Wireless Network Technology Research Articles

Assessment of the dynamic parameters of a 673-meter cable-stayed bridge with wireless three-component MEMS-based sensors

Dynamic structural monitoring of bridges under ambient vibrations is a method to assess dynamic parameters of structures with low intrusiveness, e.g. with Operational Modal Analysis (OMA) data processing. Yet, several barriers hinder its adoption for real world applications, such as a lack of adequate sensors or industrial sensor networks and more generally, a lack of integrated solutions. In this paper, we present a data collection campaign using a novel wireless sensing system for dynamic structural monitoring of civil engineering structures. The core enabler of this SHM product named S-lynks is the use of a novel ultra-low noise seismic MEMS accelerometer, QuietSeisTM, which makes possible to envision a true industrial wireless sensor network for OMA in civil engineering structures. We developed a bespoke wireless sensing system composed of Wireless Sensor Nodes (WSN) and Gateways (WSG) and combining Low Power Wide Area Network (LPWAN) and Wireless Local Area Network (WLAN) technologies to manage the WSN wake up, synchronization and data transmission to WSG. This product has been used on a 675-meter cable-stayed bridge to assess the dynamic parameters from ambient vibrations measurements. We successfully triggered 120 measure points on the deck and inside a tower in less than 10 hours. More than 20 modes are identified between 0 and 5 Hz, with well-defined mode shapes. Extra measurements on a few cables to identify the tension allow explaining the asymmetry observed in some mode shapes.

A Critical Review of the Propagation Models Employed in LoRa Systems.

LoRa systems are emerging as a promising technology for wireless sensor networks due to their exceptional range and low power consumption. The successful deployment of LoRa networks relies on accurate propagation models to facilitate effective network planning. Therefore, this review explores the landscape of propagation models supporting LoRa networks. Specifically, we examine empirical propagation models commonly employed in communication systems, assessing their applicability across various environments such as outdoor, indoor, and within vegetation. Our investigation underscores the prevalence of logarithmic decay in most empirical models. In addition, we survey the relationship between model parameters and environmental factors, clearing their nuanced interplay. Analyzing published measurement results, we extract the log-distance model parameters to decipher environmental influences comprehensively. Drawing insights from published measurement results for LoRa, we compare them with the model's outcomes, highlighting successes and limitations. We additionally explore the application of multi-slope models to LoRa measurements to evaluate its effectiveness in enhancing the accuracy of path loss prediction. Finally, we propose new lines for future research in propagation modelling to improve empirical models.

An efficient key tag missing identification protocol for multiple regions in the Industrial Internet of Things

Radio Frequency Identification (RFID) is a special wireless network technology that uses radio frequency communication to achieve non-contact automatic identification through tags affixed to items. As one of the core technologies for physical layer communication in the Industrial Internet of Things(IIoT), RFID technology has been widely used in inventory management, supply chain management, access control identification, and many other areas. In practical application scenarios, the value and importance of some special tags far exceeds that of other tags. Therefore, in many cases, managers only need to pay attention to the status of these special tags, i.e., key tags, and not the status of all tags. However, the response of ordinary tags to reader commands will seriously interfere with identifying key missing tags. Therefore, we propose a novel protocol, Random Grouped Missing Key Tag Identification (RGKMTI), designed for multi-region key tag identification. RGKMTI employs a region-based sequential tag identification method to identify each group of tags individually, which effectively reduces collision slots and utilizes the duality property of slots containing only two key tags during the missing key tag verification phase, where the reader can verify two tags by transmitting at most a single tag ID, which significantly reduces the transmission load and shortens the total execution time. The experimental results show that the RGKMTI is significantly better than the existing schemes in terms of execution time and time efficiency, even though it reduces the identification time by 19.83% and improves the time efficiency by 6.82% compared with the best existing schemes.

Empowering Aquarists a Comprehensive Study On IOT-Enabled Smart Aquarium Systems For Remote Monitoring And Control

owadays, a lot of people keep fish as pets at home and the aquarium keeper has been feeding the fish in the tanks, therefore appropriate setup and upkeep are required. Taking care of the aquarium appears to be somewhat tough for aquarists. The fish's quality of life depends on the aquarium being kept up properly. Changes in water quality, fish feeding, temperature management, light control, and the challenge of physically inspecting aquarium conditions are these problems are experienced.. As such, it is important to monitor the physical characteristics regularly and to improve the quality of the water. It has water quality management, which levels the aquarium to the perfect conditions by keeping an eye on the physical variations in the tank. Thus, the system that this model suggests is capable of real-time operation and is fitted with sensors. It carries out turbidity system, water renewal, temperature monitoring, and pH level detection in water and all these things are carried out in the ESP32 controller which contains an inbuit WiFi module. For large-scale aquaculture, an embedded system with a wireless network technology and a water quality testing unit was proposed. The aquarium's state is tracked and sent to the user's mobile application via Thingspeak IoT platfrom.

PUA-LARIN KİBERTƏHLÜKƏSİZLİYİ HAQQINDA

Currently, unmanned aerial vehicles (UAVs) are widely used in civil and military fields. Due to this development, their security also becomes an important aspect. Problems related to the safety of UAVs also arise. Here, not only hacking, but also protection against radio-electronic interference devices are very serious problems. Based on a number of real security incidents, it can be argued that cyber security for UAVs and other unmanned vehicles is of exceptional importance. Various methods and main components neutralization are presented. Examples of real-life security incidents involving UAVs are presented in different years and in several sample countries. UAV system discusses certain methods for preventing and detecting cyber-attacks, anomalous behavior, identifying intrusion attempts and responding in real-time. Cryptography is invaluable in preventing some cyber attacks. Data encryption protects its privacy and prevents interception. This article examines the cybersecurity challenges of UAVs. The integration of artificial intelligence (AI) and machine learning (ML) into wireless network technologies can enable solutions to various security challenges. AI is being researched in a wide range of applications in automated systems and aviation.

Selfish attack detection and response using cooperative backoff adjustment in wireless sensor networks

In recent times, the proliferation of intelligent internet of things (IoT) and wireless sensor network (WSN) technologies has significantly enhanced both daily life and industrial operations. Despite their widespread application, these networks face challenges with resource monopolization by selfish nodes, which detrimentally impacts network efficiency. This study introduces a novel detection and response strategy for selfish nodes, utilizing backoff adjustment mechanisms to reduce throughput deterioration. The effectiveness of this approach was rigorously evaluated using the NetSim simulator, focusing on the impact of backoff adjustment on network throughput. Simulation results indicate that the implementation of this method in the presence of selfish nodes yields a noteworthy improvement in network throughput, with approximately more than twice. This advancement holds promise for enhancing the robustness and efficiency of IoT and WSN technologies against disruptive selfish node behaviors.

Medium access control protocol based on time division multiple access scheme for wireless body area network

In recent years, the demand for wireless body area network (WBAN) technology has increased, driven by advancements in medical and healthcare applications. WBAN consists of small, low-power, and heterogeneous sensor devices attached inside or outside the body for continuous health monitoring. Medium access control (MAC) is pivotal in addressing WBAN challenges by ensuring reliability and energy efficiency under a dynamic environment caused by body movement. Therefore, to tackle these challenges, this paper presents a MAC protocol based on time division multiple access (TDMA) to enhance the WBAN performance. The proposed TDMA-MAC protocol employs a one-periodic scheduled-based access method to provide reliable data transmission while satisfying the WBAN requirements. The proposed protocol is compared to the IEEE 802.15.6 MAC, enhanced packet scheduling algorithm MAC (EPSA-MAC), and concurrent MAC (C-MAC) protocols based on the performance metrics of packet delivery ratio (PDR), network throughput, energy consumption, and average delay. The simulation results show that the TDMA-MAC protocol outperforms its competitors as it could achieve up to 98% PDR, 30% enhanced throughput, 30% energy optimization, and 20% improvement in average delay.

Efficient Authentication Steps for Vehicle Transmission in Ad-Hoc Networks

Vehicular ad-hoc networks (VANETs) are underactive development, thanks in part to recent advances in wireless communication and networking technologies. The most fundamental part of VANET is to enable message authentications between vehicles and roadside units. Message authentication using proxy vehicles has been proposed to reduce the computational overhead of roadside units significantly. In this message authentication scheme, a proxy vehicle that verifies multiple messages at the same time improves roadside units’ efficiency. In this paper first, we show that the only proxy-based authentication scheme (PBAS) presented for this goal by Liu et al. cannot guarantee message authenticity and also it is not resistant to impersonation and modification attacks and false acceptance of batched invalid signatures. Next, we propose a new identity-based message authentication scheme using proxy vehicles (ID-MAP). Then guarantee that it can satisfy the message authentication requirements, existential unforgeability of underlying signatures against adaptability chosen message, and identity attack is proved under the Elliptic Curve discrete logarithm problem (ECDLP) in the random oracle model. It should be highlighted that ID MAP not only is more efficient than PBAS since it is pairing-free and identity based and also it does not use map-to-point hash functions, also, it satisfies the security and privacy requirements of VANETs. Furthermore, analysis shows that the required time to verify 3000 messages in ID-MAP is reduced by 76% compared to that PBAS.

An efficient neighbor nodes trust tagging model for optimized route detection in smart grids with secure data transmission

Smart Grids are the control and transmission networks of the future; they allow suppliers and customers to interact in real time to optimize and intelligently manage resources. Although wireless mesh network technology can facilitate these smart functionalities, it is important to address the vulnerabilities and cyber-attack risks that are inherent to it. Smart Grid's reliance on the Internet amplifies security concerns. A number of methods have been proposed to address this issue; while some have made promises, they all need substantial amounts of computational resources. With this technology, channels based on trust can be set up. The security of the system is built utilizing a family relationship-based method, which makes use of measures that may be used to gauge a node's originality. Additionally, the power consumption and signal strength of the node are taken into account. The complexity and extent of the network need the development of new types of smart grid communication. Finding a way to detect and thwart major assaults on routing protocols is another design challenge. Smart grids rely on these protocols in its data system for efficient interchange of renewable energy. A unique secure energy routing mechanism is developed in this proposed model for secure data communication. In the proposed model, a new method for Neighbor Nodes Trust Tagging Model for Optimized Route Detection (NNTT-ORD) is proposed for establishing secure route for data communication in smart grids. The proposed model is compared with the existing model and the results represent that the proposed model route provides a secure environment for data transmission.

Rendezvous Sequence Generation Algorithm for Cognitive Radio Networks in Post-Disaster Scenario

Recent natural disasters have inflicted tremendous damage on humanity, with their scale progressively increasing and leading to numerous casualties. Events such as earthquakes can trigger secondary disasters, such as tsunamis, further complicating the situation by destroying communication infrastructures. This destruction impedes the dissemination of information about secondary disasters and complicates postdisaster rescue efforts. Consequently, there is an urgent demand for technologies capable of substituting for these destroyed communication infrastructures. This paper proposes a technique for generating rendezvous sequences to swiftly reconnect communication infrastructures in post-disaster scenarios. We compare the time required for rendezvous using the proposed technique against existing methods and analyze the average time taken to establish links with the rendezvous technique, discussing its significance. This research presents a novel approach enabling rapid recovery of destroyed communication infrastructures in disaster environments through Cognitive Radio Network (CRN) technology, showcasing the potential to significantly improve disaster response and recovery efforts. The proposed method reduces the time for the rendezvous compared to existing methods, suggesting that it can enhance the efficiency of rescue operations in postdisaster scenarios and contribute to life-saving efforts.

Disentangling composite influences of social connectivity and system interactivity on continuance intention in mobile short video applications: The pivotal moderation of user-perceived benefits

The expeditious proliferation of wireless network technology has caused the widespread integration of mobile short video apps into our daily routines, offering unprecedented opportunities for information dissemination and interactive communication. Despite extensive researches on the interactivity of mobile short video app, there has been a limited focus on understanding the underlying factors that contribute to users' perceived benefits and continuance intention. Leveraging insights from preceding studies, this study develops a conceptual research model that investigates how two distinct facets of interactivity, namely social interactivity and system interactivity, influence users' perceived benefits from mobile short video apps, ultimately impacting their intention to continue using them. This analysis rigorously scrutinizes data garnered from 808 users of mobile short video applications, employing structural equation modeling to validate the proposed hypotheses. The findings conclusively reveal that interactivity significantly affects users’ perceived benefits from mobile short video apps. Moreover, the study elucidates that perceived benefits from these apps are the significant predictors of continuance intention. Furthermore, interactivity could indirectly influence continuance intention via the mediation of perceived benefits. These findings possess the potential not only to augment our comprehension of perceived benefits and continuance intention from the interactivity perspective but also to furnish valuable insights for scholars in the field. The research sheds light on effective methodologies to encourage the adoption of mobile short video applications and to boost user engagement in a society oversaturated with mobile technology.

DESIGN AND DEVELOPMENT OF DISASTER ALERTING APPLICATION: SAJAG

The SAJAG application aims to be a crucial tool for disaster warning and management, particularly targeting flood- prone regions like the Republic of India. Its primary objective is to reduce injuries resulting from natural disasters, especially floods, by integrating with local emergency services. SAJAG delivers real- time disaster information, including location, severity, and estimated impact duration, enabling users to make informed decisions about evacuation and safe areas. Additionally, the app facilitates proactive disaster preparedness through self-awareness planning features. Leveraging wireless networking technology and artificial intelligence, SAJAG ensures timely and accurate information dissemination to users, even in adverse conditions such as power outages. Keywords: Disaster alert, Natural disaster, Floods, Real-time information, Artificial intelligence.

Research on the vehicle lane-changing decision-making system in complex traffic environments

In order to ensure the safety of vehicles changing lanes, there should be a certain degree of interaction and dynamics between vehicles on the way. In this paper, the problems of non-interactive road information and imprecise path planning are investigated. Through the research of the road condition decision-making system, autonomous lane-changing assistance system and intelligent traffic system, vehicle wireless networking technology, vehicle-road cooperation technology, and lane-changing decision-making and planning technology are used to increase the frequency of information interaction between vehicles and the external environment in order to realize information interaction between vehicles and collaborative driving. In this paper, simulation experiments of vehicle lane-changing are carried out by using MATLAB Simulink and compared with other research models. The results show that the lane change planning data based on the road condition decision system is more accurate, which leads to fast lane change driving in complex traffic scenarios.

5G SMART DIABETES TOWARDS PERSONALIZED DIABETES DIAGNOSIS WITH HEALTH CARE BIGDATA CLOUD

Recent advances in wireless networking and big data technologies, such as 5G networks, medical big data analytics, and the Internet of Things, along with recent developments in wearable computing and artificial intelligence, are enabling the development and implementation of innovative diabetes monitoring systems and applications. Due to the life-long and systematic harm suffered by diabetes patients, it is critical to design effective methods for the diagnosis and treatment of diabetes. Based on our comprehensive investigation, this article classifies those methods into Diabetes 1.0 and Diabetes 2.0, which exhibit deficiencies in terms of networking and intelligence. Thus, our goal is to design a sustainable, cost-effective, and intelligent diabetes diagnosis solution with personalized treatment. In this article, we first propose the 5G-Smart Diabetes system, which combines the state-of-the-art technologies such as wearable 2.0, machine learning, and big data to generate comprehensive sensing and analysis for patients suffering from diabetes. Then we present the data sharing mechanism and personalized data analysis model for 5G-Smart Diabetes. Finally, we build a 5G-Smart Diabetes testbed that includes smart clothing, smartphone, and big data clouds. The experimental results show that our system can effectively provide personalized diagnosis and treatment suggestions to patients. Key Words: Monitoring, Diabetes, Wearable, Big Data, Smart Systems, Machine Learning

PERANCANGAN E - TRANSAKSI PEMBAYARAN INTERNET SERVICE PROVIDER PADA PT DIGITAL NETWORK ANTANUSA (DNA.NET) KOTA JAMBI

PT Digital Network Antanusa (DNA.Net) Jambi City is an internet service provider network service company in the city of Jambi. DNA.Net operates using cable and wireless network technology (wireless). DNA.Net is not a cellular or satellite service, but something new, much better and much faster to connect to the internet and provide to homes or companies. However, there are still problems with processing, paying and checking internet bills, therefore errors and files are often lost when processing payment transaction data. These include frequent errors in internet registration data information and lost documents, delays in transactions or transactions taking too long, and even making payment reports is still not neat. This method used is the UML (Unified Modeling Language) method which describes the system model including use cases, use case specifications, activities and class diagrams and the designs used by researchers are prototypes. Therefore, the researcher aims to analyze and design an internet service provider payment transaction information system so that a computerized payment system is more flexible and efficient so that problems at PT Digital Network Antanusa can be resolved so that the obstacles currently faced can be resolved and run very well. . The final result of the analysis and design produces a maturity system. This is done to make the system more effective and efficient and of course to make the bill payment process easier

An intelligent mechanism for dynamic spectrum sharing in 5G IoT networks

Amid the rapid advancement of wireless network technologies, the forthcoming ubiquity of the Internet of Things (IoT) necessitates the development of sophisticated architectural frameworks and algorithms capable of handling the associated spectrum management complexities. This study proposes an innovative Mobile Broadband Internet of Things (MBIN) architecture, designed to facilitate efficient progression of the impending IoT revolution. The centerpiece of this architecture is a ground-breaking Advanced Cloud-Enabled Double Auction (ACEDA) algorithm. Deployed within the MBIN framework, this mechanism optimizes the sharing of spectrum resources, thereby enhancing the performance of the IoT structure. Theoretical grounding, algorithmic implementation, simulation results, and user satisfaction analysis are discussed in-depth to validate the efficacy of this approach.

Assessing the quality of experience in wireless networks for multimedia applications: A comprehensive analysis utilizing deep learning-based techniques

In the context of the burgeoning progression of wireless network technology and the corresponding escalation in the demand for mobile Internet-based multimedia transmission services, the task of preserving and augmenting user satisfaction has emerged as an imperative concern. This necessitates a sophisticated and accurate evaluation of multimedia service quality within the sphere of wireless networks. To systematically address the nuanced issue of user experience quality, the present study introduces a novel method for evaluating multimedia Quality of Experience (QoE) in wireless networks, employing an advanced deep learning model as the underlying analytical framework. Initially, the research undertakes the task of modeling the video session process, giving due consideration to the status of each temporal interval within the session's architecture. Subsequently, the challenge of QoE prediction is dissected and investigated through the lens of recurrent neural networks (RNNs), culminating in the proposition of an all-encompassing QoE prediction model that harmoniously integrates video information, Quality of Service (QoS) data, user behavior analytics, and facial expression analysis. The empirical segment of this research serves to validate the efficacy of the suggested video QoE evaluation method, engaging both quantitative and qualitative comparison metrics with contemporaneous state-of-the-art QoE models, employing the RTVCQoE dataset as the empirical foundation. The experimental findings illuminate that the QoE model elucidated in this study transcends competing models in performance metrics such as PLCC, SRCC, and KRCC. Consequently, this investigation stands as a seminal contribution to academic literature, furnishing an exacting and dependable QoE evaluation methodology. Such a contribution augments the user experience landscape in multimedia services within wireless networks, and instigates further scholarly exploration and technological innovation in the mobile Internet domain.

Graph Laplace Regularization-based pressure sensor placement strategy for leak localization in the water distribution networks under joint hydraulic and topological feature spaces

Urban water distribution networks (WDNs) have wide range and intricate topology, which include leakage, pipe burst and other abnormal states during production and operation. With the continuous development of the Internet of Things (IoT) technology in recent years, the means of monitoring the WDNs by using wireless sensor network technology has gradually received attention and extensive research. Most of the existing researches select the deployment location of sensors according to the hydraulic state of the WDNs, but the connectivity and topology between the nodes of the WDNs are not fully considered and analyzed. In this study, a new method that can integrate the topological features and hydraulic model information of the WDN is proposed to solve the problem of optimal sensor placement. First, the method preprocesses the covariance matrix of the pressure sensitivity matrix of the water distribution network by a diffusion kernel-based data prefiltering method and obtains the new network topology weights and its Laplacian matrix under the constraints of the network topology through a data-based graphical Laplacian learning method. Then, the sensor placement problem is transformed into a matrix minimum eigenvalue constraint problem by the Graph Laplace Regularization (GLR)-based method, and finally the selection of sensor nodes is accomplished by the method based on Gershgorin Disc Alignment (GDA). The proposed strategy is tested on a passive Hanoi network, an active Net 3 network, and a larger network, PA2, and is compared with some existing methods. The results show that the proposed solution achieves good performance in three different leak localization methods.

Evaluation of the Limitation of Operational Parameters of the IEEE 802.11 ac Network in the 20MHz Channel

IEEE 802.11 wireless network technologies are widely used to create corporate and personal local networks for data exchange and access to Internet resources. The main principle of operation of IEEE 802.11 networks is the principle of competitive access, according to which all wireless network users have the same access rights to the information transmission environment. This method of access leads to the occurrence of collisions in networks with a large number of users, which complicates the process of network functioning and leads to the degradation of quality indicators. The purpose of the study is to estimate the limit values of the operational characteristics of the IEEE 802.11 ac wireless network in the mode with the highest transmission rate (MCS8) in a frequency channel of 20 MHz with one spatial stream, provided that the network has a significant number of active stations with a saturated load. An alternative model of processes in IEEE 802.11 networks based on the concept of a virtual competitive window is used for research. According to the concept of virtual contention window (VCW), the process of data transmission in a network with competitive access is considered as a quasi-stationary process. Numerical data were obtained and graphs of channel bandwidth, transmission delay, and delay non-uniformity were given in the presence of one to sixteen active stations with a saturated load in the network, in the case of transmission of frames with a data volume of 512 or 1500 bytes. The maximum possible bandwidth of the channel with a frequency band of 20 MHz (68.387 bit/s) was determined, in the case of using frames with the maximum load (11454 bytes) provided by the standard. Estimated data on the number of collisions occurring in a network with a saturated load and the number of frames transmitted at various stages of channel access are also provided. The frame transmission delay increases almost proportionally to the number of active stations and varies from 0.605 ms to 5.293 ms, in the case of loading all data frames of 512 bytes, and from 0.785 to 6.41 ms, in the case of a load of 1500 bytes, for changes in the number of active stations in the network from 2 to 16. The unevenness of the delay exceeds the average delay and grows non-linearly, in the case of an increase in the number of active stations from 1 to 6 (CWmin=15), and linearly — with a further increase in the number of stations (over 6). The obtained results are useful for reasonable planning of wireless networks and configuration of network equipment parameters.

Deep reinforcement learning aided secure UAV communications in the presence of moving eavesdroppers

Integrating unmanned aerial vehicles (UAVs) with wireless systems is widely anticipated to bring enormous benefits to network users, and is considered as a key enabling technology for next generation wireless networks. However deployment of UAVs raises security concerns, as they are prone to eavesdropping attacks. In this paper we investigate secure data transmissions in a UAV-aided communication system from the physical layer security (PLS) perspective. The UAV is employed as a flying base station and transmit confidential information to a ground user. Specifically, an eavesdropper is moving in the vicinity of the ground user, attempting to intercept legitimate data transmission. We aim to optimize UAV trajectory to maximize secrecy throughput and due to the fast changing environment with unpredictable movements of the eave, this nonconvex problem is difficult to solve. Instead, we propose a deep reinforcement learning framework by reformulating this problem as a Markov Decision Process (MDP). By carefully designing state spaces, state-dependent action sets and time-dependant reward signals, we aim to help the agent learn an optimized trajectory and complete the flight task within time limit. We adapt the deep Q network (DQN) algorithm and extend it with prioritized experience replay (PER) and importance sampling method to ensure a stable and effective learning outcome. Simulation results validate the effectiveness of the learning algorithm and confirm that the agent is able to learn an effective policy with desirable and robust learning outcomes. Compared with benchmark schemes, our proposed scheme is shown to achieve considerable performance gain in secrecy throughput.