![]() However, in a complex BLE network, it can be challenging to select the optimal reference beacon, and accurate positioning becomes difficult. Resilient disaster-time networks and disaster management lifecycles, a complete framework hasīeen discussed here in this paper along with implementation and simulation of the system.Īs communication technology and smartphones develop, many indoor positioning applications based on Bluetooth low energy (BLE) beacons have emerged. Having two long-range and a short-rangeĬommunication layers ensures its operation even in extreme conditions where typical network-Įnabled devices and systems may not be very useful. Rescue operations and potentially can save more lives. Moreover, usages of UAVĪnd UGV equipped with LoRaWAN gateways and BLE receivers can expand the reach of This system not only will increase a victim’s chance of survival during or after aĭisastrous event but also, will help the rescue operation to be more accurate, organized, andĮfficient providing them geo-local and biometric data of victims. Tracks disaster victims using GNSS, transmits their bio-signs, and can be installed in disaster This paper presents an IoT-based system with redundant communication layers includingĬonventional cellular network, hybrid mesh LoRaWAN, and Bluetooth Low Energy (BLE) that Our method can reduce the noise caused by the spatial-temporal variation of the magnetic field, thus greatly improving the indoor positioning accuracy, reaching an average positioning accuracy of 0.78 m. We implement an embedded magnetic sensor array positioning system, which is evaluated in an experimental environment. The magnetic sensor array can detect subtle magnetic anomalies and spatial variations to improve the stability and accuracy of magnetic field fingerprint maps, and the RPNN model is built for recognizing magnetic field fingerprint. This research proposes a new magnetic indoor positioning method, which combines a magnetic sensor array composed of three magnetic sensors and a recurrent probabilistic neural network (RPNN) to realize a high-precision indoor positioning system. However, since the magnetic field is easily affected by external magnetic fields and magnetic storms, which can lead to “local temporal-spatial variation”, it is difficult to construct a stable and accurate magnetic field fingerprint map for indoor positioning. When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user’s location. ![]() Our approach is based on using low energy Bluetooth transmitters and a method of determining the user's position using the trilateration algorithm and the appropriate placement of transmitters in a space.īy collecting the magnetic field information of each spatial point, we can build a magnetic field fingerprint map. As a step towards solving this problem, we propose a solution supporting the navigation of users, especially the visually impaired, inside buildings. As satellite navigation systems are burdened with errors, which increase when trying to use them in confined spaces, it becomes necessary to use more accurate technology. Because as the urbanisation process, the surfaces of various buildings grow, which significantly impedes orientation in them, especially for the blind or visually impaired users. A similar revolution may also await navigations in closed spaces such as public or commercial buildings. ![]() Nowadays people cannot imagine moving in an "urban jungle” with paper maps without electronic support, but dozens of years ago those maps were more popular than satellite navigation. to the use of satellite positioning systems (including GPS) the ability to determine a user's position in open spaces has become a necessary element of everyday life. Auto tools will then average all of the pings to determine the average RSSI (reciev…Īlso, you can use the Radbeacon android app to configure each individual RADBeacon’s signal strength, which is the equivalent of setting the detection field size. Tasker will not scan continuously while walking, but every few seconds. The beacon radar app reports every ping back to tasker. Once the user is home the app will wait for you to walk, once you start walking it will scan for beacons. Basically the same as this post This will then set a smartthings presence sensor “User Home” to present. How this works, uses Autolocation and wifi connected to determine if you are home. ![]() This uses very little power and should be relatively simple to setup. I have created an app that uses several tools to determine overall and precise locations within the house using beacons. Android Presence using Beacons/Autolocation/Sharptools Projects & Stories
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