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Throughout various complex processes within hospitals, context-aware services and applications can help to improve the quality of care and reduce costs. For example, sensors and radio frequency identification (RFID) technologies for e-health have been deployed to improve the flow of material, equipment, personal, and patient. Bed tracking, patient monitoring, real-time logistic analysis, and critical equipment tracking are famous applications of real-time location systems (RTLS) in hospitals. In fact, existing case studies show that RTLS can improve service quality and safety, and optimize emergency management and time critical processes. In this paper, we propose a robust system for position and orientation determination of equipment. Our system utilizes passive (RFID) technology mounted on flooring plates and several peripherals for sensor data interpretation. The system is implemented and tested through extensive experiments. The results show that our system's average positioning and orientation measurement outperforms existing systems in terms of accuracy. The details of the system as well as the experimental results are presented in this paper.

Aggregating fine-granular data measurements from smart meters presents an opportunity for utility companies to learn about consumers' power consumption patterns. Several research studies have shown that power consumption patterns can reveal a range of information about consumers, such as how many people are in the home, the types of appliances they use, their eating and sleeping routines, and even the TV programs they watch. As we move toward liberalized energy markets, many different parties are interested in gaining access to such data, which has enormous economical, societal, and environmental benefits. However, the main concern is that many such beneficial uses of smart meter big data would be severely curtailed if the data were excessively protected due to individuals' privacy. In this paper, we propose a game theoretic mechanism that balances between beneficial uses of data and individuals' privacy in deregulated smart grids. Our mechanism solves the problem of access control by fairly compensating consumers for their participation in the data market based on the concept of differential privacy. The results of our experiments show the importance of taking consumers' attitudes toward privacy as a crucial element in designing balanced markets for fair data sharing. Furthermore, the experiments provide a principled way to choose reasonable values for privacy levels that are more relevant to real-world scenarios.INDEX TERMS Smart metering, smart grid, big data, privacy, game theory.

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