Chan-Gun Lee

A Distributed and Scalable Time Slot Allocation Protocol for Wireless Sensor Networks

There are performance deficiencies that hamper the deployment of Wireless Sensor Networks (WSNs) in critical monitoring applications. Such applications are characterized by considerable network load generated as a result of sensing some characteristics of the monitored system. Excessive packet collisions lead to packet losses and retransmissions, resulting in significant overhead costs and latency. In order to address this issue, we introduce a distributed and scalable scheduling access scheme that mitigates high data loss in data-intensive sensor networks and can also handle some mobility. Our approach alleviates transmission collisions by employing virtual grids that adopt Latin Squares characteristics to time slot assignments. We show that our algorithm derives conflict-free time slot allocation schedules without incurring global overhead in scheduling. Furthermore, we verify the effectiveness of our protocol by simulation experiments. The results demonstrate that our technique can efficiently handle sensor mobility with acceptable data loss, low packet delay, and low overhead.

Early separation of filter and refinement steps in spatial query optimization

The spatial query has been processed in two steps, the filter step and the refinement step, due to the large volume and high complexity of the spatial data. However, this approach has been considered only in the query execution phase after completion of the query optimization phase. This paper presents query optimization strategies which take the characteristics of spatial databases into account. The first strategy is the separation of filter and refinement steps not in the query execution phase but in the query optimization phase. As the second strategy, several refinement operations can be combined in processing a complex query, and as the third strategy several filter operations can also be combined. We call the optimization technique utilizing these strategies the early separated filter and refinement (ESFAR). This paper also presents a rule-based optimization technique for ESFAR.

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.

Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver.

Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX) quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE) before and after applying our mechanism are significantly reduced from 6.3×10-1 to 5.3×10-7, respectively.

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.

A Component-Based Reconfigurable Sensor Network Monitor for Adapting Time-Critical Requirements

Sensor data can be useful in various applications, but it is also critical in some areas, such as health, safety and finance. In particular, in a time critical environment, temporal correlation of streaming sensor data should be monitored. A run-time monitor is embedded in the server for such applications, in order to detect erroneous conditions, and perform appropriate reactions in a timely manner. However, modification of the monitor may require stop and restart of the server, in order to take effect. This kind of option is not permissible where the server should be performing continuously. In this paper, we propose a component based reconfigurable sensor network monitor designed for handling time critical requirements. In order to allow dynamic modification, we implement both the server and the monitor on the Fractal Component model, which enables us to reconfigure the system architecture in run-time. For detecting violation of the timing condition, we specify timing constraints, and design the monitor on Fractal Aspect Component. The architecture can be formally verified by Fractal-ADL and Alloy. We demonstrate the feasibility of the proposed framework, by developing a smart cruise control system. We aim to provide guidance to construct a monitoring system in a dynamic and time critical environment.

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.

Optimization of Processor Clock Frequency for Sensor Network Nodes Based on Energy Use and Timing Constraints

The effectiveness of sensor networks depends critically on efficient power management of the sensor nodes. Dynamic voltage frequency scaling (DVFS) and dynamic power management (DPM) have been proposed to enable energy-efficient scheduling for real-time and embedded systems. However, most power-aware scheduling algorithms are designed to deal with only those cases in which the task execution time is determined solely by the clock frequency of the processor. In this study, we propose an extended task execution model that is appropriate for the sensor nodes and an algorithm that determines the optimal clock frequency for a nodes processor. We analyze the extended model and verify that our algorithm calculates the clock frequency that optimizes energy savings while satisfying the timing constraints.

Architecture of a network performance monitor for application services on multi-clouds

Recently, many new services are being deployed on the cloud due to its flexibility of on-demand allocation and deallocation of computing resources. As the services require more advanced features such as fault-tolerance and high performance, much interest is focused on the multi-clouds. Hence, the needs for effectively managing the quality of services on multi-clouds emerge rapidly. Among the many factors affecting the quality of services, we focus on the network performance in this paper. We identify essential requirements of network performance monitor for multi-clouds and propose an architecture. In particular we address the necessity of supporting external agents and discuss how to integrate with them in a flexible and extensible way. In addition, the issues of timely delivery and off-line analysis of measured results are addressed.