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Although the exposure to PM 2.5 has serious health implications, indoor PM 2.5 monitoring is not a widely applied practice. Regulations on the indoor PM 2.5 level and measurement schemes are not well established. Compared to other indoor settings, PM 2.5 prediction models for large office buildings are particularly lacking. In response to these challenges, statistical models were developed in this paper to predict the PM 2.5 concentration in well-mixed indoor air in a commercial office building. The performances of different modeling methods, including multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (DLM), least absolute shrinkage selector operator (LASSO), simple artificial neural networks (ANN), and long-short term memory (LSTM), were compared. Various combinations of environmental and meteorological parameters were used as predictors. The root-mean-square error (RMSE) of the predicted hourly PM 2.5 was 1.73 μg/m 3 for the LSTM model and in the range of 2.20−4.71 μg/m 3 for the other models when regulatory ambient PM 2.5 data were used as predictors. The LSTM models outperformed other modeling approaches across the performance metrics used by learning the predictors' temporal patterns. Even without any ambient PM 2.5 information, the developed models still demonstrated relatively high skill in predicting the PM 2.5 levels in well-mixed indoor air.

Effective security mechanisms are essential to the widespread deployment of pervasive systems. Much of the research focus on security in pervasive computing has revolved around distributed trust management. While such mechanisms are effective in specific environments, there is no generic framework for deploying and extending these mechanisms over a variety of pervasive systems. We present the design and implementation of a novel framework called Distributed Trust Toolkit (DTT), for implementing and evaluating trust mechanisms in pervasive systems. The DTT facilitates the extension and adaptation of trust mechanisms by abstracting trust mechanisms into interchangeable components. Furthermore, the DTT provides a set of tools and interfaces to ease implementation of trust mechanisms and facilitate their execution on a variety of platforms and networks. In addition to the adaptability and extensibility provided by this design, we demonstrate through simulation that use of DTT improves utilization of resources and enhances performance of existing trust mechanisms in pervasive systems. We are currently developing an implementation of the DTT that can be easily deployed in pervasive environments.

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The assignment of tasks to employees is one of the most essential aspects of a project manager's job. A situation with employees working on tasks that they are not wellsuited for can lead to a significant loss of time and resources in addition to a sub-par product or service. The simple difference between a good and bad task assignment for employees can easily result in major differences in a company's bottom line.We utilize techniques from game theory to produce an algorithm for matching employees and tasks based on manager and employee preference, employee time, and employee skills. As a result, we have created a deterministic algorithm for task assignment with built-in feedback mechanisms for measuring the health of the project group with respect to the work given.

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Service composition schemes create high-level ap plication services by combining several basic services. Service composition schemes for dynamic, open systems, snch as those found in pervasive environments, must be cognizant of the possibility of failures and attacks. In open systems, it is seldom feasible to guarantee the reliability of each node prior to access; however, there may be several possible ways to compose the same high-level service, each having a different (though possibly overlapping) set of nodes that can satisfy the composition. We approach this problem with a Reliable Service Composition middleware component, ReSCo, to determine trustworthy compositions and nodes for service composition in dynamic, open systems. ReSCo is a modular, adaptive middle ware component that selects from possible composition paths and nodes to enhance reliability of service compositions. ReSCo can work with a broad range of both service composition algorithms and trust establishment mechanisms.