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Xu, Yihuai, Xin Hu, Yimao Sun, et al. "High-Accuracy Height-Independent 3D VLP Based on Received Signal Strength Ratio." Sensors 22, no. 19 (2022): 7165. http://dx.doi.org/10.3390/s22197165.
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Visible light positioning (VLP) has attracted intensive attention from both academic and industrial communities thanks to its high accuracy, immunity to electromagnetic interference, and low deployment cost. In general, the receiver in a VLP system determines its own position by exploring the received signal strength (RSS) from the transmitter according to a pre-built RSS attenuation model. In such model-based methods, the LED’s emission power and the receiver’s height are usually required known and constant parameters to obtain reasonable positioning accuracy. However, the LED’s emission power is normally time-varying due to the fact that the LED’s optical output power is prone to changing with the LED’s temperature, and the receiver’s height is random in a realistic application scenario. To this end, we propose a height-independent three-dimensional (3D) VLP scheme based on the RSS ratio (RSSR), rather than only using RSS. Unlike existing RSS-based VLP methods, our method is able to independently find the horizontal coordinate, i.e., two-dimensional (2D) position, without a priori height information of the receiver, and also avoids the negative effect caused by fluctuation of the LED’s emission power. Moreover, we can further infer the height of the receiver to achieve three-dimensional (3D) positioning by iterating the 2D results back into positioning equations. To quickly verify the proposed scheme, we conduct theoretical analysis with mathematical proof and experimental results with real data, which confirm that the proposed scheme can achieve high position accuracy without known information of the receiver’s height and LED’s emission power. We also implement a VLP prototype with five LED transmitters, and experimental results show that the proposed scheme can achieve very low average errors of 2.73 cm in 2D and 7.20 cm in 3D.
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Raes, Willem, Nicolas Knudde, Jorik De Bruycker, Tom Dhaene, and Nobby Stevens. "Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning." Sensors 20, no. 21 (2020): 6109. http://dx.doi.org/10.3390/s20216109.
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In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios.
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Veselý, Jiří, Petr Hubáček, and Jana Olivová. "The Power Gain Difference Method Analysis." Sensors 20, no. 11 (2020): 3018. http://dx.doi.org/10.3390/s20113018.
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In this paper, we propose a new approach to passively locate the 3D position of a signal source. This novel technique, called the power gain difference (PGD), is based only on measuring the received signal strength (RSS) with multiple sensors deployed in the area of interest, while the target transmit power or the equivalent isotropic radiated power (EIRP) is assumed to be unknown. Next, the signal source position is estimated using the knowledge of the ratios of RSS measured on different sensors. First, this article presents the geometric representation and the analytical solution of the model of the PGD technique. Second, the PGD dilution of precision was analyzed in order to gauge the accuracy of measuring the RSS. Finally, a numerical simulation of the performance of the proposed method was carried out and the results are discussed. It seems that the PGD technique has the potential to be a simple and effective solution of the 3D localization problem.
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Martínez-Ciro, Roger Alexander, Francisco Eugenio López-Giraldo, José Martín Luna-Rivera, and Atziry Magaly Ramírez-Aguilera. "An Indoor Visible Light Positioning System for Multi-Cell Networks." Photonics 9, no. 3 (2022): 146. http://dx.doi.org/10.3390/photonics9030146.
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Indoor positioning systems based on visible light communication (VLC) using white light-emitting diodes (WLEDs) have been widely studied in the literature. In this paper, we present an indoor visible-light positioning (VLP) system based on red–green–blue (RGB) LEDs and a frequency division multiplexing (FDM) scheme. This system combines the functions of an FDM scheme at the transmitters (RGB LEDs) and a received signal strength (RSS) technique to estimate the receiver position. The contribution of this work is two-fold. First, a new VLP system with RGB LEDs is proposed for a multi-cell network. Here, the RGB LEDs allow the exploitation of the chromatic space to transmit the VLP information. In addition, the VLC receiver leverages the responsivity of a single photodiode for estimating the FDM signals in RGB lighting channels. A second contribution is the derivation of an expression to calculate the optical power received by the photodiode for each incident RGB light. To this end, we consider a VLC channel model that includes both line-of-sight (LOS) and non-line-of-sight (NLOS) components. The fast Fourier transform (FFT) estimates the powers and frequencies of the received FDM signal. The receiver uses these optical signal powers in the RSS-based localization application to calculate the Euclidean distances and the frequencies for the RGB LED position. Subsequently, the receiver’s location is estimated using the Euclidean distances and RGB LED positions via a trilateration algorithm. Finally, Monte Carlo simulations are performed to evaluate the error performance of the proposed VLP system in a multi-cell scenario. The results show a high positioning accuracy performance for different color points. The average positioning error for all chromatic points was less than 2.2 cm. These results suggest that the analyzed VLP system could be used in application scenarios where white light balance or luminaire color planning are also the goals.
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Ahmad, A., P. Claudio, A. Alizadeh Naeini, and G. Sohn. "WI-FI RSS FINGERPRINTING FOR INDOOR LOCALIZATION USING AUGMENTED REALITY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2020 (August 3, 2020): 57–64. http://dx.doi.org/10.5194/isprs-annals-v-4-2020-57-2020.
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Abstract. Indoor localization has attracted the attention of researchers for wide applications in areas like construction, facility management, industries, logistics, and health. The Received Signal Strength (RSS) based fingerprinting method is widely adopted because it has a lower cost over other methods. RSS is a measurement of the power present in the received radio signal. While this fingerprinting method is very popular, there is a significant amount of effort required for collecting fingerprints for indoor space. In this paper, we propose an RSS fingerprinting method using Augmented Reality (AR) that does not rely on an external sensor resulting in ease of use and maintenance. This method uses spatial mapping techniques to help align the floor plan of existing buildings; then, after the alignment, we map local device coordinates to global coordinates. After this process, we partition the space in equally distanced reference points for RSS fingerprint collection. We developed an application for Microsoft HoloLens to align the floor plan and collect fingerprints on reference points. Then we tested collected fingerprints with existing RSS based indoor localization methods for its accuracy and performance.
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Muttair, Karrar Shakir, Mahmood Farhan Mosleh, and Oras Ahmed Shareef. "Optimal transmitter location using multi-scale algorithm based on real measurement for outdoor communication." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1384. http://dx.doi.org/10.11591/ijai.v11.i4.pp1384-1394.
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In the era of wireless network communication, the demand for determining the best transmitter (Tx) location is constantly increasing especially in outdoor environments and localization-based services. In this paper, localization optimal location of a Tx has been achieved in electrical engineering technical college by using received signal strength (RSS). We install practical devices in the college to measure the received signal strength in all the college regions. Where each device consists of two pieces which are the AirMAX sector directional antenna and signal broadcast piece (router board). We measured RSS in four directions for each received point in all selected regions using the NetSpot software. An algorithm was created in MATLAB software to determine the average received power (ARP), average signal quality (ASQ) as well as calculate the average coverage (AC) for each Tx device location based on the locations deployed in the real environment. Overall, the results obtained showed that this algorithm effectively demonstrated that it is able to determine the best location of a Tx device compared to other deployed locations in the college. Furthermore, we find large effects when a Tx location changes from one location to another on the RSS because of the surrounding environment effects.
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Sergi, Simone, Fabrizio Pancaldi, and Giorgio M. Vitetta. "Cluster-Based Ranging for Accurate Localization in Wireless Sensor Networks." International Journal of Navigation and Observation 2010 (July 29, 2010): 1–11. http://dx.doi.org/10.1155/2010/460860.
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A novel ranging technique based on received signal strength (RSS) and suitable to indoor scenarios is illustrated. In the proposed technique, multiple power measurements, associated with the signals radiated by a cluster of nodes surrounding a given target, are jointly processed to improve the quality of RSS-based estimation of the distance between the target and an anchor. Specific algorithms for the generation of a cluster and for the acquisition of power measurements are described. Simulation results show that, when used in indoor positioning systems, the proposed ranging technique is substantially more accurate than noncooperative strategies. In addition, it allows to concentrate significant processing tasks in a limited number of fixed anchors, so reducing maintenance costs and making it possible to adopt cheap and simple portable wireless nodes.
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Rzymowski, Mateusz, Krzysztof Nyka, and Lukasz Kulas. "Direction of Arrival Estimation Based on Received Signal Strength Using Two-Row Electronically Steerable Parasitic Array Radiator Antenna." Sensors 22, no. 5 (2022): 2034. http://dx.doi.org/10.3390/s22052034.
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In this paper, we present a novel approach to direction-of-arrival (DoA) estimation using two-row electronically steerable parasitic array radiator (ESPAR) antenna which has 12 passive elements and allows for elevation and azimuth beam switching using a simple microcontroller, relying solely on received signal strength (RSS) values measured at the antenna output port. To this end, we thoroughly investigate all 18 available 3D antenna radiation patterns of the antenna measured in an anechoic chamber with respect to radiation coverage in the horizontal and vertical direction and propose a generalization of the power-pattern cross-correlation (PPCC) algorithm involving a high number of multiple calibration planes (MCP) as well as specific combinations of radiation pattern sets. Additionally, a new way of RSS-based DoA estimation accuracy assessment, which involves thorough testing conducted along the elevation direction when RF signals impinging on the antenna arrive from arbitrary θ angles, has been reported in this paper to verify the overall algorithm’s performance. The results obtained for different signal-to-noise ratio (SNR) levels indicate that two-row ESPAR antenna can produce, even for low SNR values, accurate DoA estimation in the horizontal plane without prior knowledge about the elevation direction of the unknown RF signals by using appropriate combinations of only 12 3D antenna radiation patterns.
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Bi, Jingxue, Yunjia Wang, Xin Li, Hongxia Qi, Hongji Cao, and Shenglei Xu. "An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering." Sensors 18, no. 8 (2018): 2502. http://dx.doi.org/10.3390/s18082502.
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The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods.
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Perihanoglu, G. M., and H. Karaman. "SPATIAL PREDICTION OF RECEIVED SIGNAL STRENGTH FOR CELLULAR COMMUNICATION USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOURS REGRESSION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W9-2024 (March 8, 2024): 291–97. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w9-2024-291-2024.
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Abstract. Signal strength maps are of great importance for cellular system providers in network planning and operation. Accurate prediction of signal strength is important for solving problems such as link quality. In this study, Received Signal Strength (RSS) prediction model is proposed for the 900 MHz band in the Van Yüzüncü Yıl University campus environment by using machine learning regression methods such as K- Nearest Neıghbours (KNN) and Support Vector Regression (SVR) together with Geographic Information Systems. For the training of this model, signal strength values taken from the RF Spectrum Analyser at different locations and distances were used. In addition, spatial data sets such as the digital elevation model, location of base stations and measurement stations, building heights and location, and land use/cover were used in the model. The effect of these data sets on RSS power is included in the model. The model aims to predict RSS accurately, visualize the estimated signal strength, and analyze the signal field strength coverage. Different kernels from the SVR model such as Polynomial, , and Sigmoid were tested. To increase the success of the model, appropriate parameter values were selected and configured according to SVR and KNN methods. For 900 MHz, the performances of SVR and KNN models were compared and the results of the models were verified using root mean squares (RMSE). Among the measured data, the lowest prediction is found in KNN Manhattan. According to the results of the simulation was observed that the SVR model created with spatial data performs better for Signal Strength. Finally, the lowest RMSE value (1.71 dB) was obtained from the Sigmoid kernel in the best signal strength estimation SVR model. The SVR model is recommended for Campus Area signal strength estimation.
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Khan, Malak Abid Ali, Hongbin Ma, Zia Ur Rehman, Ying Jin, and Atiq Ur Rehman. "Evaluation of Distributed Machine Learning Model for LoRa-ESL." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 4 (2023): 700–709. http://dx.doi.org/10.20965/jaciii.2023.p0700.
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To overcome the previous challenges and to mitigate the retransmission and acknowledgment of LoRa for electric shelf labels, the data parallelism model is used for transmitting the concurrent data from the network server to end devices (EDs) through gateways (GWs). The EDs are designated around the GWs based on machine clustering to minimize data congestion, collision, and overlapping during signal reception. Deployment and redeployment of EDs in the defined clusters depend on arithmetic distribution to reduce the near-far effect and the overall saturation in the network. To further improve the performance and analyze the behavior of the network, constant uplink power for signal-to-noise (SNR) while dynamic for received signal strength (RSS) has been proposed. In contrast to SNR, the RSS indicator estimates the actual position of the ED to prevent the capture effect. In the experimental implementation, downlink power at the connected EDs in the clusters illustrates higher values than the defined threshold.
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Akinbolati, Akinsanmi, and Moses Oludare Ajewole. "EFFECT OF SOME RADIO CLIMATIC FACTORS ON DIGITAL TERRESTRIAL TELEVISION SIGNAL IN A SAHEL SAVANNAH CITY OF NIGERIA." FUDMA JOURNAL OF SCIENCES 4, no. 2 (2020): 111–18. http://dx.doi.org/10.33003/fjs-2020-0402-150.
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This study investigates the effect of some radio climatic factors on the Received Signal Strength (RSS) of a Digital Terrestrial Television Broadcast Station (DTTBS) in Katsina City, Nigeria. The RSS was measured at intervals along selected routes around the station using a digital signal strength meter. GPS receiver was used to log the line of sight, geographic coordinates and heights of data points from the station. The atmospheric pressure, temperature and humidity corresponding to data points were concurrently measured using a compact weather station whereas the corresponding surface radio refractivity (Ns) values were computed. Data were collected during dry and wet season months’ in the year 2017. Results shown that, Ns is inversely proportional to RSS irrespective of routes and seasons with correlation coefficients of -0.51 and -0.57 during dry and wet season months respectively. Higher mean value of, 358.50 (N-units) was deduced during wet compared to the dry season months of 301.20 (N-units). Average atmospheric pressure of 951.92 and 949.61 (hPa) and as well as humidity values of 32.25 and 77.93 %RH were deduced during dry and wet season months’ respectively. Findings also show that pressure is inversely proportional to RSS. The overall effects of pressure, temperature and humidity on RSS is better understood using the effect of Ns since the latter is derived from those parameters. This study concludes that the specified radio climatic factors have attenuation effect on UHF signal and should be put into consideration when planning link’s design and power budgets on the UHF band.
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Neunteufel, Daniel, Stefan Grebien, and Holger Arthaber. "Indoor Positioning of Low-Cost Narrowband IoT Nodes: Evaluation of a TDoA Approach in a Retail Environment." Sensors 22, no. 7 (2022): 2663. http://dx.doi.org/10.3390/s22072663.
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The localization of internet of things (IoT) nodes in indoor scenarios with strong multipath channel components is challenging. All methods using radio signals, such as received signal strength (RSS) or angle of arrival (AoA), are inherently prone to multipath fading. Especially for time of flight (ToF) measurements, the low available transmit bandwidth of the used transceiver hardware is problematic. In our previous work on this topic we showed that wideband signal generation on narrowband low-power transceiver chips is feasible without any changes to existing hardware. Together with a fixed wideband receiving anchor infrastructure, this facilitates time difference of arrival (TDoA) and AoA measurements and allows for localization of the fully asynchronously transmitting nodes. In this paper, we present a measurement campaign using a receiver infrastructure based on software-defined radio (SDR) platforms. This proves the actual usability of the proposed method within the limitations of the bandwidth available in the ISM band at 2.4 GHz. We use the results to analyze the effects of possible anchor placement schemes and scenario geometries. We further demonstrate how this node-to-infrastructure-based localization scheme can be supported by additional node-to-node RSS measurements using a simple clustering approach. In the considered scenario, an overall positioning root-mean-square error (RMSE) of 2.19 m is achieved.
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FASYA, ISTI, AKHMAD HAMBALI, and BRIAN PAMUKTI. "Penentuan Indoor Positioning System (IPS) menggunakan Metode Received Signal Strength (RSS) untuk Visible Light Communication (VLC)." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 11, no. 2 (2023): 325. http://dx.doi.org/10.26760/elkomika.v11i2.325.
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ABSTRAKPenentuan posisi di dalam ruangan merupakan hal yang penting untuk menjadikan petunjuk di dalam ruangan. Banyak metode lain yang sudah dilakukan penelitian tetapi ada beberapa kendala seperti tidak dapat digunakan di area sensitive dan biaya yang mahal. Pada penelitian ini kami mengusulkan untuk melakukan simulasi penentuan posisi menggunakan metode Received Signal Strength (RSS) dengan Light Emitting Diode (LED) dan Positive Intrinsic Negatif (PIN). Dengan asumsi pada ruangan tertutup yang berukuran 5 × 5 × 3 m3, kami melakukan simulasi 4 skenario posisi transmitter. Pada setiap skenario memiliki 3 buah transmitter pada level daya 1 watt yang lebih rendah dari penelitian-penelitian sebelumnya. Dari hasil simulasi yang ekstensif menyatakan bahwa, pada skenario 1 memiliki hasil akurasi 27,97%, skenario 2 sebesar 43,33%, skenario 3 sebesar 86,84%, dan skenario 4 dengan hasil 21,15%.Kata kunci: indoor positioning system, received signal strength, visible light communication ABSTRACTDetermining the position in the room is important to be a guide in the room. Many other methods have been researched, but there are some obstacles such as not being able to be used in sensitive areas and expensive. In this study, we suggest simulating buying a position using the Received Signal Strength (RSS) method with Light Emitting Diode (LED) and Positive Intrinsic Negative (PIN). Assuming that the room is 5 × 5 × 3 m3 closed, we simulated 4 transmitter position scenarios. Each scenario has 3 transmitters at a power level of 1 watt which is lower than previous studies. From the extensive simulation results stated that scenario 1 has an accuracy of 27.97%, scenario 2 is 43.33%, scenario 3 is 86.84%, and scenario 4 is 21.15%.Keywords: indoor positioning system, received signal strength, visible light communication
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Janssen, Thomas, Rafael Berkvens, and Maarten Weyn. "RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell." Sensors 20, no. 21 (2020): 6172. http://dx.doi.org/10.3390/s20216172.
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Low Power Wide Area Networks (LPWAN) have the ability to localize a mobile transmitter using signals of opportunity, as a low power and low cost alternative to satellite-based solutions. In this paper, we evaluate the accuracy of three localization approaches based on the Received Signal Strength (RSS). More specifically, the performance of a proximity, range-based and optimized fingerprint-based algorithm is evaluated in a large-scale urban environment using a public Narrowband Internet of Things (NB-IoT) network. The results show a mean location estimation error of 340, 320 and 204 m, respectively. During the measurement campaign, we discovered a mobility issue in NB-IoT. In contrast to other LPWAN and cellular technologies which use multiple gateways or cells to locate a device, only a single cell antenna can be used for RSS-based localization in NB-IoT. Therefore, we address this limitation in the current NB-IoT hardware and software by studying the mobility of the cellular-based 3GPP standard in a localization context. Experimental results show that the lack of handover support leads to increased cell reselection time and poor cell sector reliability, which in turn results in reduced localization performance.
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Kimoto, Ryota, Shigemi Ishida, Takahiro Yamamoto, Shigeaki Tagashira, and Akira Fukuda. "MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics." Sensors 19, no. 7 (2019): 1645. http://dx.doi.org/10.3390/s19071645.
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The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans.
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Laily Yusof, Azita, Ainnur Eiza Mohd Azhar, and Norsuzila Ya’acob. "Performance analysis of handover parameter for natural disaster in LTE-A network." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (2019): 319. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp319-324.
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<span>Natural disaster is an unpredictable event which can cause service communication disruption. The disruption of service communication can be divided into three cases which are power outage, broken backhaul and physical destruction by disaster. This paper focuses on power outage case. Although each base stations equipped with backup batteries, it only last for several hours. After natural disaster hit certain areas, traffic is highly congested due to panic and rescue operation of lifesaving. This scenario will drain the backup batteries quickly. In order to limit the UEs’ connection to the affected base station, this research proposed to adjust Received Signal Strength (RSS) based on parameter called as a distance fraction coefficient, α so that the affected coverage area is reduced when in disaster area situation. The parameter is added in the path loss equation and later in the RSS equation. The numerical results show the RSS improved whereby α=0.2 gives the optimal value and therefore limit the incoming traffic to the affected base station. </span>
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Laily Yusof, Azita, Ainnur Eiza Azhar, and Norsuzila Ya’acob. "Performance evaluation of UE-controlled intelligent handover algorithm for natural disaster." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 24. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp24-30.
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<span>This paper proposes a UE-controlled intelligent handover algorithm for natural disaster. In this handover algorithm, two variables known as modified Received Signal Strength (RSSm) and Left over Power (LoP) are identified. The RSSm is an improved formulation from RSS where distance fraction coefficient has been introduced. The fraction coefficient of 0.2 is used where the affected areas is reduced so that the users can receive good signal quality due to its location near to the base station. Meanwhile, the LoP also has been investigated to control power consumption of base station. In this research, 80% RSSm and 20% LoP has been chosen for the proposed handover algorithm as it can maintain good quality of service (QoS) for all users and also can prolong battery life. From the simulation results obtained, the average number of handovers for the proposed handover algorithm outperformed the conventional natural disaster handover algorithm.</span>
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Puspitasari, Annisa Anggun, and Byung Moo Lee. "A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications." Sensors 23, no. 5 (2023): 2554. http://dx.doi.org/10.3390/s23052554.
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A reconfigurable intelligent surface (RIS) is a development of conventional relay technology that can send a signal by reflecting the signal received from a transmitter to a receiver without additional power. RISs are a promising technology for future wireless communication due to their improvement of the quality of the received signal, energy efficiency, and power allocation. In addition, machine learning (ML) is widely used in many technologies because it can create machines that mimic human mindsets with mathematical algorithms without requiring direct human assistance. Meanwhile, it is necessary to implement a subfield of ML, reinforcement learning (RL), to automatically allow a machine to make decisions based on real-time conditions. However, few studies have provided comprehensive information related to RL algorithms—especially deep RL (DRL)—for RIS technology. Therefore, in this study, we provide an overview of RISs and an explanation of the operations and implementations of RL algorithms for optimizing the parameters of RIS technology. Optimizing the parameters of RISs can offer several benefits for communication systems, such as the maximization of the sum rate, user power allocation, and energy efficiency or the minimization of the information age. Finally, we highlight several issues to consider in implementing RL algorithms for RIS technology in wireless communications in the future and provide possible solutions.
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Laily Yusof, Azita, Ainnur Eiza Azhar, and Norsuzila Ya’acob. "Self-select target neighboring base station assisted handover for natural disaster in LTE-A network." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (2020): 309. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp309-316.
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This paper presents Self-Select Target Neighboring Base Station Assisted Handover for Natural Disaster in LTE-A Network. In this study, two parameters have been introduced which are known as received signal strength of user (RSS) and left over power of base station (LoP) in order to maintained good QoS of UE and prolong battery life of base station when there is power outage. A distance fraction coefficient (α) with value 0.2 has been introduced to RSS expression to improve the signal strength by reducing the area that the UE’s covered. Both parameters are used to calculate weighted-average score (WAS) for selection of potential target base station to avoid more users to connect to the affected base station. From the results, sRSS=0.8 gave the highest WAS with value of WAS=0.84 for users from 1 to 100 compares to other value of sRSS. Moreover, by using no natural disaster condition as reference, Condition 1 (wRSS=80%, wLoP=20% ) with the lowest percentage of improvement (3.75%.) will be chosen as handover condition as it near to base station, avoid overloaded users to the affected base station, hence prolong battery life as it only use 20% of battery usage.
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Chang, Shengming, Youming Li, Yucheng He, and Yongqing Wu. "RSS-Based Target Localization in Underwater Acoustic Sensor Networks via Convex Relaxation." Sensors 19, no. 10 (2019): 2323. http://dx.doi.org/10.3390/s19102323.
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The received signal strength (RSS) based target localization problem in underwater acoustic wireless sensor networks (UWSNs) is considered. Two cases with respect to target transmit power are considered. For the first case, under the assumption that the reference of the target transmit power is known, we derive a novel weighted least squares (WLS) estimator by using an approximation to the RSS expressions, and then transform the originally non-convex problem into a mixed semi-definite programming/second-order cone programming (SD/SOCP) problem for reaching an efficient solution. For the second case, there is no knowledge on the target transmit power, and we treat the reference power as an additional unknown parameter. In this case, we formulate a WLS estimator by using a further approximation, and present an iterative ML and mixed SD/SOCP algorithm for solving the derived WLS problem. For both cases, we also derive the closed form expressions of the Cramer–Rao Lower Bounds (CRLBs) on root mean square error (RMSE). Computer simulation results show the superior performance of the proposed methods over the existing ones in the underwater acoustic environment.
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Zhang, Yuanyuan, Huafeng Wu, Xiaojun Mei, et al. "Two-Phase Robust Target Localization in Ocean Sensor Networks Using Received Signal Strength Measurements." Sensors 21, no. 5 (2021): 1724. http://dx.doi.org/10.3390/s21051724.
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Target localization plays a vital role in ocean sensor networks (OSNs), in which accurate position information is not only a critical need of ocean observation but a necessary condition for the implementation of ocean engineering. Compared with other range-based localization technologies in OSNs, the received signal strength (RSS)-based localization technique has attracted widespread attention due to its low cost and synchronization-free nature. However, maintaining relatively good accuracy in an environment as dynamic and complex as the ocean remains challenging. One of the most damaging factors that degrade the localization accuracy is the uncertainty in transmission power. Besides the equipment loss, the uncertain factors in the fickle ocean environment may result in a significant deviation between the standard rated transmission power and the usable transmission power. The difference between the rated and actual transmission power would lead to an extra error when it comes to the localization in OSNs. In this case, a method that can locate the target without needing prior knowledge of the transmission power is proposed. The method relies on a two-phase procedure in which the location information and the transmission power are jointly estimated. First, the original nonconvex localization problem is transformed into an alternating non-negativity-constrained least square framework with the unknown transmission power (UT-ANLS). Under this framework, a two-stage optimization method based on interior point method (IPM) and majorization-minimization tactic (MMT) is proposed to search for the optimal solution. In the first stage, the barrier function method is used to limit the optimization scope to find an approximate solution to the problem. However, it is infeasible to approach the constraint boundary due to its intrinsic error. Then, in the second stage, the original objective is converted into a surrogate function consisting of a convex quadratic and concave term. The solution obtained by IPM is considered the initial guess of MMT to jointly estimate both the location and transmission power in the iteration. In addition, in order to evaluate the performance of IPM-MM, the Cramer Rao lower bound (CRLB) is derived. Numerical simulation results demonstrate that IPM-MM achieves better performance than the others in different scenarios.
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Abed, Ahmed, and Ikhlas Abdel-Qader. "RSS-Fingerprint Dimensionality Reduction for Multiple Service Set Identifier-Based Indoor Positioning Systems." Applied Sciences 9, no. 15 (2019): 3137. http://dx.doi.org/10.3390/app9153137.
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Indoor positioning systems (IPS) have been recently adopted by many researchers for their broad applications in various Internet of Things (IoT) fields such as logistics, health, construction industries, and security. Received Signal Strength (RSS)-based fingerprinting approaches have been widely used for positioning inside buildings because they have a distinct advantage of low cost over other indoor positioning techniques. The signal power RSS is a function of the distance between the Mobile System (MS) and Access Point (AP), which varies due to the multipath propagation phenomenon and human body blockage. Furthermore, fingerprinting approaches have several disadvantages such as labor cost, diversity (in signals and environment), and computational cost. Eliminating redundancy by ruling out non-informative APs not only reduces the computation time, but also improves the performance of IPS. In this article, we propose a dimensionality reduction technique in a multiple service set identifier-based indoor positioning system with Multiple Service Set Identifiers (MSSIDs), which means that each AP can be configured to transmit N signals instead of one signal, to serve different kinds of clients simultaneously. Therefore, we investigated various kinds of approaches for the selection of informative APs such as spatial variance, strongest APs, and random selection. These approaches were tested using two clustering techniques including K-means and Fuzzy C-means. Performance evaluation was focused on two elements, the number of informative APs versus the accuracy of the proposed system. To assess the proposed system, real data was acquired from within the College of Engineering and Applied Sciences (CEAS) at the Western Michigan University (WMU) building. The results exhibit the superiority of fused Multiple Service Set Identifiers (MSSID) performance over the single SSID. Moreover, the results report that the proposed system achieves a positioning accuracy <0.85 m over 3000 m2, with an accumulative density function (CDF) of 88% with a distance error of 2 m.
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Bala Subramanian, C., and G. Vijayalakshmi. "A Hybrid Privacy Preserving Scheme for Hotspots in Nested NEMO Based VANETs." Applied Mechanics and Materials 573 (June 2014): 605–11. http://dx.doi.org/10.4028/www.scientific.net/amm.573.605.
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Vehicular Ad-hoc NETwork (VANET) is an emerging technology. The integration of NEMO (NETwork MObility) with VANET is known as NEMO based VANETs. This combination supports the communication between the RSU (Road Side Unit) and Vehicles to provide the internet access through the public hotspots inside the public transportation systems. The travelers inside these systems can enjoy full internet access by using different Mobile Network Nodes (MNNs). Due to open nature of wireless system the physical layer attack can easily localize the MNNs by measuring their Received Signal Strength (RSS). The proposed system deals with the power variability and concealment techniques and propose the hybrid privacy preserving scheme to prevent the attackers from localizing the MNNs inside Nested NEMO based VANETs hotspots. The main aim of this proposed system is to make more confusion on the measurement of RSS and, hence, preserve the privacy at physical layer. The simulation is achieved by the integration of Vanet Mobisim and NS-2. In addition, our proposed scheme can be practically implemented due to having many MNNs that select the same fake point and, hence, increase estimation error on RSS measurement by the attacker. Key words: NEMO, Nested NEMO, Privacy, RSS, VANET
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Le, Yanfen, Shijialuo Jin, Hena Zhang, Weibin Shi, and Heng Yao. "Fingerprinting Indoor Positioning Method Based on Kernel Ridge Regression with Feature Reduction." Wireless Communications and Mobile Computing 2021 (January 9, 2021): 1–12. http://dx.doi.org/10.1155/2021/6631585.
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An important goal of indoor positioning systems is to improve positioning accuracy as well as reduce power consumption. In this paper, we propose an indoor positioning method based on the received signal strength (RSS) fingerprint. The proposed method used a certain criterion to select fixed access points (FPs) in an offline phase instead of an online phase for location estimation. Principal component analysis (PCA) was applied to reduce the features of the RSS measurements but retain the most information possible for establishing the positioning model. Then, a kernel-based ridge regression method was used to obtain the nonlinear relationship between the principal components of the RSS measures and the position of the target. We thoroughly investigated the performance of the proposed method in realistic wireless local area network (WLAN) and wireless sensor network (WSN) indoor environments and made comparisons with recently developed methods. The experimental results indicated that the proposed method was less dependent on the density of the reference points and had higher positioning accuracy than the commonly used positioning methods, and it adapts to different application environments.
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Wang, Yifan, Jingxiang Gao, Zengke Li, and Long Zhao. "Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network." Applied Sciences 10, no. 1 (2020): 321. http://dx.doi.org/10.3390/app10010321.
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Currently, indoor locations based on the received signal strength (RSS) of Wi-Fi are attracting more and more attention thanks to the technology’s low cost, low power consumption and wide availability in mobile devices. However, the accuracy of Wi-Fi positioning is limited, due to the signal fluctuation and indoor multipath interference. In order to overcome this problem, this paper proposes a robust and accurate Wi-Fi fingerprint location recognition method based on a deep neural network (DNN). A stacked denoising auto-encoder (SDAE) is used to extract robust features from noisy RSS to construct a feature-weighted fingerprint database offline. We use the combination of the weights of posteriori probability and geometric relationship of fingerprint points to calculate the coordinates of unknown points online. In addition, we use constrained Kalman filtering and hidden Markov models (HMM) to smooth and optimize positioning results and overcome the influence of gross error on positioning results, combined with characteristics of user movement in buildings, both dynamic and static. The experiment shows that the DNN is feasible for position recognition, and the method proposed in this paper is more accurate and stable than the commonly used Wi-Fi positioning methods in different scenes.
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Chen, Rung-Ching, Yu-Cheng Lin, Sheng-Ling Huang, and Qiangfu Zhao. "Using Radio Frequency Identification for Indoor Location Identification Based on Power Level, Signal Strength and Fuzzy Method." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (2011): 1287–98. http://dx.doi.org/10.20965/jaciii.2011.p1287.
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In recent years, there has been a dramatic proliferation of research concerned with Radio Frequency Identification (RFID). RFID technologies are getting considerable attention not only from academic research but also from the applications for enterprise. One of the most important application issues prevailing throughout the last few decades of RFID application research is the indoor position location. Many researchers have used varied technologies to perform the action of indoor position location tracking. In our research, we propose a new method using RFID tags to perform indoor position location tracking. This method uses Received Signal Strength (RSS) to collect signal strengths from reference tags beforehand, and then uses the signal strengths to set up Power Level areas of range by reference tags. Next, using the signal strengths from the reference tags we match signal strengths with track tags. Finally, when the track tags are set up in indoor environments, they can find the position of neighboring reference tags by using the fuzzy set theory and an arithmetic mean to calculate the position location values; with this method we are able to break figures down to track tag position locations. We conducted this experiment to prove that our methodology can provide better accuracy than the LANDMARC system.
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L. N. Nguyen, Thu, Tuan D. Vy, and Yoan Shin. "An Efficient Hybrid RSS-AoA Localization for 3D Wireless Sensor Networks." Sensors 19, no. 9 (2019): 2121. http://dx.doi.org/10.3390/s19092121.
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Wireless sensor networks (WSNs) enable many applications such as intelligent control, prediction, tracking, and other communication network services, which are integrated into many technologies of the Internet-of-Things. The conventional localization frameworks may not function well in practical environments since they were designed either for two-dimensional space only, or have high computational costs, or are sensitive to measurement errors. In order to build an accurate and efficient localization scheme, we consider in this paper a hybrid received signal strength and angle-of-arrival localization in three-dimensional WSNs, where sensors are randomly deployed with the transmit power and the path loss exponent unknown. Moreover, in order to avoid the difficulty of solving the conventional maximum-likelihood estimator due to its non-convex and highly complex natures, we derive a weighted least squares estimate to estimate jointly the location of the unknown node and the two aforementioned channel components through some suitable approximations. Simulation results confirm the effectiveness of the proposed method.
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Shi, Ke, Zhenjie Ma, Rentong Zhang, Wenbiao Hu, and Hongsheng Chen. "Support Vector Regression Based Indoor Location in IEEE 802.11 Environments." Mobile Information Systems 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/295652.
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The wide spread of the 802.11-based wireless technology brings about a good opportunity for the indoor positioning system. In this paper, we present a new 802.11-based indoor positioning method using support vector regression (SVR), which consists of offline training stage and online location stage. The model that describes the relations between the position and the received signal strength (RSS) of the mobile device is established at the offline training stage by SVR, and at the online location stage the exact position is determined by this model. Due to the complex indoor environment, RSS is vulnerable and changeable. To address this issue, data filtering rules obtained through statistical analysis are applied at offline training stage to improve the quality of training samples and thus improve the quality of prediction model. At the online location stage,k-times continuous measurement is utilized to obtain the high quality RSS input, which guarantees the consistency with the training samples and improves the position accuracy of mobile devices. Performance evaluation shows that the proposed method has a higher positioning accuracy compared with the probability and neutral network method, and the demand for the storage capacity and computing power is also low at the same time.
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Costa, Marcelo Salgueiro, Slavisa Tomic, and Marko Beko. "An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks." Sensors 21, no. 5 (2021): 1731. http://dx.doi.org/10.3390/s21051731.
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This work addresses the problem of target localization in three-dimensional wireless sensor networks (WSNs). The proposed algorithm is based on a hybrid system that employs angle of arrival (AOA) and received signal strength (RSS) measurements, where the target’s transmit power is considered as an unknown parameter. Although both cases of a known and unknown target’s transmit power have been addressed in the literature, most of the existing approaches for unknown transmit power are either carried out recursively, or require a high computational cost. This results in an increased execution time of these algorithms, which we avoid in this work by proposing a single-iteration solution with moderate computational complexity. By exploiting the measurement models, a non-convex least squares (LS) estimator is derived first. Then, to tackle its nonconvexity, we resort to second-order cone programming (SOCP) relaxation techniques to transform the non-convex estimator into a convex one. Additionally, to make the estimator tighter, we exploit the angle between two vectors by using the definition of their inner product, which arises naturally from the derivation steps that are taken. The proposed method not only matches the performance of a computationally more complex state-of-the-art method, but it outperforms it for small N. This result is of a significant value in practice, since one desires to localize the target using the least number of anchor nodes as possible due to network costs.
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Magiera, Jarosław. "Detection and Direction-of-Arrival Estimation of Weak Spread Spectrum Signals Received with Antenna Array." Electronics 10, no. 21 (2021): 2566. http://dx.doi.org/10.3390/electronics10212566.
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This paper presents a method for the joint detection and direction of arrival (DOA) estimation of low probability of detection (LPD) signals. The proposed approach is based on using the antenna array to receive spread-spectrum signals hidden below the noise floor. Array processing exploits the spatial correlation between phase-delayed copies of the signal and allows us to evaluate the parameter used to make the decision about the presence of LPD transmission. The DOA estimation is based on the covariance between signals received by sensors for the fixed geometry of the antenna array. Moreover, the paper provides a method for mitigating narrowband interferences prior to signal detection. The presented methods were verified through simulations which proved that the confident detection of a one-second transmission in an additive white Gaussian noise channel is possible even when the noise is 24 dB higher than the power of the received signal. The performance of DOA estimation is analyzed in a wide range of signal-to-noise and interference-to-noise ratios. It is found that the DOA may be estimated with an RMS error not exceeding 10 degrees, even if interference occupies 15% of the analyzed frequency band.
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Vallet García, José. "Characterization of the Log-Normal Model for Received Signal Strength Measurements in Real Wireless Sensor Networks." Journal of Sensor and Actuator Networks 9, no. 1 (2020): 12. http://dx.doi.org/10.3390/jsan9010012.
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Using the classical received signal strength (RSS)-distance log-normal model in wireless sensor network (WSN) applications poses a series of characteristic challenges derived from (a) the model’s structural limitations when it comes to explaining real observations, (b) the inherent hardware (HW) variability typically encountered in the low-cost nodes of WSNs, and (c) the inhomogeneity of the deployment environment. The main goal of this article is to better characterize how these factors impact the model parameters, an issue that has received little attention in the literature. For that matter, I qualitatively elaborate on their effects and interplay, and present the results of two quantitative empirical studies showing how much the parameters can vary depending on (a) the nodes used in the model identification and their position in the environment, and (b) the antenna directionality. I further show that the path loss exponent and the reference power can be highly correlated. In view of all this, I argue that real WSN deployments are better represented by random model parameters jointly accounting for HW and local environmental characteristics, rather than by deterministic independent ones. I further argue that taking this variability into account results in more realistic models and plausible results derived from their usage. The article contains example values of the mean and standard deviation of the model parameters, and of the correlation between the path loss exponent and the reference power. These can be used as a guideline in other studies. Given the sensitivity of localization algorithms to the proper model selection and identification demonstrated in the literature, the structural limitations of the log-normal model, the variability of its parameters and their interrelation are all relevant aspects that practitioners need to be aware of when devising optimal localization algorithms for real WSNs that rely on this popular model.
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Upton, David W., Keyur K. Mistry, Peter J. Mather, et al. "A Review of Techniques for RSS-Based Radiometric Partial Discharge Localization." Sensors 21, no. 3 (2021): 909. http://dx.doi.org/10.3390/s21030909.
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The lifespan assessment and maintenance planning of high-voltage power systems requires condition monitoring of all the operational equipment in a specific area. Electrical insulation of electrical apparatuses is prone to failure due to high electrical stresses, and thus it is a critical aspect that needs to be monitored. The ageing process of the electrical insulation in high voltage equipment may accelerate due to the occurrence of partial discharge (PD) that may in turn lead to catastrophic failures if the related defects are left untreated at an initial stage. Therefore, there is a requirement to monitor the PD levels so that an unexpected breakdown of high-voltage equipment is avoided. There are several ways of detecting PD, such as acoustic detection, optical detection, chemical detection, and radiometric detection. This paper focuses on reviewing techniques based on radiometric detection of PD, and more specifically, using received signal strength (RSS) for the localization of faults. This paper explores the advantages and disadvantages of radiometric techniques and presents an overview of a radiometric PD detection technique that uses a transistor reset integrator (TRI)-based wireless sensor network (WSN).
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Pamukti, Brian, Miftakhul Meiliana Rahmawati, and Nachwan Mufti Adriansyah. "Impact of The Number of Light Emitting Diode Towards The Accuracy in Indoor Positioning System Based on Visible Light Communication." Jurnal Elektronika dan Telekomunikasi 20, no. 2 (2020): 70. http://dx.doi.org/10.14203/jet.v20.70-75.
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This research detects the random position with received signal strength (RSS) method based on the received power in the room with a size of 5×5×3 cubic meter and several numbers of light emitting diode (LEDs). The first scenario uses 4 LEDs, the second scenario uses 6 LEDs and the third scenario uses 8 LEDs. Random points as the detector are placed spread at the room as many as 25 points. The simulation result shows that the use of more LEDs reduces the positioning error and increases the accuracy. If the accuracy reduces, the positioning error increase and affect the detection results. Using 8 LEDs, the number of the detected random point increases 30%, the positioning error is 47%, and the accuracy increase from 33%. In addition, our research is useful for finding the locations of small items such as sensors that are scattered in a closed room.
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Jovalekic, Nikola, Vujo Drndarevic, Ermanno Pietrosemoli, and Iain Zennaro. "Experimental Study of LoRa Transmission over Seawater." Sensors 18, no. 9 (2018): 2853. http://dx.doi.org/10.3390/s18092853.
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Low Power Wide Area Networks (LPWANs) are gaining attention in both academia and industry by offering the possibility of connecting a large number of nodes over extended distances. LoRa is one of the technologies used as a physical layer in such networks. This paper investigates the LoRa links over seawater in two typical scenarios: clear Line-of-Sight (LOS) and obstructed path in two different Industrial, Scientific and Medical (ISM) radio bands: 868 MHz and 434 MHz. We used three different LoRa devices in the experiments: the Own Developed LoRa Transceiver (ODT) and two commercial transceivers. Firstly we investigated transceivers’ Receive Signal Strength Indicator (RSSI) and Signal-to-Noise (SNR) measurement chain linearity and provided correction factors for RSSI to correlate it with actual signal levels received at transceivers’ inputs. Next, we carried out field experiments for three different LoRa Spreading Factors, S F ∈ [ 7 , 10 , 12 ] , within a bandwidth of B W = 125 kHz and Coding Rate C R = 4 / 6 . The experiments showed that LoRa links are fully feasible over seawater at distances at least 22 km long, using only low-cost off-the-shelf rubber duck antennas in LOS path condition in both ISM bands. In addition, we showed that LoRa links can be established over 28 km obstructed LOS oversea path in ISM 434 MHz band, but using costly, higher gain antennas. Furthermore, the laboratory experiments revealed that RSSI is linear in a wide range, up to - 50 dBm, whereas the SNR measurement chain goes into saturation for Received Signal Strength (RSS) values higher than - 100 dBm. These findings enabled accurate interpretation of the results obtained in field experiments.
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Youssif, Mohamed Ibrahim, Amr ElSayed Emam, and Mohamed Abd ElGhany. "Image multiplexing using residue number system coding over MIMO-OFDM communication system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4815. http://dx.doi.org/10.11591/ijece.v9i6.pp4815-4825.
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<p>Image transmission over Orthogonal Frequency-Division Multiplexing (OFDM) communication system is prone to distortion and noise due to the encountered High-Peak-to-Average-Power-Ratio (PAPR) generated from the OFDM block. This paper studies the utilization of Residue Number System (RNS) as a coding scheme for digital image transmission over Multiple-Input-Multiple-Output (MIMO) – OFDM transceiver communication system. The use of the independent parallel feature of RNS, as well as the reduced signal amplitude to convert the input signal to parallel smaller residue signals, enable to reduce the signal PAPR, decreasing the signal distortion and the Bit Error Rate (BER). Consequently, improving the received Signal-to-Noise Ratio (SNR) and enhancing the received image quality. The performance analyzed though BER, and PAPR. Moreover, image quality measurement is achieved through evaluating the Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and the correlation values between the initial and retrieved images. Simulation results had shown the performance of transmission/reception model with and without RNS coding implementation.</p><p> </p>
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Khalil, Hisham, Saeed Ur Rahman, Inam Ullah, et al. "A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications." Drones 6, no. 12 (2022): 372. http://dx.doi.org/10.3390/drones6120372.
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This paper presents a UAV-swarm-communication model using a machine-learning approach for search-and-rescue applications. Firstly, regarding the communication of UAVs, the receive signal strength (RSS) and power loss have been modeled using random forest regression, and the mathematical representation of the channel matrix has also been discussed. The second part consisted of swarm control modeling of UAVs; however, a dataset for five types of triangular swarm formations was generated, and K-means clustering was applied to predict the cluster. In order to obtain the correct swarm formation, the dendrogram of all types was investigated. Finally, the heat map and contour were plotted for all kinds of swarm clusters. Furthermore, it was observed that the RSS of proposed swarms had good agreement with swarm distances.
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Zhang, Bingxin, Guopeng Zhang, Weice Sun, and Kun Yang. "Task Offloading with Power Control for Mobile Edge Computing Using Reinforcement Learning-Based Markov Decision Process." Mobile Information Systems 2020 (February 14, 2020): 1–6. http://dx.doi.org/10.1155/2020/7630275.
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This paper proposes an efficient computation task offloading mechanism for mobile edge computing (MEC) systems. The studied MEC system consists of multiple user equipment (UEs) and multiple radio interfaces. In order to maximize the number of UEs benefitting from the MEC, the task offloading and power control strategy for a UE is optimized in a joint manner. However, the problem of finding the optimal solution is NP-hard. We then reformulate the problem as a Markov decision process (MDP) and develop a reinforcement learning- (RL-) based algorithm to solve the MDP. Simulation results show that the proposed RL-based algorithm achieves a near-optimal performance compared to the exhaustive search algorithm, and it also outperforms the received signal strength- (RSS-) based method no matter from the standpoint of the system (as it leads to a larger number of beneficial UEs) or an individual (as it generates a lower computation overhead for a UE).
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Havedanloo, Saeed, and Hamid Reza Karimi. "Improving the Performance Metric of Wireless Sensor Networks with Clustering Markov Chain Model and Multilevel Fusion." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/783543.
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The paper proposes a performance metric evaluation for a distributed detection wireless sensor network with respect to IEEE 802.15.4 standard. A distributed detection scheme is considered with presence of the fusion node and organized sensors into the clustering and non-clustering networks. Sensors are distributed in clusters uniformly and nonuniformly and network has multilevel fusion centers. Fusion centers act as heads of clusters for decision making based on majority-like received signal strength (RSS) with comparison the optimized value of the common threshold. IEEE 802.15.4 Markov chain model derived the performance metric of proposed network architecture with MAC, PHY cross-layer parameters, and Channel State Information (CSI) specifications while it is including Path-loss, Modulation, Channel coding and Rayleigh fading. Simulation results represent significant enhancement on performance of network in terms of reliability, packet failure, average delay, power consumption, and throughput.
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Yang, Heekwon, Byeol Kim, Joosung Lee, Yonghan Ahn, and Chankil Lee. "Advanced Wireless Sensor Networks for Sustainable Buildings Using Building Ducts." Sustainability 10, no. 8 (2018): 2628. http://dx.doi.org/10.3390/su10082628.
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The communication technology ZigBee has been widely adopted in wireless sensor networks (WSNs) for a wide range of industrial applications. However, although ZigBee provides low-power, low-cost mesh networking, it cannot guarantee steady and predictable network performance as channels are time-variant and highly attenuated by man-made obstacles. The networks also suffer from interference, especially in the important 2.4 GHz industrial, scientific, and medical (ISM) band. These degraded channel characteristics increase the number of hops, thus increasing both the packet error rate and transmission delays. In this paper, we report the deployment of a ZigBee-based WSN inside an existing building duct system utilized for intelligent waste collection in an industrial environment. The Received Signal Strength (RSS) and path losses were measured, revealing that the duct communication channel acts as a very effective waveguide, providing a more reliable and consistent network performance than conventional free space channels.
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Althobaiti, Turke, Ruhul Amin Khalil, and Nasir Saeed. "Robust ISAC Localization in Smart Cities: A Hybrid Network Approach for NLOS Challenges with Uncertain Parameters." Journal of Sensor and Actuator Networks 13, no. 1 (2023): 2. http://dx.doi.org/10.3390/jsan13010002.
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Accurate localization holds paramount importance across many applications within the context of smart cities, particularly in vehicular communication systems, the Internet of Things, and Integrated Sensing and Communication (ISAC) technologies. Nonetheless, achieving precise localization remains a persistent challenge, primarily attributed to the prevalence of non-line-of-sight (NLOS) conditions and the presence of uncertainties surrounding key wireless transmission parameters. This paper presents a comprehensive framework tailored to address the intricate task of localizing multiple nodes within ISAC systems significantly impacted by pervasive NLOS conditions and the ambiguity of transmission parameters. The proposed methodology integrates received signal strength (RSS) and time-of-arrival (TOA) measurements as a strategic response to effectively overcome these substantial challenges, even in situations where the precise values of transmitting power and temporal information remain elusive. An approximation approach is judiciously employed to facilitate the inherent non-convex and NP-hard nature of the original estimation problem, resulting in a notable transformation, rendering the problem amenable to a convex optimization paradigm. The comprehensive array of simulations conducted within this study corroborates the efficacy of the proposed hybrid cooperative localization method by underscoring its superior performance relative to conventional approaches relying solely on RSS or TOA measurements. This enhancement in localization accuracy in ISAC systems holds promise in the intricate urban landscape of smart cities, offering substantial contributions to infrastructure optimization and service efficiency.
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Sarfraz, Mubashar, Sheraz Alam, Sajjad A. Ghauri, et al. "Random Graph-Based M-QAM Classification for MIMO Systems." Wireless Communications and Mobile Computing 2022 (April 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/9419764.
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Automatic modulation classification (AMC) has been identified to perform a key role to realize technologies such as cognitive radio, dynamic spectrum management, and interference identification that are arguably pivotal to practical SG communication networks. Random graphs (RGs) have been used to better understand graph behavior and to tackle combinatorial challenges in general. In this research article, a novel modulation classifier is presented to recognize M-Quadrature Amplitude Modulation (QAM) signals using random graph theory. The proposed method demonstrates improved recognition rates for multiple-input multiple-output (MIMO) and single-input single-output (SISO) systems. The proposed method has the advantage of not requiring channel/signal to noise ratio estimate or timing/frequency offset correction. Undirected RGs are constructed based on features, which are extracted by taking sparse Fourier transform (SFT) of the received signal. This method is based on the graph representation of the SFT of the 2nd, 4th, and 8th power of the received signal. The simulation results are also compared to existing state-of-the-art methodologies, revealing that the suggested methodology is superior.
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Soni, Ankit, Raksha Upadhyay, and Abhay Kumar. "AvDR-Based Wireless Secure Key Generation with Colored Noise for IoT." Fluctuation and Noise Letters 19, no. 01 (2019): 2050013. http://dx.doi.org/10.1142/s0219477520500133.
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Physical layer key generation exploiting inherent channel randomness is an open research area in securing the networks with resource constraint nodes; therefore reduction of numerical computation is desirable to save battery power. However, the correlated components due to colored noise also affect the system performance. In this work, we consider the correlated colored noise components along with the additive white Gaussian noise (AWGN) in the wireless channel and analyze the effect of these correlated components on the system performance. We further propose a hybrid averaging and dimensionality reduction (AvDR), based received signal strength (RSS) preprocessing which is the combination of moving window averaging (Av) and principal component analysis (PCA) as dimensionality reduction technique (DR) to improve the system performance. Further, the system performance was evaluated by numerical simulations, and it is observed that the same improvement in system performance is achieved by generating keys from a fewer number of points selected after PCA as compared to processing all the points. Picking a few of the points in the data sequence instead of all reduces the total number of numerical calculations and saves system power, which is the primary requirement of resource constraint networks like the IoT.
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Shen, Shengqiang, Jose Miguel Menéndez Sánchez, Shiyin Li, and Heidi Steendam. "Pose Estimation for Visible Light Systems Using a Quadrature Angular Diversity Aperture Receiver." Sensors 22, no. 14 (2022): 5073. http://dx.doi.org/10.3390/s22145073.
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The quadrature angular diversity aperture (QADA) receiver, consisting of a quadrant photodiode (QPD) and an aperture placed above the QPD, has been investigated for pose estimation for visible light systems. Current work on pose estimation for the QADA receiver uses classical camera sensor algorithms well known in computer vision. To this end, however, the light spot center first has to be obtained based on the RSS. However, this is less straightforward than for camera sensors, as in contrast to such sensors where the relationships are linear, the RSS output from the QADA is a non-linear function of the light spot position. When applying closed form solutions or iterative methods for cameras on a QADA, the non-linearity will degrade their performance. Furthermore, since in practice the aperture is not always perfectly aligned with the QPD, a procedure to calibrate the receiver is needed. Current work on calibration requires additional sophisticated equipment to measure the pose during calibration, which increases the difficulty of implementation. In this paper, we target the above problems for pose estimation and calibration of the QADA receiver. To this end, we first study the effect of the strategy of differencing and normalization on the probability density function (PDF), a commonly applied strategy for the QPD’s robustness against RSS variation, and it is shown that the applied strategy results in a complex PDF, which makes an effective and efficient estimation hard to achieve. Therefore, we derive an approximated PDF in a simple closed-form, based on which the calibration and the pose estimation algorithms using the least squares principle are proposed. The proposed calibration does not require any information about the pose of the receiver and is robust to variation of the received power and imperfect knowledge of the radiation pattern of the LED, making it easy to implement. We also derive the corresponding Cramér-Rao lower bound on the misalignment to benchmark the performance of the misalignment and to serve as an indicator to determine the required signal-to-noise ratio (SNR) or number of LEDs to obtain a desired accuracy. The calibration and pose estimation are evaluated by means of a Monte Carlo simulation. Computer simulations show that this theoretical bound is close to the RMSE of the proposed estimator and that the proposed pose estimator outperforms the PnP algorithm.
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Bie, Qingyu, Yuhan Zhang, Yufeng He, and Yilin Lin. "Joint Base Station Selection and Power Allocation Design for Reconfigurable Intelligent Surface-Aided Cell-Free Networks." Electronics 13, no. 9 (2024): 1688. http://dx.doi.org/10.3390/electronics13091688.
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Cell-free (CF) networks can reduce cell boundaries by densely deploying base stations (BSs) with additional hardware costs and power sources. Integrating a reconfigurable intelligent surface (RIS) into CF networks can cost-effectively increase the capacity and coverage of future wireless systems. This paper considers an RIS-aided CF system where each user is supported by a devoted RIS and can establish connections with multiple BSs for coherent transmission. Specifically, each RIS can enhance signal transmission between users and their selected BSs through passive beam-forming, but also randomly scattered signals from other non-selected BSs to users, causing additional signals and interference in the network. To gain insights into the system performance, we first derive the average signal-to-interference-plus-noise ratio (SINR) received by each user in a closed-form expression. Subsequently, we formulate an optimization problem aimed at maximizing the weighted sum-SINR of all users in the RIS-CF network. This optimization considers both BS transmit power allocation and BS selections as variables to be jointly optimized. To tackle the complexity of this nonconvex optimization problem, we develop an innovative two-layer iterative approach that offers both efficiency and efficacy. This algorithm iteratively updates the transmit power allocation and BS selections to converge to a locally optimal solution. Numerical results demonstrate significant performance improvement for the RIS-CF network using our proposed scheme. These results also highlight the effectiveness of jointly optimizing BS transmit power allocation and BS selections in the RIS-CF network.
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Gonsioroski, L. H., and L. da Silva Mello. "Measurements of Building Transmission Loss and Delay Spread at 2.5 GHz." International Journal of Antennas and Propagation 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/683941.
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This paper presents the results of measurements of signal transmission loss at 2.5 GHz through 10 urban buildings. This allows the characterization of different types of buildings by effective attenuation constants and consideration of the contribution of the transmitted signal in microcell coverage predictions. Power delay profiles (PDPs) of the received signal were also measured and used to determine the time dispersion parameters of the channel, including the mean excess delay and the rms delay spread.
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Lutfi Hadi Azizul Adry, Muhammad Alif Najmi Ahmad Tarmizi, Rafiza Ruslan, Ahmad Yusri Dak, and Rashidah Ramle. "Smart Traffic Light Prototype Implementation using LoRa Technology." Applied Mathematics and Computational Intelligence (AMCI) 12, no. 4 (2023): 66–75. http://dx.doi.org/10.58915/amci.v12i4.350.
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Smart traffic light systems have the potential to significantly improve traffic flow and safety in both urban and suburban areas. Most smart traffic lights were deployed in a city with adequate infrastructure. However, building a smart traffic light in suburban areas can be challenging due to the limited availability of communication infrastructure. This paper aims to build and implement a smart traffic light system in a suburban area by incorporating LoRa (Long Range) technology for communication. LoRa is a low-power wireless technology that allows long-distance communication between devices and has low data rates and power consumption. It was created especially for Internet of Things (IoT) devices that run on batteries. The peer-to-peer method was used, which utilized three LoRa nodes for enabling the communication between traffic lights. Usability testing was conducted to determine the impact of the smart traffic light system on traffic flow and safety. The result of the testing shows that the system is necessary to improve traffic management in suburban areas, and the respondents were positive about its potential benefits. Received Signal Strength (RSS) was measured in two different environments: paddy field and residential areas in Kampung Alor Ara, Arau, Perlis. Results show a slight difference in RSS indicator versus distance for the placement of traffic lights in three-junction lanes. The optimum distance between any two traffic lights is 100m in the paddy field area. While for residential areas, the optimum distance is only 50m. The successful implementation of this prototype demonstrates the potential of LoRa technology in enhancing traffic management in suburban areas. Further research such as implementing artificial intelligence (AI) and utilizing advanced LoRa devices and antennas is needed to explore the scalability and long-term sustainability of the system.
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Ebrahimi Jarihani, Arash, Sahar Sarafi, Michael Koeberle, Johannes Sturm, and Andrea M. Tonello. "A 16 Gbps, Full-Duplex Transceiver over Lossy On-Chip Interconnects in 28 nm CMOS Technology." Electronics 9, no. 5 (2020): 717. http://dx.doi.org/10.3390/electronics9050717.
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A high-speed full-duplex transceiver (FDT) over lossy on-chip interconnects is presented. The FDT employs a hybrid circuit to separate the inbound and outbound signals from each other and also performs echo-cancellation with the help of the main and the auxiliary drivers. A hybrid MOS device is utilized for impedance matching and conversion of the received voltage signal into a current signal for amplification. Moreover, a compensation capacitance ( C c ) is used at the output of the main driver to minimize the residual echo signal and achieve a higher data rate. The entire FDT architecture has been designed in TSMC 28 nm CMOS standard process with 0.9 V supply voltage. The performance results validate a 16 Gbps FD operation with a root-mean-square (RMS) jitter of 16.4 ps, and a power efficiency of 0.16 pJ/b/mm over a 5 mm on-chip interconnect without significant effect due to process-voltage-temperature (PVT) variations. To the best knowledge of the authors, this work shows the highest achievable full-duplex data rate, among the solutions reported in the literature to date, yet with low complexity, low layout area of 1581 μ m 2 and competitive power efficiency.
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Mohr, Werner. "Coverage Improvements for Sub-Terahertz Systems Under Shadowing Conditions." Journal of Telecommunications and Information Technology, no. 3 (September 1, 2023): 1–10. http://dx.doi.org/10.26636/jtit.2023.3.1301.
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Radio propagation in the millimeter wave and sub-terahertz domain is heavily affected by shadowing conditions. The communication link is blocked without any additional technical means being used. Coverage improvements can be provided by using reflectors, RIS arrays, and repeaters to direct radio waves around corners or obstacles. These concepts show different performance and complexity levels affecting their network deployment. This paper investigates the achievable radio range or the received power to compare specific deployment concepts under realistic propagation conditions. Overall, the repeater solution provides either the largest radio range or the lowest necessary total transmit power compared to reflectors or RIS arrays and, thereby, is the most sustainable approach. A RIS array requires an additional centralized signal processing capacity for calculating optimized RIS settings and results in the highest level of network deployment complexity.
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Moon, Hyeon-Jin, Young Jin Kim, Jae Won Chang, and Seung-Il Moon. "Decentralised Active Power Control Strategy for Real-Time Power Balance in an Isolated Microgrid with an Energy Storage System and Diesel Generators." Energies 12, no. 3 (2019): 511. http://dx.doi.org/10.3390/en12030511.
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Remote microgrids with battery energy storage systems (BESSs), diesel generators, and renewable energy sources (RESs) have recently received significant attention because of their improved power quality and remarkable capability of continuous power supply to loads. In this paper, a new proportional control method is proposed using frequency-bus-signaling to achieve real-time power balance continuously under an abnormal condition of short-term power shortage in a remote microgrid. Specifically, in the proposed method, the frequency generated by the grid-forming BESS is used as a global signal and, based on the signal, a diesel generator is then controlled indirectly. The frequency is controlled to be proportional to the AC voltage deviation of the grid-forming BESS to detect sudden power shortages and share active power with other generators. Unlike a conventional constant-voltage constant-frequency (CVCF) control method, the proposed method can be widely applied to optimise the use of distributed energy resources (DERs), while maintaining microgrid voltages within an allowable range, particularly when active power balance cannot be achieved only using CVCF control. For case studies, a comprehensive model of an isolated microgrid is developed using real data. Simulation results are obtained using MATLAB/Simulink to verify the effectiveness of the proposed method in improving primary active power control in the microgrid.