Constrained Weighted Least Squares Location Algorithm Using Received Signal Strength Measurements

Determine the location of a target has gained considerable interest over the past few years. The Received Signal Strength(RSS) measurements and Differential RSS(DRSS) measurements can be converted to distance or distance ratio estimates for constructing a set of linear equations. Based on these linear equations, a constrained weighted least Squares(CWLS) algorithm for target localization is derived. In addition, an iterative technique based on Newton's method is utilized to give a solution. The covariance and bias of the CWLS algorithm is derived using perturbation analysis. Simulation shows that the proposed estimator achieves better performance than existing algorithms with reasonable complexity.

Multi-Variable Flocking Control for Multi-Agent Systems via a Received Signal Strength Indicator

A novel flocking control approach is proposed for multi-agent systems by integrating the variables of velocities, motion directions, and positions of agents. A received signal strength indicator （RSSI） is applied as a variable to estimate the inter-distance between agents. A key parameter that contains the local information of agents is defined, and a multi-variable controller is proposed based on the parameter. For the position control of agents, the RSSI is introduced to substitute the distance as a control variable in the systems. The advantages of RSSI include that the relative distance between every two agents can be adjusted through the communication quality under different environments, and it can shun the shortage of the limit of sensors. Simulation studies demonstrate the effectiveness of the proposed control approach.

S^3 LQA: A Link Quality Assessment Metric for WSNs Based on Symbol Error and Received Signal Strength

With the rapid evolution of WSNs technology, it is very important to evaluate link quality quickly and accurately, so that the routing protocols can take relevant strategies in time to keep the entire network working steadily and efficiently. However, the issue of layer is still open to research. To tackle this issue, a improving link quality assessment methods on physical novel link quality assessment metric called S3LQA is proposed, which estimates the link quality of wireless sensor networks by CC2420 wireless radio frequency transceiver principles and free space propagation theory. The metric adopts both complete and incomplete packages to improve the evaluation performance effectively based on IEEE802. 15.4 frame format and DSSS-O- QPSK mechanism. The experimental results show that the proposed method can improve energy cost and achieves hatter real-timin nerformance than traditional counting-based （PRR） link aualitv assessment metric.

Accuracy Analysis of Position Estimation Based on Measurements of Received Signal Strength Difference

The performance of a cellular location system based on received signal strength difference （RSSD） is investigated. In the cellular location system, each mobile station needs to measure the signal strength transmitted by surrounding base stations, and sends its measurements to the service base station. Using the strength difference between the service base station and neighboring base stations, the position of a mobile station is estimated. The related Cramer-Rao lower bound （CRLB） on the location error of this method was derived, and numerical simulations are made to discuss the influences of the number of base stations, correlation coefficient of shadowing attenuation, and cell radius on CRLB. The results show that the CRLB is positively correlated with the standard deviation of shadowing attenuation and cell radius, but negatively correlated with the number of base stations and the correlation coefficient of shadowing attenuation. In addition, the CRLB results obtained in this paper were compared with those of the cellular location system based on received signal strength （RSS） measurements, which reveals that the former is more tight.

Analysis of RFID Based Positioning Technique Using Received Signal Strength and Directional Antenna

Automatic robot navigation is being utilized in many industries for the purpose of high speed work delivery. Color follower, fix path follower robots are current solution to this activities but dynamic path configuration is not possible in these robots. Hence new system proposes effective and fully dynamic path follower robots using RFID and directional antenna. Radio Frequency Identification (RFID) system permits automatic identification of objects with RFID tags using radio waves which have been widely used in mobile robot navigation, localization and mapping both in indoor and outdoor environment. This article presents a navigation strategy for autonomous mobile robot using passive RFID system. Proposed robot system is provided with RFID tag functionality which will load tag number and direction instruction. At some turning point, user will put RF tag, this tag will be read by RF reader which is placed on robot. As per direction instruction robot will change the direction and reach to the destination. Also as per the movement, robot will send its GPS location to PC (Personal Computer) which will be displayed on PC. Hence main goal is to provide more reliable and low energy consumption based indoor positioning system which will be achieved using directional antenna.

Received signal strength based indoor positioning algorithm using advanced clustering and kernel ridge regression

We propose a novel indoor positioning algorithm based on the received signal strength(RSS) fingerprint. The proposed algorithm can be divided into three steps, an offline phase at which an advanced clustering(AC) strategy is used, an online phase of approximate localization at which cluster matching is used, and an online phase of precise localization with kernel ridge regression. Specifically, after offline fingerprint collection and similarity measurement, we employ an AC strategy based on the K-medoids clustering algorithm using additional reference points that are geographically located at the outer cluster boundary to enrich the data of each cluster. During the approximate localization, RSS measurements are compared with the cluster radio maps to determine to which cluster the target most likely belongs. Both the Euclidean distance of the RSSs and the Hamming distance of the coverage vectors between the observations and training records are explored for cluster matching. Then, a kernel-based ridge regression method is used to obtain the ultimate positioning of the target. The performance of the proposed algorithm is evaluated in two typical indoor environments, and compared with those of state-of-the-art algorithms. The experimental results demonstrate the effectiveness and advantages of the proposed algorithm in terms of positioning accuracy and complexity.

Sensor selection for received signal strength-based source localization in wireless sensor networks

Generally, localization is a nonlinear problem, while linearization is used to simplify this problem. Reasonable approximations could be achieved when signal-to-noise ratio (SNR) is large enough. Energy is a critical resource in wireless sensor networks, and system lifetime needs to be prolonged through the use of energy efficient strategies during system operation. In this paper, a closed-form solution for received signal strength (RSS)-based source localization in wireless sensor network (WSN) is obtained. A sensor selection method is proposed to improve the localization accuracy as well as to save energy consumption. By selecting only a limited number of sensor nodes based on the model accuracy and geometry structure analysis, localization performance is improved, and energy consumption is reduced. In addition, extensive simulations are presented to demonstrate that the estimation performance with the proposed sensor selection method is better than that without sensor selection.

Indoor Location Algorithm Based on the Measurement of the Received Signal Strength

The“distance-loss”model is amended by inserting a random distance-estimation variable.The estimation error is very small;thus,it does not change the log-normal distribution of the shadowing factor in the model.Then,an iterative method is introduced to reduce the influence of shadowing,and the location estimation based on the received signal strength will be improved.Simulations show that this algorithm is effective.

Design and application of wireless signal strength measurement system on the near-ground

The wireless communication system's performance is greatly constrained by the wireless channel characteristics,especially in some specific environment.Therefore,signal transmission will be greatly impacted even if not in a complicated topography.Testing results show that it is hardly to characterize the radio propagation properties for the antenna installed on the ground.In order to ensure a successful communication,the radio frequency(RF)wireless signal intensity monitor system was designed.We can get the wireless link transmission loss through measuring signal strength from received node.The test shows that the near-ground wireless signal propagation characteristics still can be characterized by the log distance propagation loss model.These results will conduce to studying the transmission characteristic of Near-Earth wireless signals and will predict the coverage of the earth's surface wireless sensor network.

Enhancing Indoor User Localization:An Adaptive Bayesian Approach for Multi-Floor Environments

Indoor localization systems are crucial in addressing the limitations of traditional global positioning system(GPS)in indoor environments due to signal attenuation issues.As complex indoor spaces become more sophisticated,indoor localization systems become essential for improving user experience,safety,and operational efficiency.Indoor localization methods based on Wi-Fi fingerprints require a high-density location fingerprint database,but this can increase the computational burden in the online phase.Bayesian networks,which integrate prior knowledge or domain expertise,are an effective solution for accurately determining indoor user locations.These networks use probabilistic reasoning to model relationships among various localization parameters for indoor environments that are challenging to navigate.This article proposes an adaptive Bayesian model for multi-floor environments based on fingerprinting techniques to minimize errors in estimating user location.The proposed system is an off-the-shelf solution that uses existing Wi-Fi infrastructures to estimate user’s location.It operates in both online and offline phases.In the offline phase,a mobile device with Wi-Fi capability collects radio signals,while in the online phase,generating samples using Gibbs sampling based on the proposed Bayesian model and radio map to predict user’s location.Experimental results unequivocally showcase the superior performance of the proposed model when compared to other existing models and methods.The proposed model achieved an impressive lower average localization error,surpassing the accuracy of competing approaches.Notably,this noteworthy achievement was attained with minimal reliance on reference points,underscoring the efficiency and efficacy of the proposed model in accurately estimating user locations in indoor environments.

基于RSSI变化序列的MAC地址与目标匹配方法

获得真实人物与所持通信设备MAC (Media Access Control)地址的联系对公共和网络安全有重大价值。针对现有方法的不足提出了更好的解决方法。方法布置多个Wi-Fi探针获取通信设备的MAC和实时RSSI (Received Signal Strength Indication)状态变化,构建RSSI状态变化序列;利用目标跟踪获取目标相对多个Wi-Fi探针的距离,构建距离状态变化序列;对2类状态变化序列进行匹配,选取最优匹配对作为目标与所携通信设备MAC地址匹配的结果。实验表明,该方法相比其他方法通用性更强,准确率更高。

Coverage of communication-based sensor nodes deployed location and energy efficient clustering algorithm in WSN

An effective algorithm based on signal coverage of effective communication and local energy-consumption saving strategy is proposed for the application in wireless sensor networks.This algorithm consists of two sub-algorithms.One is the multi-hop partition subspaces clustering algorithm for ensuring local energybalanced consumption ascribed to the deployment from another algorithm of distributed locating deployment based on efficient communication coverage probability（DLD-ECCP）.DLD-ECCP makes use of the characteristics of Markov chain and probabilistic optimization to obtain the optimum topology and number of sensor nodes.Through simulation,the relative data demonstrate the advantages of the proposed approaches on saving hardware resources and energy consumption of networks.

Compressive Sensing Based Wireless Localization in Indoor Scenarios

The sparse nature of location finding in the spatial domain makes it possible to exploit the Compressive Sensing (CS) theory for wireless location.CS-based location algorithm can largely reduce the number of online measurements while achieving a high level of localization accuracy,which makes the CS-based solution very attractive for indoor positioning.However,CS theory offers exact deterministic recovery of the sparse or compressible signals under two basic restriction conditions of sparsity and incoherence.In order to achieve a good recovery performance of sparse signals,CS-based solution needs to construct an efficient CS model.The model must satisfy the practical application requirements as well as following theoretical restrictions.In this paper,we propose two novel CS-based location solutions based on two different points of view:the CS-based algorithm with raising-dimension pre-processing and the CS-based algorithm with Minor Component Analysis (MCA).Analytical studies and simulations indicate that the proposed novel schemes achieve much higher localization accuracy.

Passive Localization of Multiple Sources Using Joint RSS and AOA Measurements in Spectrum Sharing System

In spectrum sharing systems,locating mul-tiple radiation sources can efficiently find out the in-truders,which protects the shared spectrum from ma-licious jamming or other unauthorized usage.Com-pared to single-source localization,simultaneously lo-cating multiple sources is more challenging in prac-tice since the association between measurement pa-rameters and source nodes are not known.More-over,the number of possible measurements-source as-sociations increases exponentially with the number of sensor nodes.It is crucial to discriminate which measurements correspond to the same source before localization.In this work,we propose a central-ized localization scheme to estimate the positions of multiple sources.Firstly,we develop two computa-tionally light methods to handle the unknown RSS-AOA measurements-source association problem.One method utilizes linear coordinate conversion to com-pute the minimum spatial Euclidean distance sum-mation of measurements.Another method exploits the long-short-term memory(LSTM)network to clas-sify the measurement sequences.Then,we propose a weighted least squares(WLS)approach to obtain the closed-form estimation of the positions by linearizing the non-convex localization problem.Numerical re-sults demonstrate that the proposed scheme could gain sufficient localization accuracy under adversarial sce-narios where the sources are in close proximity and the measurement noise is strong.

Reliable energy-efficient routing with novel route update in wireless sensor networks

In this paper we introduce a novel energy-aware routing protocol REPU (reliable, efficient with path update), which provides reliability and energy efficiency in data delivery. REPU utilizes the residual energy available in the nodes and the re-ceived signal strength of the nodes to identify the best possible route to the destination. Reliability is achieved by selecting a number of intermediate nodes as waypoints and the route is divided into smaller segments by the waypoints. One distinct ad-vantage of this model is that when a node on the route moves out or fails, instead of discarding the whole original route, only the two waypoint nodes of the broken segment are used to find a new path. REPU outperforms traditional schemes by establishing an energy-efficient path and also takes care of efficient route maintenance. Simulation results show that this routing scheme achieves much higher performance than the classical routing protocols, even in the presence of high node density, and overcomes simul-taneous packet forwarding.

RSS-Based Selective Clustering Technique Using Master Node for WSN

Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmission.Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span.This paper introduces the RSS-based energy-efficient selective clustering technique using a master node(RESCM)to improve energy utilization using a master node.The master node positioned at the center of the network area and base station(BS)is placed outside the network area.During cluster head(CH)selection,the node with a high RSS value is more likely to become CH.The network is divided into segments according to the distance from the master node.All nodes near BS or master node transmit their data using direct transmission without the clustering process.The simulation results showed that the RESCM method improves the total network lifespan effectively.

Grid-Based Localization Mechanism with Mobile Reference Node in Wireless Sensor Networks

Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location information to estimate their locations, which needs lots of time or costs. In this paper we proposed a localization mechanism using a mobile reference node （MRN） and trilateration in WSNs to reduce the energy consumption and location error. The simulation results demonstrate that the proposed mechanism can obtain more unknown nodes locations by the mobile reference node moving scheme and will decreases the energy consumption and average ocation error.

RSS-Based Indoor Localization System with Single Base Station

The paper proposes an Indoor Localization System(ILS)which uses only one fixed Base Station(BS)with simple non-reconfigurable antennas.The proposed algorithm measures Received Signal Strength(RSS)and maps it to the location in the room by estimating signal strength of a direct line of sight(LOS)signal and signal of the first order reflection from the wall.The algorithm is evaluated through both simulations and empirical measurements in a furnished open space office,sampling 21 different locations in the room.It is demonstrated the system can identify user’s real-time location with a maximum estimation error below 0.7 m for 80%confidence Cumulative Distribution Function(CDF)user level,demonstrating the ability to accurately estimate the receiver’s location within the room.The system is intended as a cost-efficient indoor localization technique,offering simplicity and easy integration with existing wireless communication systems.Unlike comparable single base station localization techniques,the proposed system does not require beam scanning,offering stable communication capacity while performing the localization process.

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.

Leveraging Logical Anchor into Topology Optimization forIndoor Wireless Fingerprinting

The indoor subarea localization has wide application space in dynamic hotzone identification, indoor layout optimization, store dynamic pricing and crowd flowtrend prediction. The ubiquitous mobile devices provide the opportunity for wirelessfingerprinting-based indoor localization services. However, there are two short boardwhere the existing methods have been criticized. One is that a tagging approach requiresa large number of professional surveys for wireless fingerprint construction, whichweakens the scalability of the methods. The other is that the crowdsourcing-basedmethods encounter the cold boot problem in the system initial stage. To address theseissues, the paper proposes a topology optimization approach leveraging the dynamiclogical anchor selection into a subarea localization system. First of all, a newannular-based radio map construction strategy with the feedback selection of logic anchoris designed to release the pressure of site survey. The implementation of this strategyharnesses the characteristics of the indoor building structure and inter subareaoverlapping recognition, without the topology and distribution of physical anchor (e.g.,access points or POIs). Secondly, exploiting the probabilistic support vector machinealgorithm, the target is localized in the corresponding subarea in a real-time pattern.Furthermore, the localization error is calibrated with an error recognition algorithm.Finally, massive experiments are implemented on a prototype system. The results showthat the proposed method can decrease the overhead of the system initialization andachieve higher localization accuracy compared with the existing approaches.