Despite recent advances in sensor and mobile technology, there is still a lack of an accurate, scalable, and non-intrusive way to knowing how much sunlight we are exposed to. For the first time, we devise a mobile phone software application (SUN BATH), that utilizes a variety of on-board sensors and data sets to accurately predict the sunlight exposure each person is exposed to. The algorithm is able to take into account the sunlight exposure based on the person location, the local weather, sun location, and shadow from buildings. The algorithm achieves this by using the mobile user's location and other sensors to determine whether it is indoors or outdoors, and uses building data to calculate shadow effects and weather data to calculate diffused light contributions. This will ultimately allow the user to be more informed about sunlight exposure and compare it with daily recommended levels to encourage positive behaviour change. In order to show the value added by the application, SUN BATH is distributed to a sample of students population for benchmarking and user experience trials. The latest stable version of the application, suggests a scalable and affordable way compared to survey or physical sensing methods. In this particular proposal, we examine how to live healthily in cities using a data-driven mobile-sensing approach. Cities are partly defined by a high building concentration and a human lifestyle that is predominantly spent indoors or in the shadow of buildings. Some cities in particular, also suffer from heavy pollution effects that significantly reduces the level of direct solar radiation. As a result, one area of concern is the urban dwellers lack of exposure to the ultra-violet (UV) band of sunlight and the wide range of associated health problems. The large scale and chronic nature of the health problems can lead to a time bomb in the National Health Service and cause irreversible future damage to the economy. This article proposes using the ray tracing SORAM model by Erdelyi et al. as an innovative and flexible technique for modelling and estimating the amount of solar irradiation can be collected at a time and certain location. SORAM module is already benchmarked against real measurement data, hence, our work here will benefit from this by taking the calculated ray-tracing information as a primary filter. The aim is to devise an affordable and accurate way of continuously estimating each person's UV exposure. Primarily, this is achieved by developing an Android smartphone application that uses the SORAM advanced modelling techniques to estimate the level of UV exposure each person is subjected to at any given time and location. The research novelty is that the proposed solution does not require additional purpose-built hardware such as a photovoltaic sensor, but instead utilizes a combination of accurate wireless localization, and weather-/terrain-informed sunlight propagation mapping. The challenges addressed include how to accurately locate a human and how to model the propagation of sunlight in complex urban environments. The latest stable version of the application, suggests a novel and affordable way compared to traditional or physical methods when calculating the amount of sunshine we are exposed to. We implemented and evaluated SUN BATH application with the Android platform using different mobile phone models such as Samsung S5, Asus Zen5 and Archos tablet. The application is developed using Android Studio as IDE for Android application development. The application allows the user to create a profile using a user name and some information such as date of birth, height, weight, skin colour, country of origin, and level of income. To be used later for future detailed reporting with relation to the amount of sun bathing for different groups and ethnicities. SUN BATH only relies on lightweight “sensors to server” modelling which allows continuous low-energy and low-cost tracking of the user location and state transitions. In particular, we will present the process to show that we were able to use SORAM within the smart-phone environment to accurately infer the amount of sunshine in a user is exposed to based on the accuracy of the GPS and other location modules for Android mobile phones. To meet stringent design requirements, SUN BATH utilizes a series of lightweight ‘sensors – server’ for a fault-tolerant location detection. SUN BATH primarily makes use of three types of location-aware detectors: WiFi, cellular-network, and GPS. The aforementioned three wireless location detectors are used in conjunction to improve resolution and resilience. WiFi hub SSID identifiers are used to locate the hub in known open and commercial databases up to an accuracy of a few metres. In the absence of WiFi, a combination of cell tower location area and assisted GPS is used to get an accuracy of 10–15 m in urban areas with shadow effects. WiFi detector adopts the distributed IP address to capture the source location to determine the region the user is in. Cellular-network detector detects the source and attenuation of signals caused by objects on its path (e.g., trees, buildings). It normally help to indicate the movement of the user as the mobile signal gets handed from one network to another. The Application utilizes the GPS sensor to exactly pinpoint the coordinates of the user location i.e. Latitude and Longitude. The system time clock is also used to assist the detection of the local time. The App cache-in those parameters and sends it to a remote server whenever there is an Internet connection. The server hosts the SORAM calculation algorithm which generates a live estimate of the amount of sun exposure the user is experiencing. The results are then passed back to the applications through the Open Database Connectivity (ODBC) middleware service to permanently store the results in a secure database management system (DBMS). A person positioned in an out-door environment is surrounded by solar radiation, which consists of direct and diffuse rays. Direct and diffuse radiation data on a horizontal surface are usually collected at various locations and weather stations across the universe. The raw datasets collected can be used to estimate the amount of global radiation at any point of earth of a given slope and azimuth. Due to cost and scarcity of live data, the SORAM algorithm embed and made use of the Reindel Model, to estimate the direct and diffuse irradiation from hourly horizontal global radiation data. In addition and to go light on computation and avoid calculations for the nighttime hours, the SORAM determines the sunrise time for each day of the year and the amount of solar radiation data from that point onwards which is then calculated until sunset. The algorithm also estimates and with high accuracy direct and diffuse radiation on a surface of given slope and azimuth from their counterparts on a horizontal access considering surrounding shading conditions. We tested SUN BATH in simulated and real locations for five continuous days from sunrise to sunset in around the School of Engineering building complex at the University of Warwick campus. Simulated tests were carried manually, using two fixed location parameters i.e Longitude and Latitude. A quick memory and CPU monitor view revealed that the SUN BATH energy consumptions and resource-constraint on the used smartphone devices were moderate. A full memory and CPU monitor view can be easily produced, but it is beyond the scope of this article. This research presented the architecture of SUN BATH mobile sensing application that gathers a variety of lightweight sensors information and utilised ray-tracing algorithm to derive the level of human sun exposure in urban areas. The application has demonstrated that it can be an affordable and pervasive way of accurately measuring the level of sunlight exposure each person is exposed to. Further work is required to scale the project to the global level, which requires big data sets on urban building maps and meterological data from all the cities.


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