RSSI Localization Research Articles

Experimental Comparison of UAV-Based RSSI and AoA Localization

Experimental Comparison of UAV-Based RSSI and AoA Localization

A Simplified and High Accuracy Algorithm of RSSI-Based Localization Zoning for Children Tracking In-Out the School Buses Using Bluetooth Low Energy Beacon

To avoid problems related to a school bus service such as kidnapping, children being left in a bus for hours leading to fatality, etc., it is important to have a reliable transportation service to ensure students’ safety along journeys. This research presents a high accuracy child monitoring system for locating students if they are inside or outside a school bus using the Internet of Things (IoT) via Bluetooth Low Energy (BLE) which is suitable for a signal strength indication (RSSI) algorithm. The in/out-bus child tracking system alerts a driver to determine if there is a child left on the bus or not. Distance between devices is analyzed for decision making to affiliate the zone of the current children’s position. A simplified and high accuracy machine learning of least mean square (LMS) algorithm is used in this research with model-based RSSI localization techniques. The distance is calculated with the grid size of 0.5 m × 0.5 m similar in size to an actual seat of a school bus using two zones (inside or outside a school bus). The averaged signal strength is proposed for this research, rather than using the raw value of the signal strength in typical works, providing a robust position-tracking system with high accuracy while maintaining the simplicity of the classical trilateration method leading to precise classification of each student from each zone. The test was performed to validate the effectiveness of the proposed tracking strategy which precisely shows the positions of each student. The proposed method, therefore, can be applied for future autopilot school buses where students’ home locations can be securely stored in the system used for references to transport each student to their homes without a driver.

Lacunar Stroke Lesion Extent and Location and White Matter Hyperintensities Evolution 1 Year Post-lacunar Stroke.

Lacunar strokes are a common type of ischemic stroke. They are associated with long-term disability, but the factors affecting the dynamic of the infarcted lesion and the brain imaging features associated with them, reflective of small vessel disease (SVD) severity, are still largely unknown. We investigated whether the distribution, volume and 1-year evolution of white matter hyperintensities (WMH), one of these SVD features, relate to the extent and location of these infarcts, accounting for vascular risk factors. We used imaging and clinical data from all patients [n = 118, mean age 64.9 (SD 11.75) years old] who presented to a regional hospital with a lacunar stroke syndrome within the years 2010 and 2013 and consented to participate in a study of stroke mechanisms. All patients had a brain MRI scan at presentation, and 88 had another scan 12 months after. Acute lesions (i.e., recent small subcortical infarcts, RSSI) were identified in 79 patients and lacunes in 77. Number of lacunes was associated with baseline WMH volume (B = 0.370, SE = 0.0939, P = 0.000174). RSSI volume was not associated with baseline WMH volume (B = 3.250, SE = 2.117, P = 0.129), but predicted WMH volume change (B = 2.944, SE = 0.913, P = 0.00184). RSSI location was associated with the spatial distribution of WMH and the pattern of 1-year WMH evolution. Patients with the RSSI in the centrum semiovale (n = 33) had significantly higher baseline volumes of WMH, recent and old infarcts, than patients with the RSSI located elsewhere [median 33.69, IQR (14.37 50.87) ml, 0.001 ≤ P ≤ 0.044]. But patients with the RSSI in the internal/external capsule/lentiform nucleus experienced higher increase of WMH volume after a year [n = 21, median (IQR) from 18 (11.70 31.54) ml to 27.41 (15.84 40.45) ml]. Voxel-wise analyses of WMH distribution in patients grouped per RSSI location revealed group differences increased in the presence of vascular risk factors, especially hypertension and recent or current smoking habit. In our sample of patients presenting to the clinic with lacunar strokes, lacunar strokes extent influenced WMH volume fate; and RSSI location and WMH spatial distribution and dynamics were intertwined, with differential patterns emerging in the presence of vascular risk factors. These results, if confirmed in wider samples, open potential avenues in stroke rehabilitation to be explored further.

Development of Wearable System to Help Preventing the Spread of Covid-19 in Public Indoor Area

Smart wearables as a part of the Internet of Things nowadays gaining confidence in our daily lives because of its accessibility and simplicity. Today, with the outbreak of Coronavirus around the world, a smart wearable device can become another solution to help slowing the virus spreading by ensuring public health and social measures. In this paper, a system consisting of distance detector and touchless door access is proposed to help the personal, physical and social distancing measures practice in a public indoor area. A BLE positioning method based on RSSI localization is used to ensure the physical distancing around the user. WPA2 and MAC address-based authentication for the touchless door access is used to restrict and trace the visitor of the indoor area. The system is implemented in ESP microcontroller. A proof of concept is conducted to see if the functionality of the system already satisfied the public health and social measures practice. The results show that only registered devices can give a signal to open the door and the device can guarantee the physical distance around the user with 4.51% error in indoor area.

Research on Intelligent Home Design of Internet of Things Based on ZIGBEE

With the rapid development of information technology, improving the intelligent level of family life and modern life style is the trend of future development. Based on the analysis of the development of smart home at home and abroad, a smart home system is designed by combining ZigBee wireless sensor network, gateway technology and mobile terminal remote control app. Firstly, the development and relating technologies of ZigBee are discussed. Secondly, the system overall architecture and data outlier analysis are carried out. Thirdly, the routing algorithm of ZigBee is improved based on neighbour table, and corresponding mathematical model is designed. Fourthly, the location algorithm of ZigBee is improved based on RSSI location algorithm and DV-Hop algorithm. Finally, the system performance is tested based on simulation analysis, results show that the proposed intelligent home system has better performance.

CRAMStrack: Enhanced Nonlinear RSSI Tracking by Using Circular Multi-Sectors

Indoor localization using a Received Signal Strength Indicator (namely, RSSI localization) has been considered a poor measurement for target tracking. The main cause of this inaccurate measurement is that RSSI’s behaviors heavily depend on environmental factors. That is, one significant challenge to localization using RSSI is that the strength of a signal varies with the environment confounding wireless communications power and signal control. In this paper, we propose Circular RSSI And Multi-Sector tracking (CRAMStrack), a novel approach to reducing the uncertainty of RSSI localization by modifying the relationship of RSSI-to-Distance (RtD), based on the sectors of a circle and the position of the tracked target. Traditional RSSI tracking uses one uniform RtD relationship to locate a target whereas CRAMStrack utilizes multiple RtD responses for each wireless sensor. The paper examines CRAMStrack’s tracking ability in a Euclidean space with estimation techniques. Real-world experiments demonstrate CRAMStrack in a testbed environment to locate targets in both stationary, linear, and non-linear movement patterns with single and group-based formations. The track accuracy was about 1.46m for moving targets, while CRAMStrack had a 40% reduction in Root Mean Square Error (RMSE) over Uni-RtD using neighboring sensor information.

An unmanned aerial vehicle-aided node localization using an efficient multilayer perceptron neural network in wireless sensor networks

Localization of sensor node is decisive for many localization-based scenarios of wireless sensor networks (WSNs). Node localization using fixed terrestrial anchor nodes (ANs) equipped with global positioning system (GPS) modules suffers from high deployment cost and poor localization accuracy, because the terrestrial AN propagates signals to the unknown nodes (UNs) through unreliable ground-to-ground channel. However, the ANs deployed in unmanned aerial vehicles (UAVs) with a single GPS module communicate over reliable air-to-ground channel, where almost clear line-of-sight path exists. Thus, the localization accuracy and deployment cost are better with aerial anchors than terrestrial anchors. However, still the nonlinear distortions imposed in propagation channel limit the performance of classical RSSI and least square localization schemes. So, the neural network (NN) models can become good alternative for node localization under such nonlinear conditions as they can do complex nonlinear mapping between input and output. Since the multilayer perceptron (MLP) is a robust tool in the assembly of NNs, MLP-based localization scheme is proposed for UN localization in UAV-aided WSNs. The detailed simulation analysis provided in this paper prefers the MLP localization scheme as they exhibit improved localization accuracy and deployment cost.

An Evaluation on Game Theory Problem of RSSI Localization Technique in Wireless Sensor Networks

The most interesting and challenging research areas in WSNs are routing protocol based on RSSI localization technique in wireless sensor networks. Get-up-and-go safeguarding is the most important experiment for WSNs and make the most of the energy efficiently during routing is an essential requirement and is a demanding task for all other research areas in WSNs. Enhancing the lifespan of the network be contingent on game theory based on RSSI localization technique in wireless sensor networks are the foremost purposes in Machiavellian WSNs since the course-plotting up for theory based on RSSI sensor nodes are battery operated and cannot be replenished or recharged frequently. Here game theory based on RSSI localization for increasing the Wireless Sensor Network life-time using Ant Colony Optimization metaheuristics.

A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks.

In this paper, a mobile anchor node assisted RSSI localization scheme in underwater wireless sensor networks (UWSNs) is proposed, which aims to improve location accuracy and shorten location time. First, to improve location accuracy, we design a support vector regression (SVR) based interpolation method to estimate the projection of sensor nodes on the linear trajectory of the mobile anchor node. The proposed method increases the accuracy of the nonlinear regression model of noisy measured data and synchronously decreases the estimation error caused by the discreteness of measured data. Second, to shorten location time, we develop a curve matching method to obtain the perpendicular distance from sensor nodes to the linear trajectory of the mobile anchor node. The location of the sensor node can be calculated based on the projection and the perpendicular distance. Compared with existing schemes that require the anchor node to travel at least two trajectories, the proposed scheme only needs one-time trajectory to locate sensor nodes, and the location time is shortened with the reduction in the number of trajectories. Finally, simulation results prove that the proposed scheme can obtain more accurate sensor node location in less time compared with the existing schemes.

Smartphone based intelligent indoor positioning using fuzzy logic

Smartphones are indispensable helpers of people in their daily routine, including functions that serve the disabled or aged people finding their outdoor location. This paper presents an experimental investigation of the indoor positioning algorithms based on the signal strength received from the Bluetooth Low Energy (BLE) beacons. We have implemented and compared several positioning algorithms (Proximity Localization, Centroid Localization, Weighted Centroid Localization, Weight-Compensated Weighted Centroid Localization Based on RSSI, Fingerprinting and Trilateration Localization). We also proposed and implemented a fuzzy logic based scheme to select the most fitting algorithm depending upon the size of the room, the number of beacons available and the strength of the signal. We have evaluated our scheme in real-world conditions (office building). The experimental results show that the algorithm of fingerprinting localization is the most suitable one. Finally, we propose a fuzzy logic system for the selection of an indoor localization algorithm based on the size of the room, the number of available beacons, and the strength of RSSI signal.

An Accurate Indoor RSSI Localization Algorithm Based on Active RFID System with Reference Tags

Radio frequency identification technologies are popular since their cost is very low and its data transmission based upon radio-wave communications. Where, the objects that are attached to tags are located using the reference tags. However, RSSI information suffers from the multipath propagation and anisotropic interference. So, the localization accuracy will be affected severely. Also, the multipath-propagation increases whenever the reference-tags increase, and so does for the cost, and signal interference. This paper presents a boundary virtual reference label algorithm for improving the indoor-efficiency by inserting some virtual reference tags on the boundary with establishing a linear regression model that eliminates unwanted tag information from the estimation procedure. The Results show that the localisation precision of the proposed approach has significantly increased, at least 78% without adding extra reference tags or radio frequency interference which represents a significant improvement over other algorithms .

Application of Ranging Difference Location Algorithm in Wireless Sensor Network Location

A kind of location technologies are developed used in the sensor network monitoring, the existing RSSI location algorithm are analyzed, and a localization method is proposed based on the SSDOA ranging difference method. hyperbolic equations are constructed and Chan's algorithm is used to solve the coordinates of unknown nodes. The simulation model of ranging error location algorithm is established by MATLAB. By analyzing the effect of ranging accuracy on positioning accuracy, the positioning performance of classical RSSI localization algorithm is compared. The results show that the WSN localization algorithm based on the SSDOA has a good localization performance and can meet the localization requirements of some WSN applications. Based on the above finding, It is concluded that Chan's algorithm is suitable for wireless sensor networks based on TDOA localization algorithm.

An Indoor Pedestrian Localization Algorithm Based on Multi-Sensor Information Fusion

For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.

Bayesian Optimization RSSI and Indoor location Algorithm of Iterative Least Square

Due to the wide application of range-based location algorithm for received signal strength, and according to the requirements of high accuracy and low power cost in the location algorithm for WSNs, in this paper, a Bayesian optimization RSSI and an indoor location algorithm for ILS were introduced by setting RRS ranging as location framework. Firstly, through analyzing the RSSI-based ranging model, an indoor location model was introduced. Secondly, in view of the influence on RSSI value caused by the indoor environment,the Bayesian probabilistic model was adopted to process the RSSI measured value and to screen out the big probability of RSSI value. Thirdly, Obtaining accurate measured data by estimating distance using method of minimum mean square error. Finally, Estimating the node location using least square method, and according to the TelosB node of Telos Series produced by company Crossbow, the ranging experiment can be designed and thus groups of experimental data were obtained and analyzed..The experimental results showed that the proposed location project greatly increased the location accuracy and decreased the computation complexity, and has obviously more advantage of running time over other location projects.

Design of a RSSI Location System for Greenhouse Environment

In order to improve the location accuracy in the complicated greenhouse environment, a positioning system is designed in this paper. According to RSSI data in the greenhouse, the parameters of the path-loss model are modified. Besides, least square estimation is used to filter RSSI data to eliminate random disturbance. Based on RSSI, the blind node is positioned by triangle centroid location method. Finally, to lessen the negative influence of steel pillars in the greenhouse, a region segmentation mechanism is introduced to the system. Experiments prove that our positioning system has higher accuracy in the greenhouse.

Study on Personnel Positioning System of Ship Based on Zigbee Technology

In order to pinpoint the location of the crew, master the number, location and distribution of personnel and activity track of time period, designed personnel positioning system of the ship based on zigbee technical by the gateway node, reference node, locate nodes, and designed personnel positioning system software in the IAR development environment. RSSI location algorithm has been improved, and the improved positioning algorithm using Matlab simulation to test, the test results show that the improved algorithm has higher positioning accuracy.

The Design of the Clusters Real-Time Positioning System Based on RSSI

With the rapid development of wireless communication and wireless sensor network, there are a large number of application requirements of indoor positioning service, so it is very significant to the study of indoor positioning technology. The paper studied the indoor real-time location technology of ZigBee and designed the real-time positioning system, which included gateway, reference node, and blind node, monitoring software of computer and RSSI location algorithm. STM32 was used to control the gateway, which was integrated with ZigBee module and WiFi module. Reference node and blind node were communicated through CC2530.Monitoring system calculated the blind nodes’ coordinate based on the improved three edge location algorithm. Using computer simulate it, the results show that this method has low consumption, the characteristics of high precision, and has certain practical value.

The Development of Underground Personnel Data Terminal Based on RSSI Location

Based on the emerging ZigBee wireless RF communication technology, this paper presents the design of a miner information collection terminal. The entire system is designed around the CC2431 SoC hardware circuit, through ZigBee networking, to achieve the location of underground environment and miner vital signs data acquisition, so that it can provide effective help to quickly carry out rescue work.

Modelling the Localization Scheme Integrated with a MAC Protocol in a Wireless Sensor Network

Node localization is one of the major issues of wireless sensor networks that determines the physical positions of nodes. The location data is useful to many communication protocols such as packet routing and sensing coverage in wireless sensor networks. It has been a daunting task to develop a practical algorithm for node localization in an environment where sensor nodes are with limited power, low cost etc. In this paper a novel idea to conserve energy required in a localization algorithm by integrating it with duty cycled MAC protocol in wireless sensor network has been modelled. To model the idea the RSSI localization method and a sensor SMAC protocol to regulate the control of energy wastages has been considered. Finally, every minute aspect of sensor network localization model including the topology, mobility, and channel and propagation schemes with scheduled transmission access, have been discussed in this paper.

센서 네트워크에서 RSSI 값과 위치 추정 기반의 에너지 효율적인 라우팅 프로토콜

This study has a purpose that improves efficiency of energy management and adaptation followed by movement of node better than the various early studied routing techniques. The purpose of this paper is the technique that uses RSSI value and location of sensor that is received by each sensor node to routing. This sduty does not save node information of 1-hop distance. And it solves energy-inefficient traffic problem that happens during data exchange process for middle node selection in close range multi hop transmission technique. The routing protocol technique that is proposed in this study selects a node relevant to the range of transmission which is set for RSSI value that is received by each node and selects the closest node as a middle node followed by location data. Therefore, it is for not exhaustion of node`s energy by managing energy efficiently and cutting data transmission consuming until the destination node.