Wireless Access Points Research Articles

Intelligent Logistics Tracking System Based on Wireless Sensor Network

Wireless sensor networks are widely used to collect and monitor data in the physical world and play a vital role. This article mainly studies the intelligent logistics tracking system based on wireless sensor network. In this paper, the compressed sensing theory is used to reconstruct the environment monitoring data in the cabin to verify the validity of the theory. The five types of sensors are placed in the specified position in the carriage for multi-point sampling, and each node performs data collection every 45 seconds. This paper uses the entropy weight fuzzy comprehensive evaluation method to evaluate the platform planning scheme. The wireless network adopts a multi-zone network networking method, which divides the entire coverage area into multiple sub-areas. Each wireless access point is responsible for controlling a sub-area. At the same time, each wireless access point will also become a mobile terminal and the backbone network. In the actual image transmission, there are two data transmission modes of Zigbee module involved, transparent transmission and point-to-point mode. In the transparent transmission mode, only the communication between the gateway device and the edge device can be carried out, and the communication between the edge devices cannot be carried out. In order to test that the wireless sensor node can actively track the logistics status of the logistics object, this paper uses a temperature sensor to test to test whether the wireless sensor node can actively track the logistics object at different temperatures. In order to achieve the goal of performance testing, a test program was written to simulate a large number of users logging into the system at the same time to perform operations to test the performance of the system. Starting from 15m, the communication radius increases by 5m each time to 50m, and the average positioning error is gradually decreasing. The results show that the introduction of Internet technology and mobile communication network positioning technology into the logistics tracking system can greatly improve the utilization of existing resources.

Highly Efficient Front End Direct Conversion Receiver for 28-GHz Wireless Access Point

The manuscript presents the design and characterization of a wideband front-end receiver, based on hollow waveguide technology, for the wireless access point (WAP) applications. The Ka-band receiver is comprised of a low-loss beam-switching antenna network (BSAN) with two-dimensional (2D) scanning property. For demodulation, the receiver uses an efficient six-port network (SPN) that is composed of four 90° hybrid couplers and a 90° phase shifter. To suppress the phase imbalance on the entire band of interest, 27 GHz to 33 GHz, a frequency-independent phase shifter with a minimum phase error is suggested. The SPN receiver is integrated with antenna-in-package in waveguide technology to decrease the insertion losses and noise levels at the first stages. To determine the SPN phase errors, a theoretical and experimental analysis of real-time wireless data transmission utilizing phase-shift keying (PSK) and quadrature amplitude modulated (QAM) signal is conducted. Besides, channel capacity along with beamformer influence on channel performance improvement are determined.

Multi-Criteria Prediction Mechanism for Vehicular Wi-Fi Offloading

The growing demands of vehicular network applications, which have diverse networking and multimedia capabilities that passengers use while traveling, cause an overload of cellular networks. This scenario affects the quality of service (QoS) of vehicle and non-vehicle users. Nowadays, wireless fidelity access points Wi-Fi access point (AP) and fourth generation long-term evolution advanced (4G LTE-A) networks are broadly accessible. Wi-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A networks. However, utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult task. This condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of APs. Many studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading decision. However, depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is available. The present study proposed a multi-criteria wireless availability prediction (MWAP) mechanism, which utilizes historical connection patterns, historical data rate information, and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading decisions. The proposed mechanism is decentralized, where each vehicle makes the prediction by itself. This characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications. A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms. A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several scenarios. The MWAP mechanism exhibited a lower prediction error (i.e., below 20%) and higher offloading ratio (i.e., above 90%) than the existing mechanisms for several tested scenarios.

Application of a WiFi/Geomagnetic Combined Positioning Method in a Single Access Point Environment

Because of the particularity of urban underground pipe corridor environment, the distribution of wireless access points is sparse. It causes great interference to a single WiFi positioning method or geomagnetic method. In order to meet the positioning needs of daily inspection staff, this paper proposes a WiFi/geomagnetic combined positioning method. In this combination method, firstly, the collected WiFi strength data was filtered by outlier detection method. Then, the filtered data set was used to construct the offline fingerprint database. In the following positioning operation, the classical k‐nearest neighbor algorithm was firstly used for preliminary positioning. Then, a standard circle was constructed based on the points obtained by the algorithm and the actual coordinate points. The diameter of the standard circle was the error, and the geomagnetic data were used for more accurate positioning in this circle. The method reduced the WiFi mismatch rate caused by multipath effects and improved positioning accuracy. Finally, a positioning accuracy experiment was performed in a single AP distribution environment that simulates a pipe corridor environment. The results proves that the WiFi/geomagnetic combined positioning method proposed in this paper is superior to the traditional WiFi and geomagnetic positioning methods in terms of positioning accuracy.

Flexible Virtual Cell Design for Ultradense Networks: A Machine Learning Approach

With the ever-growing demand for even higher throughput, ultradense networks (UDNs) are being deployed for the fifth generation (5G) mobile communications. Although massively distributed radio access points (APs) result in a considerable increase in throughput, they also cause some critical problems. When employing a wireless backhaul, the backhaul capacity becomes a limiting factor, which may result in a high packet loss rate. Furthermore, dense deployment of APs leads to more frequent handoffs for mobile user equipments, which results in heavy measurement and signaling overhead. To address the problem of frequent handoffs, virtual cell (VC) has been considered as a promising solution. However, the limited wireless backhaul capacity encountered by inflexible VC design may still result in an intolerable packet loss rate. For a better trade-off between the packet loss rate and the handoff overhead, a machine learning approach for flexible VC design is proposed that leverages particle swarm optimization (PSO) to quickly find the optimal VC solution. To be responsive to the dynamic traffic demand and backhaul capacity of APs, a new parameter called “weighted distance” is employed in the modified K-means algorithm, which is nested in the PSO procedures for master AP selection and VC boundary determination. Compared with an exhaustive search, optimal VC solutions can be found efficiently through considerably fewer iterations. The proposed method is generic and applicable to disparate UDN application scenarios.

Future of TCP on Wi-Fi 6

The Linux TCP/IP stack contributions have recently pointed all in the same direction: maximize the available throughput while maintaining low latency. These activities started by mitigating the buffebloat phenomenon at the network bottleneck as much as possible. So far, the deployed solutions have been designed by considering standard models for the bottleneck that could be either wireless or wired. The introduction of Wi-Fi 6 is dislocating the bottleneck of standard home and office WLAN from the radio access point to the 1 Gbps interface, the wired interface that points to the internet service provider; this bottleneck migration leads to a new real-case bottleneck model, which is hybrid. Such an environment embraces new technologies and provides new challenges for the old TCP protocol when applied to hybrid bottlenecks and operating in conjunction with the TCP side-modules, which are now part of the novel Linux kernels. This paper aims to highlight the TCP performance considering the new TCP modules and novel scenarios opened by Wi-Fi 6 with real-case hybrid bottlenecks.

Verification and Optimization of Metro Fare Clearing Models Based on Travel Route Reconstruction

How to verify and optimize metro fare clearing models efficiently and accurately is a research focus in metro operations. Metro fare clearing models are mostly based on probability distributions. In such models, the normal distribution of travel time corresponding to the section probabilities is used to calculate the route choice probabilities of passengers on a multi-route metro network. By integrating the operating mileage proportions of each metro line operator and the corresponding route choice probabilities, the fare clearing proportions are calculated for all the operators of the metro network. To verify the accuracy of the fare clearing proportions, we propose a travel route reconstruction approach based on cell phone data acquisition technique. With wireless access point (AP) sensors installed at transfer stations, the unique medium access control (MAC) address of the smart phone with Wi-Fi function turned on is recorded and transmitted to a data analysis platform. After matching the MAC address information with time and location, the travel route of the smart phone user is reconstructed. Then, the parameters in the fare clearing model are verified and optimized according to the travel route choice probabilities. The proposed methodology is applied in Hangzhou metro network for experiment, and the metro fare clearing model is verified and modified by reconstructing the actual travel routs of the local passengers.

Wireless powered IoE for 6G: Massive access meets scalable cell-free massive MIMO

A key challenge to the scalable deployment of the energy self-sustainability (ESS) Internet of Everything (IoE) for sixth-generation (6G) networks is juggling massive connectivity and high spectral efficiency (SE). Cell-free massive multiple-input multiple-output (CF mMIMO) is considered as a promising solution, where many wireless access points perform coherent signal processing to jointly serve the users. However, massive connectivity and high SE are difficult to obtain at the same time because of the limited pilot resource. To solve this problem, we propose a new framework for ESS IoE networks where the user activity detection (UAD) and channel estimation are decoupled. A UAD detector based on deep convolutional neural networks, an initial access scheme, and a scalable power control policy are proposed to enable the practical scalable CF mMIMO implementation. We derive novel and exact closed-form expressions of harvested energy and SE with maximum ratio (MR) processing. Using local partial minimum mean-square error and MR combining, simulation results prove that the proposed framework can serve more users, improve the SE performance, and achieve better user fairness for the considered ESS IoE networks.

An IoT-based bin level monitoring system for solid waste management

Improper disposal of solid waste that impacts human health and pollutes the environment, arising a need for successful and necessary collection of waste materials. However, most trash bins placed in cities can be seen overflowing due to traditional or inefficient waste management approaches. Therefore, a real-time remote monitoring system is needed to alert the level of garbage in bins to the relevant authority for immediate waste clearance. This paper presents the development and validation of a self-powered, simple connect, IoT solution to monitor the unfilled level of trash bins from a central monitoring station. The end sensor nodes of the developed IoT system are called Bin Level Monitoring Unit (BLMU) which are installed in every trash bin where the unfilled level needs to be monitored. Every BLMU measures the unfilled level of the trash bins and transmits it to a wireless access point unit (WAPU). Each WAPU receives the unfilled level data from several BLMUs and uploads it to the central server for storage and analysis. The waste collection authority can view and analyze the unfilled level of each bin using a smart graphical user interface. The following important experiments were carried out to validate the developed system: (1) the developed bin level monitoring system was tested by filling a trash bin with solid waste at various levels, and the corresponding unfilled level of the trash bin was monitored using the smart graphical user interface. (2) The life expectancy of the BLMU was evaluated as approximately 434 days. (3) The maximum transmission distance between a BLMU and a WAPU is 119 m. (4) The cost of a developed trash bin is 107 USD. Based on the results achieved, the developed trash bins can be suitable for smart cities.

Energy Efficient Relay Selection and Resource Allocation in D2D-Enabled Mobile Edge Computing

In order to improve resource utilization and network capacity, we propose the Device-to-Device (D2D) enabled Mobile Edge Computing (MEC) system, where multiple Smart Devices (SDs) transmit the offloading data to the MEC server with the help of wireless access point (WAP) selected from multiple WAPs. The SD uses the chosen WAP as the communication relay between the MEC server and itself. Aimed to minimize the total energy consumption of the system and satisfy the SDs demand on delay, we jointly optimize relay selection and resource allocation in D2D-enabled MEC system. The problem is formulated as an integer-mixed non-convex optimization problem which is a NP-hard problem. We thus propose a two-phase optimization algorithm that jointly optimizes relay selection policy and resource allocation strategy. In first phase, the original problem is converted into a convex optimization problem by using convex optimization techniques, and the optimal relay selection policy can be achieved by solving the relay selection problem. After obtaining the relay selection policy, the original problem is transformed into a resource allocation problem solved by leveraging the Lagrange Method in the second phase. Furthermore, the proposed algorithm is a low-complexity algorithm which is associated with the root finding method. The optimal relay selection policy and resource allocation strategy can be found in polynomial time. The extensive simulation results are provided to indicate that the D2D-enabled MEC system achieves remarkable results in energy saving. Compared with other baseline methods, our proposed algorithm can not only achieve the optimal solution with less time cost, but also improve the energy efficiency and network capacity.

Full duplex wireless communiation with dynamic buffer aided MAC protocol

This paper presents a dynamic buffer aided medium access control protocol for asymmetric traffic conditions in an IoT network, where the IoT devices send data to a wireless access point (WAP) which then forwards the data to the network fusion center. Specifically, the control frame structures of conventional carrier sense multiple access with collision detection (CSMA/CD) protocol are modified for incorporating the traffic asymmetry of the network. The devices and WAP are assumed to have full duplex abilities to support continuous sensing of the channel. The sensing performance of the considered system is derived for arbitrary number of devices in terms of probabilities of false alarm and miss detection. Moreover, the throughput of the network is also obtained. Simulation results show that the proposed cross-layer design achieves significant throughput improvement while incorporating traffic asymmetry of the network.

Research on the Multiobjective Optimization of Microwave Wireless Power Receiving in an Unmanned Aerial Vehicle Network

With the wide application of various wireless energy transmission technologies and unmanned aerial vehicle clusters in both production and life, the use of microwave wireless energy transmission to provide a real-time energy supply for an unmanned aerial vehicle network in flight has become an effective way to extend its working time. This paper focuses on the optimization of the energy transmission efficiency and cost in the microwave wireless power receiving process of an unmanned aerial vehicle network. Considering the overall energy transmission efficiency from the power supply terminal to the power receiving network and the cost of the wireless power transmission equipment of the network, we have established a multiobjective wireless power receiving optimization model including a microwave energy emission source and an unmanned aerial vehicle network that receives energy. The model is optimized to select the best wireless power access point and the number of wireless power receiving modules in a network node. In the case study, the optimization model is solved using an evolutionary algorithm, and the solution results verify the effectiveness and correctness of the model.

Viability of Numerical Full-Wave Techniques in Telecommunication Channel Modelling

In telecommunication channel modelling the wavelength is small compared to the physical features of interest, therefore deterministic ray tracing techniques provide solutions that are more efficient, faster and still within time constraints than current numerical full-wave techniques. Solving fundamental Maxwell's equations is at the core of computational electrodynamics and best suited for modelling electrical field interactions with physical objects where characteristic dimensions of a computing domain is on the order of a few wavelengths in size. However, extreme communication speeds, wireless access points closer to the user and smaller pico and femto cells will require increased accuracy in predicting and planning wireless signals, testing the accuracy limits of the ray tracing methods. The increased computing capabilities and the demand for better characterization of communication channels that span smaller geographical areas make numerical full-wave techniques attractive alternative even for larger problems. The paper surveys ways of overcoming excessive time requirements of numerical full-wave techniques while providing acceptable channel modelling accuracy for the smallest radio cells and possibly wider. We identify several research paths that could lead to improved channel modelling, including numerical algorithm adaptations for large-scale problems, alternative finite-difference approaches, such as meshless methods, and dedicated parallel hardware, possibly as a realization of a dataflow machine.

Wi-PSG: Detecting Rhythmic Movement Disorder Using COTS WiFi

Rhythmic movement disorder (RMD) is closely related to health problems like insomnia, daytime fatigue, anxiety disorder, and depression, or even causes severe injuries resulting from the movements. To obtain detailed information of RMD related abnormal movements for early diagnosis, there are generally three categories of solutions: 1) using camera to record image data; 2) wearing various smart devices; and 3) deploying dedicated hardware to capture sensor data. But none of such are widely accepted for different reasons due to privacy, inconvenience and excessive overhead. We believe one of the essential features in a feasible solution is nonintrusiveness , in which movement data collection should be carried out without the awareness of targets. In addition, it should be fairly accurate and low cost. In this work, we propose Wi-PSG, a contactless and nonintrusive sleep monitoring system, which exploits channel state information (CSI) from existing WiFi infrastructures to detect RMD related movements. Specifically, we introduce new set of sensitivity metrics and reconstruct the collected CSI into an ideal subcarrier sensitive to all target movements. With the estimated CSI background model derived from static propagation paths, nonmovement interference can be canceled from RMD movement detection.We then train the classifier for distinguishing different kinds of RMD movements using both time and frequency features extracted from CSI signals. We implement Wi-PSG with a pair of WiFi devices and wireless access point. We evaluate Wi-PSG with nine volunteers over a one-month period. The extensive experiments demonstrate that Wi-PSG can achieve a recognition accuracy of above 92%, even under challenging scenarios.

Hastane Hizmetlerinin İyileştirilmesinde Kalite Fonksiyonu Göçerimi (KFG) Yönteminin Kullanılması ve Nesnelerin İnterneti Model Önerisi

Technological developments aim to make our lives easier and to improve our quality of life. The hospitals in which the Health Services are provided, need to adapt to the developing technologies to provide better services as they have a direct impact on the quality of life of the people. The aim of this study is to propose a model for the use of the Internet of Things (IoT), the effect of which has been demonstrated on the health sector, in the improvement of the health services provided in hospitals by using Quality Function Deployment management in order to follow a systematic way. Customer needs are determined by expert team consultancy with a scale prepared with the help of Servqual scale, technical requirements are presented that can be suggested within the scope of the Internet of Things and the Quality Function Deployment method is applied. When the Quality House emerging in the study is analyzed, it is seen that the technical requirements with the highest technical importance are: “Establishing a warning system for health units with Wireless Body Sensors”, “Health counseling over the Internet and mobile applications” and “Increasing the number of wireless access points”.

Applying bigdata technology for campus wireless network optimization

This article introduces the use of big data analysis technology to solve the problem of campus wireless network optimization. By integrating two data sources: logs and traffic, a full range of data collection and analysis is carried out on the status of network equipment and the second to seventh layer of the network protocol. The analytics is based on the per-flow big data analysis technology and overcomes the weakness of the past analytics due to the data with scattered nature, and provides a new problem-solving idea for wireless network optimization. The wireless access point (AP) is the edge device of the wireless network. Its layout and communication quality play a decisive role in wireless network communication. Whether the effective usage of AP can be obtained by analyzing its log and traffic is the key to optimization. This research focuses on calculating the network traffic of each protocol layer of AP through a parallel computing model. The research results will provide better solutions for wireless network layout, equipment selection, system expansion, and fault diagnosis.

Electromagnetic Radiation Safety: Russian National and International Regulatory Frameworks for Radiofrequency Electromagnetic Fields

Summary. Introduction: Today, the growing human exposure to radiofrequency electromagnetic fields is of serious concern to the population and the international scientific community. Sources of radiofrequency electromagnetic fields in residential areas include mobile radio base stations, signal repeaters, wireless broadband access points, TV and radio transmitters, satellite earth stations, radar and radio relay stations. Launching of 5G mobile networks will further complicate the electromagnetic situation in inhabited areas, especially in megacities where the size of exposed population is the largest. In the system of measures contributing to protection of the population from adverse health effects of electromagnetic fields, standards (regulations and other documents establishing electromagnetic fields permissible levels) play a significant role and lay the foundation for organizational and technical measures of protection. High importance of standards in ensuring electromagnetic safety served as the basis for our study. The objective of the study was to perform a comparative analysis of radiofrequency electromagnetic fields standards for inhabited areas and approaches to setting electromagnetic fields exposure levels adopted by different countries and international organizations. Methods: We reviewed of some aspects of biological effects of radiofrequency electromagnetic fields, regulations setting electromagnetic fields exposure limits in different countries and recommended by ICNIRP, and approaches to establishing those limits. Conclusions: We found a significant discrepancy in radiofrequency electromagnetic fields limits adopted in different countries, the legal status and scope of regulatory documents. Differences in the methodology for the development, adoption and approval of electromagnetic fields standard accompanied by differences in their legal status prevent uniform EMF standard setting.

Massively Distributed Antenna Systems With Nonideal Optical Fiber Fronthauls: A Promising Technology for 6G Wireless Communication Systems

Employing massively distributed antennas brings radio access points (RAPs) closer to users, enabling aggressive spectrum reuse that can bridge gaps between the scarce spectrum resource and extremely high connection densities in future wireless systems. Examples include the cloud radio access network (C-RAN), ultradense network (UDN), and cell-free massive multiple-input, multiple-output (CF-mMIMO) systems. These systems are usually designed in the form of fiber wireless communications (FWC), where distributed antennas or RAPs are connected to a central unit (CU) through optical fronthauls. A large number of densely deployed antennas or RAPs require an extensive infrastructure of optical fronthauls. Consequently, the cost, complexity, and power consumption of the network of optical fronthauls may dominate the performance of the entire system. This article provides an overview and outlook on the architecture, modeling, design, and performance of massively distributed antenna systems (DAS) with nonideal optical fronthauls. Complex interactions between optical fronthauls and wireless access links require optimum designs across the optical and wireless domains by jointly exploiting their unique characteristics. It is demonstrated that systems with analog radio-frequency-overfiber (RFoF) links outperform their baseband-overfiber (BBoF) or intermediate-frequency-overfiber (IFoF) counterparts for systems with shorte fiber length and more RAPs, which are all desired properties for future wireless communication systems.

CampEdge: Distributed Computation Offloading Strategy Under Large-Scale AP-Based Edge Computing System for IoT Applications

With the development of multiaccess edge computing (MEC) technology at the network edge, efficient resources allocation and offloading between the resource-constrained edge clouds to maintain load balancing become a looming problem recently. However, most existing researches aimed at optimizing the allocation of computing resources are based on simulation and apply only for some typical Internet-of-Things (IoT) applications on mobile devices (i.e., they all lack of practicality and generality). In this article, we present a novel edge computing platform (CampEdge) with 36 edge nodes based on the wireless access point (AP) for adaptive resources allocation and computation offloading in a complicated dynamic campus environment. We first collected and sufficiently analyzed a large real-world WiFi data set, covering more than 8500 wireless APs and serves 44 000 active end users within an area of 3.1 km2 over three months. A multiclass classification algorithm based on the random forest was then used with this data to accurately predict the state of resources usage at each edge node (i.e., in a busy state or normal state). To reasonably offload and transmit end-users’ computational tasks to these edge nodes and optimize the total latency cost among the whole offloading process, we then illustrate a distributed computation offloading optimization strategy to formulate this complicated problem as a multiobjective latency optimization problem based on alternating direction method of multipliers (ADMM). Furthermore, we briefly discuss the convergence of our system. In experiments, CampEdge was shown to decrease user latency by up to 30%, compared to state-of-the-art methods. The proposed strategy was also shown to adaptively conform to a variety of compute-intensive and time-sensitive IoT applications for end users.

Insights into Fabrication and Measurements of PCB-Based Passive Millimeter Wave Antennas

Future cellular communication systems might be based on millimeter wave frequencies. So, there is a need to design and deploy antennas which operate in the millimeter wave spectrum integrated in mobile terminals, portable wireless devices, base stations and access points. Even though a lot of articles exist on the design of such antennas, fabrication and measurement nuances of such antennas are addressed rarely in the open literature. Initially, the fabrication process of printed circuit board (PCB) antennas is discussed in detail with sources of error and possible precautions. A case study of microstrip-based tapered slot antenna operating at 28 GHz is presented. The variations in the antenna characteristics with slight variations due to fabrication errors are presented. A detailed discussion on S-parameters, pattern and gain measurements is also presented along with possible sources of errors associated with them.