Average Received Signal Strength Research Articles

Enhancing Indoor Positioning Systems Accuracy with Optimal Placement of Wi-Fi Access Points

The indoor positioning system (IPS) has generated a considerable amount of interest in recent times, and the prosperity of the system's development is heavily reliant on its ability to accurately locate objects. The performance measure is significantly impacted by the location of access points (APs). However, the bulk of previous studies have tended to overlook the matter of optimal AP placement and efficient design for IPS due to the dependence on pre-existing installed APs, which were chiefly formulated for coverage objectives. In this investigation, an optimal placement function - which is reliant on mean and variance - has been developed using received signal strength (RSS) measurements data. The performance evaluation in this research is based on experimentation and compared with currently employed placement methods. The results indicate that the most optimal function value for the suggested method is 1.5714, which is substantially smaller than the values for rectangular, triangular, and triangular II, which are 12.468, 5.5364, and 8.5147, respectively. When the recommended placement strategy is employed instead of the existing ones, the weighted K-nearest algorithm (WKNN) for location error, using average RSS as the fingerprint radio map database, yielded a heightened degree of precision.

Uplink CoMP Transmission for Cellular-Connected UAV Networks

In this letter, we study the coordinated multipoint (CoMP) scheme in the uplink cellular-connected UAV network. By considering the LoS and NLoS components in air-to-ground (A2G) transmission, we adopt the strongest average received signal strength (RSS)-based CoMP scheme. We then develop an analytical framework to evaluate the network performance in terms of coverage probability and area spectral efficiency (ASE) for the case of two cooperative base stations (BSs), followed by an extension to a generalized number of cooperative BSs. Compared to the CoMP scheme with single UAV tier and Non-CoMP baseline, the considered CoMP scheme is able to leverage favored Line-of-Sight (LoS) transmission, lower path loss and BS cooperation gain.

Methodology for Safe and Effective Subcutaneous Implantation of Wireless Biotelemetry Sensor Devices in Rodents.

In this work, a methodology for assessing the impact of implantation surgery on laboratory mice on behavior was created. The study included the design of several implants fabricated on various printed circuit board (PCB) technologies with overall diameters between 26-28mm and weights between 4.5-6.5g. 11 adult CD1 mice were implanted with the devices and their behavior was analyzed using common behavioral benchmark tests. The results show that implants designed to be 10% of the animal's body weight showed no adverse effects on mobility or social behavior. These results illustrate a method to identify and reduce the adverse behavioral changes inherent to device implantation. Additional considerations for implant surgery are provided. These results are validated with the implantation of a Bluetooth Low Energy (BLE) wireless sensor tag. The implanted wireless tag showed an average Received Signal Strength Indicator (RSSI) of 62.96dBm with a standard deviation of 4.95dBm and a variance of 24.5 dBm2. The high RSSI and variance values show that the implant was working well inside of the mouse's body and that the mouse was fully recovered and readily exploring its surroundings.Clinical Relevance-This work 1) studies the behavioral impact of implantable wireless biopotential devices. This will help clinical researchers conducting behavioral studies using sensor implants. 2) demonstrates a working implanted BLE wireless model inside of a mouse. Various wireless connectivity metrics are studied.

Coverage Analysis for Cellular-Connected Random 3D Mobile UAVs With Directional Antennas

This letter proposes an analytical framework to evaluate the coverage performance of a cellular-connected unmanned aerial vehicle (UAV) network in which UAV user equipments (UAV-UEs) are equipped with directional antennas and move according to a three-dimensional (3D) mobility model. The ground base stations (GBSs) equipped with practical down-tilted antennas are distributed according to a Poisson point process (PPP). With tools from stochastic geometry, we derive the handover probability and coverage probability of a random UAV-UE under the strongest average received signal strength (RSS) association strategy. The proposed analytical framework allows to investigate the effect of UAV-UE antenna beamwidth, mobility speed, cell association, and vertical motions on both the handover probability and coverage probability. We conclude that the optimal UAV-UE antenna beamwidth decreases with the GBS density, and the omnidirectional antenna model is preferred in the sparse network scenario. What's more, the superiority of the strongest average RSS association over the nearest association diminishes with the increment of GBS density.

Optimized deep learning/kalman filter-based underwater localization in VLC systems

Several applications depend on the localization technique in underwater visible light communication (UVLC) systems, as military, petroleum, and diving fields. Recent research aims to develop the localization system by different methods to obtain the optimum position of the receiver. In this paper, we use Kalman Filter (KF) algorithm with average Received Signal Strength (RSS) technique using optimization. Optimized Deep Learning Models (DLMs) are utilized to improve the system performance, including such as ResNet50V2, InceptionResNetV2, SSD, and RetinaNet. Two channel modeling Weighted Double Gamma Function (WDGF) with a Combination Exponential Arbitrary Power Function (CEAPF) are used for sea water to enhance the UVLC localization system. The obtained results show that using CEAPF channel modeling with ResNetV2 strategy achieves the best accuracy of the localization for different methods. Also, the ResNetV2 outperforms other strategies for using RSS average technique. The RSS with KF and DLM achieves a higher accuracy with ResNetV2 than InceptionResNetV2, RetinaNet and SSD. Using WDGF achieves accuracy less than that in CEAPF where for using KF with average RSS method. Applying the RSS with KF with CEAPF channel modeling improves the performance than using WDGF. We use an automatic hyper-parameter (HP) approach to the Bayesian optimization models ResNet50V2, InceptionResNetV2, SSD, and RetinaNet. The ResNet50V2 based on average RSS technique hybrid with KF in CEAPF channel model achieves 99.99% accuracy, 99.99% area under the curve (AUC), 99.98% precision, 99.89% F1-score, 0.099 RMSE and 0.43 s testing time.

Deep learning/Kalman filter-based underwater localization in VLC systems

There is a huge importance for the localization system in underwater visible light communication (VLC) systems as in petroleum, military and diving fields. To enhance the localization system, we use the Kalman filter (KF) algorithm with average received signal strength (RSS) method to obtain the nearest estimated positions. In this paper, two channel modeling weighted double Gamma functions (WDGF) are applied and a combination exponential arbitrary power function (CEAPF) for enhancing localization in VLC underwater systems. Using the proposed KF enhances the localization by ~ 60% as compared to the than average RSS technique for WDGF channel modeling and ~ 78% for the CEAPF channel modeling. Based on the estimate of received signal strength (RSS) by deep learning models (DLMs), underwater localization utilizing VLC is introduced. Our proposed framework is categorized into two phases. First, data collection is collected based on MATLAB software. Second, the training and testing of DLMs, SSD, RetinaNet, ResNet50V2 and InceptionResNetV2 techniques are applied. The channel gains are the DLMs’ input data set, while the DLMs’ output is the RSS intensity technique coordinates for each detector. The DLMs are then developed and trained using Python software. The ResNet50V2 based on average RSS technique hybrid with KF in CEAPF channel model achieves 99.98% accuracy, 99.97% area under the curve, 98.99% precision, 98.88% F1-score, 0.101 RMSE and 0.32 s testing time.

Development of Wireless Network Indoor Coverage System Based on Optical Fiber Distribution System.

With the continuous development of network communication technology, the domestic network has covered the homes of ordinary people, but the indoor coverage system of the network cable network needs to be further improved. The optical fiber distribution system is a signal coverage system integrating GSM, TD-SCDMA, and other standards. It is mainly composed of system access unit, system expansion unit, and system remote unit. Wireless network is an important communication technology, which is easy to use and can be connected wirelessly over long distances. The purpose of this paper is to study an optical fiber distribution system for indoor coverage design of wireless networks, so as to achieve faster and more efficient coverage. In this paper, a wireless network signal processing algorithm is proposed, and a systematic method for designing the wireless network indoor coverage of the optical fiber distribution system is proposed, and the algorithms are compared and tested. The test results in this paper show that the signal strength obtained by the algorithm in this paper is −54.83 dB, while the traditional artificial design algorithm is only −45.97 dB. In addition, the wireless network base point of the algorithm in this paper is also 3 lower than the traditional one. Moreover, the coverage rate achieved by the algorithm in this paper is 3.17% higher than that of the network cable network base point layout given by the genetic algorithm, and the average received signal strength is 3.34 dB higher. In addition, the algorithm in this paper takes 9.1 s, 11.2 s, and 5.3 s for the three scenarios of underground parking lot, large shopping mall, and residential residential area, while the traditional manual design algorithm takes 87.9 s, 108.8 s, and 51.7 s, respectively. The algorithm in this paper is only about 1/10 of the traditional artificial design algorithm. Therefore, it can be concluded that the coverage effect of the wireless network indoor coverage system of the optical fiber distribution system designed in this paper is significantly improved.

Evaluating Android smartphone’s Bluetooth received signal strength indicator measurements for social distancing

<span>This paper presents a digital solution to reduce transmission of coronavirus disease 2019 (COVID-19) by maintaining social distance between the general public. The object is achieved by using Bluetooth, which is already handy for almost everyone in the general public and comes as a standard feature in smartphones. The present technique uses an indirect method to calculate the range between the two objects. 5,300 samples were collected by adjusting the receiver at a range of angles to calculate the intensity of the Bluetooth signal. The observations are presented in the form of plotted graphs between the number of samples and the average received signal strength indicator (RSSI) value at a particular angle of rotation.</span>

Novel Optimal Multisensor Placement for Indoor Rectilinear Line-of-Sight Coverage

Optimal multisensor placement/deployment for an arbitrary indoor geometry still remains very challenging nowadays. It is preferable to minimize the number of sensors which can still cover the entire indoor space with a balanced load. However, this multi-objective problem is quite complex in practice. In this work, we will study this critical multisensor placement problem mathematically and algorithmically and propose a systematic approach to tackle this problem. In this paper, we focus on the line-of-sight (LoS) coverage within a rectilinear indoor environment and the sensors’ locations are restricted on the perimeter. Our proposed new approach consists of two stages. First, a partitioning algorithm is designed to partition an arbitrary rectilinear geometry into a number (as few as possible) of feasible rectilinear subareas, each of which can be fully covered by a sensor located somewhere on its external perimeter. Second, the average squared Euclidean distance from a sensor to an arbitrary point within its coverage area is taken as an additional objective measure. By minimizing such average squared Euclidean distance, one can find the optimal sensor location. In our new approach, the first stage is related to solving the art gallery problem (AGP) based on discrete optimization while the second stage is based on continuous optimization. Therefore, our proposed new approach involves both continuous and discrete optimization schemes. By simulations, we also show that the number of sensors placed by our new algorithm is equal to or less than the theoretical upper-bound established in the existing AGP study for numerous rectilinear geometries. Moreover, we also show that by minimizing the average squared Euclidean distance, we maximize the average received signal strength indoors using the prevalent Feko wireless-channel emulator. Our proposed new systematic approach can also deal with practical scenarios involving internal obstacle(s) and sensing-range restriction.

Research and Design of Distributed Fire Alarm System of Indoor Internet of Things Based on LoRa

In order to comprehensively improve the sensitivity of fire warning and effectively shorten the warning time, this paper proposes and implements an indoor distributed fire alarm system based on low power wide area network. The system is mainly composed of three parts: a multisensor acquisition node based on LoRa technology, a distributed edge gateway, and a remote user monitoring system. The multisensor collection node obtains environmental parameters such as indoor temperature, smoke concentration, and air quality and then transmits the sensing data to edge gateway by LoRa after preprocessing. The edge gateway is based on an embedded Linux platform and is deployed in distributed state to collect and store data from multiple collection nodes. Besides, edge gateway forwards valid data to the remote user monitoring system by standard MQTT protocol. The user monitoring system displays current deployment area parameters to users in real time and provides early warning prompts based on relevant preset indicators to help the administrator make more accurate decisions on corresponding measures. The system has been deployed and tested in Nanjing Institute of Technology. By sensor calibration experiments, LoRa communication experiments, and system tests in different environments, the experimental results show that the average received signal strength in a small interference space is -104.12 dBm, and the average received signal strength in a noisy signal environment is -57.5 dBm. By setting the optimal transmitting power for each distance, the packet receiving rate can reach more than 95%, and the alarm accuracy can reach 100% under premise of ensuring the lowest power consumption. Finally, this paper conducts a comprehensive performance analysis on the wireless communication performance of environmental collection nodes, multisensor data fusion algorithm, distributed LoRa edge gateway deployment performance, and remote system early warning accuracy.

Survey of Radio Propagation Models for Acoustic Applications in Underwater Wireless Sensor Network

Background: Acoustic waves have a large range of applications in UWSNs from underwater monitoring to disaster management, military surveillance to assisted navigation. Acoustic waves are primarily used for wireless communication in water. But radio waves are more suitable than acoustic waves for many underwater applications (e.g. real-time applications, shallow water applications). Objectives: A propagation model is required to effectively design a radio wave based UWSN. Propagation model predicts the average received signal strength at a given distance from the transmitter and the variability of the signal strength in close spatial proximity to a particular location. Various radio propagation models are developed for air. Methods: The performance of RF-EM waves underwater is not the same as that in the air. Many parameters which have real-value in the air becomes complex valued in seawater. Thus, propagation models for air cannot be directly used to calculate propagation loss underwater. Various radio propagation models are developed for water by Al-Shamaa’a et al., Uribe and Grote, Jiang et al., Elrashidi et al., Hattab et al. Each model has some merits and demerits. Path loss model developed by Al-Shamma’a et al. is a simple model based on attenuation only. Results: Uribe and Grote have introduced distance-dependent attenuation coefficient in path loss calculation. Path loss model by Jiang et al. calculates path loss for freshwater. Model by Hattab et al. is specifically designed for UWSN. According to the authors, it is the first path loss model developed for UWSN. Elrashidi et al. have calculated path loss for freshwater and seawater at 2.4 GHz. The model includes the effect of the reflected signals on the received signal by the receiver node. Conclusion: The paper presents a comparative analysis of these various radio propagation models developed for underwater. Among these models, the radio propagation model by Hattab et al. is more realistic and covers both propagation loss and interface loss. According to the authors, it is the first radio propagation model developed for UWSNs.

Link quality estimation method based on gradient boosting decision tree

In the application of wireless sensor networks, link quality estimation is the primary problem to guarantee the reliable transmission of data and the performance of the upper layer network protocol. In order to accurately evaluate link quality, a link quality estimator based on gradient boosting decision tree (GBDT) was proposed. The physical layer parameter average received signal strength indication, mean link quality indicator and mean signal noise rate is selected as the input of the GBDT estimator and the nonlinear correlation between physical layer parameters and packet received rate is analysed by using the maximum information coefficient method. Considering the influence between outliers and different dimensionality of parameters, we used the boxplot method to carry out smoothing and normalisation processing to reduce the complexity of the estimator. At last, the improved particle swarm optimisation algorithm is used to select the optimal parameter combination in the GBDT estimator. The experimental results show that compared with the support vector machine (SVM) estimator, the estimator of this paper has higher accuracy and stability.

Multi-objective optimization framework complying IEEE 802.15.6 communication standards for wireless body area networks

Wireless body area network (WBAN) routing protocols are primarily designed for improvement of network performance parameters such as network lifetime, throughput and latency. However, these designs do not take into account the health hazards posed due to WBAN radiation. The focus of implementing IEEE 802.15.6 standards is to develop an energy-efficient, reliable and health-conscious wireless communication system around human body. This paper presents the implementation of a clustering-based routing protocol for WBAN that targets in achieving overall optimization of WBAN performance parameters including network lifetime, throughput, latency, node signal power and specific absorption rate (SAR) of human body for emf radiation. The suggested protocol applies a multi-objective NSGA-II optimization heuristic for cluster formation while taking into account various IEEE 802.15.6 standard constraints. A unique chromosome structure has been proposed for NSGA-II population in which each chromosome represents a random network topology for determining the node clusters, cluster heads and node transmission power. Furthermore, three system-level models for conducting multi-objective fitness evaluation of each chromosome have been proposed. These models predict the global transmission energy consumption; the average received signal strength (RSSI) and end to end network latency respectively. The developed NSGA-II model evolves into a non-dominated set of Pareto-optimal network topologies that renders the desired objectives of minimization of network energy consumption and network latency besides maximization of average RSSI. Enhancement in RSSI further leads to a tremendous improvement in the throughput rate. The node transmission power specifications of Pareto-optimal network topologies meet the standard SAR constraints too. The performance results shows that the suggested protocol strictly meets IEEE 802.15.6 compliance.

Reciprocal Formulation of the Modal Expansion Approach for Around‐the‐Corner Propagation in Street Canyons

AbstractIn a recent paper, we have presented an efficient and simple approach to model electromagnetic propagation in urban street canyons, when both the transmitter and the receiver are below the rooftop level. The model is based on the modal expansion approach, and we have shown that it is computationally efficient and that it allows for deriving a straightforward expression of the average received signal strength. In that paper, the Line‐of‐Sight (LoS) propagation case was described and discussed in detail, and the complete derivation of results was provided. Conversely, for the Non‐Line‐of‐Sight (NLoS) around‐the‐corner case, only the final results were presented and briefly discussed. In the present paper, first of all we provide full details on the derivation of formulas for the NLoS case. In addition, we extend the discussion of results and properly analyze the obtained NLoS propagation loss expression. Last but not least, in contrast with our previous propagation model, we here propose a new formulation of the NLoS case that satisfies a key property in electromagnetics, that is, reciprocity, which was previously fulfilled only in the LoS case. The presented models are validated using empirical models, ray‐tracing algorithms, and experimental campaigns.

A Location-Dependent Base Station Cooperation Scheme for Cellular Networks

The link quality in cellular networks strongly depends on the location of the users relative to the serving and interfering base stations (BSs). This paper proposes a location-dependent BS cooperation scheme for general cellular networks, where BSs are modeled using a stationary point process and the Voronoi diagram forms the cell structure. The cooperation scheme is based on the relative average received signal strength from the three strongest BSs. For the channel model where Rayleigh fading and power-law path loss are considered, each cell is partitioned into three regions based on the relative distance to the three nearest BSs: the cell center region, cell edge region, and cell corner region. The area fraction of each region is tuned by the so-called cooperation level $\gamma \in [ {0},{1}]$ . We study the scheme where users in the above regions receive the non-coherent joint transmission from one, two, and three nearest BSs, respectively. As such, the scheme primarily helps users vulnerable to interference. We analyze the signal-to-interference ratio (SIR) in Poisson networks and show that a moderate $\gamma $ jointly improves the average SIR performance and the network fairness.

Modal Expansion Approach for Electromagnetic Propagation in Street Canyons

We present a simple and efficient model to compute electromagnetic (EM) propagation in urban street canyons, when transmitting and receiving antennas are located below the rooftop level. A parallel-plate dielectric hollow waveguide model of the street canyon is proposed, and propagation is analyzed by using the modal expansion approach. Obtained results for the case of line-of-sight (LoS) propagation are substantially equivalent to those obtained with the ray-optics method, but, with respect to the latter, our method presents important advantages: it is more efficient from a computational viewpoint, it allows deriving a very simple and reasonably accurate formulation of the average received signal strength, and it provides a fairly simple way to compute coupling at cross-junctions, exploited to obtain an analogous formulation for the non-LoS case. Obtained results are compared with simulations of a ray-tracing-based EM solver and with experimental measurements available in the literature.

Multi-Constraints Adaptive Link Quality Index Based Mobile-RPL Routing Protocol for Low Power Lossy Networks

The importance of IPv6 Routing Protocol for Low power and Lossy Networks (LLNs), also called RPL, has motivated in the development of a robust and quality of service (QoS) oriented Multi-Constraints Adaptive Link Quality Index (MALQI) based routing protocol. Unlike classical RPL protocols, MALQI enables mobile-RPL while ensuring fault-resilient, reliable and QoS communication over LLNs. MALQI protocol exploits key novelties such as signal strength based mobile node positioning, average received signal strength indicator (ARSSI) and ETX based objective function for fault tolerant best forwarding path selection. The functional architecture of MALQI enables it to be used as the parallel to the link layer RPL that even in the case of link failure can assist efficient data delivery over LLNs. Once detecting link outage, MALQI can execute node discover and best forwarding path selection to assist QoS delivery. Contiki-Cooja based simulation reveals that MALQI based mobile-RPL outperforms other state-of-art routing protocols.

On Base Station Coordination in Cache- and Energy Harvesting-Enabled HetNets: A Stochastic Geometry Study

In this paper, we study the performance of base station (BS) coordination in heterogeneous networks (HetNets) with cache-enabled and renewable energy-powered small cell BSs (SBSs). Macrocell base stations (MBSs) provide basic coverage, while the SBSs, powered by harvested energy, conduct content-aware coordinated transmission to provide high data rate and further improve the network coverage. Specifically, a joint transmission strategy is performed based on the knowledge of the energy states and the cached contents of SBSs, along with the awareness of the availability of channel resources and the average received signal strength (RSS) of the corresponding link. Stochastic geometry is applied to characterize the statistics of the cell load at MBSs and SBSs, as well as the aggregated information and interference signal strength. Then, the average user capacity for the joint transmission is obtained. Additionally, the coverage probability is derived with gamma approximation for the aggregated information and interference signal strength. Analytical results reveal that the average user capacity and coverage probability can be maximized with optimal cache size, energy harvesting rate and cooperative RSS threshold. Finally, extensive numerical and simulation results are provided.

Outdoor Path Loss Predictions Based on Extreme Learning Machine

In a typical outdoor environment, the propagation of radio waves is usually random in nature, to the extent that the characterization of the wireless channel often becomes very difficult. Several models have been developed to predict the average Received Signal Strength (RSS) for specified distance ranges. However, the use of deterministic models requires high computational efficiency while the prediction results of empirical models may not be as accurate as required. On machine learning approach, the performances of multi-layered feed-forward network models are limited by slow convergence and local minimum, such that a global optimal solution is not guaranteed. In this paper, Extreme Learning Machine (ELM) algorithm is considered in the development of an optimal path loss prediction model for outdoor propagation scenario. Single Hidden Layer Feed-forward Neural Networks (SHLFNNs) are trained and tested with the path loss data that were computed based on the RSS data of a commercial 1800 MHz base station located along Lagos-Badagry highway in Nigeria. The training speed, learning effectiveness, and the generalization ability of Artificial Neural Network Back-Propagation (ANN-BP) and ELM algorithms are analysed. Experimental results show that ELM models are 140 times faster to train than the ANN-BP models. On prediction accuracy, the outputs of ELM, ANN-BP, Okumura–Hata, and COST-231 models have Root Mean Squared Error (RMSE) values of 2.896, 2.449, 7.456, and 6.116 dB respectively; and regression coefficient (R) values of 0.959, 0.973, 0.935, and 0.935 respectively, when compared to the target variable of the training dataset. When the models were tested with new input data that were excluded from the training process, RMSE values of 4.250, 6.622, 8.732, and 7.087 respectively; and R values of 0.893, 0.876, 0.904, and 0.904 respectively are obtained. In conclusion, the findings of this study confirm that ELM algorithm guarantees an optimal path loss model with fast training convergence, high prediction accuracy, and good generalization ability for radio network planning and optimization in outdoor environments.

Capacity- and Trust-Aware BS Cooperation in Nonuniform HetNets: Spectral Efficiency and Optimal BS Density

This paper studies base station (BS) cooperation in nonuniform heterogeneous networks. Considering the limited capacity at BS and existence of untrusted small cell BSs (SBSs) in practical scenarios, a novel capacity- and trust-aware BS cooperation strategy is proposed. The BS cooperation is performed in a user centric manner, based on the average received signal strength at users, the capacity of BSs, and the trustworthiness of SBSs. Furthermore, with the proposed BS cooperation, the statistics of aggregate information-signal strength and interference strength are theoretically analyzed, based on stochastic geometry. Then, expressions for spectral efficiency (SE) and area SE (ASE) are analytically derived. In addition, to study the impact of the SBS density on the SE and ASE, the optimal densities of normal SBSs to maximize the SE and ASE are proved to exist and obtained. Finally, simulations and numerical evaluations validate the theoretical analysis and reveal that with the awareness of BS capacity and trustworthiness, optimal cooperative thresholds achieving the maximum SE and ASE exist, which decrease with high path-loss exponent; and in high path-loss fading environment with high existence probability of untrusted SBSs, more normal SBSs are required to be deployed to achieve the maximum SE and ASE performance.