An Application of Context Middleware Based on Fuzzy Logic for Wireless Sensor Networks

The research of context-aware computing based on wireless sensor network (WSN) aims at intelligently connecting computers, users, and environment. So its application system should be flexibly adaptable to dynamic changes of context and application requirements and proactively provides the information satisfied with current context for users. The middleware can be very effective to provide the support runtime services for context-aware computing. In this paper we propose middleware architecture for context processing. This architecture is based on fuzzy logic control (FLC) system for context reasoning and sensor fusion. We propose a formal context representation model in which a user’s context is described by a set of roles and relations correspond to a context space. A middleware prototype has been developed, which detect tourist’ physical context and provide reminding. The experiments prove that the model and approach proposed are feasible.

TGAIN:Geospatial Data Recovery Algorithm Based on GAIN-LSTM

Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban planning.The deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial data.In this paper,we consider abnormal spatial data as missing data and focus on abnormal spatial data recovery.Existing geospatial data recovery methods require complete datasets for training,resulting in time-consuming data recovery and lack of generalization.To address these issues,we propose a GAIN-LSTM-based geospatial data recovery method(TGAIN),which consists of two main works:(1)it uses a long-short-term recurrent neural network(LSTM)as a generator to analyze geospatial temporal data and capture its temporal correlation;(2)it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input data.The experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error(MAE),root mean square error(RMSE),mean square error(MSE),mean absolute percentage error(MAPE),coefficient of determination(R2)and average computational time across various data missing rates.Concurrently,TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models,especially when dealing with a high rate of missing data.Our model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis.