One of the most significant researches in location-based services is the development of effective indoor localization. In this work, we propose a novel model of fingerprint localization, which divides location area into different subareas by fuzzy C-means and calculates location via relative distance fuzzy localization. In offline training stage, fuzzy C-means algorithm is used in localization model to divide localization area into different subareas and then to select the useful access points in subareas to reduce the dimensions of fingerprint. In online location stage, we use the nearest neighbor algorithm to select the subareas and to calculate the coordinate of the target point according to relative distance fuzzy localization algorithm, which converts traditional fingerprint of reference points into distance fingerprint and calculates the coordinate of the target point by fuzzy C-means algorithm. The noise and non-linear attenuation between the wireless signal and distance are taken into full consideration in relative distance fuzzy localization algorithm, which eliminates the random environmental noise. Experiments show that our proposed model is able to save the calculation time and improve the localization accuracy.

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WebIDE is leveraged for IoT application development, which could adapt to the rapid growth of IoT applications and meanwhile facilitate the rapid development. Resource allocation is of vital significance in the WebIDE cloud service system. Existing resource allocation approaches may encounter issues such as unbalanced resource assignments, which could lead to the reduced system resource utilization or extended system response time. Existing methods are typically on the basis of predetermined resource demands for each task, and not applicable to the case that the resource demands are dynamic and unknown. This article predicts the tasks to be performed by the WebIDE cloud service through task pre-scheduling, and then applies the existing resource allocation methods. Firstly, all tasks are classified, based on the execution state, execution operations and WebIDE cloud server resource requirements. Secondly, the grouped tasks are mapped to different system states, with the Markov state transition probability matrix leveraged to model the transition probability between tasks, followed by the prediction model constructed. Finally, integrating task pre-scheduling with ant colony algorithm, WebIDE cloud server resource allocation is carried out. Experiment results show that adding the task prediction model could significantly not only reduce the task response time, but also improve the cloud server resource utilization.

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