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Wireless Networks
Journal Prestige (SJR): 0.336  Citation Impact (citeScore): 2 Number of Followers: 6  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1572-8196 - ISSN (Online) 1022-0038 Published by Springer-Verlag  [2468 journals]
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- A hybrid charging scheme for efficient operation in wireless sensor
network-
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Abstract: Abstract The adoption of Wireless Sensor Networks (WSN) is persistently increasing over various applications. Being an integral part of the Internet of Things, prolonged sustainability of a sensor node is highly demanded. In this perspective, energy depends on supporting high-end sustainability factors in the WSN environment. In many recent studies, wireless transfer of energy concepts has contributed to ensuring on-demand charging of rechargeable sensor nodes to facilitate a better data aggregation process. A review of existing studies on the wireless transfer of energy exhibits adopting the clustering approach, scheduling-based approach, approaches with ambiguous assumptions, and sophisticated techniques. Most recently, it has been noticed that learning-based schemes have contributed to facilitating better charging strategy; however, they are also associated with iterative learning operation that adversely affects the operational cost of charging. Therefore, the proposed scheme has introduced a novel and simplified hybrid charging scheme considering the presence of normal and critical sensor nodes in the deployment area. A reinforcement learning scheme is used for optimizing the policy construct toward formulating a better charging strategy. At the same time, the study implements Type-I and Type-II chargers based on their static location and mobility, respectively. With an extensive simulation environment, the proposed scheme has been benchmarked with the existing charging scheme to find that the proposed hybrid scheme offers reduced operational cost, enhanced battery span, and higher sustainability in contrast to the current system.
PubDate: 2024-08-09
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- EtherVote: a secure smart contract-based e-voting system
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Abstract: Abstract Conventional electing procedures cannot fulfill advanced requirements in modern times. Secure electronic voting systems have been a concern of many researchers for years to replace traditional practices. Decentralized approaches, such as Blockchain technology, are essential to provide compulsory guarantees for secure voting platforms, that hold the properties of transparency, immutability, and confidentiality. This paper presents EtherVote, a secure decentralized electronic voting system, which is based on the Ethereum Blockchain network. The EtherVote is a serverless e-voting model, relying solely on Ethereum and smart contracts, that does not include a database, and thus it enhances security and privacy. The model incorporates an effective method for voter registration and identification to strengthen security. The main properties of EtherVote include encrypted votes, efficiency in handling elections with numerous participants, and simplicity. The system is tested and evaluated, vulnerabilities and possible attacks are exposed through a security analysis, and anonymity, integrity, and unlinkability are retained.
PubDate: 2024-08-07
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- Deep intrusion net: an efficient framework for network intrusion detection
using hybrid deep TCN and GRU with integral features-
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Abstract: Abstract In recent times, the several cyber attacks are occurred on the network and thus, essential tools are needed for detecting intrusion over the network. Moreover, the network intrusion detection systems become an important tool thus, it has the ability to safeguard the source data from all malicious activities or threats as well as protect the insecurity among individual privacy. Moreover, many existing research works are explored to detect the network intrusion model but it fails to protect the target network efficiently based on the statistical features. A major issue in the designed model is regarded as the robustness or generalization that has the capability to control the working performance when the data is attained from various distributions. To handle all the difficulties, a new meta-heuristic hybrid-based deep learning model is introduced to detect the intrusion. Initially, the input data is garnered from the standard data sources. It is then undergone the pre-processing phase, which is accomplished through duplicate removal, replacing the NAN values, and normalization. With the resultant of pre-processed data, the auto encoder is utilized for extracting the significant features. To further improve the performance, it requires choosing the optimal features with the help of an Improved chimp optimization algorithm known as IChOA. Subsequently, the optimal features are subjected to the newly developed hybrid deep learning model. The hybrid model is built by incorporating the deep temporal convolution network and gated recurrent unit, and it is termed as DINet, in which the hyper parameters are tuned by an improved IChOA algorithm for attaining optimal solutions. Finally, the proposed detection model is evaluated and compared with the former detection approaches. The analysis shows the developed model is suggested to provide 97% in terms of accuracy and precision. Thus, the enhanced model elucidates that to effectively detect malware, which tends to improve data transmission significantly and securely.
PubDate: 2024-08-03
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- Smart vision for quality apple classification using SURF–Harris
optimizing techniques-
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Abstract: Abstract Smart manufacturing optimizes processes and marketing transactions, leading to more efficient manufacturing processes. Computational modeling is used to execute the improvement, which requires trustworthy and meaningful information as input. This paper uses a vision sensor for capturing accurate data. Hybrid techniques for vision-based classification and feature extraction methods were used. The ORB (Oriented FAST and Rotated BRIEF) and BRISK (Binary Robust Invariant Scalable Keypoints) feature recognition and characterization processes were chosen based on evaluating several feature recognition and characterization processes when applied to acoustic information recorded by an Apple dataset. After the feature extraction, a Deep CNN (DCNN) method is used for the classification process to achieve efficient accuracy. The vision-based algorithms classify the apple as damaged or not damaged, allowing for automated examination, classification, and analysis. To test the classifiers, 600 apple photos were obtained. There are 300 non-faulty apples and 300 damaged apples in the photographs. The faulty group encompasses a wide range of defective kinds and severities. The effectiveness of twofold, threefold, fourfold, fivefold, and tenfold detectors is evaluated. The Softmax classifiers were used in the study, achieving 98.84% classification accuracy for a tenfold dataset.
PubDate: 2024-08-01
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- A comprehensive study on physical fitness of Wushu routine athletes based
on video-driven core strength training mechanism in wireless network-
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Abstract: Abstract Under the condition of high performance sports, the physical state of Wushu routine athletes is very different from that of commons, and they need the strength support of the core muscle tissue. Specifically, core strength training has an important impact on the physical stability of Wushu routine athletes, and strengthening core strength training can improve their physical quality. In core strength training, video training method can make up for the shortcomings of traditional training methods. In addition, with the rapid development of wireless network technology, video service has become the mainstream application of mobile Internet. At the same time, users' experience needs for video services under wireless networks have gradually changed, and the traditional video Quality of Experience (QoE) is difficult to fully reflect users' actual experience quality. Therefore, this paper proposes a QoE prediction model based on core strength training video information, data of quality of service, and behaviors of Wushu routine athletes. The experimental results show that the QoE prediction model of core strength training video converges rapidly in the training process, and has a good fitting effect on the training set and verification set. Furthermore, the QoE prediction model proposed in this paper can improve the accuracy of subjective QoE of Wushu routine athletes in wireless network environment. The construction of QoE prediction model is the premise of optimizing QoE. An effective QoE prediction model can reflect the real video experience of Wushu routine athletes and provide a comprehensive and accurate QoE reference for the construction of core strength training video, so as to improve the physical quality of Wushu routine athletes.
PubDate: 2024-08-01
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- Heterogeneous load balancing improvement on an energy-aware distributed
unequal clustering protocol using BBO algorithms-
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Abstract: Abstract With the advancement of ICT technology today, we can equip every single object with tiny and inexpensive radio modules; hence, they can interact and cooperate to perform complex tasks that have not been possible previously. During recent years several practical IoT-based applications have been proposed by deploying this improvement. With the expansion of IoT-based systems, many challenges began to emerge in this area. Energy optimization has been considered, as one of the most important problems in this domain, and data transmission has been identified as the primary accused. To tackle this problem, clustering has been suggested as a promising solution for reducing transmission distance and consequently energy consumption. The non-deterministic polynomial-time hard problem nature of clustering; alongside a variety of considerable parameters, limitations, and their contradiction, have made this problem more complex. As a result, various approaches have been proposed during recent years, each of which considered some parameters and real-world constraints. Here we present an efficient improvement on the existing energy-aware distributed unequal clustering protocol (EADUC). Our solution deploys a well-known swarm intelligence algorithm (SI), named Biogeography-based optimization (BBO) in a distributed manner to achieve heterogeneous load balancing. Our proposed work reflected a variety of real-world limitations such as energy, time, communication radius, and buffer size that have not been considered in many previous works simultaneously. Our simulations show approximately a 26% drop in the total number of dead nodes and a 1.59 % drop in energy consumption in comparison to the existing EADUC algorithm.
PubDate: 2024-08-01
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- Toward intelligent cooperation at the edge: improving the QoS of workflow
scheduling with the competitive cooperation of edge servers-
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Abstract: Abstract Advances in big data and Internet of Things devices have brought novel service modes, such as smart cities and intelligent transportation, to daily life. With the widespread deployment of smart terminals comes an exponentially increasing amount of data, which, causes conflict due to the intensive resource demand and limited computation capacity. To manage this conflict, edge computing has been introduced as an auxiliary technique to cloud computing. However, the emerging computation-intensive service chains bring high resource demands that may exceed the computation capability of a single edge server. Simply offloading them to cloud servers is hardly time saving and is challenging for typical edge-cloud schemes. In this paper, we address the challenge of coordinating the workflow scheduler from multiple users in a partially observable environment. We first partition the workflow by leveraging graph theory to split the component tasks into clusters based on their dependency constraints. We further model the possible contention on edge servers among multiple users as a Markov game and propose a multiagent reinforcement learning-based edge server coordination algorithm named partially observable multiagent workflow scheduler (POMAWS) as the solution. With fine-trained agents, the proposed scheme can intelligently activate nearby edge nodes to form a temporal workgroup and manage contention when it occurs. The numerical results validate the feasibility of our proposed scheme, as its performance exceeds typical cloud computing and traditional clustering schemes with an improved QoS in terms of processing delay.
PubDate: 2024-08-01
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- Multi-objective fog node placement strategy based on heuristic algorithms
for smart factories-
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Abstract: Abstract With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intelligent devices brings a huge task request. Fog computing, which is an emerging distributed computing paradigm, is widely applied to process the device data generated in smart manufacturing. However, as fog nodes are resource limited and geographically widely distributed limitations, proper fog node placement strategies are critical to enhance the service performance of fog computing systems. In this paper, we study the problem of fog node placement in smart factories and divide it into two scenarios, fixed device and mobile device fog node placement, depending on the mobility of the devices. The fog node placement model and objective function are built in the two scenarios, and two improved heuristic algorithms are proposed to obtain the most optimal placement scheme. In addition, we perform simulation experiments based on existing intelligent production line prototype platforms and devices to evaluate the performance of the proposed algorithms. The IGA reduces latency by an average of \(586.7 - 1089{\text{ms}}\) over the benchmark algorithm, saving \(18.3 - 39\%\) in energy consumption. The total latency of IMOA is reduced by \(59.8 - 68.5\%\) , and the maximum latency is reduced by \(48.8 - 69.2\%\) . The experimental results show that the proposed algorithms outperform other benchmark algorithms in terms of task response time and energy consumption.
PubDate: 2024-08-01
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- A blockchain ledger for securing isolated ambient intelligence deployments
using reputation and information theory metrics-
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Abstract: Abstract Ambient Intelligence deployments are very vulnerable to Cyber-Physical attacks. In these attacking strategies, intruders try to manipulate the behavior of the global system by affecting some key elements within the deployment. Typically, attackers inject false information, integrate malicious devices within the deployment, or infect communications among sensor nodes, among other possibilities. To protect Ambient Intelligence deployments against these attacks, complex data analysis algorithms are usually employed in the cloud to remove anomalous information from historical series. However, this approach presents two main problems. First, it requires all Ambient Intelligence systems to be networked and connected to the cloud. But most new applications for Ambient Intelligence are supported by isolated systems. And second, they are computationally heavy and not compatible with new decentralized architectures. Therefore, in this paper we propose a new decentralized security solution, based on a Blockchain ledger, to protect isolated Ambient Intelligence deployments. In this ledger, new sensing data are considered transactions that must be validated by edge managers, which operate a Blockchain network. This validation is based on reputation metrics evaluated by sensor nodes using historical network data and identity parameters. Through information theory, the coherence of all transactions with the behavior of the historical deployment is also analyzed and considered in the validation algorithm. The relevance of edge managers in the Blockchain network is also weighted considering the knowledge they have about the deployment. An experimental validation, supported by simulation tools and scenarios, is also described. Results show that up to 93% of Cyber-Physical attacks are correctly detected and stopped, with a maximum delay of 37 s.
PubDate: 2024-08-01
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- Deep convolutional neural network with Kalman filter based objected
tracking and detection in underwater communications-
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Abstract: Abstract Underwater autonomous operation is becoming increasingly crucial as a means to escape the hazardous high-pressure deep-sea environment. As a result, it is essential for there to be underwater exploration. The development of sophisticated computer vision is the single most significant factor for the success of underwater autonomous operations. In order to improve low-quality photos and compensate for low-light circumstances, preprocessing is used in underwater vision. This allows for clearer pictures to be seen. In this paper, we propose a deep convolutional neural network (DCNN) method for solving the weakly illuminated problem for underwater pictures. This method combines the max-RGB and shade-of-grey approaches to improve underwater visibility and to train the plotting association necessary to obtain the lighting plot. Using this method, we are able to resolve the problematic of weakly illuminated pictures in a way that is efficient. After the photos have been prepared, a deep convolutional neural network (DCNN) approach is developed for detection and classification in the water. Two updated methods are then utilized in order to adapt the architecture of the DCNN to the qualities of underwater vision. The purpose of this investigation is to present a Kalman Filter (KF) method as a solution to the difficulties associated with underwater communication in terms of object tracking and detection. We were able to separate a section of the object by employing a threshold segment and morphological technique. This allowed us to investigate the invariant moment and area properties of the section. Based on the findings, it can be decided that the suggested technique is useful for monitoring underwater targets using DCNN-KF. Furthermore, it displays high resilience, high accuracy, and real-time characteristics. Results from the simulations show that the suggested model DCNN-KF does a better job of localization than the most advanced methods at the time of the study.
PubDate: 2024-08-01
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- Analysis of electromagnetic force on wireless charging structure
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Abstract: Abstract Wireless charging for UAVs (unmanned aerial vehicles) is a significant development in the field of robotics and aerial technology. It has the potential to revolutionize the way UAVs are used and deployed, by eliminating the need for tethers and cables to provide power. However, electromagnetic force will be generated due to the interaction between coupled magnetic field and moving charge. Especially in the space isolation environment, the coil structure will produce non-contact free movement under the action of electromagnetic force, which has a side effect on the safe operation of the system. This paper analyses the characteristics of electromagnetic force on the coil structure, proposes an optimization method to reduce the electromagnetic force, and verified the optimized structure by simulation. According to the results, the electromagnetic force on the optimized coil structure is significantly lower than that of the traditional coil structure.
PubDate: 2024-08-01
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- Distributed determination method of redundant nodes in wireless
communication networks based on Bezier function-
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Abstract: Abstract Wireless sensor networks are a special kind of self-assembling network in the fields of national defense, military, item tracking, and aerial detection, etc. They have a wide range of application potential in many areas that are not accessible to people, and have led to a fundamental change in the ability of people to obtain information. The study addresses the distributed determination of redundant nodes in wire communication networks, and aims to propose an improved redundant node determination algorithm for wireless communication networks based on the Bezier function. In order to enhance a number of capabilities, including the determination efficiency of wireless sensors, the study intends to offer an enhanced method for distributed assessment of redundant nodes in wireless communication networks relying on the Bezier function. The experimental results show that MATLAB is used as a simulation platform, and the improved algorithm based on Bezier function has a slower reduction in the number of surviving nodes, a much longer life cycle, an improved number of surviving nodes per round and an improved energy loss, and an improved overall performance of the network. As can be observed, due to limitations enforced by other network elements, there are numerous uncertain starting connections in practice. The research's proposed Bezier function-based method for the distributed determination of redundant nodes in wireless communication networks indicates the reference value because the improved algorithm based on the Bezier function was used to improve the closure rate of nodes faster than when the original algorithm was used.
PubDate: 2024-08-01
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- Cloud computing-driven resource allocation method for global tennis
training: a performance optimization with game theory consideration-
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Abstract: Abstract Tennis is an elegant sport characterized by confrontation, controllability, leisure, and entertainment. With the continuous improvement of Chinese women’s tennis in the world, men’s tennis also appears in the world arena, and the popularity of Chinese sports also appears tennis hot. Since the 2008 Beijing Olympic Games, tennis courts have been built vigorously all over China. Due to the facilities, operational capabilities, and services vary in tennis courts, how to book a proper tennis court for clubs or citizens becomes the research content of this study. In the context of cloud computing, a lot of resources and information enter the cloud. This study puts tennis court resources in the cloud and allocates tennis court resources based on game theory. According to the situation that multi-users successively request cloud tennis court resources, this study designs a method of resource allocation for cloud tennis courts based on a dynamic game model. Aiming at the problems existing in traditional resource allocation, this method uses dynamic game theory to formally describe and analyze the resource allocation process and establish a game model, realize a game equilibrium solution based on the quantitative calculation of income, and realize fair resource allocation. The experimental results show that the proposed resource allocation of the tennis courts method has a good performance in success rate and task completion time. It also supports clubs or citizens in the global resource allocation of tennis courts.
PubDate: 2024-08-01
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- BLE-based secure tracking system proposal
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Abstract: Abstract As communication capabilities of mobile devices continue to advance, ensuring reliability and security has become increasingly crucial. The aerial tracking system presented in this paper provides a useful solution for tracking and tracing objects in various scenarios. To ensure reliability and security, the system incorporates appropriate mechanisms, including Lightweight Cryptography, to prioritize confidentiality and integrity. The Android component of the system has two modes of operation: Tracker Mode, running on a smartphone mounted on a drone (RPA), and Client Mode, running on mobile devices on the ground. In Client Mode, users transmit their positioning and trajectory information via Bluetooth Low Energy beacon mode, which is then relayed to the server backend via the 4G/5G network once the RPA enters an area with coverage. The system provides a reliable and secure solution for situations where tracking and tracing are essential, such as the supervision and control of public areas with capacity control or tracking and localizing people in isolated environments.
PubDate: 2024-08-01
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- Generation of high-order random key matrix for Hill Cipher encryption
using the modular multiplicative inverse of triangular matrices-
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Abstract: Abstract Hill Cipher is one of the classic symmetric encryption algorithms widely used in cloud data security. Although the hill cipher principle is relatively simple, its key matrix must be invertible, and all elements must be integers. However, the inverses of randomly generated matrix does not always exist and it is time-consuming to test whether the higher-order matrix is reversible. In this paper, we propose Random Key Matrix Generation Method (RKMGM), a novel algorithm to randomly generate a high order hill key matrix based on the modular multiplicative inverse of a triangular matrix. We prove that RKMGM extends the selection of key matrices from finite field to the rational number field and has no constraints on matrix order, and then analyze the time complexity of RKMGM. Compared to alternative hill key generation methods based on the involutory matrix, self-inversion matrix, and single mode, RKMGM has the advantages of simplicity, fewer constraints, one-time random generation, and high key space complexity.
PubDate: 2024-08-01
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- Joint activity and channel estimation for asynchronous grant-Free NOMA
with chaos sequence-
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Abstract: Abstract This paper considers an asynchronous grant-free non-orthogonal multiple access (NOMA) systems which can be applied in massive machine-type communications (mMTCs) and underwater IoT acoustic communication scenarios due to its high spectral efficiency and low power consumption characteristics. In particular, the system’s asynchronous reception of user signals can effectively reduce the additional overhead caused by synchronous reception. We investigate the joint activity and channel estimation in the asynchronous case, where an asynchronous frame structure is considered, and a pilot sequence designed by chaotic sequence is used to reduce the pilot storage space. The joint estimations are formulated as single measurement vector (SMV) and multiple measurement vector (MMV) problems for single-antenna and multiple-antenna systems. Different from the existing estimation algorithms, where prior information is considered for estimation, an adaptive alternating direction method of multiplier (ADMM)is proposed for the SMV problem and a two-stage ADMM is proposed for the MMV problem. In particular, an index set is first estimated in each iteration of our proposed adaptive ADMM, and a linear ADMM is performed based on the index set. The first stage of our proposed two-stage ADMM is to estimate the delay and the activity, and then the channel state information is estimated. Further, we analyze the complexity of the two algorithms and their sensitivity to the initial values of chaotic sequences. Finally, simulation results reflecting the detection performance of the algorithms are given. Based on the simulation results, the proposed two algorithms are computationally efficient, providing superior signal recovery accuracy and user activity detection performance. More importantly, the signal delay has a relatively small impact on the proposed algorithm.
PubDate: 2024-08-01
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- A spatiotemporal and motion information extraction network for action
recognition-
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Abstract: Abstract With the continuous advancement in Internet-of-Things and deep learning, video action recognition is gradually emerging in daily and industrial applications. Spatiotemporal and motion patterns are two crucial and complementary types of information used for action recognition. However, effectively modelling both types of information in videos remains challenging. In this paper, we propose a spatiotemporal and motion information extraction (STME) network that extracts comprehensive spatiotemporal and motion information from videos for action recognition. First, we design the STME network, which includes three efficient modules: a spatiotemporal extraction (STE) module, a short-term motion extraction (SME) module and a long-term motion extraction (LME) module. The SME and LME modules are used to model short-term and long-term motion representation, respectively. Then, we apply the STE module to capture comprehensive spatiotemporal information which can supplement the video representation for action recognition. According to our experimental results, the STME network achieves significantly better performance than existing methods on several benchmark datasets. Our codes are available at https://github.com/STME-Net/STME.
PubDate: 2024-08-01
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- Resource allocation and offloading decision for secure UAV-based MEC
wireless-powered System-
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Abstract: Abstract Unmanned aerial vehicles (UAVs) equipped with mobile edge computing (MEC) servers and featuring flexible deployment capabilities can help to reduce the computing pressure on ground user networks. However, the majority of ground users are hindered by their limited battery life, preventing them from working without interruption. To maximize the service lifetime of ground users, UAVs transmit energy to them first and then collect offload tasks afterwards. This approach allows users to work without interruption while transmitting UAVs with computing tasks which can then be processed with the help of MEC servers. This helps to reduce the pressure on ground user networks, ensuring that they remain reliable and efficient. Therefore, we propose an optimization problem that aims to maximize the minimum security offloading rate of the system. This problem involves multiple variables, so conventional methods are not suitable for solving it. Our proposed scheme utilizes block coordinate descent (BCD) and successive convex approximation (SCA) algorithms, which can better optimize the user offloading decision, energy transfer duration, and user transmit power. Numerical results demonstrate that our scheme is more effective than the two benchmark schemes in improving the system performance.
PubDate: 2024-08-01
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- A geo-location and trust-based framework with community detection
algorithms to filter attackers in 5G social networks-
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Abstract: Abstract We propose a geographical location and trust-based framework combined with community detection algorithms to filter communities of malicious users in 5G social networks. This framework utilizes geo-location information, community trust within the network and AI community detection algorithms to identify users that can cause harm. It has a benefit over some other fake user detection mechanisms because it takes into account the characteristics that a malicious user cannot easily fake like the geographical location and community trust built throughout time. We illustrate the proposed framework on synthetic social network data. Results show this framework can distinguish potential malicious users from trustworthy users based on their location, trust, and structural attributes.
PubDate: 2024-08-01
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- Research and implementation of network communication based on embedded
monitoring system-
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Abstract: Abstract With the rapid development of computer technology and network communication technology, embedded micro control system has been widely used. It is crucial in the fields of large-scale chemical equipment, mechanized production base, transportation and future robots. Combined with the current Internet of Things technology, the equipment needs to be kept on the network during operation, real-time data comparison, data feedback, data reception and other network operations to provide strong help for human society. Focusing on the network communication level, due to the improvement of hardware and software technology, the basic link work has become the foundation part of realizing network communication, and the real problem is often the information transmission. For example, for wireless channel estimation and optimization based on embedded system, it is necessary to eliminate the interference of irresistible factors as far as possible, overview the defects in algorithm level and logic analysis level since the development of network communication technology, discuss the impact of network communication module of embedded monitoring system in practical application as far as possible, and make the error infinite to zero under the premise of unchanged hardware conditions. Based on the above conditions, the lightweight channel estimation method and the optimization under the condition of sparse matrix are proposed. This paper studies the network communication of embedded devices, especially the channel link in network communication, and focuses on the channel estimation algorithm. This paper proposes the advantages of LS and MMSE algorithms. According to the experimental results, although in terms of speed indicators, it still maintains better than the traditional algorithm in terms of average throughput, transmission delay, response time, transmission rate and so on in a static environment.
PubDate: 2024-08-01
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