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The rapid spread of advanced mobile phones in Japan, a world leader in mobile telecoms, prompted Mr Nakagawa to devise Lifewatcher, Mobile Healthcare's main product. It is a "disease selfmanagement system" used in conjunction with a doctor, based around a secure online database that can be accessed via a mobile phone. Patients record what medicines they are taking and what food they are eating, taking a picture of each meal. A database of common foodstuffs, including menu items from restaurants and fast-food chains, helps users work out what they can safely eat. Patients can also call up their medical records to follow the progress of key health indicators, such as blood sugar, blood pressure, cholesterol levels and calorie intake.

Advances in Information and Communication Technologies (ICTs) especially sensors and sensor networks are producing a quite different urban environment that we have experienced hitherto [7], leading to a transformation of the cyber and real cities into smart cities [8]. As sensors can be integrated into nearly all parts of the real world, sensor-based sensing is at the heart of smart cities [9]. Hereinto, sensor-rich smart phones have made possible the recent birth of the mobile sensing research area [10]. A mobile sensing system (MSS) could be simply regarded as a user-level application (app) running on the phone to read data from an internal sensor in the phone, or external sensors in the wireless sensor network (WSN) and then reporting the sensed data to the Web. As a type of environmental-centric and human-based MSS, mobile device-based participatory sensing [11] has been gradually applied for collection of actual noise data with multiple granularities in space and time to improve the update frequency and reduce the cost of data collection for noise mapping. However, participatory data are generally contributed by volunteer participants at arbitrary locations and times in most cases [12]. Consequently, collected samples of noise measurements are randomly distributed in space and time, thus making processing and management of these data difficult, especially for the purpose of supporting noise simulations. Therefore, effective organization and management of these participatory data are needed to further support the data input for noise simulations to improve noise mapping [6,13].