Ki-Seong Lee

RETE-ADH: An Improvement to RETE for Composite Context-Aware Service

We propose a new pattern matching algorithm for composite context-aware services. The new algorithm, RETE-ADH, extends RETE to enhance systems that are based on the composite context-aware service architecture. RETE-ADH increases the speed of matching by searching only a subset of the rules that can be matched. In addition, RETE-ADH is scalable and suitable for parallelization. We describe the design of the proposed algorithm and present experimental results from a simulated smart office environment to compare the proposed algorithm with other pattern matching algorithms, showing that the proposed algorithm outperforms original RETE by 85%.

Deep learning–based real-time query processing for wireless sensor network:

The data collected from wireless sensor network indicate the system status, the environment status, or the health condition of human being, and we can use the wireless sensor network data to carry out appropriate work by processing it. In recent years, using deep learning, it is possible to construct a more intelligent context-aware system by predicting future situations as well as monitoring the current state. In this article, we propose a monitoring framework for wireless sensor network streaming data analysis based on deep learning. In particular, in an environment where time requirements are strictly enforced, data analysis results must be derived within a deterministic time. Therefore, we conduct query refinement adaptively to enable timely analysis of wireless sensor network data in the predictor. Even if some sensor data that is not synchronized in time are included or even if some data have not arrived yet, reasonably accurate query analysis results can be obtained within the deadline by performing the proposed method.

Model-Driven Monitoring of Time-Critical Systems Based on Aspect-Oriented Programming

Temporal correctness is one of the most important requirements for time-critical systems. Although time-critical systems are designed to meet their timing constraints, there can be still errors especially with timing constraints in run-time due to various reasons. Typically, time-critical systems are shipped with run-time monitors to check their temporal requirements. Hence, run-time monitors are essential to time-critical services. In this paper, we propose a model-driven monitor based on AOP for time-critical systems. The monitor is modeled by using xUML in the design time, and its timing constrains are specified by RTL-like expressions. The designed monitor model is transformed into the code automatically by our proposed tool chain. We validate the effectiveness of our approach by presenting a case study and analyzing the implemented system.

T-L Plane Abstraction-Based Energy-Efficient Real-Time Scheduling for Multi-Core Wireless Sensors

Energy efficiency is considered as a critical requirement for wireless sensor networks. As more wireless sensor nodes are equipped with multi-cores, there are emerging needs for energy-efficient real-time scheduling algorithms. The T-L plane-based scheme is known to be an optimal global scheduling technique for periodic real-time tasks on multi-cores. Unfortunately, there has been a scarcity of studies on extending T-L plane-based scheduling algorithms to exploit energy-saving techniques. In this paper, we propose a new T-L plane-based algorithm enabling energy-efficient real-time scheduling on multi-core sensor nodes with dynamic power management (DPM). Our approach addresses the overhead of processor mode transitions and reduces fragmentations of the idle time, which are inherent in T-L plane-based algorithms. Our experimental results show the effectiveness of the proposed algorithm compared to other energy-aware scheduling methods on T-L plane abstraction.

Energy Efficient Real-Time Scheduling Using DPM on Mobile Sensors with a Uniform Multi-Cores

In wireless sensor networks (WSNs), sensor nodes are deployed for collecting and analyzing data. These nodes use limited energy batteries for easy deployment and low cost. The use of limited energy batteries is closely related to the lifetime of the sensor nodes when using wireless sensor networks. Efficient-energy management is important to extending the lifetime of the sensor nodes. Most effort for improving power efficiency in tiny sensor nodes has focused mainly on reducing the power consumed during data transmission. However, recent emergence of sensor nodes equipped with multi-cores strongly requires attention to be given to the problem of reducing power consumption in multi-cores. In this paper, we propose an energy efficient scheduling method for sensor nodes supporting a uniform multi-cores. We extend the proposed T-Ler plane based scheduling for global optimal scheduling of a uniform multi-cores and multi-processors to enable power management using dynamic power management. In the proposed approach, processor selection for a scheduling and mapping method between the tasks and processors is proposed to efficiently utilize dynamic power management. Experiments show the effectiveness of the proposed approach compared to other existing methods.

A Development Framework Toward Reconfigurable Run-time Monitors

Time-critical systems are usually loaded with run-time monitors to observe their temporal requirements because there can be timing violations which may trigger fatal damages to people or systems. Since the timing constraints of run-time monitor are non-trivial, it is prone to complicate modifications as well as implementations. We propose a run-time monitor which facilitates to reconfigure monitoring conditions at design time. As the monitoring concerns are well separated in design time, we can expect the system to mitigate complexity in implementation. Our timing monitor is modeled by using xUML in early stage of development process, and specifications of timing constraints are represented by RTL - like expression. The modeled monitor is transformed into the AOP code by MDA approach. We demonstrate the effectiveness of our approach by showing a case study and analyzing our work.