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When monitoring the environment with wireless sensor networks, the data sensed by the nodes within event backbone regions can adequately represent the events. As a result, identifying event backbone regions is a key issue for wireless sensor networks. With this aim, we propose a distributed and morphological operation-based data collection algorithm. Inspired by the use of morphological erosion and dilation on binary images, the proposed distributed and morphological operation-based data collection algorithm calculates the structuring neighbors of each node based on the structuring element, and it produces an event-monitoring map of structuring neighbors with less cost and then determines whether to erode or not. The remaining nodes that are not eroded become the event backbone nodes and send their sensing data. Moreover, according to the event backbone regions, the sink can approximately recover the complete event regions by the dilation operation. The algorithm analysis and experimental results show that the proposed algorithm can lead to lower overhead, decrease the amount of transmitted data, prolong the network lifetime, and rapidly recover event regions.

IntroductionObesity is a public health concern that is becoming increasingly more serious worldwide. Effective and sustainable childhood obesity prevention strategies may help to reduce the prevalence of obesity and may have an impact on lifelong health. However, few such strategies have been rigorously evaluated for Chinese children in different regions of China.Methods and analysisThe Diet, ExerCIse and CarDiovascular hEalth-Children is a cluster-randomised controlled trial that aims to assess the effectiveness and sustainability of a school-based, multi-faceted intervention to prevent obesity among Grade 4 primary school students (8–10 years old) in China. Twenty-four schools (approximately 1200 students) from above average, average and below average developed regions in China will be randomised to an intervention (12 schools) or usual practice (12 schools) group. The intervention will last for one school year (9 months) and consists of activities towards students, parents and school environment. A smartphone application will be used to assist in providing information on, monitoring and providing feedback on the behaviours and body weight of the students. Data will be collected at baseline, 4 months, 9 months and 21 months. The primary outcome will be the difference between groups in the change in students’ body mass index at 9 months after the baseline investigation. The secondary outcomes will include the differences between groups in the changes in anthropometric measures, diet, physical activity levels and other measures at the follow-up visits. A variety of process evaluation methods will be used to evaluate the implementation process of the complex intervention.Ethics and disseminationThis study was approved by the Peking University Institution Review Board (IRB00001052-18021). The results will be disseminated through publication in peer-reviewed journals, presentations at conferences and in lay summaries provided to school staff and participants.Trial registration number NCT03665857.

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To improve the user experience, an increasing number of mobile applications offload their computing tasks to servers with powerful computing capabilities. The fog radio access network (F-RAN) incorporates the concept of "fog computing" into the access network architecture, endowing an edge network with computing, storage, communication and control functions. In this paper, we consider a multiple fog access point (F-AP) and a multiuser F-RAN, where each user generates two different tasks: communication and computation. To satisfy the diverse quality of service requirements of different users, we jointly optimize the spectrum access, computation offloading and radio resource allocation. The problem is modeled as a mixed integer nonlinear programming problem, which is difficult to solve. In view of this, we propose a genetic algorithm based on convex optimization, i.e., the genetic convex optimization algorithm (GCOA), which divides the mixed integer nonlinear programming problem into two parts, i.e., optimization and convex optimization, to solve it in polynomial time. Simulation results are provided to verify the effectiveness of the algorithm.

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