Network Of Mobile Devices Research Articles

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

Hidden influential node selection based on cost–benefit analysis for harmful information propagation control

The quick development of mobile network and smart devices provides a convenience way for information sharing in online social networks, which also accelerates the propagation of harmful information, thus how to select the hidden influential nodes with lower management cost for reducing the propagation speed of harmful information is an important task. In this article, we propose a greedy hidden influential node selection algorithm based on the epidemic model and cost–benefit analysis. First, we investigate the user behaviour dynamic characteristics from two perspectives of social relationships and interaction behaviours, and then susceptible and infected (SI) epidemic model is applied and user influence is estimated. Second, considering the management cost and benefit of different users, a greedy hidden influential node selection algorithm based on the cost–benefit analysis is proposed. Finally, a series of experiments are conducted using the public social network data set and the data set collected from Sina Weibo, to verify the performance and practicality of the developed method. The experimental results demonstrate that our method outperforms other related methods in harmful information propagation control.

EBOF: A fusion-domain color image watermarking scheme based on approximate Hessenberg factorization

With the rapid development of mobile networking devices and Internet technology, color images gradually occupy the dominant position of image applications, and the protection of color image copyright is also imminent. In this paper, a blind watermarking scheme for color images based on approximate Hessenberg factorization (AHF) in fusion domain is proposed. The formation rule of Hessenberg matrix coefficients is further researched based on the Householder transform, and the approximate values of Hessenberg matrix at position (1, 2), (2, 1), (2, 2) are directly obtained and used to hide the watermark information in the spatial domain. The proposed scheme not only consists of fractional order Chen’s chaotic system (FOCCS) to encrypt the watermark image, but also combines with Error correction coding application, B-R-G watermark embedding principle, Overflow processing of extreme pixel block and Firefly algorithm (EBOF). Moreover, the embedding position and the amount of embedding information can be also adaptively selected according to the image size. The simulation results show that the presented fusion-domain scheme outperforms state of the art schemes. On the premise of ensuring low time complexity, the presented watermarking scheme has strong robustness, high invisibility and high security.

Using Deep Learning Models to Detect Fake News about COVID-19

The proliferation of mobile networked devices has made it easier and faster than ever for people to obtain and share information. However, this occasionally results in the propagation of erroneous information, which may be difficult to distinguish from the truth. The widespread diffusion of such information can result in irrational and poor decision making on potentially important issues. In 2020, this coincided with the global outbreak of Coronavirus Disease (COVID-19) , a highly contagious and deadly virus. The proliferation of misinformation about COVID-19 on social media has already been identified as an “infodemic” by the World Health Organization (WHO) , posing significant challenges for global governments seeking to manage the pandemic. This has driven an urgent need for methods to automatically detect and identify such misinformation. The research uses multiple deep learning model frameworks to detect misinformation in Chinese and English, and compare them based on different text feature selection s. The model learns the textual characteristics of each type of true and misinformation for subsequent true/false prediction. The long and short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the bidirectional long and short-term memory (BiLSTM) model were selected for fake news detection. BiLSTM produces the best detection result, with detection accuracy reaching 94% for short-sentence English texts, and 99% for long-sentence English texts, while the accuracy for Chinese texts was 82% .

A New Method for using Wavelet-OFDM and Its Applications in Healthcare to Improve the Services of the 5G Mobile Network and IoT-Related Communication Devices

The orthogonal frequency division multiplex (OFDM), which is based on the Fourier transform, is at the center of the current wireless communication systems, including 5G.Due to its performance and capacity to support network-intensive applications like the Internet of Things (IoT), numerous studies have suggested that wavelet transform-based OFDM is a superior alternative to Fourier in physical layer solutions. In this paper, we compare the performance of wavelet transforms to the requirements of the future wireless application system and offer guidelines and methods for wavelet applications in the design of 5G waveforms. A comprehensive discussion of the impact on healthcare follows. Taking into consideration the following 5G key performance indicators (KPIs), a comprehensive performance comparison has been conducted using an image as the test data: energy savings, complexity of modulation and demodulation, dependability, latency, spectral efficiency, effect of transmission and reception under asynchronous transmission, and resistance to time- and frequency-selective channels are all factors to consider. The usage guidelines for the wavelet transform are then discussed. The guidelines are sufficient to act as a guide for future developments and as approaches to tradeoffs.

RFCam

As cameras and Wi-Fi access points are widely deployed in public places, new mobile applications and services can be developed by connecting live video analytics to the mobile Wi-Fi-enabled devices of the relevant users. To achieve this, a critical challenge is to identify the person who carries a device in the video with the mobile device's network ID, e.g., MAC address. To address this issue, we propose RFCam, a system for human identification with a fusion of Wi-Fi and camera data. RFCam uses a multi-antenna Wi-Fi radio to collect CSI of Wi-Fi packets sent by mobile devices, and a camera to monitor users in the area. With low sampling rate CSI data, RFCam derives heterogeneous embedding features on location, motion, and user activity for each device over time, and fuses them with visual user features generated from video analytics to find the best matches. To mitigate the impacts of multi-user environments on wireless sensing, we develop video-assisted learning models for different features and quantify their uncertainties, and incorporate them with video analytics to rank moments and features for robust and efficient fusion. RFCam is implemented and tested in indoor environments for over 800 minutes with 25 volunteers, and extensive evaluation results demonstrate that RFCam achieves real-time identification average accuracy of 97.01% in all experiments with up to ten users, significantly outperforming existing solutions.

Efficient processing of top-k frequent spatial keyword queries.

The rapid popularization of high-speed mobile communication technology and the continuous development of mobile network devices have given spatial textual big data (STBD) new dimensions due to their ability to record geographical objects from multiple sources and with complex attributes. Data mining from spatial textual datasets has become a meaningful study. As a popular topic for STBD, the top-k spatial keyword query has been developed in various forms to deal with different retrievals requirements. However, previous research focused mainly on indexing locational attributes and retrievals of few target attributes, and these correlations between large numbers of the textual attributes have not been fully studied and demonstrated. To further explore interrelated-knowledge in the textual attributes, this paper defines the top-k frequent spatial keyword query (tfSKQ) and proposes a novel hybrid index structure, named RCL-tree, based on the concept lattice theory. We also develop the tfSKQ algorithms to retrieve the most frequent and nearest spatial objects in STBD. One existing method and two baseline algorithms are implemented, and a series of experiments are carried out using real datasets to evaluate its performance. Results demonstrated the effectiveness and efficiency of the proposed RCL-tree in tfSKQ with the complex spatial multi keyword query conditions.

A Joint Resource Allocation Algorithm for D2D Communication

The emergence of multimedia services has meant a substantial increase in the number of devices in mobile networks and driving the demand for higher data transmission rates. The result is that, cellular networks must technically evolve to support such higher rates, to be equipped with greater capacity, and to increase the spectral and energy efficiency. Compared with 4G technology, the 5G networks are being designed to transmit up to 100 times more data volume with devices whose battery life is 10 times longer. Therefore, this new generation of networks has adopted a heterogeneous and ultra-dense architecture, where different technological advances are combined such as device-to-device (D2D) communication, which is one of the key elements of 5G networks. It has immediate applications such as the distribution of traffic load (data offloading), communications for emergency services, and the extension of cellular coverage, etc. In this communication model, two devices can communicate directly if they are close to each other without using a base station or a remote access point. Thus, eliminating the interference between the D2D and cellular communication in the network. The interference management has become a hot issue in current research. In order to address this problem, this paper proposes a joint resource allocation algorithm based on the idea of mode selection and resource assignment. Simulation results show that the proposed algorithm effectively improves the system performance and reduces the interference as compared with existing algorithms.

Study of webcasting on promoting college students’ consumption

With the advancement of Internet technology and the popularization of mobile network devices, webcasting has become a popular entertainment method, and shopping rewards mediated by webcasting have also become a new consumption marketing model. It is of strategic significance to study the consumption behavior of the main groups of consumers under the live broadcast platform. Based on the social media marketing environment, this paper takes webcasting as the research object, and takes game livecasting as the starting point. The impact of webcasting on consumer behavior, combined with relevant theories, try to put forward corresponding reference strategies.

Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks

Device-to-Device (D2D) communication-enabled Heterogeneous Cellular Networks (HCNs) have been a promising technology for satisfying the growing demands of smart mobile devices in fifth-generation mobile networks. The introduction of Millimeter Wave (mm-wave) communications into D2D-enabled HCNs allows higher system capacity and user data rates to be achieved. However, interference among cellular and D2D links remains severe due to spectrum sharing. In this paper, to guarantee user Quality of Service (QoS) requirements and effectively manage the interference among users, we focus on investigating the joint optimization problem of mode selection and channel allocation in D2D-enabled HCNs with mm-wave and cellular bands. The optimization problem is formulated as the maximization of the system sum-rate under QoS constraints of both cellular and D2D users in HCNs. To solve it, a distributed multiagent deep Q-network algorithm is proposed, where the reward function is redefined according to the optimization objective. In addition, to reduce signaling overhead, a partial information sharing strategy that does not observe global information is proposed for D2D agents to select the optimal mode and channel through learning. Simulation results illustrate that the proposed joint optimization algorithm possesses good convergence and achieves better system performance compared with other existing schemes.

SECURITY ARCHITECTURE ON WIRELESS NETWORK TRAFFIC HANDOFF : A REVIEW

This paper provides an overview of the wireless handoff process on wireless networks by investigating Authentication Protocol using EAP -AKA (Extensible Authentication Protocol) - Authentication and Key Agreement The EAP-AKA protocol was developed for LAN networks or mobile network devices, the authors found ineffective network procedures guaranteed that made the attacks MITM and DoS attacks may occur. In addition, we find the storage procedures and resource submissions not safe enough to withstand DoS attacks. Focusing on Both types of attacks, the authors are interested in existing security protocol methods and written by researchers, then perform Systematic Literature Review (SLR) by using international journals database ie from the IEEE organization with the use of wireless handoff attack keyword in the middle of the world and obtained some related journals which need further research to deepen the Systematic Literature review, by offering a structured, methodical, and meticulous approach to understanding the research trends of handoff security issues on wireless networks and mobile networks. The purpose of this research is to provide credible intellectual guidance for future researchers to help them identify areas in the study. Most SLR is limited to conference papers and journal articles published by IEEE from 2015 to 2021. This study reveals that the protocol, privacy and security of the handoff process are the least studied, while the operating problems, architecture, methods in the handoff process get much attention in the literature.

Report from the Next Generation Mobile Networks Alliance

The Next Generation Mobile Networks Alliance (NGMN) was founded by leading international mobile network operators in 2006 with the objective to ensure that functionality and performance of the next generation mobile network infrastructure, service platforms and devices will meet the requirements of operators and, ultimately, will satisfy end user demand and expectations.

Can Multipath TCP Be Robust to Cyber Attacks? A Measuring Study of MPTCP with Active Queue Management Algorithms

With the development of social networks, more and more mobile social network devices have multiple interfaces. Multipath TCP (MPTCP), as an emerging transmission protocol, can fit multiple link bandwidths to improve data transmission performance and improve user experience quality. At the same time, due to the large-scale deployment and application of emerging technologies such as the Internet of Things and cloud computing, cyber attacks against MPTCP have gradually increased. More and more network security research studies point out that low-rate distributed denial of service (LDDoS) attacks are relatively popular and difficult to detect and are recognized as one of the most severe threats to network services. This article introduces six classic queue management algorithms: DropTail, RED, FRED, REM, BLUE, and FQ. In a multihomed network environment, we perform the performance evaluation of MPTCP under LDDoS attacks in terms of throughput, delay, and packet loss rate when using the six algorithms, respectively, by simulations. The results show that in an MPTCP-enabled multihomed network, different queue management algorithms have different throughput, delay, and packet loss rate performance when subjected to LDDoS attacks. Considering these three performance indicators comprehensively, the FRED algorithm has better performance. By adopting an effective active queue management (AQM) algorithm, the MPTCP transmission system can enhance its robustness capability, thus improving transmission performance. We suggest that when designing and improving the queue management algorithm, the antiattack performance of the algorithm should be considered: (1) it can adjust the traffic speed by optimizing the congestion control mechanism; (2) the fairness of different types of data streams sharing bandwidth is taken into consideration; and (3) it has the ability to adjust the parameters of the queue management algorithm in a timely and accurate manner.

Next-Generation Internet of Things in Fintech Ecosystem

The purpose of the present study is to comprehensively evaluate the present status of the Internet of Things (IoT) in fintech ecology and ahead. The study finds that the concept of communication between the devices and financial technology is not new yet only a considerable number of studies are done on IoT in fintech. The study highlights that both the IoT and fintech show an upward trend in the marketspace. Then, the study identifies that the increase in the demand for blockchain, Internet, mobile network, cloud storage, and IoT devices among the organization are the key drivers of IoT in fintech. The study also shows that the issues related to sustainable energy, digital payments, and security are the current area of applications and challenges for IoT in Fintech. Thereafter, the study concludes that the growth of neurotech will influence the next-generation IoT in fintech as the catalytic agent. Neurotech-enabled IoT (NIoT) in Fintech will enhance the man, machine, and memory (3Ms) relationship for business process innovation.

Computational Overhead of a Novel Scheme for Identity Confidentiality on IoT Devices in 5G Mobile Networks

Mobile networks are becoming a preferred choice for the Internet of Things (IoT), due to its flexibility, broad coverage, increasing bandwidth, low latency and low subscription cost. However, a long-standing security issue in any mobile network across the various generations is identity confidentiality. In a recent technical specification standardised by 3rd Generation Partnership Project (3GPP) for 5G mobile network, a novel scheme called the Elliptic Curve Integrated Encryption Scheme (ECIES) is adopted to tackle the issue of identity confidentiality. While this mechanism seems to provide a reasonable solution for modern resource affluent smart phones, it’s suitability for resource constrained IoT devices needs to be analysed. In this paper, we study the computational overhead of the ECIES on IoT devices.

A New Approach for Enhancing the Services of the 5G Mobile Network and IOT-Related Communication Devices Using Wavelet-OFDM and Its Applications in Healthcare

The heart of the current wireless communication systems (including 5G) is the Fourier transform-based orthogonal frequency division multiplex (OFDM). Over time, a lot of research has proposed the wavelet transform-based OFDM as a better replacement of Fourier in the physical layer solutions because of its performance and ability to support network-intensive applications such as the Internet of Things (IoT). In this paper, we weigh the wavelet transform performances against the future wireless application system requirements and propose guidelines and approaches for wavelet applications in 5G waveform design. This is followed by a detailed impact on healthcare. Using an image as the test data, a comprehensive performance comparison between Fourier transform and various wavelet transforms has been done considering the following 5G key performance indicators (KPIs): energy efficiency, modulation and demodulation complexity, reliability, latency, spectral efficiency, effect of transmission/reception under asynchronous transmission, and robustness to time-/frequency-selective channels. Finally, the guidelines for wavelet transform use are presented. The guidelines are sufficient to serve as approaches for tradeoffs and also as the guide for further developments.

CrowdBLPS: A Blockchain-Based Location-Privacy-Preserving Mobile Crowdsensing System

With the popularization of intelligent terminals, especially current trends, such as “Industrie 4.0” and the Internet of Things, mobile crowdsensing is becoming one of the promising applications built on smart devices in mobile networks. However, the existing mobile crowdsensing models are mostly based on a centralized platform, which is not fully trusted in reality and results in the existence of fraud and other security problems. Furthermore, the data quality collected through crowdsensing is varied, and the location privacy is difficult to guarantee, especially at the worker selection stage. To solve these two problems, an effective blockchain-based location-privacy-preserving crowdsensing model, CrowdBLPS, is proposed in this article. First, the idea of a blockchain is introduced into this model. The decentralized structure and the consensus approach are applied to realize the nonrepudiation and nontampering of information. Second, to improve the data sensing quality and protect worker privacy, a two-stage approach, including the preregistration stage and the final selection stage, is proposed. Finally, we further implement a prototype on the Ethereum public testing network, and the experimental results show the feasibility, availability, and reliability of CrowdBLPS.

A Privacy-Preserved E2E Authenticated Key Exchange Protocol for Multi-Server Architecture in Edge Computing Networks

Edge computing has played an important role in enabling 5G technology which supports a great number of connected narrow-band IoT devices. In an edge computing architecture enabled with global mobile network, edge or IoT devices are wirelessly connected to the edge of the network. Data acquisition and processing will be handled at or close to the edge of the network in a distributed way. Since edge computing is a heterogeneous distributed interactive system with multiple domains and entities, it might suffer from potential attacks and threats. To provide a trusted edge computing, there must have a robust scheme that allows all participants to mutually authenticate in a secure and privacy-preserved way. With the rapid development of IoT technologies, mobile networks and edge computing architecture, single server has been unable to meet the needs of users. In this paper, we propose a privacy-preserved end-to-end password-based authenticated key exchange protocol for multi-server architecture in edge computing networks. Our protocol allows an end user to use an easy-to-remember password to login to the server, then through foreign agent compute a shared key with another end user for specific use of services. The proposed protocol provides strong user anonymity during communication process. Besides, the proposed protocol is proved to be secure using BAN logic and AVISPA tool. Furthermore, performance analysis shows that the proposed protocol gains stronger security and better computational efficiency. Providing lightweight computation with short key size of ECC, our work is a solution to lower latency and improve efficiency in edge computing networks.

Sustainable automatic data clustering using hybrid PSO algorithm with mutation

Widespread use of various mobiles, social networks and IOT devices results into continuous generation of the data, often leading to the formation of the big data. Sustainable grouping of such data into various clusters is an open research problem, which aims to provide solutions which are computationally efficient and maintainable over dynamic data. This paper proposes a new sustainable clustering algorithm HPSOM by hybridization of PSO with mutation operator for clustering of the data generated from different networks. The data generated by such networks is usually dynamic and heterogeneous in nature and the number of clusters is not fixed/ known in advance. Hence the proposed algorithm is further extended as AHPSOM for generating and re-adjusting the clusters automatically over the mobile network devices, and it facilitates the generation of sustainable clusters. Firstly, the performance of basic HPSOM is evaluated on six real life data sets and is also compared with some known evolutionary clustering techniques in terms of SWCD and Convergence speed. Then the performance of AHPSOM is evaluated using some synthetic and real life datasets using some validity metrics (cluster numbers, intra cluster distance, inter cluster distance, ARI and F-measure) and is also compared with some prevalent state-of-art automatic clustering techniques. The results show that the proposed algorithm is very efficient in terms of creating well separated, compact and sustainable clusters.

A Cluster-Driven Energy Routing Protocol for Optimal Network Lifetime in Ad Hoc Networks

Mobile Ad hoc Network (MANET) is a group of networked mobile devices working in a cooperative manner. Due to usage, these devices quickly run out of battery power in critical situations and consequently fail in packet transmission. Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and Dynamic State algorithm are two research works with unique energy efficiency concepts that, if harmonized and refined, will deliver a better optimized energy-efficient routing protocol for MANETs. This study, therefore, proposes PEGADyn – a hybrid version of PEGASIS and Dynamic State algorithm for a new energy-efficient routing protocol in ad hoc networks. PEGADyn creates a virtual grid classification of nodes based on current location, followed by a cluster formation of nodes in each virtual grid created. In each cluster and virtual grid formed, cluster heads (CHs) and designated cluster heads (dCH) are selected based on their energy levels. CHs and dCHs are used for communication between clusters and virtual grids. The use of CHs and dCHs limits communication overheads among nodes, reducing the energy expended and increasing the network lifetime. A simulation comparison of PEGADyn with PEGASIS and Dynamic State shows PEGADyn to be better in extending network lifetime and maintaining network throughput.