES is developed by hybridising the concepts of the Triple Data Encryption Standard (triple DES) algorithm and Advanced Encryption Standard. As per the recorded outcomes, the projected model uses the lowest energy (5.015 J), which is better than AHE = 5.9 J, CHTP = 5.5 J, SAPDA = 6.2 J, BDLA = 5.98 J, AO = 6.0 J, EBOA = 6.15 J, BOA = 6.2 J and MFO = 6.25 J. Thus, the projected model is said to be much more applicable for secured data transmission.
    Keywords: UWSN; data survivability; optimal clustering; encryption algorithm; TF-A3DES; AUCSO.
    DOI: 10.1504/IJWMC.2023.10061846
     
  • An improved algorithm for sesame seedling and weed detection based on YOLOv7   Order a copy of this article
    by Gan Yu, Huimin Sun, Zhuolei Xiao, Chaoyue Dai 
    Abstract: Sesame is a widely cultivated oilseed and edible crop with significant economic and nutritional value. Weed infestation is a primary factor that hinders sesame growth, and effective weed control is crucial for optimal sesame yield. Accurate identification of sesame seedlings and weeds can guide intelligent devices to improve weed control. However, low recognition ac-curacy and high miss-detection rates persist as major issues. To address these challenges, we propose the YOLOV7-G algorithm, which builds upon the YOLOV7 baseline network. Our approach integrates the SimAM attention mechanism in the feature extraction structure to focus the model on the morphological features of weeds and incorporates the C3 module into the backbone network to increase the perceptual field range. We also employ the SPPFCSPC module in the Neck part to replace convolutional kernels of different sizes with stacked convolutional kernels to reduce computational effort while maintaining the original perceptual field. Finally, we use the Focal-SIoU loss function to improve the regression accuracy of the prediction frame.
    Keywords: SPPFCSPC; YOLOV7; SimAM attention mechanism; Focal-SIoU; C3.
    DOI: 10.1504/IJWMC.2024.10061847
     
  • An improved mask R-CNN example segmentation algorithm based on RGB-D   Order a copy of this article
    by Gongfa Li, Boao Li, Du Jiang, Bo Tao, Juntong Yun 
    Abstract: Combining the characteristics of RGB images and depth images, we propose a reverse fusion instance segmentation algorithm that effectively combines the advantages of RGB and Depth information by fusing high level semantic features with low level edge detail features. The algorithm uses RGB and depth information in RGB-D images, extracts features from RGB and depth images separately using a feature pyramid network (FPN) and upsamples the high-level features to the same size as the bottommost features. Subsequently, we apply the inverse fusion method to fuse the high-level features with the low-level features. At the same time, a mask optimization structure is introduced in the mask branch to achieve RGB-D reverse fusion instance segmentation. On the basis of using ResNet-101 as the backbone network, the average accuracy is improved by 10.6% compared with Mask R-CNN without fusing depth information.
    Keywords: depth image; instance segmentation; feature fusion; inverse fusion.
    DOI: 10.1504/IJWMC.2024.10061995
     
  • Optimal dual-channel design for manufacturers under different consumer low-carbon preferences for products   Order a copy of this article
    by Bingda Zhang, Zixia Chen, Zelin Chen, Yang Peng 
    Abstract: Consumer demand for low-carbon products is primarily met through a green supply chain network involving manufacturers, retailers, and consumers. The manufacturers' choice between online and offline channels is influenced by consumers' varying degrees of low-carbon preferences, necessitating comprehensive considerations of factors such as product quality and sales prices. This study advances the existing product demand analysis in the manufacturer's secondary supply chain network by innovatively conducting systematic modeling and algorithmic solutions for the manufacturer's dual-channel selection under different consumer low-carbon preferences. The simulations, facilitated by MATLAB tools, reveal that consumers' low-carbon preferences tend to strengthen over time, prompting manufacturers to prefer online channel strategies.
    Keywords: manufacturer; dual-channel supply chain; consumer low-carbon preference; demand analysis; choice design.
    DOI: 10.1504/IJWMC.2023.10062014
     
  • Optimisation of a high-speed optical OFDM system for indoor atmospheric conditions   Order a copy of this article
    by B. Sridhar, S. Sridhar, Naresh K. Darimireddy 
    Abstract: VLC provides high security and broadband functionality for optical communication in free space. In particular, this proposed work focuses on analysing receiving power distribution patterns and signal-to-noise ratios for indoor and vehicle applications. The optical systems of indoor communications are more suitable than wireless radio systems. The significant advantage of optical wireless communication (OWC) is providing high-speed data up to 2.5 Gbps at a low cost. In indoor areas such as auditoriums and public places, the OWC systems are more suitable. But optical signals are distorted by the signal propagation effects due to obstacles, walls, etc. The proposed system is an OFDM-based system that can transmit multiple channels and connects many modems over a given indoor area. Proposed methods initially focus on the LED/LD transmitter sources placement at the ceiling of indoor space and observed signal power distribution; in an IM/DD-based OWC system, the information signal must be accurate and nonnegative. The proposed asymmetric optical OFDM (ACO-OFDM) system is implemented for indoor communications, and the system's performance is evaluated with the Bit error rate. In particular, the performance of the specific M-QAM ACO-OFDM method with adaptive frequency is assessed by using theoretical analysis and simulations. Compared to the M-QAM ACO-OFDM method, the ACO-OFDM and DCO-OFDM showed lower spectral efficiency performance for the OWC system in the frequency selective channel.
    Keywords: ACO-OFDM; indoor networks; power distribution; clipping; bit error rate.

  • Cache-aided MISO-NOMA concept   Order a copy of this article
    by Natasa Paunkoska (Dimoska), Venceslav Kafedziski 
    Abstract: Coded caching, non-orthogonal multiple access (NOMA), and massive multiple-input multiple-output (MIMO) are challenging and attractive technologies for rapidly emerging mobile networks, offering numerous significant advantages. The literature shows that a match between two diverse techniques outperforms the current existing benefits. This paper proposes a new joint scheme assuming cooperation between all three methods. Namely, a multiple-input single-output (MISO) base station equipped with multiple antennas serves a group of cached-aided end-users using the NOMA pairing approach to deliver requested files. Using MISO allows simultaneous transmissions; NOMA uses SIC (successive interference cancellation) to decode the signal consisting of more messages; coded caching speeds up the interference elimination and file decoding by applying CIC (cache-enabled interference cancellation). The zero-forcing method deals with inter-pair interference cancellation. This newly proposed concept improves the achievable sum rate for file delivery compared with other joint conventional constructions. Hence, simulation results show that the schemes using join CIC and SIC decoding processes exceed the sum-rate performance.
    Keywords: caching; non-orthogonal multiple access; multiple-input single-output; cache-enabled interference cancellation; successive interference cancellation.
    DOI: 10.1504/IJWMC.2023.10062301
     
  • Bidirectional ConvLSTM based interactive query focused video summarisation   Order a copy of this article
    by Vasudha Tiwari, Charul Bhatnagar 
    Abstract: Video summarisation presents the essence of a video in a compact form by extracting the most salient frames from it in a temporal sequence. Although, over time, video summarisation has gained much focus from the researchers, yet the need of personalised summaries that are based on user’s intent still needs exploration. The authors propose an interactive, Query-Focused Video Summarisation(QFVS) approach which attempts to find those frames of a video that have maximum pertinence to user’s text query. The proposed model consists of a bidirectional ConvLSTM as an enhancement over ConvLSTM along with Resnet-50 for feature extraction. The input text query is matched with the predicted labels produced by the model and the frames with maximum similarity are selected for summary generation. The proposed method is evaluated on performance with previous state-of-art works and the results clearly demonstrate a significant improvement in the performance and proves the efficiency of the approach.
    Keywords: video summarisation; query based; interactive summarisation; bidirectional ConvLSTM; Resnet-50; text query; LSTM; ConvLSTM.
    DOI: 10.1504/IJWMC.2024.10062374
     
  • Joint metaheuristics-based optimisation in wireless cooperative network   Order a copy of this article
    by Sonika Pahuja, Poonam Jindal 
    Abstract: Considering the limitations of natural energy resources, energy-efficient wireless networks have gained significant attention in recent years. A wireless cooperative communication network (WCCN) provides better spectrum utilization and reduces interference. Therefore, new technologies and methods are explored significantly to optimize the existing wireless networks. The system efficiency improves in WCCN as multiple relay nodes are used to enable path transmission sharing. In this paper, the delay, relay selection, and energy optimization are done using joint metaheuristics algorithm. Here, a novel Black-widow optimization and Tunicate swarm algorithm (BWO-TSA) based energy efficient joint metaheuristics algorithm is proposed. The algorithm demonstrates reduced delay, optimal relay selection, and optimal power allocation through numerical simulations. The results achieved are further compared with the existing Branch and Bound (BB) algorithm. The optimal solution for the network is achieved with lower computational complexity for different network parameters like outage probability, and energy consumption.
    Keywords: wireless-powered cooperative communication network; relay selection; resource allocation; optimisation algorithm; scheduling; power control.
    DOI: 10.1504/IJWMC.2024.10062516