Embracing Localization Inaccuracy: A Case Study

IEEE Journal of Biomedical and Health Informatics, 2014

In current healthcare environments, a trend towards mobile and personalized interactions between people and nurse call systems is strongly noticeable. Therefore it should be possible to locate patients at all times and in all places throughout the care facility. The present article aims at describing a method by which a mobile node can locate itself indoors, based on signal strength measurements and a minimal amount of yes/no decisions. The algorithm has been developed specifically for use in a healthcare environment. With extensive testing and statistical support, we prove our algorithm can be used in a healthcare setting with an envisioned level of localization accuracy up to room revel (or region level in a corridor), while avoiding heavy investments since the hardware of an existing nurse call network can be reused. The approach opted for leads to very high scalability, since thousands of mobile nodes can locate themselves. Network timing issues and localization update delays are avoided, which ensures a patient can receive the needed care in a time and resources efficient way.

2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2015

Device-free localization algorithms attract, among others, the attention of researchers working in the Ambient Assisted Living (AAL) scenarios, where the target user might not be able or willing to wear any devices. We concentrate on systems that exploit the Received Signal Strength indicator coming from wireless devices whose position is known, called anchors. In this paper we select and test the main device-free localization solutions and experimentally compare their performance using a smaller number of anchors than commonly found in the literature. We illustrate the procedure used to validate our comparing procedure and we give suggestions on usability in the application scenarios typical of AAL. To the best of our knowledge, this is the first direct comparison between different device-free algorithms using the same input data for all of them, and the first one that compares their performance with a varying number of anchors. Thanks to the characteristics of our comparison procedure, we can make suggestions about the more appropriate algorithms to use for different kinds of applications.

Sensors, 2017

Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error.

2014

Abstract—Wireless Sensor Networks (WSNs) have attracted a great deal of research interest during the last few years. Potential applications make them ideal for the development of the envisioned world of ubiquitous and pervasive computing. Localization is a key aspect of such networks, since the knowledge of a sensor’s location is critical in order to process information originating from this sensor, or to actuate responses to the environment, or to infer regarding an emerging situation etc. Indoor localization in the literature is based on various techniques, ranging from simple Received-Signal-Strength (RSS) to the more demanding Time-of-Arrival (ToA) or Direction-of-Arrival (DoA) of the incoming signals. In the context of several EU research projects, various WSN platforms for indoor localization have been developed, evaluated and tested within real-world emergency medical services applications. These platforms were selected in order to deal with all principal localization techniq...

Journal of Robotics and Control (JRC), 2020

Wireless sensor networks (WSNs) have a vital role in indoor localization development. As today, there are more demands in location-based service (LBS), mainly indoor environments, which put the researches on indoor localization massive attention. As the global-positioning-system (GPS) is unreliable indoor, some methods in WSNs-based indoor localization have been developed. Path loss model-based can be useful for providing the power-distance relationship the distance-based indoor localization. Received signal strength indicator (RSSI) has been commonly utilized and proven to be a reliable yet straightforward metric in the distance-based method. We face issues related to the complexity of indoor localization to be deployed in a real situation. Hence, it motivates us to propose a simple yet having acceptable accuracy results. In this research, we applied the standard distance-based methods, which are is trilateration and min-max or bounding box algorithm. We used the RSSI values as the localization parameter from the ZigBee standard. We utilized the general path loss model to estimate the traveling distance between the transmitter (TX) and receiver (RX) based on the RSSI values. We conducted measurements in a simple indoor lobby environment to validate the performance of our proposed localization system. The results show that the min-max algorithm performs better accuracy compared to the trilateration, which yields an error distance of up to 3m. By these results, we conclude that the distance-based method using ZigBee standard working on 2.4 GHz center frequency can be reliable in the range of 1-3m. This small range is affected by the existence of interference objects (IOs) lead to signal multipath, causing the unreliability of RSSI values. These results can be the first step for building the indoor localization system, which low-cost, lowcomplexity, and can be applied in many fields, especially indoor robots and small devices in internet-of-things (IoT) world's today.

2010

Wireless Sensor Networks (WSNs) have attracted a great deal of research interest during the last few years. Potential applications make them ideal for the development of the envisioned world of ubiquitous and pervasive computing. Localization is a key aspect of such networks, since the knowledge of a sensor's location is critical in order to process information originating from this sensor, or to actuate responses to the environment, or to infer regarding an emerging situation etc. Indoor localization in the literature is based on various techniques, ranging from simple Received-Signal-Strength (RSS) to the more demanding Time-of-Arrival (ToA) or Direction- of-Arrival (DoA) of the incoming signals. In the context of several EU research projects, various WSN platforms for indoor localization have been developed, evaluated and tested within real-world emergency medical services applications. These platforms were selected in order to deal with all principal localization techniques,...

Journal of the American Medical Directors Association, 2012

Objectives: To assess the effectiveness of a wireless network (WiFi-based) localization system (devices mounted on resident wheelchairs) in decreasing caretaker time spent searching for residents and providing alerts of residents going outdoors in a skilled nursing facility. Design: A controlled study over two 2-month periods approved by the institutional review board. Setting: A long-term skilled nursing facility in Massachusetts specializing in multiple sclerosis previously instrumented with wireless network infrastructure. Participants: Nineteen residents and 9 staff members at the facility for the first 2-month period; 9 residents and 3 staff members at the facility for the second 2-month period. Intervention: Software was installed on 4 staff computers to display the locations of residents enrolled in the study. This software was made available to enrolled staff for the second half of the first 2-month period and the entirety of the second 2-month study. In the second 2-month study, the software was modified to provide alerts if any 1 of 9 participating "high-risk"' residents went outdoors, and the accuracy of the alert system was evaluated. Measurements: In the first 2-month study, 9 staff members recorded the amount of time it took them to locate participating residents (as and when needed during the course of their daily activities). In the second 2-month study, 3 staff members recorded whether outdoor-alerts correctly identified a resident leaving the building or if it was a false alarm. Results: In both phases, participating staff members made frequent use of the system (44 searches and 215 outdoor alerts). Overall, the localization information decreased the average time needed to find residents by about two-thirds (from 311.1 seconds to 110.9 seconds). For outdoor alerts, the system had a false-alarm rate of 9.1% (under normal facility operations); systematic tests of the outdoor-alert system carried out by the authors had a false-negative, or missed-alarm, rate of 1.7%. Conclusion: Using timely resident location information can provide significant gains for both operational efficiency (finding residents) and enhanced resident safety (outdoor alerts). This approach may provide an inexpensive alternative for facilities that have sufficient wireless infrastructure; future work should assess its effectiveness in additional settings. (W. Li). JAMDA j o u r n a l h o m e p a g e : w w w . j a m d a . c o m 1525-8610/$ -see front matter

2014 Twentieth National Conference on Communications (NCC), 2014

In a Wireless Sensor Network (WSN), it is often necessary to know the accurate location of one or more nodes. Various localization methods have been proposed, namely: TDOA, TOA, AOA, GPS and RSSI. All methods except RSSI require added hardware which increases the cost of the WSN. Moreover, the RSSI value of received radio signals is easily available on most sensor nodes. We propose a set of three techniques (mean+filter, mode, mode+filter) for evaluating RSSI for accurate indoor localization. The techniques are developed with the help of rigorous experimental data collected with TelosB nodes. Localization performance with all the models is compared with an existing technique, the mean technique. We assert that the mode-filter technique achieves a 64.86% less error over the mean technique in distance estimation between the transmitter and the receiver, and it has a 21.33% lower error in location estimation.

ArXiv, 2019

This paper details a number of indoor localization techniques developed for real-time monitoring of older adults. These were developed within the framework of the i-Light research project that was funded by the European Union. The project targeted the development and initial evaluation of a configurable and cost-effective cyber-physical system for monitoring the safety of older adults who are living in their own homes. Localization hardware consists of a number of custom-developed devices that replace existing luminaires. In addition to lighting capabilities, they measure the strength of a Bluetooth Low Energy signal emitted by a wearable device on the user. Readings are recorded in real time and sent to a software server for analysis. We present a comparative evaluation of the accuracy achieved by several server-side algorithms, including Kalman filtering, a look-back heuristic as well as a neural network-based approach. It is known that approaches based on measuring signal strengt...

International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2024

This paper presents a novel hybrid localization algorithm designed for healthcare systems, integrating Received Signal Strength Indicator (RSSI) and Time of Arrival (ToA) measurements with machine learning techniques. The algorithm aims to enhance the accuracy, robustness, and computational efficiency of sensor localization in dynamic healthcare environments. Experimental results demonstrate that the hybrid algorithm achieves a significantly lower localization error, averaging 0.5 meters, compared to traditional RSSI-only and ToAonly methods. The algorithm's rapid convergence and low computational time make it suitable for real-time applications. Additionally, its robustness to measurement noise, a common challenge in healthcare settings, underscores its reliability. This research underscores the potential of advanced localization technologies to improve patient monitoring, safety, and overall healthcare delivery, with future work poised to further enhance performance and adaptability.