Automatic definition of web service call zones based on the classification of context of the mobile device

Предложен метод автоматизации активации устройств Интернета вещей на основе классификации геопозиции мобильного устройства. В отличие от других методов пользователь обучает систему активации устройств с помощью примеров и контрпримеров, что значительно снижает требования к квалификации пользователя. Проведено тестирование метода на таких двух устройствах, как шлагбаум и электромеханический замок двери. Полученные результаты тестирования позволяют судить о работоспособности метода и возможности его использования в системах умного дома и города. Most IoT devices provide an application programming interface such as web service that allows controlling these IoT devices over Internet using a mobile phone. Activation of IoT devices is performed according to the status of user behavior. Both user behavior and activation of IoT devices are periodical. An activation of IoT device is often related with a user geolocation which can be defined by sensors of the mobile device. A method for automated activation of IoT devices based on classification of geolocation of mobile device is proposed. The method implements a supervised learning that simplifies automate activation of IoT devices for the end users. Existing methods demand appropriate end user qualification and require long time to automate activation. For indoor geolocation of the mobile device information from Wi-Fi access points and geolocation GPS sensor is utilized. Data of Wi-Fi and GPS sensors is used to form context of a mobile device. Based on context examples of invoking/not invoking web services the spatial areas are formed. When the mobile device context is within the web service invocation area, the web service is invoked and the associated IoT device is activated. To implement the method, an Android application was developed. The method was tested on a training set that contained 100 training examples of calling two web services: opening an electromechanical door lock and opening a barrier. As a result of testing, the accuracy of classifying the context of a mobile device was 98 percent. The results obtained can be used in the development of smart home and smart city systems.

Real-Time Indoor Geolocation Tracking for Assisted Healthcare Facilities

A leading cause of physical injury sustained by elderly persons is the event of unintentionally falling. A delay between the time of fall and the time of medical attention can exacerbate injury if the fall resulted in a concussion, traumatic brain injury, or bone fracture. The authors present a solution capable of finding and tracking, in real-time, the location of an elderly person within an indoor facility, using only existing Wi-Fi infrastructure. This paper discusses the development of an open source software framework capable of finding the location of an individual within 3m accuracy using 802.11 Wi-Fi in good coverage areas. This framework is comprised of an embedded software layer, a Web Services layer, and a mobile application for monitoring the location of individuals, calculated using trilateration, with Kalman filtering employed to reduce the effect of multipath interference. The solution provides a real-time, low cost, extendible solution to the problem of indoor geolocation to mitigate potential harm to elderly persons who have fallen and require immediate medical help.

Indoor Vehicles Geolocalization Using LoRaWAN

One of the main drawbacks of Global Navigation Satellite Sytems (GNSS) is that they do not work indoors. When inside, there is often no direct line from the satellite signals to the device and the ultra high frequency (UHF) used is blocked by thick, solid materials such as brick, metal, stone or wood. In this paper, we describe a solution based on the Long Range Wide Area Network (LoRaWAN) technology to geolocalise vehicles indoors. Through estimation of the behaviour of a LoRaWAN channel and using trilateration, the localisation of a vehicle can be obtained within a 20–30 m range. Indoor geolocation for Intelligent Transporation Systems (ITS) can be used to locate vehicles of any type in underground parkings, keep a platoon of trucks in formation or create geo-fences, that is, sending an alert if an object moves outside a defined area, like a bicycle being stolen. Routing of heavy vehicles within an industrial setting is another possibility.