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This letter proposes matrix factorization (MF) based interpolation of received signal strength (RSS) from a transmitter mounted on moving robot in factory environment. For realizing the reliable wireless communication, machine learning based channel prediction methods have been intensively studied in the past decade. However, some traffic models will make the observation of RSS sequence be intermittent, and the missing values must be interpolated before input to the predictor. Classical interpolation such as linear interpolation cannot appropriately estimate the missing values because the result of the interpolation depends on the observation time. In this letter, we propose to apply an MF-based interpolation technique to RSS interpolation in order to restore the true RSS variation pattern. Moreover, the basic MF-based interpolation is improved by introducing a smoothing term in an objective function to represent the smooth variation of the RSS sequence. The simulation results show that the MF-based interpolation can improve the prediction accuracy of the machine learning based channel prediction method.

Factory communication systems require highly re- liable links with predictable performance and quality of service in order to avoid outages that can damage the production-line process. Communication anomalies can be caused by narrowband interference which is difficult to identify and track from the time- domain information only. This paper describes a methodology for classifying increasing severity and types of interference in order to improve throughput prediction. Received signal strength (RSS) data is collected from both a ray-tracing simulation and a Wireless Local Area Network (WLAN) measurement campaign with a transmitter mounted on an actual automated guided vehicle (AGV). Scalogram time-frequency images are computed from the RSS signal and a convolutional neural network (CNN) is then trained to recognize the spectral features and enable the interference classification. The block random interference could be correctly classified on over 65% of the occasions in the ray- traced channel at 30 dB SNR.

Recently, the explosive increase in the number of IoT devices makes the IoT becomes extremely large-scaled, and the security of such a large scale IoT emerges as a big challenge. As a classic security technique, the port scan is widely used around the world. However, as IP resources are limited, a large number of devices are located in the LAN or WLAN behind the NAT which cannot be directly accessed by the port scanner. Furthermore, port scanning generated a tremendous number of probe and response packets which may cause heavy traffic load and frequent congestion. To conquer those problems, in this article, we first propose a reverse proxy based NAT penetration system for scanning ports behind NAT. Based on the NAT penetration system, we proposed a probe delay based adaptive scanning algorithm referred to as ProDASA, which adaptively changes port scanning frequency and scanning methods to balance the network performance and security requirements of the IoT. The experiment in a real environment demonstrates the feasibility of the proposed NAT penetration system and the computational simulation with multiple virtual devices shows the advantage of our proposed ProDASA in terms of both network performance and security by comparing with a conventional method.

Timestamp of frame reception timing of each wireless communication node is useful information to realize effective access control in a coordinated manner among multiple nodes. In order to obtain fine frame reception timestamp from multiple nodes with an independent clock, this paper proposed a method to compensate for the timestamp error due to clock drift among multiple nodes. The proposed method forms a node tree to determine the order of timestamp synchronization using the information of the commonly received frames among the nodes. From the root to the leaf node(s) in the formed tree, the timestamps are synchronized applying linear regression to the timestamps of commonly received frames for the parent and child nodes. The tree is formed so that the number of commonly received frames increases in order to improve the accuracy of the linear regression. Then, the jitter performance of the original timestamp obtained at each node is evaluated by experimental evaluation in an anechoic chamber using four different node configurations. This experimental evaluation revealed that timing synchronization function (TSF) timestamp is obtained with a jitter of a few µs. Finally, the system-level simulation assuming IEEE 802.11n WLAN was conducted to evaluate the residual timestamp error among the coordinating nodes. The simulation results revealed that the proposed method can keep the residual timestamp error around 1 µs if TSF stamp is available and the clock drift rate is stable in a couple of minutes.

In this paper, using the time-series throughput data generated from a simulated factory scenario, we study throughput prediction using convolutional neural network (CNN). Different with image or numerical recognition using CNN, in which the distribution of the prediction target during training stage usually has the similar level, the distribution of the throughput data concentrates only on several values. This centralized distribution may degrade the prediction accuracy. Therefore, we will propose a new CNN prediction method employing target vectorization which can mitigate the centralization of distribution. This method makes training process of CNN hold more possibility and improves the prediction accuracy of the throughput.

The demand for relay-transmission technologies has recently been increasing. We study relay transmission using multiband to reduce transmission delay and meet the increasing demand. In the relay transmission, the relay node initiates relay transmission while receiving data on another frequency band. However, despite its potential, the difference of data rates between reception and forwarding causes data loss from buffer overflow. To solve this problem, we propose an algorithm that selects appropriate channels, such that the transmission rates become as high as possible without buffer overflow, and evaluate the effectiveness of the proposal.

For efficient network scan to narrow-band wireless networks, this paper proposes a method to estimate the retransmission rate of the background traffic at the target scan rate from a scan response delay obtained at a different scan rate. First, through computer simulations considering a Wi-SUN sensor network with two different network situations and multiple scan rates, we derive a regression function using the least-squares method for the mean of the scan response delay and the COR of the background traffic, and the COR of background traffic and the retransmission rate of background traffic. Then, we propose the new estimation scheme of the retransmission rate of the background traffic. First, Network situation estimator estimates the situation of the target network from observed value and estimated value regarding the variance of scan response delay. Second, COR estimator estimates the COR of the background traffic from the mean of scan response delay, the estimated situation of target network, and the scan rate which is used for observing the scan response delay. Third, Retransmission rate estimator estimates the retransmission rate of the background traffic from the COR of the background traffic the estimated situation of the target network, and a scan rate of estimation target. Through computer simulations, it is confirmed that the proposed method can estimate the retransmission rate of the target network with the average error lower than 5.8% regardless of the situation of the target network and the scan rate.

The large number of Internet of things (IoT) terminals increases the cost and difficulty of IoT network management such as network scan. Some wireless IoT networks that have low transmission data rate especially require low overhead for network management to avoid interruption. Hence, it is important to make automatic identification of network type for effective network management. In this study, we at first carry out laboratory experiments of network scan to wireless terminals in wireless LAN (WLAN), Long Term Evolution (LTE) and Low Power, Wide Area (LPWA) networks that include Wi-SUN and LoRa network. From the result of response delay obtained from each network in the experiments, we then examine the basic property of the response delay, especially about the minimum response delay. Finally, we propose a scheme of network-type estimation based on the property of the minimum delay in different networks. The performance of the proposed scheme of network-type estimation is evaluated regarding the network objects of WLAN, LTE and LPWA networks. The evaluation results show that the scan response delay is a useful factor for successfully identifying different types of IoT networks.

For effective computation of the interference in an environment with multiple nodes, we propose a scheme clustering multiple interference nodes and estimating the total interference power of the cluster. The total interference power is obtained by adding a margin to a reference interference power calculated using geographic information of a limited number of representative nodes. The margin is empirically set to avoid under-estimation of the interference power at an outage probability. Through simulations of the total interference power in a street canyon environment, we evaluated the excess amount of the estimated total interference power in several methods to calculate the reference interference power. As a result, we show that the amount of the excessively estimated power can be reduced by selecting an appropriate way to calculate the reference interference power.

This paper proposes a method of identifying the scan response state based on a similarity measure between scan response preliminary obtained in various network states and an unknown scan response data in order to estimate the cause of network scan failure/delay which is necessary in detecting compromised IoT wireless devices. Existing methods for measuring similarity between two vectors fail to find similarity between two scan response data where the distribution, dimension, or magnitude of such scan response data differs, therefore, a method that can clearly measure the similarity between scan responses data is required. In particular, a similarity measure based on the average absolute deviation with a baseline scheme that can be utilized for easy identification of the network scan response scenario is proposed. Thereafter, the estimation of cause of scan failure/delay is presented. The goal of our proposed method is to detect early the state of the target (scanned) network, in order to accurately estimate the cause of scan failure/delay from the scan response data. The performance of the proposed method is evaluated using computer simulation. The evaluation results show that the proposed similarity measure achieved high similarity ratio of all unknown scan response samples to correct scenario and outperforms other conventional similarity measure methods. Also, it is confirmed that the cause of scan failure/delay could be estimated with over 90% correct estimation accuracy in various scenarios.

Recently, WLANs using multiple frequency bands (e.g., 5 GHz, 2.4 GHz, and 920 MHz) have become popular as a means to increase spectral efficiency. However, during multi-hop transmissions in such multi-band WLANs, the relay node experiences a significant increase in delay and loss of data frames. This happens because of the difference of link rates between the receiving and sending sides of the relay node. In order to address this problem, we consider the channel conditions of each band on either side of the relay. Accordingly, the receiving and sending rates at the relay node are measured and calculated, respectively. Based on the measured receiving rate and calculated sending rate, we propose an algorithm for the relay node to determine the optimal coding rate and modulation method so as to dynamically control the best band and channel selection for the sending side. Simulation results demonstrate the effectiveness of our proposed algorithm in terms of improvement of delay and transmission failure probability.

The large number of Internet of things (IoT) terminals increases the cost and difficulty of IoT network management such as network scan. Wireless IoT networks that have low data rate especially require low overhead for network management to avoid interruption. Hence, it is important to make an automatic identification of network type for effective network management. To successfully estimate the type of a network, in which IoT terminals are involved, this study examines the fundamental properties of the response delay of IoT terminals in scan events. We carry out indoor experiments of network scan to wireless terminals in wireless LAN (WLAN) and Low Power, Wide Area (LPWA) networks, including Wi-SUN and LoRa network. From the result of response delay obtained from the experiments, we then examine the basic property of the response delay, especially about the minimum response delay. Finally, we propose a scheme of network-type estimation based on the property of the minimum delay in different networks. The evaluation results show that the scan response delay is a useful factor for successfully identifying different types of IoT networks.

As part of the countermeasures against cyber-attacks on IoT wireless devices, this paper proposes a technique to determine the success or failure of network scan, and the cause of failure/delay from communication result of network scan.

In manufacturing and logistics, various applications exploiting IoT devices are started to be used. Although there is a demand for wireless connection between the IoT devices to networks, obstacles such as radio frequency interference, multipath-rich propagation, and movement of objects make communication unstable. The instability can cause a system failure of IoT applications. Since the QoS outage does not occur frequently in actual systems, the training of QoS outage events may be insufficient to accurately predict future QoS values. Therefore, we propose a method to estimate QoS outage probability from the predicted future QoS, distribution of prediction error, and required QoS. The proposed method can achieve high estimation accuracy by obtaining and using different error distributions according to the rarity of the observed sequence. Simulation results based on a theoretical throughput analysis of wireless LAN show that the proposed method can appropriately predict the QoS outage probability.

This paper studies the prediction of the received signal strength at a receiver that tracks an automated guided vehicle (AGV) as it moves along a factory route. We apply machine learning to predict a sliding-window pattern of the received signal strength indication (RSSI) signal and further improve the prediction performance by using multiple receivers. The performance evaluation processes wireless data collected from actual received signal strength measurement experiments recorded from an OFDM transmitter in the 2.4 GHz band. The performance is evaluated for vehicle movement along routes with both repetitive and random sections and with and without position errors.

The advances of cloud and edge computing allow networked robots to acquire supporting information anywhere. Meanwhile, cloud and edge computing give rise to a large amount of data information for user to acquire. This leads to a challenging issue for wireless networks because radio resources is limited. To accommodate a number of robots that download map information from wireless LANs (WLANs), it is important for a robotic wireless network (RWN) to improve the efficiency of using wireless resources. The objective of this paper is to investigate how to efficiently use multiple wireless channels for a multi-layered RWN, which employs local data sharing among robots to save wireless resource consumption. Based on the hierarchical structure of multi-layered RWN, we proposed a multi-channel utilization scheme for local data delivery in robot clusters. The joint design of local data sharing via robot clustering and multi-channel utilization increases the available wireless resources for accommodating of the increasing number of service robots. Quantitative analysis results illustrate that the proposed channel separation-and-sharing system increases significantly the capability of robot accommodation and improve the channel availability. Furthermore, the preliminary experiment results confirm the functionality of the proposed system.

This paper studies a channel allocation algorithm for next generation wireless local area networks (WLANs). The aim is to maintain a fairness in usage of all Wi-Fi channels in a network. A probabilistic neural network (PNN) is employed for predicting the upcoming busy-line status on each channel and this information is distributed to other access-points (APs) in the network. The probability of switching to a new less-busy channel which is modeled by a Markov chain is solved by sampling from a Gibbs distribution with low computational complexity. Simulation results show that the application layer and MAC layer transmission delays are reduced by up to 10% and 5% respectively with respect to the current IEEE 802.11ac system.

The authors have studied a multiband wireless local area network (MB-WLAN) which can effectively detect and exploit unused radio resources scattered in time and frequency domains. The MB-WLAN sets one or more primary channels (PCHs) in multiple frequency bands, and each station (STA) carries out random back-off process on the multiple primary channels to obtain a transmission opportunity (TXOP). Once a STA obtains a TXOP on any PCH, it checks whether or not another TXOP can be obtained on any other PCH in near future. If the STA judges that it can obtain another TXOP, it pends its transmission until another TXOP is obtained on any other PCH, and then a channel-bonded frame is transmitted. A suitable pending duration depends on the level of congestion on each PCH because the STA lose its TXOP more frequently to other STA’s frame transmission as the PCH gets more crowded. This paper, therefore, proposes a method to control the maximum pending duration with the aid of idle length prediction based on probabilistic neural network (PNN). This paper also proposes a method to control the timing to invoke learning of channel usage for PNN in order to get rid of the impact of self-transmission on the characteristics of channel usage. In order to validate the effectiveness of the proposals, this paper evaluates the achievable throughput of the MB-WLAN by computer simulation assuming IEEE 802.11n/ac-based WLAN operated in the 2.4GHz and 5GHz bands and 4-antenna STA. It is confirmed that the MBWLAN with two proposals can achieve almost best performance regardless the level of congestion on PCHs.

Wireless communications, and in particular wireless local area network (WLAN) technology, has undergone a tremendous evolution in the past decades. After the release of the WLAN standard IEEE 802.11a/b in 1999, Wi-Fi technology soon became pervasive, thanks mainly to its deployment on the unlicensed ISM band. However, high traffic, especially in hotspots and areas with dense deployment of Wi-Fi access points (APs) (e.g., stations, airports, etc.) has caused major issues and a severe degradation of communications quality. The latest WLAN standards (e.g., 802.11ac, 802.11ax) have largely succeeded in improving the link quality and data rate by adopting state-of-the-art PHY layer technologies, e.g., OFDMA, MU-MIMO. However, improvement of the MAC layer in these standards is not noticeable due to restrictions such as hardware limitation and backward compatibility issues for legacy APs. As an effort to improve the MAC layer for the next-generation WLAN standard, in this paper we propose a simple algorithm with low computational complexity for channel selection in Wi-Fi networks. The main idea is to take advantage of the potential of the IEEE 802.11ax MAC to avoid major standard modifications. For this purpose, we employ the channel utilization ratio (CUR), which is measured periodically by each AP based on its channel sensing. Time-averaged CUR values are weighted based on a Gibbs sampling approach and a probability associated to each channel is updated. Finally, a channel is selected based on the aforementioned probabilities in predefined time slots. Simulation results show that the proposed approach can improve the system throughput by up to 5% and transmission delay by up to 20%.

Service robots in a robotic wireless network (RWN) download map information from map servers for safe movement. A RWN needs to support a number of service robots. However, this functionality is constrained by the limited radio resources of a RWN. This paper addresses the issue of improving wireless-resource efficiency for a number of robots to download map information. We propose a data sharing scheme for robots to save consumption of wireless resources in map information acquisition. In the proposed scheme, robots are clustered, and a majority of client robots locally obtain the map information from a few cluster-head robots that acquire the map information directly from the server. To reduce wireless resource consumption, local data sharing among robots employs a lower transmission power than that is used in directly downloading from the server. The proposed scheme carries out efficient cluster formation so as to reduce the number of robots that download the map from the server, and to increase the number of robots that obtain the map information from the local data sharing.

In order to increase area spectral efficiency by exploiting unused spectral resources scattering in multiple frequency bands, we have been studying a wireless LAN (WLAN) system which simultaneously transmits multiple wireless frames over multiple frequency bands such as 2.4 GHz and 5 GHz, and we developed its initial hardware prototype. This paper introduces our developed prototype, and fundamental experiment to validate the effectiveness of our proposed multi-band WLAN. Evaluation results show that simultaneous multi-band transmission technique improves a throughput performance by about 2.5 times compared with conventional single-band transmission under the crowded channel condition.

This paper describes a technique to predict the fading channel of an automated guided vehicle (AGV) that moves along a pre-determined route. A probabilistic neural network (PNN) estimates the most likely signal by performing pattern matching between a stored and current fading signal window. The prediction unit is being developed as part of an anomaly detection unit that together will provide advance information on pending communication outages in a factory communications network. Multiple distributed receivers are employed in order to further improve the accuracy of the prediction. Performance is evaluated using a ray-tracing model of the moving AGV and results show that the mean squared error (MSE) can be reduced four orders of magnitude by employing eight receivers.

This paper will study the busy/idle duration model and its prediction performance of autoregressive (AR) based predictor using the real environment data collected during rush-hour weekday evening at a railway station. The analysis shows that both busy and idle duration distribution largely appear as a generalized Pareto (GP) distribution with a different scale value. In addition, the scale value highly decides the prediction performance of the low-complexity linear AR based predictor. We also propose a new AR based predictor by separating busy/idle duration data into different streams to differentiate the scale value of the streams. The prediction performance of the proposed predictor can be improved for the streams with small scale value.

Multi-band wireless local area network (WLAN) allows WLAN to adaptively select and jointly use wireless channels in multiple frequency bands to improve spectral efficiency. Monitoring of multi-band wireless resources is a significant and challenging issue for multi-band WLAN. Traditional methods of sensing by access point (AP) for each basic service set (BSS) of multi-cell WLAN incurs constraints of sensing capacity as well as the spatial and spectral coverage, especially for multi-band WLAN. We consider a resource monitoring system that employs collective sensing for each BSS via multi-RF stations (STAs). Distributed and collective sensing at each BSS alleviates the constraints of AP based sensing. Meanwhile, the STA based sensing for a BSS leads to a large sensing-report overhead. In this paper, we especially address the problem of potential large spectrum consumption caused by the sensing reports from a collective of sensing entities of STAs in multi-band WLAN. We propose a distributed-and-interactive scheme to efficiently report sensing results, such as channel occupancy ratio (COR) from sensing entities. The sensing results are collected at the AP in each BSS in a WLAN. The evaluation results show that the proposed scheme leads to a very small number of reports to obtain the necessary sensing results, even for a WLAN that has a large number of sensing entities.

Multiband simultaneous transmission (MST) system has been studied to enhance the spectral efficiency of wireless local area network (WLAN). In this system, information bits are divided into multiple bands and appropriately modulated taking account for the channel condition of each frequency band, and then a transmission frame consists of differently modulated symbols. To achieve higher spectrum efficiency, it is important to select an appropriate modulation and coding scheme satisfying an allowable frame error rate (FER). It means that FER prediction is a key technique to avoid low rate transmission and the frequent frame error. In this paper, we propose a FER prediction method for MST system. To predict the FER, we derive pairwise error probability (PEP) for this system. Moreover, we also consider the FER calculation based on the PEP for the WLAN system with a block interleaver. Furthermore, in order to improve prediction accuracy, we consider the impact of symbol error occurring beyond adjacent symbols on the PEP derivation. The simulation results show the accuracy of the proposed FER prediction method for both the additive white Gaussian noise channel and a multipath channel.

In this paper, we propose a low latency relay transmission scheme for WLAN which simultaneously uses multiple bands. In order to reduce the latency, the proposed relay transmission scheme forwards a bit sequence which is obtained by truncated decoding. In the proposed relay transmission scheme, a relay node receives a header of a frame on a frequency band. If relay transmission is required, the relay node starts relay transmission on another frequency band while the relay node receives the frame. Furthermore, we also propose an adaptive relay transmission selection (ATRS) scheme which selects the relay transmission scheme according to channel condition between a source node and a relay node. The ATRS scheme estimates an expected latency using FER estimated by mutual information which is obtained from SNR. Thus, the ATRS scheme selects appropriate relay transmission scheme. Simulation results show that the proposed schemes can attain the lowest latency or close to the lowest latency under various conditions.

In the current IEEE 802.11 wireless LAN (WLAN), each station (STA) must obtain transmission opportunity (TXOP) on its primary channel. It means that current WLAN hardly utilizes the whole spectral resources in a frequency band if congestion occurs in some WLAN channels or bands. To overcome this problem, a concept of alternative primary channel (APCH) has been proposed. The APCH is a channel on which each STA can obtain TXOP if it is idle for a while, even when the primary channel is busy. This paper proposes a channel access procedure to realize load balancing of channel access among multiple WLAN channels by using APCH. Through computer simulation based on IEEE 802.11ac WLAN and the evaluation methodology of IEEE 802.11 WLAN, it is confirmed that the proposed channel access procedure can realize the load balancing. It is also confirmed that the performance of average area throughput and transmission delay can be improved by using APCH.

In this paper, we propose a synchronization scheme to obtain frequency diversity in a WLAN system which simultaneously uses the multiple bands such as 5 GHz, 2.4 GHz and 920 MHz simultaneously. The proposed scheme is to combine the output of correlators after compensation for the difference of the center frequency on each frequency band. The proposed scheme obtains a large frequency diversity gain by joint synchronization processing across multiple bands, and improves the accuracy of synchronization. Simulation results show that the proposed scheme can achieve a better performance of synchronization than a conventional scheme which performs synchronization process only in a band.

A poly-polarization multiplexing (PPM) scheme, that simultaneously utilizes two orthogonal polarization signals to create virtual additional planes, has been studied to enhance the spectrum efficiency for fixed satellite communication in the Ku band (12, 14 GHz). In this paper, to further increase the spectrum efficiency, we propose an enhanced PPM (E-PPM) scheme. By formulating the generalized linear mapping for two orthogonal polarization planes, the proposed scheme derives the optimal E-PPM signal set that has the largest minimum Euclidean distance. As a fundamental study, this paper focuses on an AWGN channel, and we show the achievable performance of the proposed scheme. In the computer simulations assuming no depolarization effects, we show that the E-PPM scheme reduces the required Eb/N0 by 1.80 dB and 0.70 dB at BER =10-7 compared with the conventional orthogonal polarization multiplexing (OPM) and PPM scheme, respectively.

This paper proposes a new overlay transmission system for wireless LAN with RTS/CTS exchange. Conventional timing synchronization schemes may fail in the presence of inter-system interference, because they have not been designed for overlay transmission. In the proposed system, a transmitter estimates the transmission timing of the next wireless LAN DATA frame, and then sends its DATA frame at almost the same time as the estimated transmission timing to easily establish timing synchronization at the receiver. Moreover, we employ a tapped delay line adaptive array antenna at both transmitter and receiver to effectively suppress interference due to overlay transmission in a rich multipath propagation environment. The frame error rate performances of the proposed system and the IEEE 802.11a wireless LAN are evaluated through computer simulations that assume an exponentially decaying 8-path non-line-of-sight fading channel and include a timing synchronization process. Simulation results demonstrate that the proposed system can achieve overlay transmission while avoiding interference in a rich multipath propagation environment.

This paper introduces our proposed pre-FFT type MMSE-AAA for an OFDM packet transmission system to suppress sporadic interference. The AAA scheme controls an antenna weight to minimize the mean square error between its output signals of two periods with identical transmitted waveform and iterates the weight updating process in an OFDM symbol to rapidly converge the weight. The average PER performance of the proposed AAA with the presence of a sporadic inter-system/intra-system interference signal is evaluated through computer simulations that assume an exponentially decaying 12-path LOS fading channel and IEEE 802.11a data frame transmission. Simulation results show that the proposed AAA can effectively suppress sporadic inter-system interference that is irrelevant to its arrival timing. Sporadic intra-system interference can also be suppressed by the proposed AAA more efficiently than inter-system interference as long as the interference arrives between 13% and 90% of the OFDM symbol duration after the beginning of an OFDM symbol of the desired signal.