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Journal articles on the topic 'Wi-Fi Sensors'
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1
Choi, Woo-Yong. "Efficient Node Insertion Algorithm for Connectivity-Based Multipolling MAC Protocol in Wi-Fi Sensor Networks." Applied Sciences 13, no. 21 (November 2, 2023): 11974. http://dx.doi.org/10.3390/app132111974.
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Since low-power Wi-Fi sensors are connected to the Internet, effective radio spectrum use is crucial for developing an efficient Medium Access Control (MAC) protocol for Wi-Fi sensor networks. A connectivity-based multipolling mechanism was employed for Access Points to grant uplink transmission opportunities to Wi-Fi nodes with a reduced number of multipolling frame transmissions. The existing connectivity-based multipolling mechanism in IEEE 802.11 wireless LANs with many nodes may require excessive time to derive the optimal number of serially connected sequences due to the backtracking algorithm based on the Traveling Salesman Problem model. This limitation hinders the real-time implementation of the connectivity-based multipolling mechanism in Wi-Fi sensor networks. In this study, an efficient node insertion algorithm is proposed, by which the number of derived serially connected multipolling sequences that cover nodes in Wi-Fi sensor networks converges to only one as the number of Wi-Fi sensors increases in Wi-Fi sensor networks. As verified by simulation experiments for Wi-Fi sensor networks, the proposed node insertion algorithm produces a near-optimal number of multipolling sequences that cover the nodes in Wi-Fi sensor networks. This study proposes a node insertion algorithm for the real-time implementation of the connectivity-based multipolling mechanism in MAC protocol for Wi-Fi sensor networks.
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Yu, Yue, Ruizhi Chen, Liang Chen, Guangyi Guo, Feng Ye, and Zuoya Liu. "A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors." Remote Sensing 11, no. 5 (March 1, 2019): 504. http://dx.doi.org/10.3390/rs11050504.
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More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.
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Lin, Jen-Yung, Huan-Liang Tsai, and Wei-Hong Lyu. "An Integrated Wireless Multi-Sensor System for Monitoring the Water Quality of Aquaculture." Sensors 21, no. 24 (December 7, 2021): 8179. http://dx.doi.org/10.3390/s21248179.
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Water temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), and salinity levels are the critical cultivation factors for freshwater aquaculture. This paper proposes a novel wireless multi-sensor system by integrating the temperature, pH, DO, and EC sensors with an ESP 32 Wi-Fi module for monitoring the water quality of freshwater aquaculture, which acquires the sensing data and salinity information directly derived from the EC level. The information of water temperature, pH, DO, EC, and salinity levels was displayed in the ThingSpeak IoT platform and was visualized in a user-friendly manner by ThingView APP. Firstly, these sensors were integrated with an ESP32 Wi-Fi platform. The observations of sensors and the estimated salinity from the EC level were then transmitted by a Wi-Fi network to an on-site Wi-Fi access point (AP). The acquired information was further transmitted to the ThingSpeak IoT and displayed in the form of a web-based monitoring system which can be directly visualized by online browsing or the ThingView APP. Through the complete processes of pre-calibration, in situ measurement, and post-calibration, the results illustrate that the proposed wireless multi-sensor IoT system has sufficient accuracy, reliable confidence, and a good tolerance for monitoring the water quality of freshwater aquaculture.
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Duives, Dorine C., Tim van Oijen, and Serge P. Hoogendoorn. "Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data." Sensors 20, no. 21 (October 23, 2020): 6032. http://dx.doi.org/10.3390/s20216032.
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Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd’s traffic state. Incorporating multiple functionally complementary sensor types is expensive. CMSs are needed that exploit data fusion opportunities to limit the number of (more expensive) sensors. This research estimates a data fusion algorithm to enhance the functionality of a CMS featuring Wi-Fi sensors by means of a small number of automated counting systems. Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best.
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Sun, Chao, Junhao Zhou, Kyongseok Jang, and Youngok Kim. "Indoor Localization Based on Integration of Wi-Fi with Geomagnetic and Light Sensors on an Android Device Using a DFF Network." Electronics 12, no. 24 (December 16, 2023): 5032. http://dx.doi.org/10.3390/electronics12245032.
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Sensor-related indoor localization has attracted considerable attention in recent years. The accuracy of conventional fingerprint solutions based on a single sensor, such as a Wi-Fi sensor, is affected by multipath interferences from other electronic devices that are produced as a result of complex indoor environments. Light sensors and magnetic (i.e., geomagnetic) field sensors can be used to enhance the accuracy of a system since they are less vulnerable to disturbances. In this paper, we propose a deep feedforward (DFF)-neural-network-based method, termed DFF-WGL, which integrates the data from the embedded Wi-Fi sensor, geomagnetic field sensor, and light sensor (WGL) in a smart device to localize the device in an indoor environment. DFF-WGL does not require complex and expensive auxiliary equipment, except for basic fluorescent lamps and low-density Wi-Fi signal coverage, conditions that are easily satisfied in modern offices or educational buildings. The proposed system was implemented on a commercial off-the-shelf android device, and performance was evaluated through an experimental analysis conducted in two different indoor testbeds, one measuring 60.5 m2 and the other measuring 38 m2, with 242 and 60 reference points, respectively. The results indicate that the model prediction with an input consisting of the combination of light, a magnetic field sensor, and two Wi-Fi RSS signals achieved mean localization errors of 0.01 m and 0.04 m in the two testbeds, respectively, compared with any subset of combination of sensors, verifying the effectiveness of the proposed DFF-WGL method.
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Silva , Ivo, Cristiano Pendão, Joaquín Torres-Sospedra, and Adriano Moreira. "Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data." Data 8, no. 10 (October 23, 2023): 157. http://dx.doi.org/10.3390/data8100157.
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This paper describes a dataset collected in an industrial setting using a mobile unit resembling an industrial vehicle equipped with several sensors. Wi-Fi interfaces collect signals from available Access Points (APs), while motion sensors collect data regarding the mobile unit’s movement (orientation and displacement). The distinctive features of this dataset include synchronous data collection from multiple sensors, such as Wi-Fi data acquired from multiple interfaces (including a radio map), orientation provided by two low-cost Inertial Measurement Unit (IMU) sensors, and displacement (travelled distance) measured by an absolute encoder attached to the mobile unit’s wheel. Accurate ground-truth information was determined using a computer vision approach that recorded timestamps as the mobile unit passed through reference locations. We assessed the quality of the proposed dataset by applying baseline methods for dead reckoning and Wi-Fi fingerprinting. The average positioning error for simple dead reckoning, without using any other absolute positioning technique, is 8.25 m and 11.66 m for IMU1 and IMU2, respectively. The average positioning error for simple Wi-Fi fingerprinting is 2.19 m when combining the RSSI information from five Wi-Fi interfaces. This dataset contributes to the fields of Industry 4.0 and mobile sensing, providing researchers with a resource to develop, test, and evaluate indoor tracking solutions for industrial vehicles.
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Jiang, Xinlong, Yiqiang Chen, Junfa Liu, Dingjun Liu, Yang Gu, and Zhenyu Chen. "Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/545792.
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As the development of Indoor Location Based Service (Indoor LBS), a timely localization and smooth tracking with high accuracy are desperately needed. Unfortunately, any single method cannot meet the requirement of both high accuracy and real-time ability at the same time. In this paper, we propose a fusion location framework with Particle Filter using Wi-Fi signals and motion sensors. In this framework, we use Extreme Learning Machine (ELM) regression algorithm to predict position based on motion sensors and use Wi-Fi fingerprint location result to solve the error accumulation of motion sensors based location occasionally with Particle Filter. The experiments show that the trajectory is smoother as the real one than the traditional Wi-Fi fingerprint method.
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Milani, Ileana, Carlo Bongioanni, Fabiola Colone, and Pierfrancesco Lombardo. "Fusing Measurements from Wi-Fi Emission-Based and Passive Radar Sensors for Short-Range Surveillance." Remote Sensing 13, no. 18 (September 7, 2021): 3556. http://dx.doi.org/10.3390/rs13183556.
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In this work, we consider the joint use of different passive sensors for the localization and tracking of human targets and small drones at short ranges, based on the parasitic exploitation of Wi-Fi signals. Two different sensors are considered in this paper: (i) Passive Bistatic Radar (PBR) that exploits the Wi-Fi Access Point (AP) as an illuminator of opportunity to perform uncooperative target detection and localization and (ii) Passive Source Location (PSL) that uses radio frequency (RF) transmissions from the target to passively localize it, assuming that it is equipped with Wi-Fi devices. First, we show that these techniques have complementary characteristics with respect to the considered surveillance applications that typically include targets with highly variable motion parameters. Therefore, an appropriate sensor fusion strategy is proposed, based on a modified version of the Interacting Multiple Model (IMM) tracking algorithm, in order to benefit from the information diversity provided by the two sensors. The performance of the proposed strategy is evaluated against both simulated and experimental data and compared to the performance of the single sensors. The results confirm that the joint exploitation of the considered sensors based on the proposed strategy largely improves the positioning accuracy, target motion recognition capability and continuity in target tracking.
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Naik, M. Renubabu. "Greenhouse Environment Monitoring and Controlling Through IoT." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2412–17. http://dx.doi.org/10.22214/ijraset.2022.44318.
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Abstract: Our project is based on IOT (Internet of things) which is very useful for monitoring and controlling the greenhouse environment, Agriculture under the greenhouse environment has more benefit of getting more crops by making proper climatic conditions for plants, fruits and vegetables. This greenhouse monitoring environment system have the transparent paper on the top and it contains the five main sensors they are temperature, humidity, rain, soil, LDR sensors. Most of the farmers are fail to get good crops by various reasons such as diseases due to temperature and humidity, if farmers really concerned about suitable temperature and humidity then they can get good crops and this can possible by providing greenhouse environment. The Arduino Nano is the heart of this project, and the five sensors are senses of their respective value and send to the Arduino Nano, through Wi-Fi module the respective detected value is monitored on the smart mobile where Wi-Fi controller app is there. Temperature sensor detects temperature, if temperature exceeds the threshold value then the fan is automatically on, there by temperature are decreases in the greenhouse environment. If LDR detects the sunlight then light will be off and when the sunlight not fall on the LDR then the light will be on in the greenhouse environment. If Rain sensor detects Rain then through the Wi-Fi controller we can open the top of the Greenhouse environment. The top is to be closed after the rain stop, by the Wi-Fi controller. If Soil sensor detects soil is to be dry then automatically the water pump is ON, and water pump is OFF automatically when soil becomes wet.
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Faydhe, Fatima, Majida Saud Ibrahim, and Kamal Y. Kamal. "HaLow Wi-Fi performance in multiusers and channels environment with MATLAB Simulink." International Journal of Communication Networks and Information Security (IJCNIS) 15, no. 1 (May 26, 2023): 01–11. http://dx.doi.org/10.17762/ijcnis.v15i1.5487.
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HaLow Wi-Fi (IEEE 802.11ah) wireless networking standard. As opposed to 2.4 GHz and 5 GHz-based conventional Wi-Fi networks, it leverages 900 MHz frequencies license-exempt for enabling networks Wi-Fi with a longer range. Lower energy usage makes it possible to build extensive networks of sensors or stations that work together to communicate signals, which is another advantage. In this paper IEEE 802.11ah Wi-Fi system design and implemented using MATLAB Simulink and tested under multiusers and channels environment in terms of Spectrum analyzer and constellation Diagram where 4 users, 2 MHz and 4 MHz channels bandwidth used to perfume the test also power of coarse synchronization, fine synchronization and initial channel estimation, to make Wi-Fi networks with a greater range possible were illustrated in space time stream.
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Ali, Muhammad, Soojung Hur, and Yongwan Park. "Wi-Fi-Based Effortless Indoor Positioning System Using IoT Sensors." Sensors 19, no. 7 (March 27, 2019): 1496. http://dx.doi.org/10.3390/s19071496.
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Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique.
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Bensky, William C. "Wi-Fi-based Wireless Sensors for Data Acquisition." Physics Teacher 56, no. 6 (September 2018): 393–97. http://dx.doi.org/10.1119/1.5051157.
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Tsyrulnyk, Serhii. "MOBILE APPLICATIONS AND ONLINE WI-FI MONITORING PLATFORMS OF WEATHER STATIONS." OPEN EDUCATIONAL E-ENVIRONMENT OF MODERN UNIVERSITY, no. 9 (2020): 181–92. http://dx.doi.org/10.28925/2414-0325.2020.9.15.
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Networks that allow digital devices to connect and transmit data are covering the world fast. Thanks to the networks, it is possible to connect all mobile devices, electronic sensors, electronic measuring devices, medical devices and sensors. They track, share, evaluate, and in some cases automatically adjust the data that is collected and transmitted. The concept of "Internet of Things" is complex and has several levels: end devices (sensors, actuators), transport layer (telecommunications environment, including wired and wireless networks) and the level of data processing (collection, storage and processing). The market environment creates requirements for young professionals, and competition between higher education institutions and vocational education institutions provides an opportunity to train a highly qualified specialist who can study and create modern hardware and software for smart electronic devices and systems that are nodes of the Internet of Things network. The article deals with issues related to the peculiarities of creating simple devices within the concept of the Internet of Things based on the popular Wi-Fi module ESP8266 and the introduction of this research into the educational process. The technical possibilities, features of connection and interaction of the ESP8266 module for meteorological monitoring are revealed. The organization of the module's access to the Internet, data sending and their monitoring using the popular mobile applications Blynk, Virtuino and the cloud IoT service ThingSpeak is shown. Their work in non-commercial tasks and ease of use for educational institutions are analyzed. The article provides the source codes of programs for the Wi-Fi module ESP8266 with a digital sensor BME280
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Retscher, Guenther, Jonathan Kleine, and Lisa Whitemore. "Trilateration Approaches for Indoor Wi-Fi Positioning." E3S Web of Conferences 94 (2019): 02002. http://dx.doi.org/10.1051/e3sconf/20199402002.
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In smartphones several sensors and receivers are embedded which enable positioning in Location-based Services and other navigation applications. They include GNSS receivers and Wireless Fidelity (Wi-Fi) cards as well as inertial sensors, such as accelerometers, gyroscope and magnetometer. In this paper, indoor Wi-Fi positioning is studied based on trilateration. Three methods are investigated which are a resection, a calculation of the center of gravity point and a differential approach. The first approach is a commonly employed resection using the ranges to the Wi-Fi Access Points (APs) as radii and intersect the circles around the APs. In the second method, the center of gravity in a triangle of APs is calculated with weighting of the received signal strength (RSS) of the Wi-Fi signals. The third approach is developed by analogy to Differential GNSS (DGNSS) and therefore termed Differential Wi-Fi (DWi-Fi). Its advantage is that a real-time modeling of the temporal RSS variations and fluctuations is possible. For that purpose, reference stations realized by low-cost Raspberry Pi units are deployed which serve at the same time as APs. The experiments conducted in a laboratory and entrance of an office building showed that position deviations from the ground truth of around 2 m are achievable with the second and third method. Thereby the positioning accuracies depend mainly on the geometrical point location in the triangle of APs and reference stations and the RSS scan duration.
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Kim, Hyunsoon, Mungyu Bae, Woonghee Lee, and Hwangnam Kim. "Adaptive Decision of Wireless Access Network for Higher User Satisfaction." Wireless Communications and Mobile Computing 2018 (July 10, 2018): 1–19. http://dx.doi.org/10.1155/2018/3427238.
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Today’s mobile devices mostly contain more than one wireless interface for Internet connection. Smartphones mostly have both LTE and Wi-Fi for data communication through Internet. Although the availability of Wi-Fi is incomparable to that of cellular network, its strength lies in the low cost and high data rate due to continuous PHY and MAC advancement of 802.11 protocol extensions. In this paper, we propose a device-centric system that performs cost-effective network connectivity to the mobile device by selectively activating Wi-Fi communication according to the device mobility and corresponding history of Wi-Fi usage. By analyzing the device mobility using attached sensors, the system selects appropriate Wi-Fi that has been often used in that mobility state. The system was implemented in actual mobile devices that were used to several experiments we designed to prove high performance of the system.
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Dinesh, Supriya, and Bharti Chourasia. "Need of Li-Fi (light fidelity) technology for the world to track COVID-19 patients." Journal of Autonomous Intelligence 6, no. 1 (July 5, 2023): 602. http://dx.doi.org/10.32629/jai.v6i1.602.
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In this modern world, a single day without light or the internet is unimaginable. Nowadays, wireless fidelity, often known as Wi-Fi, is the most well-known and commonly utilized conventional wireless technology. Wi-Fi employs radio waves or electromagnetic waves to carry data across networks. Imagine if a basic LED light in and around the hospital could link us to high-speed wireless internet with just a simple flickering of light at a very high speed where eyes cannot detect it. This technology is known as Li-Fi, or light fidelity, and it is 10,000 times faster than Wi-Fi. Hospitals are among the locations where Wi-Fi is absolutely forbidden. As doctors are the frontline soldiers against COVID-19, the objective of this project is to develop smart healthcare systems that use green communications to monitor COVID-19 patients using temperature, pressure, and heart rate sensors from Li-Fi transmitter to Li-Fi receiver by using simple LED light as a medium to transmit the data or information of COVID-19 to the cloud by using Li-Fi Dongle.
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P.Mathiyalagan, Et al. "Intelligent Anti-Larceny Device in an IOT-Enabled Car." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 1 (January 31, 2023): 235–38. http://dx.doi.org/10.17762/ijritcc.v11i1.9816.
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The research presents a comprehensive study on an Automobile smart anti-larceny system that leverages Internet of Things (IoT) technology. The system comprises an smart anti-larceny device and an anti-larceny management system, both of which utilize 3G wi-fi communication for seamless data exchange. The smart anti-larceny device integrates various sensor modules such as microwave, gyroscope, acceleration, magnetometer, temperature, and smog sensors, along with essential components like a system microprocessor MCU, GPS positioning module, voice alarm module, LED scintillation alarm module, siren continuous alarm module, 3G wi-fi communication module, and LCD display module. These modules are interconnected through the system microprocessor MCU. In the event of Automobile intrusion, larceny, or unauthorized movement, the sensors detect the anomalies in real-time, triggering voice alarms, activating sirens, and displaying warning signals through LED lamps.
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H, Mr Vinay Kumar, K. Uma, Pallavi S. Kyama, Vishnavi M. M, and Sangeetha Bai. "Lora Based Tree Poaching Detector using Arduino." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 4472–74. http://dx.doi.org/10.22214/ijraset.2023.51298.
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Abstract: These days there are numerous occurrences about pirating of trees like sandal, pinewood and so forth. These trees are in all respects exorbitant. They are utilized in the restorative sciences, beauty care products. To confine their sneaking and to spare timberlands around the world some preventive estimates should be conveyed. We have built up a framework which can be utilized to confine pirating. The structure framework utilizes three sensors PIR motion detector sensor, sound detection sensor, vibration sensor module. information created from these sensors is constantly checked with the guide of LCD 16*2 Alphanumeric display. Through GPS the location of the tree will be tracked easily. As for the sensors, their yield gadgets are actuated through transfer switch. For PIR sensor and sound sensor, a signal is enacted and for temperature sensor a water siphon is actuated. For PIR sensor and sound sensor, a signal is enacted and for temperaturesensor a water siphon is actuated. For further implementation we are using LoRa wan Wi-Fi module that covers around 10km to 15km for wide range of communication to receiver moduleand we can also use satellite communication (satcom) for longer range of communication. At present we are using a Wi-Fi module in our project.
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M. Abinaya, Akash Lal, Anandhu.V, S.Kathiravan, and Krishnaraj.K. "Li-Fi BASED HEALTH MONITORING SYSTEM FOR INFANTS." international journal of engineering technology and management sciences 7, no. 3 (2023): 188–95. http://dx.doi.org/10.46647/ijetms.2023.v07i03.025.
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The objective of the project is to design an infant health monitoring system based on LI-FI Technology. In this project we are continuously monitoring an infant through LI-FI Technology, it transmits data faster than WI-FI. The patient parameters are quickly transmitted via LI-FI transmitter, and it is received by LI-FI Receiver. For each parameter different sensors are used to monitor patient health in real time. We are transmitting and receiving data via LI-FI Technology. The sensors like SpO2 sensor for monitoring patient’s blood oxygen saturation and pulse level, temperature sensor is used to monitor patient body temperature, All these parameters are stored in Arduino microcontroller and then it will be uploaded and Receiver receive a data from LI-FI transmitter and it will be displayed, In case of emergency doctor can provide treatment for the particular infant based on the parametric value. This method is efficient than conventional systems. The main advantage of this project is implementation of LI-FI technology for faster data transmission and to avoid the presence of electromagnetic radiations.
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Zhang, Yi, Yue Zheng, Guidong Zhang, Kun Qian, Chen Qian, and Zheng Yang. "GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi." ACM Transactions on Sensor Networks 18, no. 1 (February 28, 2022): 1–24. http://dx.doi.org/10.1145/3466638.
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Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.
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Zhang, Yi, Yue Zheng, Guidong Zhang, Kun Qian, Chen Qian, and Zheng Yang. "GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi." ACM Transactions on Sensor Networks 18, no. 1 (February 28, 2022): 1–24. http://dx.doi.org/10.1145/3466638.
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Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.
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Bassoli, Marco, Valentina Bianchi, and Ilaria Munari. "A Plug and Play IoT Wi-Fi Smart Home System for Human Monitoring." Electronics 7, no. 9 (September 16, 2018): 200. http://dx.doi.org/10.3390/electronics7090200.
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The trend toward technology ubiquity in human life is constantly increasing and the same tendency is clear in all technologies aimed at human monitoring. In this framework, several smart home system architectures have been presented in literature, realized by combining sensors, home servers, and online platforms. In this paper, a new system architecture suitable for human monitoring based on Wi-Fi connectivity is introduced. The proposed solution lowers costs and implementation burden by using the Internet connection that leans on standard home modem-routers, already present normally in the homes, and reducing the need for range extenders thanks to the long range of the Wi-Fi signal. Since the main drawback of the Wi-Fi implementation is the high energy drain, low power design strategies have been considered to provide each battery-powered sensor with a lifetime suitable for a consumer application. Moreover, in order to consider the higher consumption arising in the case of the Wi-Fi/Internet connectivity loss, dedicated operating cycles have been introduced obtaining an energy savings of up to 91%. Performance was evaluated: in order to validate the use of the system as a hardware platform for behavioral services, an activity profile of a user for two months in a real context has been extracted.
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Choi, Woo-Yong. "Combined Sweeping and Jumping Method to Enhance Node Insertion Algorithm for Wi-Fi Sensor Networks." Applied Sciences 14, no. 17 (September 3, 2024): 7762. http://dx.doi.org/10.3390/app14177762.
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Two dominant driving forces for evolving communication technologies in the current society have been the proliferation of wireless access networks to the Internet and the broadbandization of access and infrastructure networks. Through these evolutions of communication technologies, high-resolution contents are instantly delivered to wireless devices such as mobile phones, wireless tablets, and headsets. Recently, wireless sensor networks, where up to 1000 low-power sensors are wirelessly connected to each other, have been created and connected to the Internet, which presents a new challenge of efficiently coordinating the transmissions of many wireless sensors with minimal transmission overheads. Developing an efficient Medium Access Control (MAC) protocol governing the transmissions of wireless sensor networks is crucial for the success of wireless sensor networks for the realization of the Internet of Things (IoT). In 2023, the node insertion algorithm was proposed to efficiently derive the minimal number of serially connected multipolling sequences of many wireless sensors, by which Access Points (APs) can poll wireless sensors with minimal polling overheads. In this paper, the combined sweeping and jumping method is presented to dramatically enhance the searching performance of the node insertion algorithm. To validate the performance of the combined sweeping and jumping method, simulation results are presented for wireless sensor networks where wireless sensors with varying transmission ranges exist.
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Ji, Yuxiong, Jizhou Zhao, Zhiming Zhang, and Yuchuan Du. "Estimating Bus Loads and OD Flows Using Location-Stamped Farebox and Wi-Fi Signal Data." Journal of Advanced Transportation 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6374858.
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Electronic fareboxes integrated with Automatic Vehicle Location (AVL) systems can provide location-stamped records to infer passenger boarding at individual stops. However, bus loads and Origin-Destination (OD) flows, which are useful for route planning, design, and real-time controls, cannot be derived directly from farebox data. Recently, Wi-Fi sensors have been used to collect passenger OD flow information. But the data are insufficient to capture the variation of passenger demand across bus trips. In this study, we propose a hierarchical Bayesian model to estimate trip-level OD flow matrices and a period-level OD flow matrix using sampled OD flow data collected by Wi-Fi sensors and boarding data provided by fareboxes. Bus loads on each bus trip are derived directly from the estimated trip-level OD flow matrices. The proposed method is evaluated empirically on an operational bus route and the results demonstrate that it provides good and detailed transit route-level passenger demand information by combining farebox and Wi-Fi signal data.
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Poulose, Alwin, Jihun Kim, and Dong Seog Han. "A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements." Applied Sciences 9, no. 20 (October 16, 2019): 4379. http://dx.doi.org/10.3390/app9204379.
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Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.
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Cui, Wei, Qingde Liu, Linhan Zhang, Haixia Wang, Xiao Lu, and Junliang Li. "A robust mobile robot indoor positioning system based on Wi-Fi." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988141989666. http://dx.doi.org/10.1177/1729881419896660.
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Recently, most of the existing mobile robot indoor positioning systems (IPSs) use infrared sensors, cameras, and other extra infrastructures. They usually suffer from high cost and special hardware implementation. In order to address the above problems, this article proposes a Wi-Fi-based indoor mobile robot positioning system and designs and develops a robot positioning platform based on the commercial Wi-Fi devices, such as Wi-Fi routers. Furthermore, a robust principal component analysis-based extreme learning machine algorithm is proposed to address the issue of noisy measurements in IPSs. Real-world robot indoor positioning experiments are extensively carried out and the results verify the effectiveness and superiority of the proposed system.
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Wan, Qiao, Xiaoqi Duan, Yue Yu, Ruizhi Chen, and Liang Chen. "Self-Calibrated Multi-Floor Localization Based on Wi-Fi Ranging/Crowdsourced Fingerprinting and Low-Cost Sensors." Remote Sensing 14, no. 21 (October 27, 2022): 5376. http://dx.doi.org/10.3390/rs14215376.
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Crowdsourced localization using geo-spatial big data has become an effective approach for constructing smart-city-based location services with the fast growing number of Internet of Things terminals. This paper presents a self-calibrated multi-floor indoor positioning framework using a combination of Wi-Fi ranging, crowdsourced fingerprinting and low-cost sensors (SM-WRFS). The localization parameters, such as heading and altitude biases, step-length scale factor, and Wi-Fi ranging bias are autonomously calibrated to provide a more accurate forward 3D localization performance. In addition, the backward smoothing algorithm and a novel deep-learning model are applied in order to construct an autonomous and efficient crowdsourced Wi-Fi fingerprinting database using the detected quick response (QR) code-based landmarks. Finally, the adaptive extended Kalman filter is adopted to combine the corresponding location sources using different integration models to provide a precise multi-source fusion based multi-floor indoor localization performance. The real-world experiments demonstrate that the presented SM-WRFS is proven to realize precise 3D indoor positioning under different environments, and the meter-level positioning accuracy can be acquired in Wi-Fi ranging supported indoor areas.
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Liu, Tao, Qingquan Li, and Xing Zhang. "AUTOMATIC CONSTRUCTION OF WI-FI RADIO MAP USING SMARTPHONES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 309–12. http://dx.doi.org/10.5194/isprs-archives-xli-b4-309-2016.
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Indoor positioning could provide interesting services and applications. As one of the most popular indoor positioning methods, location fingerprinting determines the location of mobile users by matching the received signal strength (RSS) which is location dependent. However, fingerprinting-based indoor positioning requires calibration and updating of the fingerprints which is labor-intensive and time-consuming. In this paper, we propose a visual-based approach for the construction of radio map for anonymous indoor environments without any prior knowledge. This approach collects multi-sensors data, e.g. video, accelerometer, gyroscope, Wi-Fi signals, etc., when people (with smartphones) walks freely in indoor environments. Then, it uses the multi-sensor data to restore the trajectories of people based on an integrated structure from motion (SFM) and image matching method, and finally estimates location of sampling points on the trajectories and construct Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53 m.
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Liu, Tao, Qingquan Li, and Xing Zhang. "AUTOMATIC CONSTRUCTION OF WI-FI RADIO MAP USING SMARTPHONES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 309–12. http://dx.doi.org/10.5194/isprsarchives-xli-b4-309-2016.
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Indoor positioning could provide interesting services and applications. As one of the most popular indoor positioning methods, location fingerprinting determines the location of mobile users by matching the received signal strength (RSS) which is location dependent. However, fingerprinting-based indoor positioning requires calibration and updating of the fingerprints which is labor-intensive and time-consuming. In this paper, we propose a visual-based approach for the construction of radio map for anonymous indoor environments without any prior knowledge. This approach collects multi-sensors data, e.g. video, accelerometer, gyroscope, Wi-Fi signals, etc., when people (with smartphones) walks freely in indoor environments. Then, it uses the multi-sensor data to restore the trajectories of people based on an integrated structure from motion (SFM) and image matching method, and finally estimates location of sampling points on the trajectories and construct Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53 m.
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Zhou, Mu, Kunjie Xu, Zengshan Tian, Haibo Wu, and Ruikang Shi. "Crowd-Sourced Mobility Mapping for Location Tracking Using Unlabeled Wi-Fi Simultaneous Localization and Mapping." Mobile Information Systems 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/416197.
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Due to the increasing requirements of the seamless and round-the-clock Location-based services (LBSs), a growing interest in Wi-Fi network aided location tracking is witnessed in the past decade. One of the significant problems of the conventional Wi-Fi location tracking approaches based on received signal strength (RSS) fingerprinting is the time-consuming and labor intensive work involved in location fingerprint calibration. To solve this problem, a novel unlabeled Wi-Fi simultaneous localization and mapping (SLAM) approach is developed to avoid the location fingerprinting and additional inertial or vision sensors. In this approach, an unlabeled mobility map of the coverage area is first constructed by using the crowd-sourcing from a batch of sporadically recorded Wi-Fi RSS sequences based on the spectral cluster assembling. Then, the sequence alignment algorithm is applied to conduct location tracking and mobility map updating. Finally, the effectiveness of this approach is verified by the extensive experiments carried out in a campus-wide area.
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Nguyen, Duc-Thang, and Taehong Kim. "An SDN-Based Connectivity Control System for Wi-Fi Devices." Wireless Communications and Mobile Computing 2018 (July 24, 2018): 1–10. http://dx.doi.org/10.1155/2018/9359878.
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In recent years, the prevalence of Wi-Fi-enabled devices such as smartphones, smart appliances, and various sensors has increased. As most IoT devices lack a display or a keypad owing to their tiny size, it is difficult to set connectivity information such as service set identifier (SSID) and password without any help from external devices such as smartphones. Moreover, it is much more complex to apply advanced connectivity options such as SSID hiding, MAC ID filtering, and Wi-Fi Protected Access (WPA) to these devices. Thus, we need a new Wi-Fi network management system which not only facilitates client access operations but also provides a high-level authentication procedure. In this paper, we introduce a remote connectivity control system for Wi-Fi devices based on software-defined networking (SDN) in a wireless environment. The main contributions of the proposed system are twofold: (i) it enables network owner/administrator to manage and approve connection request from Wi-Fi devices through remote services, which is essential for easy connection management across diverse IoT devices; (ii) it also allows fine-grained access control at the device level through remote control. We describe the architecture of SDN-based remote connectivity control of Wi-Fi devices. While verifying the feasibility and performance of the proposed system, we discuss how the proposed system can benefit both service providers and users.
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Vega-Barbas, Mario, Manuel Álvarez-Campana, Diego Rivera, Mario Sanz, and Julio Berrocal. "AFOROS: A Low-Cost Wi-Fi-Based Monitoring System for Estimating Occupancy of Public Spaces." Sensors 21, no. 11 (June 3, 2021): 3863. http://dx.doi.org/10.3390/s21113863.
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Estimating the number of people present in a given venue in real-time is extremely useful from a security, management, and resource optimization perspective. This article presents the architecture of a system based on the use of Wi-Fi sensor devices that allows estimating, almost in real-time, the number of people attending an event that is taking place in a venue. The estimate is based on the analysis of the “probe request” messages periodically transmitted by smartphones to determine the existence of Wi-Fi access points in the vicinity. The method considers the MAC address randomization mechanisms introduced in recent years in smartphones, which prevents the estimation of the number of devices by simply counting different MAC addresses. To solve this difficulty, our Wi-Fi sensors analyze other fields present in the header of the IEEE 802.11 frames, the information elements, to extract a unique fingerprint from each smartphone. The designed system was tested in a set of real scenarios, obtaining an estimate of attendance at different public events with an accuracy close to 95%.
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Retscher, Guenther, Jonathan Kleine, and Lisa Whitemore. "Trilateration approaches for seamless out-/indoor GNSS and Wi-Fi smartphone positioning." Journal of Applied Geodesy 13, no. 1 (January 28, 2019): 47–61. http://dx.doi.org/10.1515/jag-2018-0022.
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Abstract More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.
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Yu, Yue, Yi Zhang, Liang Chen, and Ruizhi Chen. "Intelligent Fusion Structure for Wi-Fi/BLE/QR/MEMS Sensor-Based Indoor Localization." Remote Sensing 15, no. 5 (February 22, 2023): 1202. http://dx.doi.org/10.3390/rs15051202.
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Due to the complexity of urban environments, localizing pedestrians indoors using mobile terminals is an urgent task in many emerging areas. Multi-source fusion-based localization is considered to be an effective way to provide location-based services in large-scale indoor areas. This paper presents an intelligent 3D indoor localization framework that uses the integration of Wi-Fi, Bluetooth Low Energy (BLE), quick response (QR) code, and micro-electro-mechanical system sensors (the 3D-WBQM framework). An enhanced inertial odometry was developed for accurate pedestrian localization and trajectory optimization in indoor spaces containing magnetic interference and external acceleration, and Wi-Fi fine time Measurement stations, BLE nodes, and QR codes were applied for landmark detection to provide an absolute reference for trajectory optimization and crowdsourced navigation database construction. In addition, the robust unscented Kalman filter (RUKF) was applied as a generic integrated model to combine the estimated location results from inertial odometry, BLE, QR, Wi-Fi FTM, and the crowdsourced Wi-Fi fingerprinting for large-scale indoor positioning. The experimental results indicated that the proposed 3D-WBQM framework was verified to realize autonomous and accurate positioning performance in large-scale indoor areas using different location sources, and meter-level positioning accuracy can be acquired in Wi-Fi FTM supported areas.
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Нарожный, В. В., А. С. Назаров, and Т. Г. Дегтярева. "ДОСЛІДЖЕННЯ ДАТЧИКА ТЕМПЕРАТУРИ DS18B20 WI-FI МОДУЛЕМ NODEMCU V3 ESP8266." Open Information and Computer Integrated Technologies, no. 85 (July 29, 2019): 167–74. http://dx.doi.org/10.32620/oikit.2019.85.10.
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The past decade can be characterized by the accelerating Internet of Things (IoT) development. Currently, the European Research Cluster on the Internet of Things (IERC) defines IoT as a dynamic global network infrastructure with the possibility of self-tuning based on standard and compatible communication protocols. The Internet and microprocessor technology development caused the rise of IoT. Other factors influencing the rapid IoT development were cloud computing and wireless networks popularity growth. As a result, the widespread use of IoT required an increase in the reliability of the devices.In many areas of modern technological processes and physical researches, the temperature is a significant physical characteristic. The paper describes the hardware and software complex connecting the DS18B20 temperature meter (sensor). The complex is designed to study the fault-tolerance of temperature measurements in IoT. The Wi-Fi module NodeMCU V3 based on ESP8266 is applied as a control unit of the complex.The IoT appearance brought to a new level such an important segment of technical researches as the development of the fault-tolerant solutions. One of the important subsystems of such an application is the physical parameters detection of various devices in real-time. The temperature is a significant physical characteristic in many areas of modern technological processes and physical researches. The hardware and software complex for connecting a DS18B20 temperature measurer (sensor) is described in the paper. The complex is designed to examine the temperature measurement fault-tolerance in IoT. The Wi-Fi module NodeMCU V3 based on ESP8266 is applied as the complex controller.As far as the work of IoT depends mainly on the information provided by the sensors, the sensor performance monitoring is critically important. The autonomous system architecture of IoT includes such tasks as perception, localization, planning, management and control over systems exchanging information with each other. For this reason, the reliability of the sensors is of high concern. Therefore, one failure can lead to the IoT system dangerous behavior.The IoT fault-tolerance is an important direction of modern systems design. The research of the ensuring possibility of the IoT fault-tolerance functioning is an urgent task. For such studies, hardware and software complexes are developed.
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Ashraf, Imran, Soojung Hur, Muhammad Shafiq, and Yongwan Park. "Floor Identification Using Magnetic Field Data with Smartphone Sensors." Sensors 19, no. 11 (June 3, 2019): 2538. http://dx.doi.org/10.3390/s19112538.
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Floor identification plays a key role in multi-story indoor positioning and localization systems. Current floor identification systems rely primarily on Wi-Fi signals and barometric pressure data. Barometric systems require installation of additional standalone sensors to perform floor identification. Wi-Fi systems, on the other hand, are vulnerable to the dynamic environment and adverse effects of path loss, shadowing, and multipath fading. In this paper, we take advantage of a pervasive magnetic field to compensate for the limitations of these systems. We employ smartphone sensors to make the proposed scheme infrastructure free and cost-effective. We use smartphone magnetic sensors to identify the floors in a multi-story building with improved accuracy. Floor identification is performed with user activities of normal walking, call listening, and phone swinging. Various machine learning techniques are leveraged to identify user activities. Extensive experiments are performed to evaluate the proposed magnetic-data-based floor identification scheme. Additionally, the impact of device heterogeneity on floor identification is investigated using Samsung Galaxy S8, LG G6, and LG G7 smartphones. Research results demonstrate that the magnetic floor identification outperforms barometric and Wi-Fi-enabled floor detection techniques. A floor change module is incorporated to further enhance the accuracy of floor identification.
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Kim, Si-Hun, Do-Hwa Kang, Kwan-woo Kim, and Chang Heon Lim. "Indoor Location Estimation Using Wi-Fi RSSI Signals and Geomagnetic Sensors." IEMEK Journal of Embedded Systems and Applications 12, no. 1 (February 28, 2017): 19–25. http://dx.doi.org/10.14372/iemek.2017.12.1.19.
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Julham, Julham, Hikmah Adwin Adam, Arif Ridho Lubis, and Muharman Lubis. "Development of soil moisture measurement with wireless sensor web-based concept." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 2 (February 1, 2019): 514. http://dx.doi.org/10.11591/ijeecs.v13.i2.pp514-520.
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<span lang="EN-US">Measurement of soil moisture commonly by applying the soil moisture sensors is to measure the condition of the ground around which is relatively not wide. Therefore if applied for the large-scale, repeated measurements are required in accordance with the determined point. As a result it takes time to get the whole results. With the existence of wireless sensor technology then this problem can be overcome. This wireless sensor system will create a network consisting of nodes and server. In this study the server part is a server computer that requires a web server application together with its script to display and store data, while the node part is the data reader system. In the data system reader module, the sensor device is required as the input that is SEN0114, the processor is a microcontroller, while the wireless uses Wi-Fi module that is ESP8266. Wi-Fi topology used later is infrastructure (using access points). In this research, it begins by testing the sensor and then testing the data validation between the node and the server. SEN0114 sensor has different value from the American Standard Method (ASM) that is 0.922%. While the data validation test of the measurement result is Wi-Fi ESP8266 module which has a maximum distance of 14 meters toward the access points.</span>
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Feng, Chenwei, Huangbin Zeng, Yu Sun, Lin Tao, Huazhi Ji, and Zhiwei Cai. "Design of Monitoring and Controlling System for Smart Home." Journal of Physics: Conference Series 2160, no. 1 (January 1, 2022): 012001. http://dx.doi.org/10.1088/1742-6596/2160/1/012001.
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Abstract An intelligent lifestyle has become a hotspot for researchers and industries nowadays. The smart home monitoring and controlling system with the Arduino as the main controller is designed in this paper, combined with sensors, Wi-Fi, and cloud technologies. Various sensors collect household environmental information, such as indoor temperature and humidity, soil moisture, combustible gas concentration, and light intensity. The main controller processes the collected signals and automatically operates the devices, including a refrigeration equipment, water pump, buzzer, fan, stepping motor. The data can also be transmitted to the cloud platform through Wi-Fi for processing, and the home environment information and device can be remotely monitored and controlled by the cloud platform or smartphone APP.
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Fan, Yao-Chung, and Hsueh-Wen Tseng. "Effective Schemes for Place Name Annotations with Mobile Crowd." Mobile Information Systems 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/809168.
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With the popularity of mobile devices, numerous mobile applications have been and will continue to be developed for various interesting usage scenarios. Riding this trend, recent research community envisions a novel information retrieving and information-sharing platform, which views the users with mobile devices, being willing to accept crowdsourcing tasks ascrowd sensors. With the neat idea, a set of crowd sensors applications have emerged. Among the applications, the geospatial information systems based on crowd sensors show significant potentials beyond traditional ones by providing real-time geospatial information. In the applications, user positioning is of great importance. However, existing positioning techniques have their own disadvantages. In this paper, we study using pervasive Wi-Fi access point as user position indicators. The major challenge for using Wi-Fi access point is that there is no mechanism for mapping observed Wi-Fi signals to human-defined places. To this end, our idea is to employ crowdsourcing model to perform place name annotations by mobile participants to bridge the gap between signals and human-defined places. In this paper, we propose schemes for effectively enabling crowdsourcing-based place name annotation, and conduct real trials with recruited participants to study the effectiveness of the proposed schemes. The experiment results demonstrate the effectiveness of the proposed schemes over existing solutions.
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Kurkcu, Abdullah, and Kaan Ozbay. "Estimating Pedestrian Densities, Wait Times, and Flows with Wi-Fi and Bluetooth Sensors." Transportation Research Record: Journal of the Transportation Research Board 2644, no. 1 (January 2017): 72–82. http://dx.doi.org/10.3141/2644-09.
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Monitoring nonmotorized traffic is gaining more attention in the context of transportation studies. Most of the traditional pedestrian monitoring technologies focus on counting pedestrians passing through a fixed location in the network. It is thus not possible to anonymously track the movement of individuals or groups as they move outside each particular sensor’s range. Moreover, most agencies do not have continuous pedestrian counts mainly because of technological limitations. Wireless data collection technologies, however, can capture crowd dynamics by scanning mobile devices. Data collection that takes advantage of mobile devices has gained much interest in the transportation literature as a result of its low cost, ease of implementation, and richness of the captured data. In this paper, algorithms to filter and aggregate data collected by wireless sensors are investigated, as well as how to fuse additional data sources to improve the estimation of various pedestrian-based performance measures. Procedures to accurately filter the noise in the collected data and to find pedestrian flows, wait times, and counts with wireless sensors are presented. The developed methods are applied to a 2-month-long collection of public transportation terminal data carried out with the use of six sensors. Results point out that if the penetration rate of discoverable devices is known, then it is possible to accurately estimate the number of pedestrians, pedestrian flows, and average wait times in the detection zone of the developed sensors.
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Fourie, Christiaan M., and Hermanus Carel Myburgh. "An Intra-Vehicular Wireless Multimedia Sensor Network for Smartphone-Based Low-Cost Advanced Driver-Assistance Systems." Sensors 22, no. 8 (April 15, 2022): 3026. http://dx.doi.org/10.3390/s22083026.
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Advanced driver-assistance system(s) (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. Wired solutions of vision sensors in ADAS already exist, but are costly and do not cater for low-end vehicles. General ADAS use wired harnessing for communication; this approach eliminates the need for cable harnessing and, therefore, the practicality of a novel wireless ADAS solution was tested. A low-cost alternative is proposed that extends a smartphone’s sensor perception, using a camera-based wireless sensor network. This paper presents the design of a low-cost ADAS alternative that uses an intra-vehicle wireless sensor network structured by a Wi-Fi Direct topology, using a smartphone as the processing platform. The proposed system makes ADAS features accessible to cheaper vehicles and investigates the possibility of using a wireless network to communicate ADAS information in a intra-vehicle environment. Other ADAS smartphone approaches make use of a smartphone’s onboard sensors; however, this paper shows the application of essential ADAS features developed on the smartphone’s ADAS application, carrying out both lane detection and collision detection on a vehicle by using wireless sensor data. A smartphone’s processing power was harnessed and used as a generic object detector through a convolution neural network, using the sensory network’s video streams. The network’s performance was analysed to ensure that the network could carry out detection in real-time. A low-cost CMOS camera sensor network with a smartphone found an application, using Wi-Fi Direct, to create an intra-vehicle wireless network as a low-cost advanced driver-assistance system.
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Yu, Hu, and Wang. "A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning." ISPRS International Journal of Geo-Information 8, no. 11 (October 23, 2019): 468. http://dx.doi.org/10.3390/ijgi8110468.
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The uncertainty of indoor Wi-Fi positioning is susceptible to many factors, such as sensor distribution, the internal environment (e.g., of a shopping mall), differences between receivers, and the flow of people. In this paper, an indoor pedestrian trajectory pattern mining approach for the assessment of the error and accuracy of indoor Wi-Fi positioning is proposed. First, the stay points of the customer were extracted from the pedestrian trajectories based on the spatiotemporal staying patterns of the customers in a shopping mall. Second, the drift points were distinguished from the stay points through analysis of noncustomer behavior patterns. Finally, the drift points were presented to calculate the errors in the pedestrian trajectories for the accuracy assessment of the indoor Wi-Fi positioning system. A one-month indoor pedestrian trajectories dataset from the Xinxiang Baolong shopping mall in Henan Province, China, was used for the assessment of the error and accuracy values with the proposed approach. The experimental results were verified by incorporating the distribution of the AP sensors. The proposed approach using big data pattern mining can explore the error distribution of indoor positioning systems, which can provide strong support for improving indoor positioning accuracy in the future.
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Bankov, Dmitry, Evgeny Khorov, Andrey Lyakhov, and Jeroen Famaey. "Resource Allocation for Machine-Type Communication of Energy-Harvesting Devices in Wi-Fi HaLow Networks." Sensors 20, no. 9 (April 25, 2020): 2449. http://dx.doi.org/10.3390/s20092449.
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The recent Wi-Fi HaLow technology focuses on adopting Wi-Fi for the needs of the Internet of Things. A key feature of Wi-Fi HaLow is the Restricted Access Window (RAW) mechanism that allows an access point to divide the sensors into groups and to assign each group to an exclusively reserved time interval where only the stations of a particular group can transmit. In this work, we study how to optimally configure RAW in a scenario with a high number of energy harvesting sensor devices. For such a scenario, we consider a problem of device grouping and develop a model of data transmission, which takes into account the peculiarities of channel access and the fact that the devices can run out of energy within the allocated intervals. We show how to use the developed model in order to determine the optimal duration of RAW intervals and the optimal number of groups that provide the required probability of data delivery and minimize the amount of consumed channel resources. The numerical results show that the optimal RAW configuration can reduce the amount of consumed channel resources by almost 50%.
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Friad Qadr, Runahi, Halgurd S. Maghdid, and Azhin T. Sabir. "Novel Integration of Wi-Fi Signal and Magnetometer Sensor Measurements in Fingerprinting Technique for Indoors Smartphone positioning." ITM Web of Conferences 42 (2022): 01016. http://dx.doi.org/10.1051/itmconf/20224201016.
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Smartphones are becoming more widespread, and location-based services (LBS) have become one of the most important uses in people’s daily lives. While outdoor location is reasonably simple thanks to GNSS signals, however, indoor location is more problematic due to the lack of GNSS signals. As a result of the widespread deployment of alternative technologies such as wireless and sensors technologies, various studies on wireless-based indoor positioning have been conducted. However, each technology has its own limitations including multipath fading of wireless signals causes time-varying received signal strength as well as the accumulated error of the onboard sensors (i.e. sensor drift) resulting in poor localization accuracy. Motivated by these restrictions, this work integrates the applicability of two technologies for indoor positioning that are already available in smartphones by avoiding their limitation. The integration is based on fingerprinting-positioning technique by including magnetometer sensor measurements and WiFi signal strength. Android-based smartphones with low-cost sensors in real indoor scenarios are utilized to create a dataset and collect independent track tests to confirm results. The performance of different scenarios, such as Wi-Fi alone, magnetometer alone, and magnetometer-aided Wi-Fi, is compared. The experimental results show that the combination of magnetometer sensor and WiFi signal strength provides significant results in which leads to reducing the location error to 0.7224 meters.
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Wu, Jiaxuan, Yunfei Feng, and Peng Sun. "Sensor Fusion for Recognition of Activities of Daily Living." Sensors 18, no. 11 (November 19, 2018): 4029. http://dx.doi.org/10.3390/s18114029.
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Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.
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Shukla, Anita, and Ankit Jain. "Smart Automated Farming System using IOT and Solar Panel." Science & Technology Journal 7, no. 2 (July 1, 2019): 22–32. http://dx.doi.org/10.22232/stj.2019.07.02.03.
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Present work focuses on need of automation in farming by using IOT technology. Automation of farming envisages monitoring and controlling of various parameters which could be helpful in increasing productivity. The proposed system provides a technological solution to the various problems like, maintenance of water requirements, humidity level, maintenance of proper temperature, and proper availability of light for sophisticated plants, fire alert and to keep a check on unwanted entry in the farming lands including timely and sufficient supply of electricity. This hardware provides an effective and efficient solution to the defined problems in Indian farming system by using node MCU Wi-Fi module. Different sensors like humidity sensor, soil moisture sensor, PIR sensor, fire sensor, light sensor and temperature sensor have been used for monitoring and controlling of various problems technologically. In proposed system a Wi-Fi module has been used which automatically informs the farmer about the water requirement, site temperature, humidity and moisture, light, fire alert and about the unwanted occupancy or encroachment by displaying real time data which can be seen and accessed over internet using IOT technology from anywhere in the world. System is equipped with solar panel which provides power backup to the system even in the absence of power supply. We have used five different sensors on three different plants with different environmental conditions and the performances of different sensors are found to be upto the desired expectations.
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Klančar, Gregor, and Igor Škrjanc. "Robot Localization using Inertial and Wi-Fi Signal Strength Sensors." IFAC Proceedings Volumes 45, no. 22 (2012): 139–44. http://dx.doi.org/10.3182/20120905-3-hr-2030.00054.
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Banin, Leor, Ofer Bar-Shalom, Nir Dvorecki, and Yuval Amizur. "Scalable Wi-Fi Client Self-Positioning Using Cooperative FTM-Sensors." IEEE Transactions on Instrumentation and Measurement 68, no. 10 (October 2019): 3686–98. http://dx.doi.org/10.1109/tim.2018.2880887.
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Hwang, Hwanwoong, Jae-Han Lim, Ji-Hoon Yun, and Byung Jeong. "Pattern-Based Decoding for Wi-Fi Backscatter Communication of Passive Sensors." Sensors 19, no. 5 (March 7, 2019): 1157. http://dx.doi.org/10.3390/s19051157.
Full textAbstract:
Ambient backscatter communication enables passive sensors to convey sensing data on ambient RF signals in the air at ultralow power consumption. To extract data bits from such signals, threshold-based decoding has generally been considered, but suffers against Wi-Fi signals due to severe fluctuation of OFDM signals. In this paper, we propose a pattern-matching-based decoding algorithm for Wi-Fi backscatter communications. The key idea is the identification of unique patterns of signal samples that arise from the inevitable smoothing of Wi-Fi signals to filter out noisy fluctuation. We provide the mathematical basis of obtaining the pattern of smoothed signal samples as the slope of a line expressed in a closed-form equation. Then, the new decoding algorithm was designed to identify the pattern of received signal samples as a slope rather than classifying their amplitude levels. Thus, it is more robust against signal fluctuation and does not need tricky threshold configuration. Moreover, for even higher reliability, the pattern was identified for a pair of adjacent bits, and the algorithm decodes a bit pair at a time rather than a single bit. We demonstrate via testbed experiments that the proposed algorithm significantly outperforms conventional threshold-based decoding variants in terms of bit error rate for various distances and data rates.