Beacon Frames Research Articles

Throughput and coverage based Base Station–Relay Station deployment for 5G cellular network

SummaryFifth generation cellular networks have high data rates and connectivity demands. The relaying approaches are widely used in cellular networks to improve coverage, user throughput, and capacity at low cost. The increasing data requirements of the mobile network increase energy consumption. Energy efficiency‐based approaches also impact network coverage and throughput. Therefore, developing an energy‐efficient network with optimal coverage and throughput is considered a challenging issue. It can be resolved with optimal deployment of Base Station (BS), Relay Station (RS), and minimizing power consumption. In this research, a joint clustering‐based deployment technique is proposed for BS–RS deployment by considering the network throughput and coverage ratio. After enabling optimal BS–RS deployment, the network traffic is estimated with an On‐demand real‐time traffic estimation framework (ODTE). It estimates user association values using Beacon frames. In addition, the Relay Assisted Base station Power Scheduling (RABPS) approach to minimize power consumption is included. Based on Erlang's probability, the RABPS algorithm is enhanced with sleep mode activation during off‐peak hours. The proposed 5G network ensures uninterrupted connectivity and energy efficiency with varying time slots of power scheduling. The throughput performance of the clustering‐based approach is increased with a coverage ratio of 88%. The simulation results show the superiority of the proposed 5G BS–RS deployment and power scheduling in terms of throughput, coverage ratio, and power consumption.

Improving performance metrics in WBANs with a dynamic next beacon interval and superframe duration scheme

The advancement of networking, information, and communication technologies has fueled the popularity of Wireless Body Area Networks (WBANs) in both medical (remote patient monitoring) and non-medical sectors. Due to low, medium, and high data traffic requirements, WBAN performance suffers during the synchronization process that generates periodic beacon frames between sensor nodes and the coordinator. It also suffers when sensor nodes implicitly send data to the coordinator during the fixed time slot using the Contention Access Period (CAP). In this study, we propose a solution called Dynamic Next Beacon Interval and Superframe Duration Scheme (DNBISD) to tackle these issues of the IEEE 802.15.4 standard. This standard relies on a Beacon Interval (BI) and CAP for synchronization and data transmission between sensor nodes and the coordinator. However, the standard must adapt to BI and CAP's changing traffic load requirements, resulting in drawbacks such as prolonged packet delays, increased energy consumption, and potential data loss, particularly in real-time patient monitoring applications. In order to overcome these challenges, our DNBISD scheme employs a fuzzy approach to adapt the BI and CAP based on requested synchronization and data considering input parameters like packet received ratio and buffer ratio. The inference system utilizes the Takagi, Sugeno, and Kang (TSK) fuzzy model for rational quantitative analysis. Simulations demonstrate that our proposed scheme significantly enhances data transmission, boosts the average packet delivery ratio and throughput, and reduces the coordinator's average packet loss ratio and energy consumption. Consequently, this improvement allows for more efficient data transfer among numerous nodes within the specified superframe structure.

Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network

Security identification solutions against WLAN network attacks according to straightforward digital detectors, such as SSID, IP addresses, and MAC addresses, are not efficient in identifying such hacking or router impersonation. These detectors can be simply mocked. Therefore, a further protected key uses new information by combining these simple digital identifiers with an RF signature of the radio link. In this work, a design of a convolutional neural network (CNN) based on fingerprinting radio frequency (RF) is developed with computer-generated data. The developed CNN is trained with beacon frames of a wireless local area network (WLAN) that is simulated as a result of identified and unidentified router nodes of fingerprinting RF. The proposed CNN is able to detect router impersonators by comparing the data pair of the MAC address and RF signature of the received signal from the known and unknown routers. ADAM optimizer, which is the extended version of stochastic gradient descent, has been used with a developed deep learning convolutional neural network containing three fully connected and two convolutional layers. According to the training progress graphic, the network converges to around 100% accuracy within the first epoch, which indicates that the developed architecture was efficient in detecting router impersonations.

Research on security encryption mechanism of physical Layer based on iterative quantization method

Third-party eavesdropping is a unsolved problem in the process of data transmission in the physical layer of IoT (Internet of Things) in Power Systems. The security encryption effect is affected by channel noise and the half-duplex nature of the wireless channel, which leads to low key consistency and key generation rate. To address this problem, a reliable solution for physical layer communication security is proposed in this paper. First, the solution improved the key consistency by dynamically adjusting the length of the training sequence during feature extraction; Second, using an iterative quantization method to quantify the RSS (Received Signal Strength) measurements to improve generation rate of the key. Finally, based on the short-time energy method for the extraction of wireless frame interval features, by monitoring the change of inter-frame interval features, we can quickly determine whether there is an eavesdropping device into the link. Simulation results show that the reciprocity of legitimate channels R (R will be explained in detail in the following) is improved by 0.1, the key generation rate is increased by about 70%, and the beacon frames are extracted from the wireless link with good results compared to the methods that do not use dynamic adjustment of the pilot signal during the channel probing phase. The result shows that this method can effectively prevents third-party eavesdropping, effectively improves the key consistency and generation rate, and effectively implements beacon frame detection.

EasyAPPos: Positioning Wi-Fi Access Points by Using a Mobile Phone

Determining the location of Wi-Fi access points (APs) is vital for various Wi-Fi-based applications such as localization, security, and AP deployment. Considerable effort has been exerted in the field of AP localization. In contrast to studies that require additional robots with specialized antenna arrays, we present EasyAPPos, a lightweight, always-on, and user-centered AP positioning solution that utilizes widely available mobile phones. We focus on addressing three challenges in AP positioning. First, the patch antenna on a mobile phone has a limited angular range due to its size, but our approach proposes a method for utilizing human natural rotation to enhance angular diversity. Second, our angle-based algorithm does not require synchronous clocks between the mobile device and the APs, in contrast to existing algorithms that require this synchrony to transform propagation delays into positions. Nevertheless, our algorithm can still utilize asynchronous delay information. Third, the low bandwidth of Wi-Fi beacon frames, which only provide limited capacity to counteract the effects of multipath, is addressed by performing AP positioning under challenging conditions. We validate EasyAPPos through simulations and experiments, which demonstrate its ability to achieve decimeter-level positioning accuracy even under harsh conditions.

Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks

Wireless sensor networks (WSNs) have been utilized as communication infrastructure for smart grid applications. The primary requirement of WSNs for smart grid applications is to transmit delay-critical data from smart grid assets ether at the maximum rate or by reducing collision rates. Additionally, WSNs should utilize the limited resources of the network to provide the required long-term QoS. The achievement of these objectives requires a remarkable design of WSN protocols to satisfy the requirements of smart grid applications. In this study, a multi-channel cluster tree protocol is proposed to prevent collisions and increase network performance. In the proposed scheme, the cluster head serves to broadcast a beacon frame containing information on the allocated channels and time slots. This enables the new node to determine its channel and timeslot. A performance analysis reveals that the proposed scheme can achieve a low end-to-end delay and low collision rates compared with the well-known IEEE 802.15.4 MAC protocols widely used in the literature to provide QoS to smart-grid applications.

A simple metric that correlates with public Wi‐Fi throughput

AbstractIn the IEEE 802.11 specifications, a wireless fidelity (Wi‐Fi) access point (AP) broadcasts a beacon frame every 0.1024 s, and the received signal strength indicator (RSSI) is measured at a user terminal. RSSI is used as a metric for AP selection, but it does unlikely correlate with the actual quality of service, as has been pointed out in previous literature. In this letter, a simple metric is proposed that correlates with the effective throughput offered by a public Wi‐Fi AP. Specifically, the number of beacon frames within a time window is counted instead of using RSSI. The proposed metric can be easily calculated as it does not rely on advanced techniques such as machine learning. In a realistic IEEE 802.11a/n/ac network, the correlation coefficient between the proposed metric and the effective total throughput is 0.874 in the time window of 1 s, which is higher than a conventional metric, indicating the potential for low‐latency identification of whether a public Wi‐Fi AP can provide good throughput.

Multilayer Joint Optimization of Packet Size and Adaptive Transmission Scheduling of Wireless Sensor Networks for Mechanical Vibration Monitoring

Limitations pertaining to the storage capacity and bandwidth of wireless sensor networks (WSNs) for mechanical vibration monitoring (MVM) pose difficulties in the realization of reliable and energy-efficient communication of big data. To prolong the lifetime of battery-limited WSNs, this paper proposes a multi-layer joint optimization of packet size and adaptive transmission scheduling (ATS) to jointly optimize the energy consumption of acquisition nodes from the storage and transmission layers. First, owing to the frequent storage and transmission of massive mechanical vibration data in WSNs, the packet size significantly affects the energy consumption during storage and transmission. The proposed packet size optimization based on the lowest energy consumption (PSO-LEC) model optimizes the energy consumption of acquisition nodes from three aspects: writing, reading, and transmission of data. Second, when transmitting massive vibration data frequently, the direct beacon frame conflict and indirect beacon frame conflict markedly induce data conflict and delay problems. Therefore, adaptive transmission scheduling based on the time division multiple access (TDMA/ATS) method is proposed to optimize the errors in data collision and transmission delay. Thus, the packet size and ATS are considered jointly to improve the network lifetime for MVM. Comprehensive experimental results indicate that the transmission reliability and energy-efficiency of the proposed scheme are significantly improved, and the network life is effectively prolonged for MVM.

WiFi6 Dynamic Channel Optimization Method for Fault Tolerance in Power Communication Network

As the scale of power networks has expanded, the demand for multi-service transmission has gradually increased. The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks. However, it still cannot cope with situations such as wireless access point (AP) failure. To solve this problem, this paper combines orthogonal frequency division multiple access (OFDMA) technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails. First, under the premise of AP layout with strong coverage over the whole area, a faulty AP determination method based on beacon frames (BF) is designed. Then, the maximum signal-to-interference ratio (SINR) is used as the principle to select AP reconnection for the affected users. Finally, this paper designs a dynamic access selection model (DASM) for service frames of power Internet of Things (IoTs) and a scheduling access optimization model (SAO-MF) based on multi-frame transmission, which enables access optimization for differentiated services. For the above mechanisms, a heuristic resource allocation algorithm is proposed in SAO-MF. Simulation results show that the method can reduce the delay by 15% and improve the throughput by 55%, ensuring high-quality communication in power wireless networks.

Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

Mobility support to change the connection from one access point (AP) to the next (i.e., handover) becomes one of the important issues in IEEE 802.11 wireless local area networks (WLANs). During handover, the channel scanning procedure, which aims to collect neighbor AP (NAP) information on all available channels, accounts for most of the delay time. To reduce the channel scanning procedure, a neighbor beacon frame transmission scheme (N-BTS) was proposed for a seamless handover. N-BTS can provide a seamless handover by removing the channel scanning procedure. However, N-BTS always requires operating overhead even if there are few mobile stations (MSs) for the handover. Therefore, this paper proposes a reinforcement learning-based handover scheme with neighbor beacon frame transmission (MAN-BTS) to properly consider the use of N-BTS. The optimization equation is defined to maximize the expected reward to find the optimal policy and is solved using Q-learning. Simulation results show that the proposed scheme outperforms the comparison schemes in terms of the expected reward.

An Enhancement for IEEE 802.11 STA Power Saving and Access Point Memory Management Mechanism

Wi-Fi researchers are trying hard to extend battery life by optimizing 802.11 power save. The rising number of Wi-Fi devices and IoT devices and daily demands have reduced Station (STA) device power consumption. Better memory management at the Access Point (AP) side is also needed, so that AP can store maximum data to deliver sleepy STA devices. There are three main contributions of this study. The first one focuses on a power-saving mechanism scheme with an adaptive change to Listen Interval (LI) based on the battery status of station devices. The second contribution aims to examine better memory management for the AP buffer to store packets that will in the future deliver power-saving STA when awake. The third contribution, under the implementation of the proposed method, includes Wi-Fi corner cases covered as Beacon frames missed via STA, the keep-alive factor, and the upper-layer time taken to care for and ensure the delivery of unicast/multicast/broadcast data. The proposed approach introduced 802.11 protocols to share battery status, a protocol to announce proposed features via AP, and a protocol to change LI at runtime. Simulation results show that the proposed scheme performs better than 802.11 power saving in terms of power usage at the STA and access point memory management.

A Detection Technique Using Dual Authentication Stages Framework for Rogue Access Point Identification

Wi-Fi hotspot or Access point indicates many threats that lead to compromise a user’s identity, personal data, and network integrity. Therefore, this will uncover the users to the adversaries who deploy Rogue Access Point (RAP) by purpose to pilfer sensitive information. Hence, the adversaries can exchange the communication anonymously or mimic it as a client to pose a threat to public safety. The research aims to propose a new approach based on the beacon frame anomaly to detect the threats of RAP in the Wi-Fi hotspot. This research uses dual-pass identification steps to detect the RAP to identify the anomalies between the first and second responses. First, the identification will check the AP legitimacy to uncover the true identity of an AP. Second, the connection type of RAP is necessary to identify once the AP is determined as a rogue. The five (5) features have been selected in this research: the beacon response, OUI Number, address 1-4, retry bit, and IBSS status. The combination of all the features was 100% successfully detected the presence of RAP and identify its connection source.

Time-slotted LoRa MAC with variable payload support

LoRaWAN is an upcoming Low Power Wide Area Network (LPWAN) technology for Internet of Things implementations in various application domains. Despite its various advantages, LoRaWAN employs an Aloha-based MAC, which in terms of performance cannot guarantee high packet delivery ratio and low latency. To overcome this issue, we propose a time-slotted scheme, called TS-VP-LoRa, which supports multiple transmission times and packet sizes at the same time. In TS-VP-LoRa, scheduling is coordinated by the LoRa gateway, broadcasting beacon frames periodically for the synchronization of LoRa end-devices. A channel hopping mechanism is also proposed in order to minimize the occurrence of collisions and to evenly split the transmission load among all channels. TS-VP-LoRa is evaluated and compared to three other MAC-layer schemes in single gateway simulation scenarios with up to 500 nodes. The proposed scheme has proven to achieve low latency with high packet delivery ratios, significantly minimize collisions and maintain a relatively low energy consumption despite the scaling of the LoRa network.

Sensor-Aided Learning for Wi-Fi Positioning With Beacon Channel State Information

Because each indoor site has its own radio propagation characteristics, a site survey process is essential to optimize a Wi-Fi ranging strategy for range-based positioning solutions. This study examines an unsupervised learning technique that autonomously learns an optimal ranging strategy for each site using Wi-Fi and sensor data accumulated while users access a positioning application. Using the collected sensor data, the device trajectory is regenerated, and a Wi-Fi ranging module is optimized to generate the shape of the estimated trajectory using Wi-Fi, similar to that obtained from sensors. In this process, the ranging module learns the way to identify the channel conditions from each Wi-Fi access point (AP) and produces ranging results accordingly. Furthermore, we collect the channel state information (CSI) from beacon frames to investigate the benefit of using CSI in addition to received signal strength measurements. With the CSI, the ranging module can identify diverse channel conditions from each AP and more accurately generate the reliability of each distance estimate to achieve accurate positioning results. The effectiveness of the proposed learning technique is verified using a real-time positioning application implemented on a PC platform.

Interference-Aware Two-Level Differentiated Transmission for Improving Downlink Spatial Reuse in Dense WLANs.

In this study, we address the problem of downlink throughput degradation in dense wireless local area networks (WLANs) based on the IEEE 802.11ax standard. We demonstrate that this problem essentially results from the asymmetric characteristic of carrier sense multiple access between downlink and uplink transmissions in infrastructure WLANs, and it is exacerbated by a dynamic sensitivity control algorithm that aims to improve spatial reuse (SR) in IEEE 802.11ax. To solve this problem, we propose the interference-aware two-level differentiation mechanism consisting of the dual channel access (DCA) and supplemental power control (SPC) schemes. The proposed mechanism introduces a new measure called a spatial reusability indicator, which roughly estimates the signal-to-interference ratio from the received signal strength of beacon frames. Based on this measure, stations (STAs) are classified into the following two categories: spatial reusable STAs (SR-STAs) and non-spatial reusable STAs (NSR-STAs). Because SR-STAs are more robust to interference than NSR-STAs, the DCA scheme prioritizes transmissions to SR-STAs over those to NSR-STAs by using differentiated carrier sensing thresholds. Moreover, the SPC scheme selectively increases the transmission power to NSR-STAs to compensate for transmission failure due to interference. By combining the SPC and DCA schemes, the proposed mechanism effectively differentiates the downlink transmissions to SR-STAs and NSR-STAs in terms of channel access and transmission power, and it can boost the possibility of successful SR. The proposed mechanism can be easily implemented in IEEE 802.11ax without any complex calculation or significant signaling overhead. Moreover, we provide a practical guideline to determine appropriate parameter values for use in the proposed mechanism. The extensive simulation results obtained in this study confirm that the proposed mechanism increases the downlink throughput by more than several times without decreasing the overall throughput, compared to the existing mechanisms, and it maintains fairness between SR-STAs and NSR-STAs in terms of the ratio of successful transmission.

Approach of recognition and precision location for the beacon in satellite optical communications

Satellite optical communications have high communication speed and good confidentiality, which can establish high-speed and large-capacity communication links between satellites and the ground or other satellites. The requirements for the PAT (pointing, acquisition and tracking) system of satellite optical communication terminals are very high. In satellite optical communications, the biggest influence on the beacon’s recognition and tracking is the background light on CCD (Charge Coupled Device). Therefore, denoising work is required during the recognition or tracking process. This paper uses corner detection method to extract the beacon frame by frame. Mark the spot, and use the optical flow method to calculate the optical flow displacement based on the two consecutive frames. In addition, calculation accuracy of the beacon’s centroid is compared with that of the tradition method. The feasibility is verified by simulation experiment, the tracking process is completed, and the noise can be precisely eliminated.

Location Prediction of Rogue Access Point Based on Deep Neural Network Approach

One of the serious security problems in wireless local networks (WLAN) is the existence of the rogue access points (RAPs). To prevent our network from the RAP attacks, we need to identify the RAPs by using the RAP detection methods. However, the identification of RAP location is also a challenging task. The objective of this paper is to propose the location prediction scheme for the RAP. We call our proposed scheme as the location prediction of rogue access point (LPRAP). The LPRAP scheme consists of two mechanisms, the RAP detection mechanism and the RAP location prediction mechanism. We apply the concept of the fingerprint in the RAP detection mechanism by considering the SSID, time duration of broadcasting beacon frame and MAC address. We show that this mechanism can detect the number of RAP. For the RAP location prediction mechanism, we utilize the deep neuron network (DNN) to predict the location of RAPs and evaluate its effectiveness. We evaluate the performance of LPRAP by comparing with those of other machine learning methods such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes, and Multi-layer Perceptron (MLP). We also compare with particle swarm optimization algorithm. The results show that LPRAP can accurately predict the location of RAP up to 99.29%.

RAST: Rapid and energy-efficient network formation in TSCH-based Industrial Internet of Things

The Time Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4 standard is expected to revolutionize the Industrial Internet of Things. Indeed, it can achieve high reliability and deterministic latency with a very low duty cycle. Nevertheless, forming a TSCH network with the standard approach might not be as efficient, constituting, thus, one of the TSCH’s major issues. Such a network formation process relies on nodes passively scanning for advertised Enhanced Beacon (EB) frames to join the network. Doing so, a node wishing to join a TSCH network may stay awake randomly scanning for EBs for a considerable period of time, leading to a lengthy formation process with excessive energy consumption. To deal with these issues, this paper presents a practical and effective Radio duty-cycled, Active-Scan based network formation process for TSCH networks (RAST). Our proposal leans on active-scan procedures combined with radio duty cycling mechanisms to shorten joining delays and reduce energy consumption. Obtained results from extensive and realistic simulations show that our solution is efficient and outperforms state-of-the-art solutions, regarding the association time and energy consumption by up to two orders of magnitude.

The rogue access point identification: a model and classification review

Most people around the world make use of public Wi-Fi hotspots, as their daily routine companion in communication. The access points (APs) of public Wi-Fi are easily deployed by anyone and everywhere, to provide hassle-free Internet connectivity. The availability of Wi-Fi increases the danger of adversaries, taking advantages of sniffing the sensitive data. One of the most serious security issues encountered by Wi-Fi users, is the presence of rogue access points (RAP). Several studies have been published regarding how to identify the RAP. Using systematic literature review, this research aims to explore the various methods on how to distinguish the AP, as a rogue or legitimate, based on the hardware and software approach model. In conclusion, all the classifications were summarized, and produced an alternative solution using beacon frame manipulation technique. Therefore, further research is needed to identify the RAP.

Optimization of Citizen Broadband Radio Service Frequency Allocation for Dynamic Spectrum Access System

With the increase in mobile broadband utilization, more spectrum release is recommended by the Federal Communications Commission for spectrum sharing under a three-tire system called Citizens Broadband Radio Service. The standardization, functional and operational necessities of this framework are defined by the Wireless Innovation Forum. If an unavoidable shipborne radar appears on the channel, the channel must be vacated by the lower tier users. The timing constraints on CBRS is also stringent. Wireless stations transmit short beacon frames termed as heartbeat signals. These signals consist of the wireless channel encryption data, Service Set Identifier (SSID) and other credential data. These signals also transmit commands to vacate a channel. The heartbeat interval, timing constraint and domain proxy features are analyzed in this paper. CBSD renunciation and spectrum acquisition is performed with the help of domain proxy based communication. The CBRS-SAS channel allocation algorithm is further investigated. The communication interoperability and network robustness can improved with the introduction of secondary SAS and secondary domain proxy respectively.