International Journal of Wireless and Mobile Computing (69 papers in press)

Regular Issues

• Multicast stable path routing protocol for wireless ad-hoc networks  
  by K.S. Saravanan, N. Rajendran 
  Abstract: Wireless Ad-Hoc Networks (WANETs) enable steady communication between moving nodes through multi-hop wireless routing path. The problem identified is how to improve the lifetime of the route and reduce the need for route maintenance. This helps to save bandwidth and reduce the congestion control available in the network. This paper aims to focus on redesign and development of multicast stable path routing protocol with special features that determine long-living routes in these networks. An extensive ns-2 simulation based performance has been analysed of three widely recognised stability oriented wireless ad-hoc network routing protocols, namely are Associativity Based Routing (ABR) protocol, Flow Oriented Routing Protocol (FORP) and Lifetime Route Assessment Based Routing Protocol (LRABP). The order of ranking of the protocols in terms of packet delivery ratio, average hop count per route, end-to end delay per packet and the number of route transitions is presented.
  Keywords: wireless ad-hoc networks; multicast routing protocol; wireless communication; routing protocol.
  
  
• Research of small fabric defects detection method based on deep learning network  
  by Siqing You, Kexin Fu, Peiran Peng, Ying Wang 
  Abstract: For quality improvement of textile products, fabric defects detection is significant. In this paper, the detection capacity of SSD for small defects was studied. The loss of feature information was reduced through the reduction of layers of SSD network; then the size of the default box was adjusted based on the K-means clustering algorithm, and the adaptive histogram equalisation algorithm was applied to enhance the defect features and effectively improve the detection accuracy. The improved SSD network model was tested to verify the fabric defects dataset, which further improved the accuracy of detection. In addition, the two-stage algorithm was compared to find the optimal algorithm for small object detection. According to the test results, the subsequent improvement method for small object detection with SSD was proposed.
  Keywords: fabric defects detection; default box; feature enhancement; SSD; faster RCNN.
  
  
• Field theory trusted measurement model for IoT transactions  
  by Meng Xu, Bei Gong, Wei Wang 
  Abstract: The Internet of Things (IoT) allows the concept of connecting billions of tiny devices to retrieve and share information regarding numerous applications, such as healthcare, environment, and industries. Trusted measurement technology is crucial for the security of the sensing layer of the IoT, especially the trusted measurement technology oriented to transaction IoT nodes. In the traditional trust management system, historical behaviour data are considered to predict the trust value of the network entity, while the nodes' trust between network entities is rarely considered. This paper proposes a novel field theory trusted measurement model of the sensing layer network, which can well adapt to the transaction scenarios of the IoT.
  Keywords: field theory; internet of things; trust measurement; transaction scenario.
  
  
• Dynamic time warping-based evolutionary robotic vision for gesture recognition in physical exercises  
  by Quan Wei, Kubota Naoyuki, Ahmad Lotfi 
  Abstract: In this paper, we propose a three-dimensional posture evaluating system from two-dimensional images, which can be implemented in physical exercises for elderly people. In this system, two-dimensional coordinates of human joints are first captured and calculated, then our proposed Dynamic Time Warping Steady State Genetic algorithm (DTW-based SSGA) is used for the evaluation of three-dimensional rotational variables from RGB images for the human arm. Finally, these predicted rotational variables would be compared with the template of sample posture by Dynamic Time Warping (DTW) to check the complement of physical exercises. The experimental result shows that our proposed DTW-based SSGA performs with higher accuracy than other evolutionary algorithms, such as standard Steady State Genetic Algorithm (SSGA) and Particle Swarm Optimisation (PSO) when evaluating human joint variables with templates, especially in the physical exercises for rehabilitation.
  Keywords: gesture recognition; forward kinematics; evolutionary computing; dynamic time warping.
  
  
• Research on trusted SDN network construction technology  
  by Fazhi Qi, Zhihui Sun, Yongli Yang 
  Abstract: In this paper, we combine trusted computing with SDN. By active measurement of the SDN controller when it is starting and running, we can guarantee the trust of the SDN controller. By actively measuring the behaviour of the SDN data transponder in the domain, we can guarantee trust of the SDN data transponder. When the cross-domain data interaction is involved, by trusted network connection mechanism, we can guarantee the trust of the transmission of data in different domains so as to build a trusted SDN network as a whole.
  Keywords: trusted computing; SDN; active measurement.
  
  
• A method of spatial place representation based on visual place cell firing  
  by Naigong Yu, Hui Feng 
  Abstract: Constructing a model of visual place cells (VPCs), which produce sensitive firing to visual information, is of great significance for studying bionic positioning and bionic navigation. Based on the physiological research of place cells and the analysis of existing VPC generation models, a firing model of VPCs based on the distance perception of landmarks by the agent is proposed in the paper. Based on the firing activity of VPCs, a spatial place representation method is proposed. The method mainly includes exploring the environment and detecting landmarks, calculating the firing rate of VPCs, adding VPCs and constructing the map of VPCs. Through simulation experiments, the reliability of the positioning performance of the proposed method is verified, and the influence of various parameters in the model on the accuracy of spatial representation of the VPCs map is analysed.
  Keywords: visual place cell; spatial representation; bionic positioning; bionic navigation.
  
  
• Research on leak detection and location of urban gas pipeline network based on RSSI algorithm  
  by Liming Wei 
  Abstract: To solve the leakage problem of urban gas pipelines, this paper presents a method of detecting and locating leakages based on the RSSI algorithm. This technique can analyse and calculate the signal strength received between ZigBee nodes when a pipeline leaks and ultimately obtain the location of the leak. Firstly, the algorithm model is established by using the RSSI signal strength values between the leak target point and each receiving point. Secondly, the distance between the leak point and each receiving point is obtained by the model. Lastly, the approximate coordinates of the leak point are obtained by the least squares method. The simulation results show that the proposed algorithm has high positioning accuracy and wide application prospects.
  Keywords: gas pipeline network; fire early warning; least squares method; RSSI algorithm; ZigBee technology.
  
  
• Multi-objective workflow scheduling in the cloud environment based on NSGA-II  
  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.
  
  
• Application of improved deep reinforcement learning algorithm in traffic signal control  
  by Wang Qiang, Song Shuaidi, Zhang Tengyun, Wang Zelin 
  Abstract: For a regional road network, the signal control system lights at multiple intersections belong to the core technology of intelligent innovation. The relevant personnel need to integrate and analyse the relevant status information of the intersection area based on the DQN method and strategy, and strengthen the control of the signal light system effect, to achieve fast and effective detection. In this paper, we propose a reinforcement learning DQN+ algorithm by using the improved DQN reward and punishment function. Experiments show that DQN+ has obvious advantages in terms of average queue length (AQL), average speed (AS) and average waiting time (AWT) at four intersections.
  Keywords: intelligent transportation; traffic signal control; reinforcement learning; deep reinforcement learning.
  
  
• Enhancing artificial bee colony algorithm with depth-first search and direction information  
  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.
  
  
• Network lifetime maximisation with low power consumption by the use of an ANFIS-based technique in wireless sensor networks  
  by Nune SrinivasRao, K.V.S.N. Rama Rao 
  Abstract: The routing in a wireless sensor network (WSN) is important for improving the network's functioning, because inappropriate routing methods and routing degrade the sensor network energy, impacting the network lifetime. Clustering strategies for reducing the energy consumption and extending the network life have been employed widely. The clustering mechanism can extend the network's service life and network failure. In this study, the IoT has contributed in improving network performance with a new energy efficient ANFIS-based routing approach for WSN. A new distributed cluster creation methodology that enables the self-organisation of local nodes, a novel method for the adjustment of clusters and the turning of the cluster head centre location to distribute the energy burden equally through all sensing nodes incorporates the suggested ANFIS-based routing. The simulation result shows that the proposed scheme outperforms conventional methods with an improvement of 80% in network lifetime and 27% in throughput.
  Keywords: energy-efficient routing protocol; base station; cluster head; network lifetime; wireless sensor network.
  
  
• Machine learning-based approach for the detection of phishing websites  
  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  
  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  
  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  
  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  
  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  
  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;.
  
  
• Fast retrieval of similar images of pulmonary nodules based on deep multi-index hashing  
  by Rui Hao, Yaxue Qin, Yan Qiang 
  Abstract: CT image retrieval of pulmonary nodules mainly uses deep neural network embedded in hash layer to extract hash codes, which are directly as the index address for linear search. With the increasing number of clinical lung CT images and the complexity of image expression, the traditional retrieval methods are inefficient. We propose a multi-index hash retrieval algorithm based on deep hash features. First, a hash layer is added to the convolutional neural network (CNN) which can simultaneously learn the high-level semantic features of images and the corresponding hash function expression. Secondly, the hash codes extracted are effectively divided and multi-index tables are constructed. The query algorithm is designed based on the drawer principle. Finally, the complexity analysis of the whole index algorithm is carried out and experimental results show that the proposed algorithm can effectively reduce the retrieval cost while maintaining the accuracy.
  Keywords: pulmonary nodule; deep learning; hash feature; image retrieval; multi-index hashing.
  
  
• A novel trust-based approach for intrusion detection architecture in wireless sensor networks  
  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  
  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  
  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  
  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.
  
  
• Performance comparison of TOA-based indoor positioning algorithms using ultrawideband technology in 3D  
  by B. Venkata Krishnaveni, K. Suresh Reddy, P. Ramana Reddy Reddy 
  Abstract: In internet of things, localisation of devices is very important. The key point of location-based service is how to calculate the information of position in indoor environments. Distinctive imaginative methodologies and improvements have been proposed; however, definite solid indoor localisation is still a challenging work. Ultra wideband innovation has arisen as a feasible contender for exact indoor situating. For a noise-free environment, the mathematical technique of trilateration is best for position assessment yet genuine conditions which turns out to be uproarious offer ascent to various point convergence position issue and the circumstance turns out to be far more detestable with the expansion in number of reference points. We acknowledge that the study provided in this paper gives a coordinated audit and connection of the positioning techniques, algorithms using the ultrawideband will be valuable for professionals to keep up to date with the continuous upgrades in the field.
  Keywords: internet of things; localisation; TOA positioning; ultrawideband.
  
  
• Aggregation techniques in wireless communication using federated learning: a survey  
  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.
  
  
• Performance analysis of Rayleigh fading wireless networks with multiple propagation paths and spatial diversity  
  by Ridhima Mehta 
  Abstract: Electromagnetic signals propagating through the wireless medium undergo scattering, short-term fading and random attenuations coupled with the power loss due to user mobile devices. This results in multipath components of the transmitted information arriving with different phase, frequency, power and delay at the receiver with fluctuating signal quality. The generalised model for multipath signal estimation is developed in this work with distinct optimal gain and receive diversity operation deployed in the wireless system. The presented modelling technique determines the channel impulse response and complex fading factor for different multipath transmission elements. This study investigates the performance of radio signal propagation in Rayleigh distributed fading channels with antenna diversity at mobile receiver. For this purpose, several network attributes including the bit error rate (BER), signal-to-noise ratio (SNR), average delay and root mean square (RMS) delay spread characterising the efficiency of wireless communications are estimated for varying number of multipath scattered components. These quantitative parameters can be exploited to predict the channel conditions for successful data transmission in typical multipath scenarios. In contrast to the previous related works, our proposed work effectively combines the influence of multiple propagation paths affected by fading and scattering, and spatial receiver diversity principles for extensive information modelling and analysis in the specific wireless communication environment.
  Keywords: MFSK; multipath propagation; Rayleigh fading; spatial diversity.
  
  
• Two-phase approach for the detection and isolation of black hole attack in mobile ad hoc network  
  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.
  
  
• A novel approach to control the sidelobe levels in OFDM radar waveform design using a hybrid of subcarrier weighting and time domain windowing  
  by C.G. Raghavendra, D. Ashish, Chitirala V. S. S. P. K. Chaitanya 
  Abstract: Taking the advantage of orthogonal frequency division multiplexing (OFDM), a novel waveform is developed, which performs well for radar communications. A major bottleneck of using OFDM is out-of-band (OOB) radiations, which weaken the ability of radar systems. To successfully design an OFDM system, it is necessary to curtail the sidelobe levels of OFDM signals. We propose a technology to deal with such issues. In this paper, a novel technique for reducing the sidelobes in OFDM radar signals is projected and examined. Subcarrier weighting technique is the method used to scale down the sidelobe peaks by multiplying real valued weighting coefficients with the used subcarriers. In order to obtain optimal subcarrier, further it is subjected to windowing. The proposed scheme is the hybrid of subcarrier weighting associated with pattern based schemes and time domain windowing which enhances the performance of OFDM radar signal. To validate the merit of the proposed radar waveform, we have obtained numerous simulation results to correlate with existing radar waveform. The results demonstrate that proposed OFDM waveform shows the superiority in functioning with reduction in sidelobe levels.
  Keywords: OFDM; MCPC; sidelobe suppression; radar communication; subcarrier weighting; windowing.
  
  
• Optimised recurrent neural network based localisation in wireless sensor networks: a composite approach  
  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  
  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  
  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  
  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.
  
  
• DC-PHD: multitarget counting and tracking using binary proximity sensors  
  by Nourhan Abdelnaiem, Hossam Fahmy, Anar A. Hady 
  Abstract: Efficient multiple target tracking and counting has become an essential requirement for many wireless binary sensor networks (WSN) applications. WSNs are inexpensive, such that sensor nodes could be easily deployed in any area of interest (AOI). Sensor nodes are simple, cheap and could sense the presence of a target that lies within its range. The simplest type of WSNs is the wireless binary sensor networks (WBSN), in which the deployed sensor nodes are binary. This paper investigates the problem of tracking and counting multiple individual targets that are present in a binary sensor network. An enhanced probability hypothesis density-based filter is proposed by introducing the spatial and temporal dependencies to improve the targets localization accuracy. The implementation of dynamic counting techniques is considered to improve the efficiency of the estimations of targets trajectories. These enhancements were motivated by the lack to differentiate between multiple targets when using the PHD filtering techniques. Simulations compare the performance of the proposed algorithm with the previously mentioned target tracking approaches, to verify the efficiency and accuracy of the proposed target counting and tracking technique in binary sensor networks.
  Keywords: dynamic counting; multi-target counting and tracking; particle filter; probability hypothesis density-based filter; wireless binary sensor networks.
  
  
• Competitive crow search algorithm-based hierarchical attention network for dysarthric speech recognition  
  by Bhuvaneshwari Jolad, Rajashri Khanai 
  Abstract: The common difficulty of speech recognition is articulation deficiency produced by an athetoid, a kind of cerebral palsy. In this paper, the effectual dysarthria speech recognition approach is introduced using the developed Competitive Crow Search Algorithm-based Hierarchical Attention Network (CCSA-based HAN). Here, the spectral subtraction method is used for removing unwanted noises. Then, the specific features are extracted, and then to improve the performance the data augmentation is done. The data augmentation process is performed by adding various noises, like street noise, train noise, and party crowd noise to the input signal. In addition, the HAN classifier is employed for recognising dysarthric speech. Here, CCSA is devised for obtaining effective recognition output, which is designed by incorporating Competitive Swarm Optimiser (CSO) and Crow Search Algorithm (CSA). The developed dysarthria speech recognition approach outperforms other existing methods with accuracy of 0.9141, sensitivity of 0.9208, and specificity of 0.9172.
  Keywords: hierarchical attention network; dysarthric speech recognition; competitive swarm optimiser; crow search algorithm.
  
  
• QoS-based handover approach for 5G mobile communication system  
  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  
  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  
  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  
  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.
  
  
• Review of sensors for detecting insulating gas ozone  
  by Xiaoyu Li, Pengfei Jia, Min Xu, Lin Zhang 
  Abstract: Air quality monitoring and analysis have been highlighted as a significant issue at the moment. So people began to pay more attention to the research direction of pollution gas detection equipment. Ozone (O3) is one of the primary indoor as well as environmental air pollutants. It can cause several health problems. Therefore, the research of pollution gas detection equipment has also become a direction of interest to experts. Existing gas sensors can not only measure a range of common gases (O2, CO2, etc.), but also monitor harmful gases (SO2, NO2, CO, etc.). The existing literature mainly optimises the sensor performance by changing the sensor materials and improving the material synthesis technology, so that the sensor detects ozone more accurately and quickly. Based on the diverse types of gas sensors, this paper reviews a variety of sensors to detect ozone gas.
  Keywords: insulating gas; ozone; gas sensor; gas detection.
  
  
• Performance analysis of various shortest-path routing algorithms using the RYU controller in SDN  
  by Deepak Kumar, Jawahar Thakur 
  Abstract: In this paper, our objective is to establish efficient routing in SDN with low latency and high throughput. To implement this research work, we have used the mininet emulator, Abilene topology, RYU controller, and various shortest path algorithms such as Dijkstra (Dij), Extended Dijkstra (EDij), and Modified Extended Dijkstra (MEDij) and Round Robin (RR). Each algorithm runs separately, one by one, for 120 seconds. These algorithms are analysed in terms of throughput and latency as performance parameters by evaluating their average mean, geometric mean, and harmonic mean value to find which algorithm is the optimal choice among the above-discussed algorithms. The research work is novel because previous studies have implemented the shortest path algorithms using Floodlight or POX controller, but this study use Python supported RYU controller and performs better compared to previous studies This research can be extended to improve load balancing and security across nodes, and routing links using meta-heuristic algorithms such as particle swarm optimisation. There is also scope for exploring and implementing deep learning-supported QoS-based approaches for effective routing optimisation.
  Keywords: Dijkstra; latency; OpenFlow; performance; throughput.
  
  
• Towards energy-efficient 5G heterogeneous networks through dynamic small cell zoom and sleep control algorithm  
  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  
  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  
  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.
  
  
• Optimisation driven generative adversarial network for course recommendation in e-learning  
  by Jobin P. Varghese, R. Vijayakumar 
  Abstract: This research created a mechanism for course recommendation based on collaborative filtering to categorise the attitudes. Here, the positively reviewed courses are identified using sentiment categorisation using the recommended Shuffled Shepherd Bat Optimisation-based Generative Adversarial network (SSBO-based GAN). The input data is fed into a matrix creation process where the data-driven matrix form is created. For course grouping, the Enhanced Fuzzy C-means method (FCM) is used. The course matching is then completed using Canberra distance and holoentropy. The GAN classifier then does the sentiment categorisation. The Bat Algorithm (BA) and the Shuffled Shepherd Optimisation Algorithm (SSOA) are combined to create the Shuffled Shepherd Bat Optimization (SSBO), which is used to train the GAN. Positive course reviews are gleaned from categorised attitudes in this case, aiding in course selection. The suggested SSBO-based GAN displayed improved performance with an F-measure of 96.6%, a recall of 97.1%, and a precision of 96.1%.
  Keywords: collaborative filtering; enhanced fuzzy C-means algorithm; course recommendation; Canberra distance; generative adversarial network.
  
  
• Research on fire alarm system of the intelligent building based on information fusion  
  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.
  
  
• Hand target detection based on improved YOLOv5  
  by Zhu Xu, Jinbao Meng, Juanyan Fang 
  Abstract: With the growing maturity of deep learning-based target detection algorithms, their deployment in intelligent service robots for target detection has become popular nowadays. In order to improve the precision of real-time hand detection and recognition by intelligent service robots, enabling them to detect hands accurately in a variety of environments. This paper proposes a hand detection method based on improved YOLOv5 deep convolutional neural network. YOLOv5s is selected as the base target detection model, the SE attention module is added to the network neck detection layer to guide the model to pay more attention to the channel features of small target to improve the detection performance, and the detection layer is added to enhance the feature learning ability of the network for target regions. The loss function of the detection model is optimized according to the hand image features to improve the confidence of the prediction frame. The experimental results show that the proposed hand detection method based on the improved YOLOv5 deep convolutional neural network can achieve a precision of 99.02%, which is 6.54% better than the original YOLOv5.
  Keywords: target detection; YOLOv5; convolutional neural network; hand detection.
  
  
• A hybrid meta-heuristic algorithm to detect malicious activity based on dynamic ON VANET environmental information  
  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  
  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  
  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.
  
  
• ACCO: adaptive congestion control protocol for opportunistic networks  
  by Deepika Kukreja, Deepak Kumar Sharma 
  Abstract: In Opportunistic Network (OppNet), intermittent connections and limited buffer space of mobile nodes lead to congestion. Various parameters, such as meeting predictability of nodes, current speed of nodes, their buffer space, message size and Time-to-Live (TTL) of messages, have been analysed for proposing a congestion control strategy protocol. The proposed protocol is named as Adaptive Congestion Control Protocol for Opportunistic networks (ACCO). The TTL of a message is calculated using values of message size and meeting predictability of its source node with destination node. As a part of congestion avoidance strategy, largest message with least TTL is dropped on detecting congestion in the network. The connection stability of a node is considered while forwarding a message to another node. Through simulations, it has been shown that the proposed congestion control strategy gives high message delivery ratio, low overhead ratio and low message dropping when compared with Epidemic, PRoPHET, DP, LR-ROP and IWDNN protocols.
  Keywords: mobile ad-hoc network; opportunistic network; congestion control; routing; time-to-live.
  
  
• RPL-OFs analysis and dynamic OF selection for QoS optimisation of RPL protocol  
  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  
  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  
  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.
  
  
• Metaheuristic assisted deep ensemble technique for identifying sarcasm from social media data  
  by Geeta Abakash Sahu, Manoj Hudnurkar 
  Abstract: Sarcasm is regarded as the enveloping linguistic factor in online documents that describes the deeply-felt subjective and opinions. This paper intends to introduce a sarcasm detection model that classifies words under sarcastic or non-sarcastic forms. Pre-processing is the initial phase, where the stop word removal and tokenisation are performed. The pre-processed data is then subjected to extracting the features, where 'information gain, chi-square, mutual information, and symmetrical uncertainty based features' are extracted. As the curse of dimensionality becomes the greatest crisis, optimal feature selection is carried out. For sarcasm detection, an ensemble classifier such as NN, RF, SVM, and optimised DCNN is used, in which the weights of DCNN are optimally selected. For optimal feature selection and optimised DCNN, a hybrid optimisation model termed as Clan Updated Grey Wolf Optimization(CU-GWO) is proposed. Finally, the effectiveness of the proposed algorithm is compared with extant methods in terms of various measures.
  Keywords: sarcasm; tokenisation; information gain; optimised DCNN; CU-GWO optimisation.
  
  
• OLSR-ETX: a parameterised solution for oscillatory network packet losses  
  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  
  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  
  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  
  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  
  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.
  
  
• The construction of the competency model and its application in talent cultivation  
  by Juyan Che, Weinan Liu, Jie Wang, Zaiyang Xie 
  Abstract: In the context of regular epidemic prevention and control, the prominent role of cross-border e-commerce in China’s economic development has become increasingly evident. With the continuous development of China’s cross-border e-commerce industry, there are higher requirements for talents. Whether or not they possess post competency has become an important indicator in corporate recruitment of cross-border e-commerce talents. By collecting the real data of cross-border e-commerce posts on 51job, the requirements for post competency are analysed and obtained. According to the data analysis, a competency model oriented to post data is constructed, on the basis of which the talent cultivation mode in higher vocational colleges is innovated. Moreover, from the perspective of talent cultivation policy network, several policy suggestions are put forward for colleges and college students, with the purpose of cultivating high-quality and high-skilled cross-border e-commerce talents that meet the needs of enterprises and promote social development.
  Keywords: competency model; talent cultivation; higher vocational education; cross-border e-commerce.
  DOI: 10.1504/IJWMC.2023.10057426
   
  
• A hybrid malicious node detection approach based on fuzzy trust model and Bayesian belief in wireless sensor networks  
  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.
  
  
• Bifurcation analysis of a predator-prey model with volume-filling mechanism  
  by Hui Hao, Yan Li, Fengrong Zhang, Zhiyi Lv 
  Abstract: In this paper, a predator-prey model with the tendency mechanism of volume-filling effect under homogeneous Neumann boundary conditions is studied. Firstly, in the case of predation tendency and spatial diffusion, the stability of non-negative equilibria is discussed by analysing the characteristic equation of the corresponding linearization system. Secondly, by Hopf bifurcation theorem, the existence of periodic patterns is investigated. Next, we mainly study the steady state bifurcation when the parameter $chi$ is selected. It is shown that the chemotactic mechanism has no effect on the existence of Hopf bifurcation when $alpha$ is selected as a bifurcation parameter. Whereas, the parameter $chi$ can induce the steady state bifurcation. Finally, to illustrate the theoretical analysis, numerical simulations are carried out.
  Keywords: predator-prey model; stability; Hopf bifurcation; volume-filling; steady state bifurcation.
  DOI: 10.1504/IJWMC.2023.10057449
   
  
• A trusted management mechanism based on trust domain in hierarchical internet of things  
  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.
  
  
• Waste plastic bottles classification with deep learning model  
  by Jixu Hou, Xiaofeng Xie, Wenwen Wang, Qian Cai, Zhengjie Deng, Houqun Yang, Hongnian Huang, Yizhen Wnag 
  Abstract: The misuse of plastic products has led to serious environmental problem. To alleviate such phenomenon, we need to recover the plastic waste with a precise distinction. In this work, we applied a deep learning model, e.g., Faster-RCNN, to identify the class of plastic bottle. We have designed a waste plastic bottle recycling system, which can cooperate with the manipulator and conveyor to automatically sort the bottles in the garbage. During the experiment, we established a data set containing 8400 images. Different backbone networks are used to train on the data set. The experimental results show that the skeleton network using Resnet-50 as Faster-RCNN has higher detection performance than other networks. The system can also be applied to the identification and classification of other solid wastes.
  Keywords: plastic pollution; object detection; garbage classification; plastic bottle recycling; automatic sorting.
  
  
• Task scheduling in multi-cloud environment via improved optimisation theory  
  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.
  
  
• Study of adaptive clutter suppression method used for airborne forward-looking wind shear radar  
  by Meng Jia, Ye Zhang 
  Abstract: Low-level wind-shear has a significant impact on aviation safety. Airborne forward-looking wind-shear weather radar detects the existence of wind-shear during the aircraft taking off and landing by using the Doppler Effect. This article first summarises the analysis of the characteristics of ground clutter in the airborne foresight wind-shear radar. For the practical application to clutter suppression of the airborne predictive wind-shear radar, a new adaptive clutter suppression filter based on the least mean square error (LMS) is presented in this paper. This article introduces the basic principle and working process of this kind of adaptive filter, and gives the computer filter results. The theoretical analysis and filter results indicate that this kind of adaptive clutter suppression technology based on the LMS has the good ground clutter inhibiting ability in the really low signal-to-clutter ratio situation. The structure of this filter is simple, and is easy to implement.
  Keywords: wind-shear radar; ground clutter suppression; adaptive filter; signal-to-clutter ratio.
  DOI: 10.1504/IJWMC.2022.10058309
   
  
• Experimental study on modal analysis of horizontally splitting compressor casing  
  by Guanbing Cheng, Yuxiang WU 
  Abstract: The compressor casing, as one key component in gas turbine engine, suffers from complex loads such as gas dynamic force, vibration and flow pulsation because it encloses the compressor rotor and ensures it operating with high performance and reliability. Thus, understanding the mechanical vibration characteristics of compressor casing is one important issue during its structural design and optimization. In present paper, we investigated the structural modal features of one typical compressor splitting casing by both finite element method (FEM) and experimental one. Firstly, we established one calculated model of the casing in Solidworks and obtained the first six orders parameters like resonant frequency and vibrating shape. Secondly, we adopted classical hammer impact method to acquire 132 arrays of vibration data, then identified the casing structural modal parameters such as frequency and vibrating mode. Finally, those calculated parameters were compared with the experimental ones. The results show that in the calculation, the casing first order frequency is about 489 Hz, the next three orders frequencies are two time the first order one.
  Keywords: aero engine; compressor casing; experimental modal analysis; finite element method; resonant frequency; vibration shape.
  DOI: 10.1504/IJWMC.2023.10058753
   
  
• A modified advanced encryption standard-based model for secure data transmission in cognitive radio with multi-channels  
  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.
  
  
• A new time-frequency synchronisation algorithm based on preamble sequence in OFDM system  
  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.
  
  
• IoT concepts, characteristics, enabling technologies, applications, and protocol stack: issues and imperatives  
  by Bimal Patel, Jalpesh Vasa, Parth Shah 
  Abstract: Internet of Things(IoT) has fueled the growth momentum exponentially by connecting billions of smart devices. Anything communication is now widespread to the “Internet of People”, “Internet of Content” and “Internet of Services” using facilities of IoT. IoT showcases network connectivity, heterogeneity among sensing devices, and future-generation enabling technologies for smart connectivity. In short, IoT extended 4A vision i.e.”Anytime”,”Anywhere” with “Anyone” and “Anything” to 6A vision by introducing additional concepts “Any path/network” and “Any Service”. Within the help of smart applications like home, agriculture, cities, grid, supply chain management individual's life get smoother, faster and comfortable. Finally, IoT protocol stack information ranging from 3-layered to 5-layered architecture including various features its future supporting technologies, and open research security issues is explored.
  Keywords: internet of things; futuristic technologies; smart applications; protocol stack; security issues.