Wi-Fi Coverage Research Articles

Analysis of Elfan Bookless Library System Innovation at the Barru Regency Library and Archives Office

This qualitative study explores the innovation of the Elfan Bookless Library System at the Barru Regency Library and Archives Office. Data were collected through observation, interviews, and documentation, with validity ensured via triangulation, negative case analysis, member checking, and extended observations. The findings reveal that the Elfan Bookless Library System enhances the reading experience by providing its own Wi-Fi network, allowing online book access. While the system effectively meets visitors' needs, its Wi-Fi coverage is limited to 500 meters, posing a connectivity challenge beyond this range. Despite this, the system is accessible without special requirements; visitors only need a cellphone or Android device. User guidance is provided through tutorials on billboards and banners, and library staff offers additional support. This study highlights the Elfan Bookless Library System's potential to innovate public library services, offering valuable insights into its advantages and limitations. These findings contribute to understanding how digital library systems can be optimized to improve user experience and accessibility in public libraries.

Design and Performance Analysis of Prioritized On-the-Spot Mobile Data Offloading Scheme

Recent years have witnessed a surge in the mobile data traffic which is only expected to grow exponentially in the coming years. To cope up with this huge data demand, mobile operators have restored to a novel scheme called on-the-spot mobile data offloading wherein mobile data is offloaded onto Wi-Fi network whenever the user enters a Wi-Fi coverage area. Further reports indicate that a sizable portion of the mobile data traffic is multimedia in nature that demands good quality of service. In this context, we design and analyze a scheme to prioritize different data traffic encountered in on-the-spot mobile data offloading. Using a Markov chain model for on-the-spot mobile data offloading scheme, we derive the packet transmission delay of different traffic categories that carry different priorities. We validate out analytical results against simulation and demonstrate the effectiveness of the proposed prioritization scheme.

Design and Implementation of 2x4 Octagonal Array Patch Microstrip Antennas using T-Slots at 2.4 GHz Frequency for Wifi Applications

Wireless communication requires a useful device to transmit and receive electromagnetic waves. WiFi is a particular kind of wireless communication device used to send data over the internet network. The bandwidth and signal coverage of WiFi have significant limitations. As a result, an antenna is utilized to enhance signal reception in order to solve this issue. The research method used is to compare the design of the 2x4 octagonal microstrip array antenna using a T-slot and without the T-slot to see the results of simulation and testing in terms of return loss, VSWR, gain, and bandwidth. The results of bandwidth testing for octagonal microstrip antennas without a 2x4 T-slot array are 32 MHz, while for octagonal microstrip antennas using a 2x4 T-slot array of 40MHz. The octagonal microstrip antenna without a 2x4 T-Slot array has Return Loss of -18.2 dB and a VSWR of 1.280. The 2x4 octagonal T-Slot array microstrip antenna has Return Loss of -17.6 dB and a VSWR of 1.303. The test results 2x4 octagonal array microstrip antenna gain without using T-slot produces the largest gain of 9.55dBi, and the antenna using T-slot produces the largest gain of 12.55dBi.

Design and Implementation of an IOT Based Smart Home Monitoring and Control System Using NodeMCU

This paper presents the design and implementation of an IoT-based smart home monitoring and control system using a NodeMCU firmware and prototyping board. The system allows the user to remotely control electrical appliances through a web-based user interface dashboard. The system uses sensors DHT11 to monitor the temperature and humidity of the home and MQ 2 to monitor the air quality and alerts the user in case smoke or fire is detected. Various HTML scripts are executed to develop a web-based dashboard interface where the user interacts with the system. When connected to the internet, the system generates an IP address that is used to assess the web page for monitoring and control of various house appliances. A maximum coverage range of up to 42.05 meters indoors was achieved by the system using the onboard Wi-Fi module on the NodeMCU. In addition, a correlation was observed between the Wi-Fi coverage distance and the response time to a command signal from the web-based dashboard. The system provides energy efficiency, home security, and centralized remote control of appliances thereby minimizing human efforts and energy losses and providing a friendly and secure home.

Arduino-Based Home Automation System Using ESP8266 Wi-Fi Module

This paper presents the design and prototype execution of a new home automation system that connects its components using Wireless-Fidelity (Wi-Fi) technology. The system under consideration comprises two primary parts. The first element is the web server, which serves as the system core and is responsible for managing, controlling, and keeping an eye on users' homes. Remotely using the internet and locally using a Local Area Network (LAN) the user and system administrator can manage and control the system code. The hardware interface part is the second element which acts as an interface between sensors and actuators. The proposed system is more scalable since it can manage many hardware modules available in Wi-Fi coverage using a single server. The system which is proposed in this paper outperforms the recently proposed home automation systems in terms of scalability and flexibility.

PENGGUNAAN METODE SIMULATED ANNEALING DAN TRILATERATION UNTUK OPTIMASI PENEMPATAN POSISI TITIK AKSES STUDI KASUS: KANTOR KESYAHBANDARAN DAN OTORITAS PELABUHAN (KSOP) KELAS 1 TANJUNG EMAS

Computer network technology is developing rapidly, and wired networks are being replaced by WiFi technology.
 The limitation of the WiFi coverage area is the main problem with this technology. Therefore, it is necessary to design for optimal placement of access points.
 This study analyzes the need for access point placement at KSOP Tanjung Mas Port, Semarang, where the need for internet connection via WiFi is very high.
 Simulation Annealing and Trilateration method is used in this study to determine the position of the access point. With the point blankspot method can be reduced.

A Novel Anchor-Free Localization Method Using Cross-Technology Communication for Wireless Sensor Network

In recent years, wireless sensor networks have been used in a wide range of indoor localization-based applications. Although promising, the existing works are dependent on a large number of anchor nodes to achieve localization, which brings the issues of increasing cost and additional maintenance. Inspired by the cross-technology communication, an emerging technique that enables direct communication among heterogeneous wireless devices, we propose an anchor-free distributed method, which leverages the installed Wi-Fi APs to range instead of traditional anchor nodes. More specifically, for the asymmetric coverage of Wi-Fi and ZigBee nodes, we first design a progressive method, where the first unknown node estimates its location based on two Wi-Fi APs and a sink node, then once achieving its position, it acts as the alternative sink node of the next hop node. This process is repeated until the new members can obtain their positions. Second, as a low-power technology, ZigBee signal may be submerged in strong signal such as Wi-Fi. To overcome this problem, a maximum prime number is deployed to be the Wi-Fi broadcasting period based on the numerical analysis theory. Among many of prime numbers, we have the opportunity to select an appropriate one to achieve full coverage with the relatively small packet collisions. Last, simulations and experiments are performed to evaluate the proposal. The evaluation results show that the proposal can achieve decimeter level accuracy without deploying any anchor node. Moreover, the proposal demonstrates the anti-interference ability in the crowded open spectrum environment.

Skeleton-Based Human Pose Recognition Using Channel State Information: A Survey.

With the increasing demand for human-computer interaction and health monitoring, human behavior recognition with device-free patterns has attracted extensive attention. The fluctuations of the Wi-Fi signal caused by human actions in a Wi-Fi coverage area can be used to precisely identify the human skeleton and pose, which effectively overcomes the problems of the traditional solution. Although many promising results have been achieved, no survey summarizes the research progress. This paper aims to comprehensively investigate and analyze the latest applications of human behavior recognition based on channel state information (CSI) and the human skeleton. First, we review the human profile perception and skeleton recognition progress based on wireless perception technologies. Second, we summarize the general framework of precise pose recognition, including signal preprocessing methods, neural network models, and performance results. Then, we classify skeleton model generation methods into three categories and emphasize the crucial difference among these typical applications. Furthermore, we discuss two aspects, such as experimental scenarios and recognition targets. Finally, we conclude the paper by summarizing the issues in typical systems and the main research directions for the future.

A Novel Plug-in Board for Remote Insect Monitoring

The conventional approach to monitoring insect swarming is based on traps that are periodically checked by human operators. However, human checking of trap contents is expensive, and in many environments, the pest species most frequently encountered in the traps can be detected and monitored automatically. To achieve this goal, a dedicated data acquisition device is necessary, which makes real-time and online pest monitoring possible from a distant location. In addition, it is beneficial for the device to run machine learning algorithms that count and identify insects automatically from pictures. Thanks to the advantages of integrated circuits, more systems have been designed to improve integrated pest management in the context of precision agriculture. However, in our opinion, all of those systems have one or more disadvantages, such as high cost, low power autonomy, low picture quality, a WIFI coverage requirement, intensive human control, and poor software support. Therefore, the aim of this work is to present a novel plug-in board for automatic pest detection and counting. The plug-in board is dedicated to Raspberry Pi devices, especially the Raspberry Pi Zero. The proposed board, in combination with a Raspberry Pi device and a Pi camera, overcomes the limitations of other prototypes found in the literature. In this paper, a detailed description can be found about the schematic and characteristics of the board with a deep-learning-based insect-counting method.

Affirmation of Cultural Heritage Digital Promotion within Smart Destination Concept

The paper explores the perspectives of implementing digital, online promotion within the concept of smart destination development, aiming for the competitiveness that innovative tools and systems enable. Built heritage in developed tourism destinations presents an attractive resource, and its presentation through digital technology impacts culture's perception. With the development of new technologies and the adoption new digital and tourism trends, e-tourism cultural heritage promotion represents a significant challenge for destinations. Research design includes the identification of cultural heritage in tourism destinations followed by the longitudinal study, the research of progress in technology implementation, and an analysis of key tools for digital promotion. Several aspects are explored: online promotion, digitalization of heritage, and WiFi coverage for social networks' real-time streaming. Qualitative and quantitative methods aim to contribute new knowledge about the important use of online digital tools. The paper points to how the advantages and potentials of developing a smart culture in a destination, recognised as a strategic policy of development, are implemented in the digital marketing of heritage sites.

Using Software-Defined Networking for Data Traffic Control in Smart Cities with WiFi Coverage

The growth of smart cities is fueled by the vast rise in wireless smart gadgets and uninterrupted connectivity. WiFi is the dominant wireless technology, enabling Internet-of-Things (IoT) connectivity in smart cities due to its ubiquitous access points and low deployment cost. However, smart city applications offer a wide range of services with different quality-of-service (QoS) demands. This paper addresses packet delivery latency as one of the QoS metrics affecting many time-sensitive smart city services. Thus, the paper proposes employing software-defined networking (SDN) to control the traffic load of WiFi access points (APs), preserving its symmetry in a city-wide coverage of WiFi-connected IoT gateways or fog nodes. These gateways receive data packets from smart city/IoT devices via wireless links and forward them over a city-deployed WiFi network to their management entities or servers. Three SDN-based algorithms are devised to reduce the gateways’ packet-forwarding delay and keep a symmetric traffic load at the WiFi network APs. The algorithms are developed and tested using a real hardware setup constituting WiFi devices without additional requirements on the IoT gateways (WiFi clients) or the APs, such as support for a specific roaming protocol or bandwidth-consuming signaling such as sending probe packets. Extensive hardware experimentation shows that the SDN controller, via the proposed algorithms, can effectively reduce the packet forwarding latency of IoT gateways by carefully selecting the IoT gateway with the highest packet latency and seamlessly handing it over to the least-loaded covering AP.

PKM Pendampinggan Perluasan Jaringan Wi-Fi Pada Madrasah Ibitidaiyah Al-Azhari

In this era of digitalization, the internet is an important need to be fulfilled, including in educational institutions. There are several ways that can be used to connect electronic devices to the internet, one of which is a wireless network. Wireless networks are quite attractive because they are more practical and have a fairly wide range. One of the widely used wireless network technologies is Wi-Fi (Wireless Fidelity). The internet network at Madrasah Ibitidaiyah Al-Azhar Karangren has used Wi-Fi technology to connect electronic devices to the internet. However, the Wi-Fi coverage area is liMadrasah Ibitidaiyahted due to the geographical problems of the building that block the Wi-Fi signal, so there are several buildings at Madrasah Ibitidaiyah Al-Azhar Karangren that are not covered by the Wi-Fi network. Therefore, it is necessary to develop a Wi-Fi network by expanding the Wi-Fi coverage area and managing user management, so that all areas and parts of Madrasah Ibitidaiyah Al-Azhar Karangren can use the internet according to their individual needs.

Justice as fairness in preparing for emergency remote teaching: A case from Botswana

Background. The COVID-19 pandemic necessitated drastic changes to undergraduate medical training at the University of Botswana (UB). To save the academic year when campus was locked down, the Department of Medical Education conducted a needs assessment to determine the readiness for emergency remote teaching (ERT) of the Faculty of Medicine, UB. Objectives. To report on the findings of needs assessment surveys to assess learner and teaching staff preparedness for fair and just ERT, as defined by philosopher John Rawls. Methods. Needs assessment surveys were conducted using Office 365 Forms distributed via WhatsApp, targeting medical students and teaching staff during the 5 undergraduate years. Data were analysed quantitatively and qualitatively. Results. Ninety-two percent (266/289) of students and 73.5% (62/84) of teaching staff responded. Surveys revealed a high penetration of smartphones among students, but poor internet accessibility and affordability in homes. Some teaching staff also reported internet and device insufficiencies. Only WhatsApp was accessible to students and teaching staff. Conclusions. For equitable access to ERT in the future, the surveys revealed infrastructural improvement needs, including wider, stronger, affordable WiFi coverage within Botswana and enhanced digital infrastructures in educational institutions, with increased support for students.

Does the improvement of public Wi-Fi technology undermine mobile network operators’ profits? Evidence from consumer preferences

Cellular data consumption by users is the main revenue source for mobile network operators (MNOs). On the one hand, technological development and expansion in Wi-Fi coverage can reduce cellular data usage. On the other hand, Wi-Fi offloading, which entails redirecting cellular traffic to a Wi-Fi network, can prevent network quality degradation as a result of explosive cellular traffic. Thus, the advancement and expansion of public Wi-Fi are both beneficial and detrimental for MNOs. Therefore, in this study, we analyze consumer preferences based on changes in the properties of Wi-Fi and cellular networks by employing a mixed logit model with stated preference data. The results indicate high consumer preference when the cellular and Wi-Fi networks are fast, Wi-Fi usability range is wide, and price is low. To analyze the impact of improving public Wi-Fi technology on MNOs, we conduct a scenario analysis. When the Wi-Fi service speed increases to 100 Mbps or more and its coverage expands to span an entire public transportation area, consumers reduce the amount of their monthly cell phone plan by 1347 Korean won (KRW) (1.15 USD) and 5141 KRW (4.40 USD), respectively. The results of this study can facilitate the determination of appropriate public Wi-Fi speeds, establish the quality of cellular networks by considering the effect on MNOs, and improve social welfare by expanding public Wi-Fi networks.

To Help or Disturb: Introduction of Crowdsourced WiFi to 5G Networks

After upgrading to 5G, a network operator still faces congestion when providing the ubiquitous wireless service to the crowd. To meet users' ever-increasing demand, some other operators (e.g., Fon) have been developing another crowdsourced WiFi network to combine many users' home WiFi access points and provide enlarged WiFi coverage to them. While the 5G network experiences negative network externality, the crowdsourced WiFi network helps offload traffic from 5G and its service coverage exhibits positive externality with its subscription number. To our best knowledge, we are the first to investigate how these two heterogeneous networks of diverse network externalities co-exist from an economic perspective. We propose a dynamic game theoretic model to analyze the hybrid interaction among the 5G operator, the crowdsourced WiFi operator, and users. Our user choice model with WiFi's complementarity for 5G allows users to choose both services, departing from the traditional economics literature where a user chooses one over another alternative. Despite of non-convexity of the operators pricing problems, we prove that the 5G operator facing severe congestion may lower his price, and he benefits from the introduction of crowdsourced WiFi. However, 5G operator with mild congestion charges users more and all the users' payoffs may decrease.

Crowd Counting Based on WiFi Channel State Information and Transfer Learning

With the popularity and development of indoor WiFi equipment, they have more sensing capability and can be used as a human monitoring device. We can collect the channel state information (CSI) from WiFi device and acquire the human state based on the measurements. These studies have attracted wide attention and become a hot research topic. This paper concentrated on the crowd counting based on CSI and transfer learning. We utilized the CSI signal fluctuations caused by human motion in WiFi coverage to identify the person count because different person counts would lead to unique signal propagation characteristics. First, this paper presented recent studies of crowd counting based on CSI. Then, we introduced the basic concept of CSI, and described the fundamental principle of CSI-based crowd counting. We also presented the system framework, experiment scenario, and neural network structure transferred from the ResNet. Next, we presented the experiment results and compared the accuracy using different neural network models. The system achieved recognition accuracy of this 100 percent for seven participants using the transfer learning technique. Finally, we concluded the paper by discussing the current problems and future work.

Reliability Analysis of Wireless Sensor Network for Smart Farming Applications.

Wireless Sensor Networks are subjected to some design constraints (e.g., processing capability, storage memory, energy consumption, fixed deployment, etc.) and to outdoor harsh conditions that deeply affect the network reliability. The aim of this work is to provide a deeper understanding about the way redundancy and node deployment affect the network reliability. In more detail, the paper analyzes the design and implementation of a wireless sensor network for low-power and low-cost applications and calculates its reliability considering the real environmental conditions and the real arrangement of the nodes deployed in the field. The reliability of the system has been evaluated by looking for both hardware failures and communication errors. A reliability prediction based on different handbooks has been carried out to estimate the failure rate of the nodes self-designed and self-developed to be used under harsh environments. Then, using the Fault Tree Analysis the real deployment of the nodes is taken into account considering the Wi-Fi coverage area and the possible communication link between nearby nodes. The findings show how different node arrangements provide significantly different reliability. The positioning is therefore essential in order to obtain maximum performance from a Wireless sensor network.

Embedded System-Based Sticky Paper Trap with Deep Learning-Based Insect-Counting Algorithm

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network

Small cells deliver cost-effective capacity and coverage enhancement in a cellular network. In this work, we present the interplay of two technologies, namely Wi-Fi offloading and small-cell cooperation that help in achieving this goal. Both these technologies are also being considered for 5G and B5G (Beyond 5G). We simultaneously consider Wi-Fi offloading and small-cell cooperation to maximize average user throughput in the small-cell network. We propose two heuristic methods, namely Sequential Cooperative Rate Enhancement (SCRE) and Sequential Offloading Rate Enhancement (SORE) to demonstrate cooperation and Wi-Fi offloading, respectively. SCRE is based on cooperative communication in which a user data rate requirement is satisfied through association with multiple small-cell base stations (SBSs). However, SORE is based on Wi-Fi offloading, in which users are offloaded to the nearest Wi-Fi Access Point and use its leftover capacity when they are unable to satisfy their rate constraint from a single SBS. Moreover, we propose an algorithm to switch between the two schemes (cooperation and Wi-Fi offloading) to ensure maximum average user throughput in the network. This is called the Switching between Cooperation and Offloading (SCO) algorithm and it switches depending upon the network conditions. We analyze these algorithms under varying requirements of rate threshold, number of resource blocks and user density in the network. The results indicate that SCRE is more beneficial for a sparse network where it also delivers relatively higher average data rates to cell-edge users. On the other hand, SORE is more advantageous in a dense network provided sufficient leftover Wi-Fi capacity is available and more users are present in the Wi-Fi coverage area.

Data Augmentation and Dense-LSTM for Human Activity Recognition Using WiFi Signal

Recent research has devoted significant efforts on the utilization of WiFi signals to recognize various human activities. An individual’s limb motions in the WiFi coverage area could interfere with wireless signal propagation, that manifested as unique patterns for activity recognition. Existing approaches though yielding reasonable performance in certain cases, are ignorant of two major challenges. The performed activities of the individual normally have inconsistent speed in different situations and time. Besides that the wireless signal reflected by human bodies normally carries substantial information that is specific to that subject. The activity recognition model trained on a certain individual may not work well when being applied to predict another individual’s activities. Since only recording activities of limited subjects in a certain speed and scale, recent works commonly have a moderate amount of activity data for training the recognition model. The small-size data could often incur the overfitting issue that negative affect the traditional classification model. To address these challenges, we propose a WiFi-based human activity recognition system that synthesizes variant activities data through eight channel state information (CSI) transformation methods to mitigate the impact of activity inconsistency and subject-specific issues, and also design a novel deep-learning model that caters to the small-size WiFi activity data. We conduct extensive experiments and show synthetic data improve performance by up to 34.6% and our system achieves around 90% of accuracy with well robustness in adapting to small-size CSI data.